python /home/admin/mtr/script_for_cron.py -j datou_current3 -m 20 -a ' -a 3318 ' -s datou_3318 -M 0 -S 0 -U 95,95,120 import MySQLdb succeeded Import error (python version) ['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/caffe_cuda8_python3/python', '/home/admin/workarea/install/darknet', '/home/admin/workarea/git/Velours/python', '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python', '/home/admin/mtr/.credentials', '/home/admin/workarea/install/caffe/python', '/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools', '/home/admin/workarea/git/fotonowerpip', '/home/admin/workarea/install/segment-anything', '/home/admin/workarea/git/pyfvs', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages'] process id : 4006170 load datou : 3318 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected type for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? [(photo_id, hashtag_id, hashtag_type, x0, x1, y0, y1, score, seg_temp, polygons), ...] was removed should we ? chemin de la photo was removed should we ? [ (photo_id_loc, hashtag_id, hashtag_type, x0, x1, y0, y1, score, None), ...] was removed should we ? chemin de la photo was removed should we ? id de la photo (peut être local ou global) was removed should we ? chemin de la photo was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? donnée sous forme de texte was removed should we ? [ (photo_id, photo_id_loc, hashtag_type, x0, x1, y0, y1, score), ...] was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? id de la photo (peut être local ou global) was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? None was removed should we ? donnée sous forme de nombre was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? donnée sous forme de texte was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? [ptf_id0,ptf_id1...] was removed should we ? FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) load thcls load THCL from format json or kwargs add thcl : 2847 in CacheModelConfig load pdts add pdt : 5275 in CacheModelConfig Running datou job : batch_current TODO datou_current to load to do maybe to take outside batchDatouExec updating current state to 1 list_input_json: [] Current got : datou_id : 3318, datou_cur_ids : ['3740996'] with mtr_portfolio_ids : ['26999691'] and first list_photo_ids : [] new path : /proc/4006170/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 18 ; length of list_pids : 18 ; length of list_args : 18 time to download the photos : 2.3875815868377686 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 Thu Sep 18 14:20:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10590 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-09-18 14:20:34.825419: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-09-18 14:20:34.852491: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-09-18 14:20:34.854597: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fc578000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-09-18 14:20:34.854645: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-09-18 14:20:34.858143: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-09-18 14:20:35.014788: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2c135cc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-09-18 14:20:35.014832: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-09-18 14:20:35.016233: 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 2025-09-18 14:20:35.016628: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-18 14:20:35.019577: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-18 14:20:35.022181: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-18 14:20:35.022651: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-18 14:20:35.025745: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-18 14:20:35.026748: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-18 14:20:35.030713: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-18 14:20:35.032062: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-18 14:20:35.032114: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-18 14:20:35.032858: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-18 14:20:35.032874: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-18 14:20:35.032882: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-18 14:20:35.034179: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9809 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. 2025-09-18 14:20:35.300007: 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 2025-09-18 14:20:35.300103: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-18 14:20:35.300131: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-18 14:20:35.300157: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-18 14:20:35.300182: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-18 14:20:35.300206: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-18 14:20:35.300231: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-18 14:20:35.300256: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-18 14:20:35.301805: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-18 14:20:35.302894: 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 2025-09-18 14:20:35.302922: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-18 14:20:35.302938: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-18 14:20:35.302952: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-18 14:20:35.302966: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-18 14:20:35.302980: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-18 14:20:35.302994: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-18 14:20:35.303008: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-18 14:20:35.304273: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-18 14:20:35.304298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-18 14:20:35.304306: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-18 14:20:35.304313: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-18 14:20:35.305599: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9809 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 : [] 2025-09-18 14:20:43.504819: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-18 14:20:43.712495: 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 : 18 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 18.00000 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: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 15 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 25.00000 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: 22.00000 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: 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: 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 : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 25.00000 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 : 10 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 41.00000 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 : 0 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 16.00000 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: 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 : 2 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: 22.00000 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: 35.00000 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 : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 28.00000 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 : 10 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 : 12 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 40.00000 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: 40.00000 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: 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 Detection mask done ! Trying to reset tf kernel 4006680 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 417 tf kernel not reseted sub process len(results) : 18 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 18 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 : 5706 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 : 0.0002918243408203125 nb_pixel_total : 11243 time to create 1 rle with old method : 0.011983394622802734 length of segment : 104 time for calcul the mask position with numpy : 0.0001266002655029297 nb_pixel_total : 5225 time to create 1 rle with old method : 0.0056934356689453125 length of segment : 104 time for calcul the mask position with numpy : 0.0003514289855957031 nb_pixel_total : 13540 time to create 1 rle with old method : 0.014644145965576172 length of segment : 144 time for calcul the mask position with numpy : 0.0002262592315673828 nb_pixel_total : 13371 time to create 1 rle with old method : 0.014223098754882812 length of segment : 189 time for calcul the mask position with numpy : 0.00010085105895996094 nb_pixel_total : 3817 time to create 1 rle with old method : 0.004272937774658203 length of segment : 89 time for calcul the mask position with numpy : 0.00010156631469726562 nb_pixel_total : 3804 time to create 1 rle with old method : 0.004553318023681641 length of segment : 60 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 3126 time to create 1 rle with old method : 0.0036628246307373047 length of segment : 40 time for calcul the mask position with numpy : 0.000194549560546875 nb_pixel_total : 9488 time to create 1 rle with old method : 0.010478019714355469 length of segment : 96 time for calcul the mask position with numpy : 0.002062559127807617 nb_pixel_total : 108261 time to create 1 rle with old method : 0.11480188369750977 length of segment : 536 time for calcul the mask position with numpy : 9.655952453613281e-05 nb_pixel_total : 3388 time to create 1 rle with old method : 0.004308223724365234 length of segment : 59 time for calcul the mask position with numpy : 0.2702012062072754 nb_pixel_total : 729665 time to create 1 rle with new method : 0.05787181854248047 length of segment : 1036 time for calcul the mask position with numpy : 0.00025343894958496094 nb_pixel_total : 6027 time to create 1 rle with old method : 0.007791996002197266 length of segment : 146 time for calcul the mask position with numpy : 0.0015079975128173828 nb_pixel_total : 98223 time to create 1 rle with old method : 0.10610532760620117 length of segment : 479 time for calcul the mask position with numpy : 0.0003619194030761719 nb_pixel_total : 5861 time to create 1 rle with old method : 0.006346225738525391 length of segment : 142 time for calcul the mask position with numpy : 0.001007080078125 nb_pixel_total : 25186 time to create 1 rle with old method : 0.026120424270629883 length of segment : 339 time for calcul the mask position with numpy : 0.00038552284240722656 nb_pixel_total : 14698 time to create 1 rle with old method : 0.016067981719970703 length of segment : 106 time for calcul the mask position with numpy : 0.00016641616821289062 nb_pixel_total : 2834 time to create 1 rle with old method : 0.0032432079315185547 length of segment : 79 time for calcul the mask position with numpy : 0.00030493736267089844 nb_pixel_total : 3907 time to create 1 rle with old method : 0.004519939422607422 length of segment : 170 time for calcul the mask position with numpy : 0.0158383846282959 nb_pixel_total : 603101 time to create 1 rle with new method : 0.03131604194641113 length of segment : 1420 time for calcul the mask position with numpy : 0.000396728515625 nb_pixel_total : 10681 time to create 1 rle with old method : 0.011815547943115234 length of segment : 112 time for calcul the mask position with numpy : 0.0002522468566894531 nb_pixel_total : 5142 time to create 1 rle with old method : 0.005760669708251953 length of segment : 94 time for calcul the mask position with numpy : 0.0001926422119140625 nb_pixel_total : 4700 time to create 1 rle with old method : 0.005541086196899414 length of segment : 68 time for calcul the mask position with numpy : 0.0002589225769042969 nb_pixel_total : 4514 time to create 1 rle with old method : 0.005068302154541016 length of segment : 114 time for calcul the mask position with numpy : 0.00022101402282714844 nb_pixel_total : 5394 time to create 1 rle with old method : 0.005946636199951172 length of segment : 70 time for calcul the mask position with numpy : 0.00016951560974121094 nb_pixel_total : 3051 time to create 1 rle with old method : 0.0035262107849121094 length of segment : 62 time for calcul the mask position with numpy : 9.036064147949219e-05 nb_pixel_total : 1859 time to create 1 rle with old method : 0.002175569534301758 length of segment : 38 time for calcul the mask position with numpy : 0.00021696090698242188 nb_pixel_total : 4598 time to create 1 rle with old method : 0.0050089359283447266 length of segment : 88 time for calcul the mask position with numpy : 0.00019884109497070312 nb_pixel_total : 5227 time to create 1 rle with old method : 0.0058972835540771484 length of segment : 68 time for calcul the mask position with numpy : 0.0001373291015625 nb_pixel_total : 2739 time to create 1 rle with old method : 0.003240823745727539 length of segment : 79 time for calcul the mask position with numpy : 0.008595466613769531 nb_pixel_total : 445327 time to create 1 rle with new method : 0.017704010009765625 length of segment : 902 time for calcul the mask position with numpy : 0.0053713321685791016 nb_pixel_total : 156158 time to create 1 rle with new method : 0.013335943222045898 length of segment : 1233 time for calcul the mask position with numpy : 0.0001373291015625 nb_pixel_total : 3144 time to create 1 rle with old method : 0.0035581588745117188 length of segment : 86 time for calcul the mask position with numpy : 0.00017213821411132812 nb_pixel_total : 4795 time to create 1 rle with old method : 0.005407810211181641 length of segment : 55 time for calcul the mask position with numpy : 0.00017595291137695312 nb_pixel_total : 2992 time to create 1 rle with old method : 0.003444194793701172 length of segment : 49 time for calcul the mask position with numpy : 0.002393484115600586 nb_pixel_total : 99105 time to create 1 rle with old method : 0.10575270652770996 length of segment : 525 time for calcul the mask position with numpy : 0.00043082237243652344 nb_pixel_total : 6486 time to create 1 rle with old method : 0.007494211196899414 length of segment : 123 time for calcul the mask position with numpy : 0.00039505958557128906 nb_pixel_total : 9266 time to create 1 rle with old method : 0.01018381118774414 length of segment : 178 time for calcul the mask position with numpy : 0.0012602806091308594 nb_pixel_total : 42033 time to create 1 rle with old method : 0.04400157928466797 length of segment : 393 time for calcul the mask position with numpy : 0.00025010108947753906 nb_pixel_total : 4326 time to create 1 rle with old method : 0.005156278610229492 length of segment : 80 time for calcul the mask position with numpy : 0.0006005764007568359 nb_pixel_total : 15299 time to create 1 rle with old method : 0.018149614334106445 length of segment : 139 time for calcul the mask position with numpy : 0.0013308525085449219 nb_pixel_total : 32077 time to create 1 rle with old method : 0.0361940860748291 length of segment : 337 time for calcul the mask position with numpy : 0.0004591941833496094 nb_pixel_total : 5835 time to create 1 rle with old method : 0.010861396789550781 length of segment : 86 time for calcul the mask position with numpy : 0.0002849102020263672 nb_pixel_total : 2655 time to create 1 rle with old method : 0.005136966705322266 length of segment : 50 time for calcul the mask position with numpy : 0.0006778240203857422 nb_pixel_total : 19856 time to create 1 rle with old method : 0.033596038818359375 length of segment : 164 time for calcul the mask position with numpy : 0.00015115737915039062 nb_pixel_total : 1593 time to create 1 rle with old method : 0.003168344497680664 length of segment : 46 time for calcul the mask position with numpy : 0.00038123130798339844 nb_pixel_total : 4648 time to create 1 rle with old method : 0.00984644889831543 length of segment : 69 time for calcul the mask position with numpy : 0.003482818603515625 nb_pixel_total : 111791 time to create 1 rle with old method : 0.1274561882019043 length of segment : 521 time for calcul the mask position with numpy : 0.0003161430358886719 nb_pixel_total : 8859 time to create 1 rle with old method : 0.010076761245727539 length of segment : 91 time for calcul the mask position with numpy : 0.00024580955505371094 nb_pixel_total : 7814 time to create 1 rle with old method : 0.008792877197265625 length of segment : 175 time for calcul the mask position with numpy : 0.0003018379211425781 nb_pixel_total : 7233 time to create 1 rle with old method : 0.009658336639404297 length of segment : 115 time for calcul the mask position with numpy : 0.00026607513427734375 nb_pixel_total : 8837 time to create 1 rle with old method : 0.01056528091430664 length of segment : 78 time for calcul the mask position with numpy : 0.0003228187561035156 nb_pixel_total : 16897 time to create 1 rle with old method : 0.02016735076904297 length of segment : 90 time for calcul the mask position with numpy : 0.01079249382019043 nb_pixel_total : 542715 time to create 1 rle with new method : 0.016652822494506836 length of segment : 1272 time for calcul the mask position with numpy : 0.010301828384399414 nb_pixel_total : 484241 time to create 1 rle with new method : 0.020016193389892578 length of segment : 840 time for calcul the mask position with numpy : 0.0002703666687011719 nb_pixel_total : 7254 time to create 1 rle with old method : 0.008636236190795898 length of segment : 95 time for calcul the mask position with numpy : 0.006970643997192383 nb_pixel_total : 324579 time to create 1 rle with new method : 0.01324152946472168 length of segment : 867 time spent for convertir_results : 3.696289539337158 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 56 chid ids of type : 3594 Number RLEs to save : 14790 save missing photos in datou_result : time spend for datou_step_exec : 30.307067394256592 time spend to save output : 0.9564878940582275 total time spend for step 1 : 31.26355528831482 step2:crop_condition Thu Sep 18 14:21:03 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 : 18 ! batch 1 Loaded 56 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 33 About to insert : list_path_to_insert length 33 new photo from crops ! About to upload 33 photos upload in portfolio : 3736932 init cache_photo without model_param we have 33 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758198065_4006170 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 33 photos in the portfolio 3736932 time of upload the photos Elapsed time : 9.084683656692505 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 3736932 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758198076_4006170 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.6215095520019531 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 14 About to insert : list_path_to_insert length 14 new photo from crops ! About to upload 14 photos upload in portfolio : 3736932 init cache_photo without model_param we have 14 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758198091_4006170 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 14 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.3534770011901855 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 3736932 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758198095_4006170 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.5251262187957764 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1384850753, 1384850729, 1384850726, 1384850722, 1384850703, 1384850686, 1384850685, 1384850684, 1384850683, 1384850677, 1384850675, 1384850673, 1384850671, 1384850670, 1384850668, 1384850613, 1384850612, 1384850610] Looping around the photos to save general results len do output : 56 /1384863014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863015Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863016Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863018Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863019Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863021Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863023Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863024Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863026Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863031Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863038Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863040Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863042Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863044Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863048Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863050Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863052Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863054Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863055Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863063Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863064Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863065Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863066Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863069Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863070Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863072Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863074Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863076Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863077Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384863078Didn'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, '3740996') ('3318', '26999691', '1384850753', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850729', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850726', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850722', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850703', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850686', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850685', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850684', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850683', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850677', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850675', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850673', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850671', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850670', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850668', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850613', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850612', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850610', None, None, None, None, None, '3740996') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 186 time used for this insertion : 0.030451297760009766 save_final save missing photos in datou_result : time spend for datou_step_exec : 33.60482573509216 time spend to save output : 0.0327146053314209 total time spend for step 2 : 33.637540340423584 step3:rle_unique_nms_with_priority Thu Sep 18 14:21:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 56 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.20750164985656738 time for calcul the mask position with numpy : 0.1291179656982422 nb_pixel_total : 2057132 time to create 1 rle with new method : 0.14388227462768555 time for calcul the mask position with numpy : 0.005986928939819336 nb_pixel_total : 5225 time to create 1 rle with old method : 0.0057277679443359375 time for calcul the mask position with numpy : 0.0058519840240478516 nb_pixel_total : 11243 time to create 1 rle with old method : 0.012565135955810547 create new chi : 0.3136274814605713 time to delete rle : 0.024617910385131836 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1496 TO DO : save crop sub photo not yet done ! save time : 0.12911677360534668 nb_obj : 8 nb_hashtags : 3 time to prepare the origin masks : 0.595423698425293 time for calcul the mask position with numpy : 0.055539608001708984 nb_pixel_total : 1914805 time to create 1 rle with new method : 0.22279000282287598 time for calcul the mask position with numpy : 0.0060231685638427734 nb_pixel_total : 3388 time to create 1 rle with old method : 0.0037348270416259766 time for calcul the mask position with numpy : 0.007025480270385742 nb_pixel_total : 108261 time to create 1 rle with old method : 0.11674332618713379 time for calcul the mask position with numpy : 0.0061109066009521484 nb_pixel_total : 9488 time to create 1 rle with old method : 0.010457992553710938 time for calcul the mask position with numpy : 0.006317138671875 nb_pixel_total : 3126 time to create 1 rle with old method : 0.0033540725708007812 time for calcul the mask position with numpy : 0.006163597106933594 nb_pixel_total : 3804 time to create 1 rle with old method : 0.00409388542175293 time for calcul the mask position with numpy : 0.00638890266418457 nb_pixel_total : 3817 time to create 1 rle with old method : 0.004335641860961914 time for calcul the mask position with numpy : 0.006500720977783203 nb_pixel_total : 13371 time to create 1 rle with old method : 0.015024662017822266 time for calcul the mask position with numpy : 0.006211042404174805 nb_pixel_total : 13540 time to create 1 rle with old method : 0.014889001846313477 create new chi : 0.511589527130127 time to delete rle : 0.00047707557678222656 batch 1 Loaded 17 chid ids of type : 3594 +++++++++++++Number RLEs to save : 3506 TO DO : save crop sub photo not yet done ! save time : 0.2617833614349365 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.0367436408996582 time for calcul the mask position with numpy : 0.01602911949157715 nb_pixel_total : 1343935 time to create 1 rle with new method : 0.02927875518798828 time for calcul the mask position with numpy : 0.011509895324707031 nb_pixel_total : 729665 time to create 1 rle with new method : 0.027581453323364258 create new chi : 0.09273838996887207 time to delete rle : 0.0002772808074951172 batch 1 Loaded 3 chid ids of type : 3594 ++Number RLEs to save : 3152 TO DO : save crop sub photo not yet done ! save time : 0.22010207176208496 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.17416954040527344 time for calcul the mask position with numpy : 0.11538004875183105 nb_pixel_total : 1969350 time to create 1 rle with new method : 0.0762171745300293 time for calcul the mask position with numpy : 0.0067408084869384766 nb_pixel_total : 98223 time to create 1 rle with old method : 0.11010575294494629 time for calcul the mask position with numpy : 0.006093502044677734 nb_pixel_total : 6027 time to create 1 rle with old method : 0.006247997283935547 create new chi : 0.33139801025390625 time to delete rle : 0.00042819976806640625 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2330 TO DO : save crop sub photo not yet done ! save time : 0.18353724479675293 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.03874969482421875 time for calcul the mask position with numpy : 0.11952400207519531 nb_pixel_total : 2042553 time to create 1 rle with new method : 0.07928347587585449 time for calcul the mask position with numpy : 0.006138801574707031 nb_pixel_total : 25186 time to create 1 rle with old method : 0.027139663696289062 time for calcul the mask position with numpy : 0.0058536529541015625 nb_pixel_total : 5861 time to create 1 rle with old method : 0.006278276443481445 create new chi : 0.25468897819519043 time to delete rle : 0.0003333091735839844 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2042 TO DO : save crop sub photo not yet done ! save time : 0.16089272499084473 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.14886116981506348 time for calcul the mask position with numpy : 0.12609052658081055 nb_pixel_total : 1451894 time to create 1 rle with new method : 0.07993936538696289 time for calcul the mask position with numpy : 0.01055145263671875 nb_pixel_total : 600267 time to create 1 rle with new method : 0.1493546962738037 time for calcul the mask position with numpy : 0.006009817123413086 nb_pixel_total : 3907 time to create 1 rle with old method : 0.004190683364868164 time for calcul the mask position with numpy : 0.005975961685180664 nb_pixel_total : 2834 time to create 1 rle with old method : 0.0030493736267089844 time for calcul the mask position with numpy : 0.006178140640258789 nb_pixel_total : 14698 time to create 1 rle with old method : 0.015543937683105469 create new chi : 0.41681814193725586 time to delete rle : 0.0006864070892333984 batch 1 Loaded 9 chid ids of type : 3594 ++++++++++Number RLEs to save : 4630 TO DO : save crop sub photo not yet done ! save time : 0.30687880516052246 nb_obj : 7 nb_hashtags : 2 time to prepare the origin masks : 0.0844273567199707 time for calcul the mask position with numpy : 0.05815887451171875 nb_pixel_total : 2038259 time to create 1 rle with new method : 0.2147660255432129 time for calcul the mask position with numpy : 0.005939960479736328 nb_pixel_total : 1859 time to create 1 rle with old method : 0.0020563602447509766 time for calcul the mask position with numpy : 0.00561213493347168 nb_pixel_total : 3051 time to create 1 rle with old method : 0.0035643577575683594 time for calcul the mask position with numpy : 0.0058345794677734375 nb_pixel_total : 5394 time to create 1 rle with old method : 0.006196498870849609 time for calcul the mask position with numpy : 0.005812406539916992 nb_pixel_total : 4514 time to create 1 rle with old method : 0.0049915313720703125 time for calcul the mask position with numpy : 0.0058438777923583984 nb_pixel_total : 4700 time to create 1 rle with old method : 0.00522923469543457 time for calcul the mask position with numpy : 0.005986452102661133 nb_pixel_total : 5142 time to create 1 rle with old method : 0.005621671676635742 time for calcul the mask position with numpy : 0.006128072738647461 nb_pixel_total : 10681 time to create 1 rle with old method : 0.011787176132202148 create new chi : 0.3636741638183594 time to delete rle : 0.0003552436828613281 batch 1 Loaded 15 chid ids of type : 3594 +++++++Number RLEs to save : 2196 TO DO : save crop sub photo not yet done ! save time : 0.17438721656799316 No data in photo_id : 1384850684 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.04719376564025879 time for calcul the mask position with numpy : 0.01853632926940918 nb_pixel_total : 2061036 time to create 1 rle with new method : 0.1478135585784912 time for calcul the mask position with numpy : 0.005966901779174805 nb_pixel_total : 2739 time to create 1 rle with old method : 0.003016233444213867 time for calcul the mask position with numpy : 0.005843639373779297 nb_pixel_total : 5227 time to create 1 rle with old method : 0.005530595779418945 time for calcul the mask position with numpy : 0.005771160125732422 nb_pixel_total : 4598 time to create 1 rle with old method : 0.0048177242279052734 create new chi : 0.19756555557250977 time to delete rle : 0.0002636909484863281 batch 1 Loaded 7 chid ids of type : 3594 ++++++Number RLEs to save : 1550 TO DO : save crop sub photo not yet done ! save time : 0.14058780670166016 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.047336578369140625 time for calcul the mask position with numpy : 0.04638481140136719 nb_pixel_total : 1628273 time to create 1 rle with new method : 0.13939690589904785 time for calcul the mask position with numpy : 0.008756399154663086 nb_pixel_total : 445327 time to create 1 rle with new method : 0.14046239852905273 create new chi : 0.3446941375732422 time to delete rle : 0.0002779960632324219 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 2884 TO DO : save crop sub photo not yet done ! save time : 0.204789400100708 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.10540270805358887 time for calcul the mask position with numpy : 0.12088131904602051 nb_pixel_total : 1914298 time to create 1 rle with new method : 0.16048312187194824 time for calcul the mask position with numpy : 0.006852149963378906 nb_pixel_total : 3144 time to create 1 rle with old method : 0.003413677215576172 time for calcul the mask position with numpy : 0.006771087646484375 nb_pixel_total : 156158 time to create 1 rle with new method : 0.07307004928588867 create new chi : 0.38158082962036133 time to delete rle : 0.00041413307189941406 batch 1 Loaded 5 chid ids of type : 3594 ++++++++Number RLEs to save : 3718 TO DO : save crop sub photo not yet done ! save time : 0.2642858028411865 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.1707296371459961 time for calcul the mask position with numpy : 0.11472749710083008 nb_pixel_total : 1960222 time to create 1 rle with new method : 0.19560694694519043 time for calcul the mask position with numpy : 0.005916595458984375 nb_pixel_total : 6486 time to create 1 rle with old method : 0.00691986083984375 time for calcul the mask position with numpy : 0.006320953369140625 nb_pixel_total : 99105 time to create 1 rle with old method : 0.10466408729553223 time for calcul the mask position with numpy : 0.006026506423950195 nb_pixel_total : 2992 time to create 1 rle with old method : 0.003276824951171875 time for calcul the mask position with numpy : 0.005936861038208008 nb_pixel_total : 4795 time to create 1 rle with old method : 0.0051996707916259766 create new chi : 0.4639284610748291 time to delete rle : 0.0004889965057373047 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 2584 TO DO : save crop sub photo not yet done ! save time : 0.19195890426635742 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03225994110107422 time for calcul the mask position with numpy : 0.018968582153320312 nb_pixel_total : 2064334 time to create 1 rle with new method : 0.06813335418701172 time for calcul the mask position with numpy : 0.006035566329956055 nb_pixel_total : 9266 time to create 1 rle with old method : 0.009946346282958984 create new chi : 0.10331559181213379 time to delete rle : 0.00022125244140625 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1436 TO DO : save crop sub photo not yet done ! save time : 0.12833380699157715 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.050667762756347656 time for calcul the mask position with numpy : 0.018558979034423828 nb_pixel_total : 2011942 time to create 1 rle with new method : 0.158280611038208 time for calcul the mask position with numpy : 0.006113290786743164 nb_pixel_total : 15299 time to create 1 rle with old method : 0.01659536361694336 time for calcul the mask position with numpy : 0.005909919738769531 nb_pixel_total : 4326 time to create 1 rle with old method : 0.004811763763427734 time for calcul the mask position with numpy : 0.0059583187103271484 nb_pixel_total : 42033 time to create 1 rle with old method : 0.0449066162109375 create new chi : 0.2712240219116211 time to delete rle : 0.0003540515899658203 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2304 TO DO : save crop sub photo not yet done ! save time : 0.17942404747009277 nb_obj : 9 nb_hashtags : 4 time to prepare the origin masks : 0.5018773078918457 time for calcul the mask position with numpy : 0.20760226249694824 nb_pixel_total : 1878472 time to create 1 rle with new method : 0.07976174354553223 time for calcul the mask position with numpy : 0.006063222885131836 nb_pixel_total : 7814 time to create 1 rle with old method : 0.00907278060913086 time for calcul the mask position with numpy : 0.0059435367584228516 nb_pixel_total : 8859 time to create 1 rle with old method : 0.009627819061279297 time for calcul the mask position with numpy : 0.006508350372314453 nb_pixel_total : 111791 time to create 1 rle with old method : 0.11801767349243164 time for calcul the mask position with numpy : 0.005827665328979492 nb_pixel_total : 4648 time to create 1 rle with old method : 0.0051000118255615234 time for calcul the mask position with numpy : 0.006089210510253906 nb_pixel_total : 1593 time to create 1 rle with old method : 0.0017237663269042969 time for calcul the mask position with numpy : 0.006237506866455078 nb_pixel_total : 19856 time to create 1 rle with old method : 0.021364688873291016 time for calcul the mask position with numpy : 0.005880594253540039 nb_pixel_total : 2655 time to create 1 rle with old method : 0.002975940704345703 time for calcul the mask position with numpy : 0.006278038024902344 nb_pixel_total : 5835 time to create 1 rle with old method : 0.006453037261962891 time for calcul the mask position with numpy : 0.006563663482666016 nb_pixel_total : 32077 time to create 1 rle with old method : 0.03493094444274902 create new chi : 0.5625896453857422 time to delete rle : 0.0006771087646484375 batch 1 Loaded 19 chid ids of type : 3594 +++++++++Number RLEs to save : 4158 TO DO : save crop sub photo not yet done ! save time : 0.2885925769805908 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.059357643127441406 time for calcul the mask position with numpy : 0.015399694442749023 nb_pixel_total : 1497918 time to create 1 rle with new method : 0.030139684677124023 time for calcul the mask position with numpy : 0.009469270706176758 nb_pixel_total : 542715 time to create 1 rle with new method : 0.027986526489257812 time for calcul the mask position with numpy : 0.005994558334350586 nb_pixel_total : 16897 time to create 1 rle with old method : 0.018364906311035156 time for calcul the mask position with numpy : 0.0062940120697021484 nb_pixel_total : 8837 time to create 1 rle with old method : 0.009705305099487305 time for calcul the mask position with numpy : 0.005936622619628906 nb_pixel_total : 7233 time to create 1 rle with old method : 0.007885456085205078 create new chi : 0.13777732849121094 time to delete rle : 0.0006945133209228516 batch 1 Loaded 9 chid ids of type : 3594 +++++Number RLEs to save : 4190 TO DO : save crop sub photo not yet done ! save time : 0.2835702896118164 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03399300575256348 time for calcul the mask position with numpy : 0.01640605926513672 nb_pixel_total : 1589359 time to create 1 rle with new method : 0.028337955474853516 time for calcul the mask position with numpy : 0.008933305740356445 nb_pixel_total : 484241 time to create 1 rle with new method : 0.027819395065307617 create new chi : 0.0818791389465332 time to delete rle : 0.00027251243591308594 batch 1 Loaded 3 chid ids of type : 3594 +++Number RLEs to save : 2760 TO DO : save crop sub photo not yet done ! save time : 0.19105839729309082 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.3207676410675049 time for calcul the mask position with numpy : 0.053340911865234375 nb_pixel_total : 1741767 time to create 1 rle with new method : 0.15680646896362305 time for calcul the mask position with numpy : 0.007804393768310547 nb_pixel_total : 324579 time to create 1 rle with new method : 0.15528154373168945 time for calcul the mask position with numpy : 0.01025700569152832 nb_pixel_total : 7254 time to create 1 rle with old method : 0.007814884185791016 create new chi : 0.4016754627227783 time to delete rle : 0.0007450580596923828 batch 1 Loaded 5 chid ids of type : 3594 +++++Number RLEs to save : 3004 TO DO : save crop sub photo not yet done ! save time : 0.23479747772216797 map_output_result : {1384850753: (0.0, 'Should be the crop_list due to order', 0), 1384850729: (0.0, 'Should be the crop_list due to order', 0), 1384850726: (0.0, 'Should be the crop_list due to order', 0), 1384850722: (0.0, 'Should be the crop_list due to order', 0), 1384850703: (0.0, 'Should be the crop_list due to order', 0), 1384850686: (0.0, 'Should be the crop_list due to order', 0), 1384850685: (0.0, 'Should be the crop_list due to order', 0), 1384850684: (0.0, 'Should be the crop_list due to order', 0.0), 1384850683: (0.0, 'Should be the crop_list due to order', 0), 1384850677: (0.0, 'Should be the crop_list due to order', 0), 1384850675: (0.0, 'Should be the crop_list due to order', 0), 1384850673: (0.0, 'Should be the crop_list due to order', 0), 1384850671: (0.0, 'Should be the crop_list due to order', 0), 1384850670: (0.0, 'Should be the crop_list due to order', 0), 1384850668: (0.0, 'Should be the crop_list due to order', 0), 1384850613: (0.0, 'Should be the crop_list due to order', 0), 1384850612: (0.0, 'Should be the crop_list due to order', 0), 1384850610: (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 [1384850753, 1384850729, 1384850726, 1384850722, 1384850703, 1384850686, 1384850685, 1384850684, 1384850683, 1384850677, 1384850675, 1384850673, 1384850671, 1384850670, 1384850668, 1384850613, 1384850612, 1384850610] Looping around the photos to save general results len do output : 18 /1384850753.Didn't retrieve data . /1384850729.Didn't retrieve data . /1384850726.Didn't retrieve data . /1384850722.Didn't retrieve data . /1384850703.Didn't retrieve data . /1384850686.Didn't retrieve data . /1384850685.Didn't retrieve data . /1384850684.Didn't retrieve data . /1384850683.Didn't retrieve data . /1384850677.Didn't retrieve data . /1384850675.Didn't retrieve data . /1384850673.Didn't retrieve data . /1384850671.Didn't retrieve data . /1384850670.Didn't retrieve data . /1384850668.Didn't retrieve data . /1384850613.Didn't retrieve data . /1384850612.Didn't retrieve data . /1384850610.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, '3740996') ('3318', '26999691', '1384850753', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850729', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850726', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850722', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850703', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850686', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850685', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850684', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850683', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850677', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850675', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850673', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850671', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850670', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850668', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850613', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850612', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850610', None, None, None, None, None, '3740996') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 54 time used for this insertion : 0.017933368682861328 save_final save missing photos in datou_result : time spend for datou_step_exec : 12.056495428085327 time spend to save output : 0.018507719039916992 total time spend for step 3 : 12.075003147125244 step4:ventilate_hashtags_in_portfolio Thu Sep 18 14:21:49 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 : 26999691 get user id for portfolio 26999691 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`=26999691 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','background','carton','metal','mal_croppe','flou','pet_fonce','pehd','autre','pet_clair','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`=26999691 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','background','carton','metal','mal_croppe','flou','pet_fonce','pehd','autre','pet_clair','environnement')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=26999691 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','background','carton','metal','mal_croppe','flou','pet_fonce','pehd','autre','pet_clair','environnement')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/26999763,26999764,26999765,26999766,26999767,26999768,26999769,26999770,26999771,26999772,26999773?tags=papier,background,carton,metal,mal_croppe,flou,pet_fonce,pehd,autre,pet_clair,environnement Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1384850753, 1384850729, 1384850726, 1384850722, 1384850703, 1384850686, 1384850685, 1384850684, 1384850683, 1384850677, 1384850675, 1384850673, 1384850671, 1384850670, 1384850668, 1384850613, 1384850612, 1384850610] Looping around the photos to save general results len do output : 1 /26999691. 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, '3740996') ('3318', '26999691', '1384850753', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850729', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850726', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850722', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850703', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850686', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850685', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850684', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850683', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850677', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850675', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850673', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850671', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850670', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850668', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850613', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850612', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850610', None, None, None, None, None, '3740996') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 19 time used for this insertion : 0.018604278564453125 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.6844239234924316 time spend to save output : 0.018902063369750977 total time spend for step 4 : 1.7033259868621826 step5:final Thu Sep 18 14:21:50 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 : {1384850753: ('0.10946210026577505',), 1384850729: ('0.10946210026577505',), 1384850726: ('0.10946210026577505',), 1384850722: ('0.10946210026577505',), 1384850703: ('0.10946210026577505',), 1384850686: ('0.10946210026577505',), 1384850685: ('0.10946210026577505',), 1384850684: ('0.10946210026577505',), 1384850683: ('0.10946210026577505',), 1384850677: ('0.10946210026577505',), 1384850675: ('0.10946210026577505',), 1384850673: ('0.10946210026577505',), 1384850671: ('0.10946210026577505',), 1384850670: ('0.10946210026577505',), 1384850668: ('0.10946210026577505',), 1384850613: ('0.10946210026577505',), 1384850612: ('0.10946210026577505',), 1384850610: ('0.10946210026577505',)} new output for save of step final : {1384850753: ('0.10946210026577505',), 1384850729: ('0.10946210026577505',), 1384850726: ('0.10946210026577505',), 1384850722: ('0.10946210026577505',), 1384850703: ('0.10946210026577505',), 1384850686: ('0.10946210026577505',), 1384850685: ('0.10946210026577505',), 1384850684: ('0.10946210026577505',), 1384850683: ('0.10946210026577505',), 1384850677: ('0.10946210026577505',), 1384850675: ('0.10946210026577505',), 1384850673: ('0.10946210026577505',), 1384850671: ('0.10946210026577505',), 1384850670: ('0.10946210026577505',), 1384850668: ('0.10946210026577505',), 1384850613: ('0.10946210026577505',), 1384850612: ('0.10946210026577505',), 1384850610: ('0.10946210026577505',)} [1384850753, 1384850729, 1384850726, 1384850722, 1384850703, 1384850686, 1384850685, 1384850684, 1384850683, 1384850677, 1384850675, 1384850673, 1384850671, 1384850670, 1384850668, 1384850613, 1384850612, 1384850610] Looping around the photos to save general results len do output : 18 /1384850753.Didn't retrieve data . /1384850729.Didn't retrieve data . /1384850726.Didn't retrieve data . /1384850722.Didn't retrieve data . /1384850703.Didn't retrieve data . /1384850686.Didn't retrieve data . /1384850685.Didn't retrieve data . /1384850684.Didn't retrieve data . /1384850683.Didn't retrieve data . /1384850677.Didn't retrieve data . /1384850675.Didn't retrieve data . /1384850673.Didn't retrieve data . /1384850671.Didn't retrieve data . /1384850670.Didn't retrieve data . /1384850668.Didn't retrieve data . /1384850613.Didn't retrieve data . /1384850612.Didn't retrieve data . /1384850610.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, '3740996') ('3318', '26999691', '1384850753', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850729', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850726', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850722', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850703', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850686', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850685', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850684', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850683', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850677', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850675', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850673', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850671', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850670', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850668', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850613', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850612', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850610', None, None, None, None, None, '3740996') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 54 time used for this insertion : 0.01627373695373535 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.1241147518157959 time spend to save output : 0.017038345336914062 total time spend for step 5 : 0.14115309715270996 step6:blur_detection Thu Sep 18 14:21:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1758198029_4006170_1384850753_c5c5b5b8d698366a3819e6ea8c9b6ee1.jpg resize: (1080, 1920) 1384850753 4.88585799636155 treat image : temp/1758198029_4006170_1384850729_f42d309e7a30363302debf3c7d432a41.jpg resize: (1080, 1920) 1384850729 -0.720511877623299 treat image : temp/1758198029_4006170_1384850726_78df7e357674e934ae9bdaf76e948c78.jpg resize: (1080, 1920) 1384850726 1.5106830136886378 treat image : temp/1758198029_4006170_1384850722_c717d77a98cc2268c223855d712e4818.jpg resize: (1080, 1920) 1384850722 9.2550078565653 treat image : temp/1758198029_4006170_1384850703_7c15f891e1ef7ec54efb68ed5b1703a4.jpg resize: (1080, 1920) 1384850703 9.272146521302126 treat image : temp/1758198029_4006170_1384850686_053df4c5f779bb1de39720c18d6e2c0b.jpg resize: (1080, 1920) 1384850686 1.7735260734879967 treat image : temp/1758198029_4006170_1384850685_3bbbf00c112a0043a3716ff587dc8de7.jpg resize: (1080, 1920) 1384850685 -4.954937095072724 treat image : temp/1758198029_4006170_1384850684_ca7159f1631d66bda8638f680475291a.jpg resize: (1080, 1920) 1384850684 -0.6238696186403665 treat image : temp/1758198029_4006170_1384850683_2ac3774f579f9a667bcfac85efab3f06.jpg resize: (1080, 1920) 1384850683 -4.537435159757455 treat image : temp/1758198029_4006170_1384850677_67ca7abe0d094565eec6b799147e1af8.jpg resize: (1080, 1920) 1384850677 -0.4167053757462965 treat image : temp/1758198029_4006170_1384850675_cfc4225798fbda71ba6d2e873db3d106.jpg resize: (1080, 1920) 1384850675 0.6135673514449889 treat image : temp/1758198029_4006170_1384850673_bd6c136b1ceec3ca5fd5b26d4f2876b2.jpg resize: (1080, 1920) 1384850673 -4.54830080691086 treat image : temp/1758198029_4006170_1384850671_39c7afce43c58b06817e40ad54ff98d3.jpg resize: (1080, 1920) 1384850671 -0.3879169290070413 treat image : temp/1758198029_4006170_1384850670_bf1b03a1f2b811493fff7395c7b08eaa.jpg resize: (1080, 1920) 1384850670 -3.94055152002057 treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9.jpg resize: (1080, 1920) 1384850668 0.6214910852666703 treat image : temp/1758198029_4006170_1384850613_3ae29d637a677d1569032dbd14570888.jpg resize: (1080, 1920) 1384850613 -1.085116204660593 treat image : temp/1758198029_4006170_1384850612_23de45557d1cd363c2023810298c05b1.jpg resize: (1080, 1920) 1384850612 0.36593712832299263 treat image : temp/1758198029_4006170_1384850610_1e7534bae8cbd44c3eb88b1995d38fea.jpg resize: (1080, 1920) 1384850610 0.6047496727638167 treat image : temp/1758198029_4006170_1384850753_c5c5b5b8d698366a3819e6ea8c9b6ee1_rle_crop_3966367189_0.png resize: (89, 157) 1384863014 2.0847464223266634 treat image : temp/1758198029_4006170_1384850753_c5c5b5b8d698366a3819e6ea8c9b6ee1_rle_crop_3966367190_0.png resize: (104, 86) 1384863015 -0.27793007327998775 treat image : temp/1758198029_4006170_1384850729_f42d309e7a30363302debf3c7d432a41_rle_crop_3966367191_0.png resize: (122, 208) 1384863016 -0.9396364077106407 treat image : temp/1758198029_4006170_1384850729_f42d309e7a30363302debf3c7d432a41_rle_crop_3966367193_0.png resize: (89, 56) 1384863017 -0.002139389058632448 treat image : temp/1758198029_4006170_1384850729_f42d309e7a30363302debf3c7d432a41_rle_crop_3966367194_0.png resize: (59, 92) 1384863018 0.6289764246792022 treat image : temp/1758198029_4006170_1384850729_f42d309e7a30363302debf3c7d432a41_rle_crop_3966367198_0.png resize: (59, 74) 1384863019 -2.484908419911985 treat image : temp/1758198029_4006170_1384850722_c717d77a98cc2268c223855d712e4818_rle_crop_3966367200_0.png resize: (146, 96) 1384863020 -2.1702120657822994 treat image : temp/1758198029_4006170_1384850703_7c15f891e1ef7ec54efb68ed5b1703a4_rle_crop_3966367202_0.png resize: (142, 96) 1384863021 -2.0484939009806262 treat image : temp/1758198029_4006170_1384850686_053df4c5f779bb1de39720c18d6e2c0b_rle_crop_3966367204_0.png resize: (105, 184) 1384863022 1.2234096760363717 treat image : temp/1758198029_4006170_1384850686_053df4c5f779bb1de39720c18d6e2c0b_rle_crop_3966367205_0.png resize: (79, 60) 1384863023 -1.6766308727573127 treat image : temp/1758198029_4006170_1384850686_053df4c5f779bb1de39720c18d6e2c0b_rle_crop_3966367206_0.png resize: (137, 83) 1384863024 -2.7435507943148494 treat image : temp/1758198029_4006170_1384850685_3bbbf00c112a0043a3716ff587dc8de7_rle_crop_3966367208_0.png resize: (105, 171) 1384863025 -4.121182031019918 treat image : temp/1758198029_4006170_1384850685_3bbbf00c112a0043a3716ff587dc8de7_rle_crop_3966367209_0.png resize: (94, 96) 1384863026 -1.5115502598353574 treat image : temp/1758198029_4006170_1384850685_3bbbf00c112a0043a3716ff587dc8de7_rle_crop_3966367211_0.png resize: (114, 78) 1384863027 -2.8449824434279614 treat image : temp/1758198029_4006170_1384850685_3bbbf00c112a0043a3716ff587dc8de7_rle_crop_3966367212_0.png resize: (70, 103) 1384863028 -5.492063377658868 treat image : temp/1758198029_4006170_1384850685_3bbbf00c112a0043a3716ff587dc8de7_rle_crop_3966367213_0.png resize: (62, 62) 1384863029 -3.142376265612424 treat image : temp/1758198029_4006170_1384850685_3bbbf00c112a0043a3716ff587dc8de7_rle_crop_3966367214_0.png resize: (38, 73) 1384863030 -4.291190640033266 treat image : temp/1758198029_4006170_1384850683_2ac3774f579f9a667bcfac85efab3f06_rle_crop_3966367215_0.png resize: (88, 76) 1384863031 -0.5441599265424802 treat image : temp/1758198029_4006170_1384850683_2ac3774f579f9a667bcfac85efab3f06_rle_crop_3966367217_0.png resize: (69, 65) 1384863032 -2.287221959082604 treat image : temp/1758198029_4006170_1384850675_cfc4225798fbda71ba6d2e873db3d106_rle_crop_3966367220_0.png resize: (86, 76) 1384863034 -1.1736912317311574 treat image : temp/1758198029_4006170_1384850673_bd6c136b1ceec3ca5fd5b26d4f2876b2_rle_crop_3966367222_0.png resize: (48, 88) 1384863035 -4.525990604850941 treat image : temp/1758198029_4006170_1384850673_bd6c136b1ceec3ca5fd5b26d4f2876b2_rle_crop_3966367224_0.png resize: (85, 139) 1384863036 -4.443493368141887 treat image : temp/1758198029_4006170_1384850671_39c7afce43c58b06817e40ad54ff98d3_rle_crop_3966367225_0.png resize: (178, 89) 1384863038 -0.6912746398815972 treat image : temp/1758198029_4006170_1384850670_bf1b03a1f2b811493fff7395c7b08eaa_rle_crop_3966367227_0.png resize: (80, 80) 1384863039 -1.3887321587842114 treat image : temp/1758198029_4006170_1384850670_bf1b03a1f2b811493fff7395c7b08eaa_rle_crop_3966367228_0.png resize: (130, 176) 1384863040 -2.2746102559214134 treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367230_0.png resize: (83, 78) 1384863042 6.747241268057185 treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367231_0.png resize: (50, 80) 1384863043 1.2635133842039723 treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367232_0.png resize: (148, 218) 1384863044 -1.2681127856919656 treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367234_0.png resize: (68, 111) 1384863045 -1.1743142453237976 treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367237_0.png resize: (175, 83) 1384863047 -1.667322496916589 treat image : temp/1758198029_4006170_1384850613_3ae29d637a677d1569032dbd14570888_rle_crop_3966367238_0.png resize: (114, 128) 1384863048 -2.820167510203855 treat image : temp/1758198029_4006170_1384850613_3ae29d637a677d1569032dbd14570888_rle_crop_3966367239_0.png resize: (76, 156) 1384863049 1.1858903152630296 treat image : temp/1758198029_4006170_1384850610_1e7534bae8cbd44c3eb88b1995d38fea_rle_crop_3966367243_0.png resize: (95, 110) 1384863050 0.5773072054565902 treat image : temp/1758198029_4006170_1384850729_f42d309e7a30363302debf3c7d432a41_rle_crop_3966367195_0.png resize: (40, 100) 1384863051 3.5536210814355975 treat image : temp/1758198029_4006170_1384850729_f42d309e7a30363302debf3c7d432a41_rle_crop_3966367197_0.png resize: (533, 332) 1384863052 0.23712268151980986 treat image : temp/1758198029_4006170_1384850703_7c15f891e1ef7ec54efb68ed5b1703a4_rle_crop_3966367203_0.png resize: (327, 108) 1384863053 -1.3807298135888095 treat image : temp/1758198029_4006170_1384850675_cfc4225798fbda71ba6d2e873db3d106_rle_crop_3966367219_0.png resize: (754, 780) 1384863054 -0.2916123001088732 treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367235_0.png resize: (520, 345) 1384863055 0.35148756344301835 treat image : temp/1758198029_4006170_1384850729_f42d309e7a30363302debf3c7d432a41_rle_crop_3966367192_0.png resize: (146, 138) 1384863061 -4.087951670491632 treat image : temp/1758198029_4006170_1384850729_f42d309e7a30363302debf3c7d432a41_rle_crop_3966367196_0.png resize: (94, 146) 1384863062 -3.2945718275756857 treat image : temp/1758198029_4006170_1384850726_78df7e357674e934ae9bdaf76e948c78_rle_crop_3966367199_0.png resize: (972, 1086) 1384863063 -0.36727377759016616 treat image : temp/1758198029_4006170_1384850722_c717d77a98cc2268c223855d712e4818_rle_crop_3966367201_0.png resize: (478, 327) 1384863064 0.3693778565612755 treat image : temp/1758198029_4006170_1384850686_053df4c5f779bb1de39720c18d6e2c0b_rle_crop_3966367207_0.png resize: (971, 1090) 1384863065 -0.37626531556656995 treat image : temp/1758198029_4006170_1384850685_3bbbf00c112a0043a3716ff587dc8de7_rle_crop_3966367210_0.png resize: (68, 96) 1384863066 -4.077832885601545 treat image : temp/1758198029_4006170_1384850677_67ca7abe0d094565eec6b799147e1af8_rle_crop_3966367218_0.png resize: (702, 929) 1384863067 0.19103100976176196 treat image : temp/1758198029_4006170_1384850673_bd6c136b1ceec3ca5fd5b26d4f2876b2_rle_crop_3966367221_0.png resize: (54, 109) 1384863068 -2.8599594913055006 treat image : temp/1758198029_4006170_1384850673_bd6c136b1ceec3ca5fd5b26d4f2876b2_rle_crop_3966367223_0.png resize: (521, 306) 1384863069 0.27665543979018953 treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367236_0.png resize: (87, 126) 1384863070 -0.6701943272311949 treat image : temp/1758198029_4006170_1384850613_3ae29d637a677d1569032dbd14570888_rle_crop_3966367240_0.png resize: (86, 253) 1384863071 -0.41266495122367663 treat image : temp/1758198029_4006170_1384850613_3ae29d637a677d1569032dbd14570888_rle_crop_3966367241_0.png resize: (910, 854) 1384863072 -0.1798944832801055 treat image : temp/1758198029_4006170_1384850612_23de45557d1cd363c2023810298c05b1_rle_crop_3966367242_0.png resize: (739, 968) 1384863073 0.9230744749430455 treat image : temp/1758198029_4006170_1384850610_1e7534bae8cbd44c3eb88b1995d38fea_rle_crop_3966367244_0.png resize: (660, 907) 1384863074 0.4612765270621806 treat image : temp/1758198029_4006170_1384850683_2ac3774f579f9a667bcfac85efab3f06_rle_crop_3966367216_0.png resize: (65, 124) 1384863075 -3.2469722799165535 treat image : temp/1758198029_4006170_1384850670_bf1b03a1f2b811493fff7395c7b08eaa_rle_crop_3966367226_0.png resize: (387, 143) 1384863076 -1.0423495549519095 treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367229_0.png resize: (322, 133) 1384863077 -0.6530818660638811 treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367233_0.png resize: (46, 45) 1384863078 3.50454878424827 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 : 74 time used for this insertion : 0.014684677124023438 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 74 time used for this insertion : 0.014348983764648438 save missing photos in datou_result : time spend for datou_step_exec : 15.849285125732422 time spend to save output : 0.03373456001281738 total time spend for step 6 : 15.88301968574524 step7:brightness Thu Sep 18 14:22:06 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/1758198029_4006170_1384850753_c5c5b5b8d698366a3819e6ea8c9b6ee1.jpg treat image : temp/1758198029_4006170_1384850729_f42d309e7a30363302debf3c7d432a41.jpg treat image : temp/1758198029_4006170_1384850726_78df7e357674e934ae9bdaf76e948c78.jpg treat image : temp/1758198029_4006170_1384850722_c717d77a98cc2268c223855d712e4818.jpg treat image : temp/1758198029_4006170_1384850703_7c15f891e1ef7ec54efb68ed5b1703a4.jpg treat image : temp/1758198029_4006170_1384850686_053df4c5f779bb1de39720c18d6e2c0b.jpg treat image : temp/1758198029_4006170_1384850685_3bbbf00c112a0043a3716ff587dc8de7.jpg treat image : temp/1758198029_4006170_1384850684_ca7159f1631d66bda8638f680475291a.jpg treat image : temp/1758198029_4006170_1384850683_2ac3774f579f9a667bcfac85efab3f06.jpg treat image : temp/1758198029_4006170_1384850677_67ca7abe0d094565eec6b799147e1af8.jpg treat image : 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temp/1758198029_4006170_1384850613_3ae29d637a677d1569032dbd14570888_rle_crop_3966367240_0.png treat image : temp/1758198029_4006170_1384850613_3ae29d637a677d1569032dbd14570888_rle_crop_3966367241_0.png treat image : temp/1758198029_4006170_1384850612_23de45557d1cd363c2023810298c05b1_rle_crop_3966367242_0.png treat image : temp/1758198029_4006170_1384850610_1e7534bae8cbd44c3eb88b1995d38fea_rle_crop_3966367244_0.png treat image : temp/1758198029_4006170_1384850683_2ac3774f579f9a667bcfac85efab3f06_rle_crop_3966367216_0.png treat image : temp/1758198029_4006170_1384850670_bf1b03a1f2b811493fff7395c7b08eaa_rle_crop_3966367226_0.png treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367229_0.png treat image : temp/1758198029_4006170_1384850668_4c981c40e39070e5e22a7337f9796dc9_rle_crop_3966367233_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 : 74 time used for this insertion : 0.015913009643554688 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 74 time used for this insertion : 0.01849508285522461 save missing photos in datou_result : time spend for datou_step_exec : 4.641308784484863 time spend to save output : 0.03898477554321289 total time spend for step 7 : 4.680293560028076 step8:velours_tree Thu Sep 18 14:22:11 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 : 0.09119987487792969 time spend to save output : 4.029273986816406e-05 total time spend for step 8 : 0.09124016761779785 step9:send_mail_cod Thu Sep 18 14:22:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin/workarea/git/Velours/python in order to get the selector url, please entre the license of selector results_Auto_P26999691_18-09-2025_14_22_11.pdf 26999763 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 .imagette269997631758198131 26999764 imagette269997641758198133 26999765 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette269997651758198133 26999766 imagette269997661758198133 26999767 imagette269997671758198133 26999768 imagette269997681758198133 26999769 imagette269997691758198133 26999770 imagette269997701758198133 26999771 change filename to text .change filename to text .change filename to text .change filename to text .imagette269997711758198133 26999772 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 .imagette269997721758198133 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=26999691 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/26999763,26999764,26999765,26999766,26999767,26999768,26999769,26999770,26999771,26999772,26999773?tags=papier,background,carton,metal,mal_croppe,flou,pet_fonce,pehd,autre,pet_clair,environnement args[1384850753] : ((1384850753, 4.88585799636155, 492688767), (1384850753, 0.38690352214288404, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850729] : ((1384850729, -0.720511877623299, 492688767), (1384850729, 0.18668089646218824, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850726] : ((1384850726, 1.5106830136886378, 492688767), (1384850726, 0.27008591646147073, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850722] : ((1384850722, 9.2550078565653, 492688767), (1384850722, 0.2119852298023897, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850703] : ((1384850703, 9.272146521302126, 492688767), (1384850703, 0.3114098067689661, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850686] : ((1384850686, 1.7735260734879967, 492688767), (1384850686, 0.35695421058470433, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850685] : ((1384850685, -4.954937095072724, 492609224), (1384850685, 0.520556368742535, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850684] : ((1384850684, -0.6238696186403665, 492688767), (1384850684, 0.9554817822169357, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850683] : ((1384850683, -4.537435159757455, 492609224), (1384850683, 0.42087119211534946, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850677] : ((1384850677, -0.4167053757462965, 492688767), (1384850677, 0.5519218432811098, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850675] : ((1384850675, 0.6135673514449889, 492688767), (1384850675, 0.6723007023947732, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850673] : ((1384850673, -4.54830080691086, 492609224), (1384850673, 0.312048849057159, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850671] : ((1384850671, -0.3879169290070413, 492688767), (1384850671, 0.5151131407802159, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850670] : ((1384850670, -3.94055152002057, 492609224), (1384850670, 0.4312123313766224, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850668] : ((1384850668, 0.6214910852666703, 492688767), (1384850668, 0.31616378712141696, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850613] : ((1384850613, -1.085116204660593, 492688767), (1384850613, 0.9724058991215905, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850612] : ((1384850612, 0.36593712832299263, 492688767), (1384850612, 0.7201106775830854, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com args[1384850610] : ((1384850610, 0.6047496727638167, 492688767), (1384850610, 1.0034215262703228, 2107752395), '0.10946210026577505') We are sending mail with results at report@fotonower.com refus_total : 0.10946210026577505 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=26999691 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_P26999691_18-09-2025_14_22_11.pdf results_Auto_P26999691_18-09-2025_14_22_11.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26999691_18-09-2025_14_22_11.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','26999691','results_Auto_P26999691_18-09-2025_14_22_11.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26999691_18-09-2025_14_22_11.pdf','pdf','','0.52','0.10946210026577505') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/26999691

https://www.fotonower.com/image?json=false&list_photos_id=1384850753
La photo est trop floue, merci de reprendre une photo.(avec le score = 4.88585799636155)
https://www.fotonower.com/image?json=false&list_photos_id=1384850729
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850726
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.5106830136886378)
https://www.fotonower.com/image?json=false&list_photos_id=1384850722
La photo est trop floue, merci de reprendre une photo.(avec le score = 9.2550078565653)
https://www.fotonower.com/image?json=false&list_photos_id=1384850703
La photo est trop floue, merci de reprendre une photo.(avec le score = 9.272146521302126)
https://www.fotonower.com/image?json=false&list_photos_id=1384850686
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.7735260734879967)
https://www.fotonower.com/image?json=false&list_photos_id=1384850685
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850684
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850683
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850677
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850675
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850673
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850671
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850670
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850668
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850613
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850612
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1384850610
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 10.95%
Veuillez trouver les photos des contaminants.

exemples de contaminants: papier: https://www.fotonower.com/view/26999763?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/26999765?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/26999771?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/26999772?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26999691_18-09-2025_14_22_11.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/26999763,26999764,26999765,26999766,26999767,26999768,26999769,26999770,26999771,26999772,26999773?tags=papier,background,carton,metal,mal_croppe,flou,pet_fonce,pehd,autre,pet_clair,environnement.


L'équipe Fotonower 202 b'' Server: nginx Date: Thu, 18 Sep 2025 12:22:17 GMT Content-Length: 0 Connection: close X-Message-Id: Vfxom5WYQKagGRjmxz8LFQ 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 [1384850753, 1384850729, 1384850726, 1384850722, 1384850703, 1384850686, 1384850685, 1384850684, 1384850683, 1384850677, 1384850675, 1384850673, 1384850671, 1384850670, 1384850668, 1384850613, 1384850612, 1384850610] 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, '3740996') ('3318', '26999691', '1384850753', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850729', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850726', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850722', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850703', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850686', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850685', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850684', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850683', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850677', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850675', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850673', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850671', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850670', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850668', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850613', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850612', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850610', None, None, None, None, None, '3740996') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 18 time used for this insertion : 0.015214681625366211 save_final save missing photos in datou_result : time spend for datou_step_exec : 5.390564680099487 time spend to save output : 0.015563011169433594 total time spend for step 9 : 5.406127691268921 step10:split_time_score Thu Sep 18 14:22:17 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('13', 18),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 18092025 26999691 Nombre de photos uploadées : 18 / 23040 (0%) 18092025 26999691 Nombre de photos taguées (types de déchets): 0 / 18 (0%) 18092025 26999691 Nombre de photos taguées (volume) : 0 / 18 (0%) elapsed_time : load_data_split_time_score 2.86102294921875e-06 elapsed_time : order_list_meta_photo_and_scores 9.059906005859375e-06 ?????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0008749961853027344 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.2133183479309082 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 : 0.029968609708193043 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26993632_18-09-2025_09_01_37.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26993632 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`=26993632 AND mptpi.`type`=3594 To do Qualite : 0.07461375911896746 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26994368_18-09-2025_09_32_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26994368 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`=26994368 AND mptpi.`type`=3594 To do Qualite : 0.13248333547668037 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26994376_18-09-2025_09_21_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26994376 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`=26994376 AND mptpi.`type`=3594 To do Qualite : 0.02829922027290448 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26994377_18-09-2025_09_11_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26994377 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`=26994377 AND mptpi.`type`=3594 To do Qualite : 0.22073720421810702 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26998416_18-09-2025_13_02_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26998416 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`=26998416 AND mptpi.`type`=3594 To do Qualite : 0.06378718171296298 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26997919_18-09-2025_12_41_36.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26997919 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`=26997919 AND mptpi.`type`=3594 To do Qualite : 0.10073354713220166 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26996058_18-09-2025_13_11_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26996058 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`=26996058 AND mptpi.`type`=3594 To do Qualite : 0.14480436599794244 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26997922_18-09-2025_12_31_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26997922 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`=26997922 AND mptpi.`type`=3594 To do Qualite : 0.10946210026577505 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26999691_18-09-2025_14_22_11.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26999691 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`=26999691 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'18092025': {'nb_upload': 18, '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 [1384850753, 1384850729, 1384850726, 1384850722, 1384850703, 1384850686, 1384850685, 1384850684, 1384850683, 1384850677, 1384850675, 1384850673, 1384850671, 1384850670, 1384850668, 1384850613, 1384850612, 1384850610] Looping around the photos to save general results len do output : 1 /26999691Didn'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, '3740996') ('3318', '26999691', '1384850753', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850729', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850726', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850722', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850703', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850686', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850685', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850684', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850683', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850677', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850675', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850673', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850671', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850670', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850668', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850613', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850612', None, None, None, None, None, '3740996') ('3318', None, None, None, None, None, None, None, '3740996') ('3318', '26999691', '1384850610', None, None, None, None, None, '3740996') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 19 time used for this insertion : 0.02209186553955078 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.9028642177581787 time spend to save output : 0.022293806076049805 total time spend for step 10 : 0.9251580238342285 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 18 set_done_treatment 62.05user 26.73system 1:51.94elapsed 79%CPU (0avgtext+0avgdata 2932992maxresident)k 501520inputs+35032outputs (20major+1969980minor)pagefaults 0swaps