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 : 2784332 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 : ['3535195'] with mtr_portfolio_ids : ['25983542'] and first list_photo_ids : [] new path : /proc/2784332/ 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 : 2.3910598754882812 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 Aug 14 14:20:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6839 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-08-14 14:20:34.013893: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-08-14 14:20:34.040569: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-08-14 14:20:34.042703: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd7cc000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-08-14 14:20:34.042744: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-08-14 14:20:34.046311: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-08-14 14:20:34.218308: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xa84a660 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-08-14 14:20:34.218360: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-08-14 14:20:34.219817: 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 2025-08-14 14:20:34.220193: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-08-14 14:20:34.223156: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-08-14 14:20:34.225636: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-08-14 14:20:34.225979: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-08-14 14:20:34.228312: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-08-14 14:20:34.229562: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-08-14 14:20:34.234213: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-08-14 14:20:34.235663: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-08-14 14:20:34.235723: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-08-14 14:20:34.236501: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-08-14 14:20:34.236518: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-08-14 14:20:34.236527: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-08-14 14:20:34.241524: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9836 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. 2025-08-14 14:20:34.595409: 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 2025-08-14 14:20:34.595487: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-08-14 14:20:34.595508: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-08-14 14:20:34.595526: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-08-14 14:20:34.595544: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-08-14 14:20:34.595561: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-08-14 14:20:34.595579: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-08-14 14:20:34.595596: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-08-14 14:20:34.597204: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-08-14 14:20:34.598596: 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 2025-08-14 14:20:34.598639: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-08-14 14:20:34.598661: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-08-14 14:20:34.598678: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-08-14 14:20:34.598695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-08-14 14:20:34.598715: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-08-14 14:20:34.598741: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-08-14 14:20:34.598761: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-08-14 14:20:34.600427: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-08-14 14:20:34.600464: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-08-14 14:20:34.600476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-08-14 14:20:34.600487: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-08-14 14:20:34.602159: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9836 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 : [] 2025-08-14 14:20:41.660068: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-08-14 14:20:41.821583: 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: 30.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 22 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 24.00000 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: 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 : 17 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 : 16 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 21.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 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 : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 20.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 19.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 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 : 18 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 21.00000 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: 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 : 13 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 : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 11.00000 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: 31.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 24.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 14 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 17.00000 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 : 2 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 26.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 5 Detection mask done ! Trying to reset tf kernel 2784816 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1145 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 : 6300 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 : 0.0002624988555908203 nb_pixel_total : 5801 time to create 1 rle with old method : 0.007048368453979492 length of segment : 85 time for calcul the mask position with numpy : 0.00048160552978515625 nb_pixel_total : 10050 time to create 1 rle with old method : 0.011837005615234375 length of segment : 138 time for calcul the mask position with numpy : 0.00039958953857421875 nb_pixel_total : 6903 time to create 1 rle with old method : 0.008257627487182617 length of segment : 95 time for calcul the mask position with numpy : 0.0004410743713378906 nb_pixel_total : 18008 time to create 1 rle with old method : 0.02075815200805664 length of segment : 177 time for calcul the mask position with numpy : 0.002707958221435547 nb_pixel_total : 104775 time to create 1 rle with old method : 0.11928057670593262 length of segment : 534 time for calcul the mask position with numpy : 0.0002760887145996094 nb_pixel_total : 3903 time to create 1 rle with old method : 0.004566669464111328 length of segment : 117 time for calcul the mask position with numpy : 0.0029327869415283203 nb_pixel_total : 107346 time to create 1 rle with old method : 0.12171506881713867 length of segment : 536 time for calcul the mask position with numpy : 0.00011682510375976562 nb_pixel_total : 1187 time to create 1 rle with old method : 0.0014755725860595703 length of segment : 54 time for calcul the mask position with numpy : 0.0004849433898925781 nb_pixel_total : 9513 time to create 1 rle with old method : 0.011031866073608398 length of segment : 167 time for calcul the mask position with numpy : 0.000213623046875 nb_pixel_total : 1719 time to create 1 rle with old method : 0.0021660327911376953 length of segment : 118 time for calcul the mask position with numpy : 0.000171661376953125 nb_pixel_total : 3617 time to create 1 rle with old method : 0.0043718814849853516 length of segment : 57 time for calcul the mask position with numpy : 9.846687316894531e-05 nb_pixel_total : 3781 time to create 1 rle with old method : 0.0045986175537109375 length of segment : 77 time for calcul the mask position with numpy : 0.0009458065032958984 nb_pixel_total : 19005 time to create 1 rle with old method : 0.02185654640197754 length of segment : 294 time for calcul the mask position with numpy : 0.0002999305725097656 nb_pixel_total : 3751 time to create 1 rle with old method : 0.004374265670776367 length of segment : 125 time for calcul the mask position with numpy : 0.0006263256072998047 nb_pixel_total : 18954 time to create 1 rle with old method : 0.022300004959106445 length of segment : 87 time for calcul the mask position with numpy : 0.00045490264892578125 nb_pixel_total : 11355 time to create 1 rle with old method : 0.013278484344482422 length of segment : 147 time for calcul the mask position with numpy : 0.0003714561462402344 nb_pixel_total : 9224 time to create 1 rle with old method : 0.010962247848510742 length of segment : 149 time for calcul the mask position with numpy : 0.0034482479095458984 nb_pixel_total : 90918 time to create 1 rle with old method : 0.10488438606262207 length of segment : 549 time for calcul the mask position with numpy : 0.001062154769897461 nb_pixel_total : 18449 time to create 1 rle with old method : 0.02166295051574707 length of segment : 197 time for calcul the mask position with numpy : 0.0005784034729003906 nb_pixel_total : 9367 time to create 1 rle with old method : 0.010869503021240234 length of segment : 183 time for calcul the mask position with numpy : 0.0003864765167236328 nb_pixel_total : 17262 time to create 1 rle with old method : 0.020228147506713867 length of segment : 205 time for calcul the mask position with numpy : 0.0005574226379394531 nb_pixel_total : 8331 time to create 1 rle with old method : 0.009744644165039062 length of segment : 164 time for calcul the mask position with numpy : 0.0008339881896972656 nb_pixel_total : 33823 time to create 1 rle with old method : 0.038243770599365234 length of segment : 461 time for calcul the mask position with numpy : 0.0003094673156738281 nb_pixel_total : 8976 time to create 1 rle with old method : 0.010671854019165039 length of segment : 66 time for calcul the mask position with numpy : 0.0030329227447509766 nb_pixel_total : 95966 time to create 1 rle with old method : 0.10906386375427246 length of segment : 471 time for calcul the mask position with numpy : 0.00035500526428222656 nb_pixel_total : 7241 time to create 1 rle with old method : 0.008379936218261719 length of segment : 104 time for calcul the mask position with numpy : 0.0005998611450195312 nb_pixel_total : 15387 time to create 1 rle with old method : 0.01807403564453125 length of segment : 175 time for calcul the mask position with numpy : 0.0006120204925537109 nb_pixel_total : 13569 time to create 1 rle with old method : 0.015804052352905273 length of segment : 238 time for calcul the mask position with numpy : 0.00025725364685058594 nb_pixel_total : 4905 time to create 1 rle with old method : 0.005936622619628906 length of segment : 77 time for calcul the mask position with numpy : 0.00043082237243652344 nb_pixel_total : 7214 time to create 1 rle with old method : 0.008717536926269531 length of segment : 106 time for calcul the mask position with numpy : 0.00027441978454589844 nb_pixel_total : 6590 time to create 1 rle with old method : 0.008303165435791016 length of segment : 105 time for calcul the mask position with numpy : 0.0004210472106933594 nb_pixel_total : 16256 time to create 1 rle with old method : 0.018579483032226562 length of segment : 191 time for calcul the mask position with numpy : 0.0005800724029541016 nb_pixel_total : 13419 time to create 1 rle with old method : 0.015820980072021484 length of segment : 130 time for calcul the mask position with numpy : 0.00031495094299316406 nb_pixel_total : 7619 time to create 1 rle with old method : 0.008870840072631836 length of segment : 127 time for calcul the mask position with numpy : 0.00039768218994140625 nb_pixel_total : 9732 time to create 1 rle with old method : 0.011374473571777344 length of segment : 129 time for calcul the mask position with numpy : 0.0002181529998779297 nb_pixel_total : 4410 time to create 1 rle with old method : 0.00545954704284668 length of segment : 66 time for calcul the mask position with numpy : 0.0027878284454345703 nb_pixel_total : 114214 time to create 1 rle with old method : 0.12849020957946777 length of segment : 524 time for calcul the mask position with numpy : 0.0009684562683105469 nb_pixel_total : 35503 time to create 1 rle with old method : 0.04044628143310547 length of segment : 228 time for calcul the mask position with numpy : 0.0010356903076171875 nb_pixel_total : 56220 time to create 1 rle with old method : 0.06446623802185059 length of segment : 236 time for calcul the mask position with numpy : 0.00012612342834472656 nb_pixel_total : 4778 time to create 1 rle with old method : 0.005658626556396484 length of segment : 78 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 2821 time to create 1 rle with old method : 0.0034532546997070312 length of segment : 47 time for calcul the mask position with numpy : 0.0011799335479736328 nb_pixel_total : 48502 time to create 1 rle with old method : 0.058747053146362305 length of segment : 492 time for calcul the mask position with numpy : 0.00010371208190917969 nb_pixel_total : 3847 time to create 1 rle with old method : 0.0047512054443359375 length of segment : 50 time for calcul the mask position with numpy : 0.0006043910980224609 nb_pixel_total : 30546 time to create 1 rle with old method : 0.03577017784118652 length of segment : 502 time for calcul the mask position with numpy : 0.000225067138671875 nb_pixel_total : 8706 time to create 1 rle with old method : 0.010807037353515625 length of segment : 130 time for calcul the mask position with numpy : 0.0005173683166503906 nb_pixel_total : 33222 time to create 1 rle with old method : 0.04158973693847656 length of segment : 235 time for calcul the mask position with numpy : 0.00021767616271972656 nb_pixel_total : 11176 time to create 1 rle with old method : 0.013097763061523438 length of segment : 129 time for calcul the mask position with numpy : 0.00011563301086425781 nb_pixel_total : 4765 time to create 1 rle with old method : 0.005930185317993164 length of segment : 62 time for calcul the mask position with numpy : 0.00013518333435058594 nb_pixel_total : 5599 time to create 1 rle with old method : 0.007058382034301758 length of segment : 103 time for calcul the mask position with numpy : 0.00011444091796875 nb_pixel_total : 2261 time to create 1 rle with old method : 0.0029616355895996094 length of segment : 128 time for calcul the mask position with numpy : 0.0005052089691162109 nb_pixel_total : 16583 time to create 1 rle with old method : 0.023858070373535156 length of segment : 221 time for calcul the mask position with numpy : 0.0001971721649169922 nb_pixel_total : 6050 time to create 1 rle with old method : 0.007470130920410156 length of segment : 90 time for calcul the mask position with numpy : 0.0005679130554199219 nb_pixel_total : 30650 time to create 1 rle with old method : 0.03529524803161621 length of segment : 292 time for calcul the mask position with numpy : 0.0003066062927246094 nb_pixel_total : 6741 time to create 1 rle with old method : 0.008425474166870117 length of segment : 127 time for calcul the mask position with numpy : 0.0001697540283203125 nb_pixel_total : 6276 time to create 1 rle with old method : 0.007538318634033203 length of segment : 101 time for calcul the mask position with numpy : 0.00014090538024902344 nb_pixel_total : 7833 time to create 1 rle with old method : 0.011771917343139648 length of segment : 109 time for calcul the mask position with numpy : 0.00027561187744140625 nb_pixel_total : 11182 time to create 1 rle with old method : 0.018967151641845703 length of segment : 155 time for calcul the mask position with numpy : 0.00018334388732910156 nb_pixel_total : 8281 time to create 1 rle with old method : 0.009829998016357422 length of segment : 113 time for calcul the mask position with numpy : 9.465217590332031e-05 nb_pixel_total : 2990 time to create 1 rle with old method : 0.003755331039428711 length of segment : 62 time for calcul the mask position with numpy : 0.00011396408081054688 nb_pixel_total : 2856 time to create 1 rle with old method : 0.003528594970703125 length of segment : 108 time for calcul the mask position with numpy : 0.000644683837890625 nb_pixel_total : 32407 time to create 1 rle with old method : 0.037830352783203125 length of segment : 192 time for calcul the mask position with numpy : 0.00012230873107910156 nb_pixel_total : 3962 time to create 1 rle with old method : 0.0047185420989990234 length of segment : 70 time for calcul the mask position with numpy : 0.00010800361633300781 nb_pixel_total : 5000 time to create 1 rle with old method : 0.005905628204345703 length of segment : 86 time for calcul the mask position with numpy : 0.00016951560974121094 nb_pixel_total : 7124 time to create 1 rle with old method : 0.008487224578857422 length of segment : 149 time for calcul the mask position with numpy : 0.00022149085998535156 nb_pixel_total : 11849 time to create 1 rle with old method : 0.013870954513549805 length of segment : 101 time for calcul the mask position with numpy : 0.00010037422180175781 nb_pixel_total : 2792 time to create 1 rle with old method : 0.0034079551696777344 length of segment : 57 time for calcul the mask position with numpy : 0.0004794597625732422 nb_pixel_total : 21079 time to create 1 rle with old method : 0.02451324462890625 length of segment : 204 time for calcul the mask position with numpy : 0.0022780895233154297 nb_pixel_total : 14773 time to create 1 rle with old method : 0.024944543838500977 length of segment : 235 time for calcul the mask position with numpy : 0.00034308433532714844 nb_pixel_total : 10479 time to create 1 rle with old method : 0.012282371520996094 length of segment : 180 time spent for convertir_results : 3.387509346008301 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 69 chid ids of type : 3594 Number RLEs to save : 12297 save missing photos in datou_result : time spend for datou_step_exec : 25.3770809173584 time spend to save output : 0.7411308288574219 total time spend for step 1 : 26.11821174621582 step2:crop_condition Thu Aug 14 14:20:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 69 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 35 About to insert : list_path_to_insert length 35 new photo from crops ! About to upload 35 photos upload in portfolio : 3736932 init cache_photo without model_param we have 35 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1755174060_2784332 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : 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 35 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.300655841827393 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 ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1755174069_2784332 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6704456806182861 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1755174070_2784332 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6079001426696777 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 26 About to insert : list_path_to_insert length 26 new photo from crops ! About to upload 26 photos upload in portfolio : 3736932 init cache_photo without model_param we have 26 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1755174074_2784332 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 26 photos in the portfolio 3736932 time of upload the photos Elapsed time : 7.202616214752197 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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/1755174082_2784332 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : 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 : 1.6379835605621338 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1755174084_2784332 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6014904975891113 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1377008661, 1377008606, 1377008576, 1377008546, 1377008517, 1377008514, 1377008511, 1377008459, 1377008455, 1377008447, 1377008416, 1377008407, 1377008403, 1377008247, 1377008246, 1377008244, 1377008242, 1377008239] Looping around the photos to save general results len do output : 69 /1377019343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019346Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019347Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019353Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019354Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019356Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019357Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019359Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019360Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019362Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019363Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019364Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019369Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019370Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019371Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019375Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019381Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019383Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019390Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019391Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019393Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019394Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019395Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019397Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019398Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019400Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019401Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019405Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019407Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019408Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019409Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019411Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019412Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019413Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019414Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019417Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019418Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019420Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019422Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019423Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1377019425Didn'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, '3535195') ('3318', '25983542', '1377008661', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008606', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008576', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008546', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008517', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008514', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008511', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008459', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008455', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008447', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008416', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008407', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008403', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008247', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008246', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008244', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008242', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008239', None, None, None, None, None, '3535195') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 225 time used for this insertion : 0.027794837951660156 save_final save missing photos in datou_result : time spend for datou_step_exec : 27.51313805580139 time spend to save output : 0.03015303611755371 total time spend for step 2 : 27.543291091918945 step3:rle_unique_nms_with_priority Thu Aug 14 14:21:24 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 69 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 5 nb_hashtags : 3 time to prepare the origin masks : 0.10307717323303223 time for calcul the mask position with numpy : 0.0228424072265625 nb_pixel_total : 1928063 time to create 1 rle with new method : 0.04174065589904785 time for calcul the mask position with numpy : 0.006789684295654297 nb_pixel_total : 104775 time to create 1 rle with old method : 0.11827445030212402 time for calcul the mask position with numpy : 0.006223917007446289 nb_pixel_total : 18008 time to create 1 rle with old method : 0.02063727378845215 time for calcul the mask position with numpy : 0.0072557926177978516 nb_pixel_total : 6903 time to create 1 rle with old method : 0.007901430130004883 time for calcul the mask position with numpy : 0.007418155670166016 nb_pixel_total : 10050 time to create 1 rle with old method : 0.0110931396484375 time for calcul the mask position with numpy : 0.0060329437255859375 nb_pixel_total : 5801 time to create 1 rle with old method : 0.0064280033111572266 create new chi : 0.26735472679138184 time to delete rle : 0.017911911010742188 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 3138 TO DO : save crop sub photo not yet done ! save time : 0.23124027252197266 nb_obj : 7 nb_hashtags : 3 time to prepare the origin masks : 0.16382980346679688 time for calcul the mask position with numpy : 0.052306413650512695 nb_pixel_total : 1943113 time to create 1 rle with new method : 0.059571027755737305 time for calcul the mask position with numpy : 0.0067882537841796875 nb_pixel_total : 3781 time to create 1 rle with old method : 0.004442691802978516 time for calcul the mask position with numpy : 0.006316184997558594 nb_pixel_total : 3617 time to create 1 rle with old method : 0.00424504280090332 time for calcul the mask position with numpy : 0.007094144821166992 nb_pixel_total : 1719 time to create 1 rle with old method : 0.0020627975463867188 time for calcul the mask position with numpy : 0.007170438766479492 nb_pixel_total : 9513 time to create 1 rle with old method : 0.012084722518920898 time for calcul the mask position with numpy : 0.007062435150146484 nb_pixel_total : 608 time to create 1 rle with old method : 0.0007326602935791016 time for calcul the mask position with numpy : 0.007581233978271484 nb_pixel_total : 107346 time to create 1 rle with old method : 0.1209261417388916 time for calcul the mask position with numpy : 0.006836652755737305 nb_pixel_total : 3903 time to create 1 rle with old method : 0.004472970962524414 create new chi : 0.31424427032470703 time to delete rle : 0.0005567073822021484 batch 1 Loaded 15 chid ids of type : 3594 ++++++++Number RLEs to save : 3295 TO DO : save crop sub photo not yet done ! save time : 0.23024988174438477 nb_obj : 6 nb_hashtags : 2 time to prepare the origin masks : 0.13326597213745117 time for calcul the mask position with numpy : 0.022945880889892578 nb_pixel_total : 1920393 time to create 1 rle with new method : 0.042757272720336914 time for calcul the mask position with numpy : 0.006974220275878906 nb_pixel_total : 90918 time to create 1 rle with old method : 0.11123204231262207 time for calcul the mask position with numpy : 0.006428956985473633 nb_pixel_total : 9224 time to create 1 rle with old method : 0.010509252548217773 time for calcul the mask position with numpy : 0.006269693374633789 nb_pixel_total : 11355 time to create 1 rle with old method : 0.013068675994873047 time for calcul the mask position with numpy : 0.006535768508911133 nb_pixel_total : 18954 time to create 1 rle with old method : 0.021740198135375977 time for calcul the mask position with numpy : 0.0061490535736083984 nb_pixel_total : 3751 time to create 1 rle with old method : 0.004394054412841797 time for calcul the mask position with numpy : 0.0062372684478759766 nb_pixel_total : 19005 time to create 1 rle with old method : 0.02188396453857422 create new chi : 0.2901430130004883 time to delete rle : 0.0006096363067626953 batch 1 Loaded 13 chid ids of type : 3594 ++++++Number RLEs to save : 3782 TO DO : save crop sub photo not yet done ! save time : 0.36038708686828613 nb_obj : 8 nb_hashtags : 2 time to prepare the origin masks : 0.11571526527404785 time for calcul the mask position with numpy : 0.025860071182250977 nb_pixel_total : 1891523 time to create 1 rle with new method : 0.04382801055908203 time for calcul the mask position with numpy : 0.006516695022583008 nb_pixel_total : 7241 time to create 1 rle with old method : 0.00845646858215332 time for calcul the mask position with numpy : 0.006834983825683594 nb_pixel_total : 95966 time to create 1 rle with old method : 0.10842037200927734 time for calcul the mask position with numpy : 0.006423473358154297 nb_pixel_total : 8976 time to create 1 rle with old method : 0.010369062423706055 time for calcul the mask position with numpy : 0.006690263748168945 nb_pixel_total : 33615 time to create 1 rle with old method : 0.0381922721862793 time for calcul the mask position with numpy : 0.006266593933105469 nb_pixel_total : 8331 time to create 1 rle with old method : 0.009359598159790039 time for calcul the mask position with numpy : 0.006413698196411133 nb_pixel_total : 132 time to create 1 rle with old method : 0.0003714561462402344 time for calcul the mask position with numpy : 0.008533239364624023 nb_pixel_total : 9367 time to create 1 rle with old method : 0.014158487319946289 time for calcul the mask position with numpy : 0.0070722103118896484 nb_pixel_total : 18449 time to create 1 rle with old method : 0.02084517478942871 create new chi : 0.33919262886047363 time to delete rle : 0.0006709098815917969 batch 1 Loaded 17 chid ids of type : 3594 ++++++++++Number RLEs to save : 4419 TO DO : save crop sub photo not yet done ! save time : 0.3221578598022461 nb_obj : 7 nb_hashtags : 2 time to prepare the origin masks : 0.11760449409484863 time for calcul the mask position with numpy : 0.07113003730773926 nb_pixel_total : 1996260 time to create 1 rle with new method : 0.04839944839477539 time for calcul the mask position with numpy : 0.006288766860961914 nb_pixel_total : 13419 time to create 1 rle with old method : 0.015462636947631836 time for calcul the mask position with numpy : 0.006076335906982422 nb_pixel_total : 16256 time to create 1 rle with old method : 0.01879262924194336 time for calcul the mask position with numpy : 0.0060863494873046875 nb_pixel_total : 6590 time to create 1 rle with old method : 0.007745027542114258 time for calcul the mask position with numpy : 0.0061414241790771484 nb_pixel_total : 7214 time to create 1 rle with old method : 0.008426666259765625 time for calcul the mask position with numpy : 0.006079196929931641 nb_pixel_total : 4905 time to create 1 rle with old method : 0.005975961685180664 time for calcul the mask position with numpy : 0.006447553634643555 nb_pixel_total : 13569 time to create 1 rle with old method : 0.01567220687866211 time for calcul the mask position with numpy : 0.006209611892700195 nb_pixel_total : 15387 time to create 1 rle with old method : 0.017835140228271484 create new chi : 0.25600266456604004 time to delete rle : 0.0004763603210449219 batch 1 Loaded 15 chid ids of type : 3594 ++++++++Number RLEs to save : 3124 TO DO : save crop sub photo not yet done ! save time : 0.2910583019256592 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.04158639907836914 time for calcul the mask position with numpy : 0.019869565963745117 nb_pixel_total : 2056249 time to create 1 rle with new method : 0.02761077880859375 time for calcul the mask position with numpy : 0.006118297576904297 nb_pixel_total : 9732 time to create 1 rle with old method : 0.011130571365356445 time for calcul the mask position with numpy : 0.006073474884033203 nb_pixel_total : 7619 time to create 1 rle with old method : 0.008863210678100586 create new chi : 0.07994270324707031 time to delete rle : 0.00025582313537597656 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1592 TO DO : save crop sub photo not yet done ! save time : 0.18984007835388184 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.06365728378295898 time for calcul the mask position with numpy : 0.022272586822509766 nb_pixel_total : 1863253 time to create 1 rle with new method : 0.042936086654663086 time for calcul the mask position with numpy : 0.006563901901245117 nb_pixel_total : 56220 time to create 1 rle with old method : 0.06501078605651855 time for calcul the mask position with numpy : 0.0063629150390625 nb_pixel_total : 35503 time to create 1 rle with old method : 0.040346384048461914 time for calcul the mask position with numpy : 0.006773710250854492 nb_pixel_total : 114214 time to create 1 rle with old method : 0.1304917335510254 time for calcul the mask position with numpy : 0.007203817367553711 nb_pixel_total : 4410 time to create 1 rle with old method : 0.0050656795501708984 create new chi : 0.3378291130065918 time to delete rle : 0.0005323886871337891 batch 1 Loaded 9 chid ids of type : 3594 +++++Number RLEs to save : 3188 TO DO : save crop sub photo not yet done ! save time : 0.24215435981750488 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.07374739646911621 time for calcul the mask position with numpy : 0.02501392364501953 nb_pixel_total : 1984089 time to create 1 rle with new method : 0.04635214805603027 time for calcul the mask position with numpy : 0.007745265960693359 nb_pixel_total : 29563 time to create 1 rle with old method : 0.03378868103027344 time for calcul the mask position with numpy : 0.006224393844604492 nb_pixel_total : 3847 time to create 1 rle with old method : 0.004391908645629883 time for calcul the mask position with numpy : 0.006318569183349609 nb_pixel_total : 48502 time to create 1 rle with old method : 0.0549471378326416 time for calcul the mask position with numpy : 0.0061457157135009766 nb_pixel_total : 2821 time to create 1 rle with old method : 0.0032470226287841797 time for calcul the mask position with numpy : 0.006436586380004883 nb_pixel_total : 4778 time to create 1 rle with old method : 0.005496978759765625 create new chi : 0.2123708724975586 time to delete rle : 0.0005779266357421875 batch 1 Loaded 11 chid ids of type : 3594 +++++++++++Number RLEs to save : 3394 TO DO : save crop sub photo not yet done ! save time : 0.29929375648498535 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.05982017517089844 time for calcul the mask position with numpy : 0.025089502334594727 nb_pixel_total : 2015731 time to create 1 rle with new method : 0.03782820701599121 time for calcul the mask position with numpy : 0.006265401840209961 nb_pixel_total : 4765 time to create 1 rle with old method : 0.0054857730865478516 time for calcul the mask position with numpy : 0.006105661392211914 nb_pixel_total : 11176 time to create 1 rle with old method : 0.01272439956665039 time for calcul the mask position with numpy : 0.006304502487182617 nb_pixel_total : 33222 time to create 1 rle with old method : 0.03998231887817383 time for calcul the mask position with numpy : 0.006160259246826172 nb_pixel_total : 8706 time to create 1 rle with old method : 0.010450363159179688 create new chi : 0.15940380096435547 time to delete rle : 0.0003528594970703125 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 2192 TO DO : save crop sub photo not yet done ! save time : 0.1748497486114502 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.057530879974365234 time for calcul the mask position with numpy : 0.027279138565063477 nb_pixel_total : 2049157 time to create 1 rle with new method : 0.04393625259399414 time for calcul the mask position with numpy : 0.0065000057220458984 nb_pixel_total : 16583 time to create 1 rle with old method : 0.01894235610961914 time for calcul the mask position with numpy : 0.006380319595336914 nb_pixel_total : 2261 time to create 1 rle with old method : 0.0026917457580566406 time for calcul the mask position with numpy : 0.0063724517822265625 nb_pixel_total : 5599 time to create 1 rle with old method : 0.0064983367919921875 create new chi : 0.12305283546447754 time to delete rle : 0.0003440380096435547 batch 1 Loaded 7 chid ids of type : 3594 +++++Number RLEs to save : 1984 TO DO : save crop sub photo not yet done ! save time : 0.1438744068145752 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.07085585594177246 time for calcul the mask position with numpy : 0.02381443977355957 nb_pixel_total : 2016050 time to create 1 rle with new method : 0.04208254814147949 time for calcul the mask position with numpy : 0.006197452545166016 nb_pixel_total : 7833 time to create 1 rle with old method : 0.008836984634399414 time for calcul the mask position with numpy : 0.006016969680786133 nb_pixel_total : 6276 time to create 1 rle with old method : 0.007195949554443359 time for calcul the mask position with numpy : 0.006033420562744141 nb_pixel_total : 6741 time to create 1 rle with old method : 0.007907629013061523 time for calcul the mask position with numpy : 0.006127834320068359 nb_pixel_total : 30650 time to create 1 rle with old method : 0.035124778747558594 time for calcul the mask position with numpy : 0.006150007247924805 nb_pixel_total : 6050 time to create 1 rle with old method : 0.006990194320678711 create new chi : 0.1671302318572998 time to delete rle : 0.00043272972106933594 batch 1 Loaded 11 chid ids of type : 3594 ++++++Number RLEs to save : 2518 TO DO : save crop sub photo not yet done ! save time : 0.20270299911499023 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03608131408691406 time for calcul the mask position with numpy : 0.02063298225402832 nb_pixel_total : 2062418 time to create 1 rle with new method : 0.031070947647094727 time for calcul the mask position with numpy : 0.007235288619995117 nb_pixel_total : 11182 time to create 1 rle with old method : 0.021138668060302734 create new chi : 0.08041739463806152 time to delete rle : 0.0003523826599121094 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1390 TO DO : save crop sub photo not yet done ! save time : 0.11935091018676758 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.035292863845825195 time for calcul the mask position with numpy : 0.01948261260986328 nb_pixel_total : 2065319 time to create 1 rle with new method : 0.02764439582824707 time for calcul the mask position with numpy : 0.006121158599853516 nb_pixel_total : 8281 time to create 1 rle with old method : 0.009509086608886719 create new chi : 0.06298184394836426 time to delete rle : 0.00021886825561523438 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1306 TO DO : save crop sub photo not yet done ! save time : 0.11836004257202148 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.05004239082336426 time for calcul the mask position with numpy : 0.02347087860107422 nb_pixel_total : 2067754 time to create 1 rle with new method : 0.03987288475036621 time for calcul the mask position with numpy : 0.006408214569091797 nb_pixel_total : 2856 time to create 1 rle with old method : 0.003309965133666992 time for calcul the mask position with numpy : 0.006093740463256836 nb_pixel_total : 2990 time to create 1 rle with old method : 0.003460407257080078 create new chi : 0.08688902854919434 time to delete rle : 0.0002505779266357422 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1420 TO DO : save crop sub photo not yet done ! save time : 0.13038277626037598 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.05891704559326172 time for calcul the mask position with numpy : 0.025667190551757812 nb_pixel_total : 2032231 time to create 1 rle with new method : 0.046167850494384766 time for calcul the mask position with numpy : 0.006264686584472656 nb_pixel_total : 5000 time to create 1 rle with old method : 0.0066568851470947266 time for calcul the mask position with numpy : 0.006701946258544922 nb_pixel_total : 3962 time to create 1 rle with old method : 0.004644632339477539 time for calcul the mask position with numpy : 0.006777763366699219 nb_pixel_total : 32407 time to create 1 rle with old method : 0.03656625747680664 create new chi : 0.14368510246276855 time to delete rle : 0.0005590915679931641 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1776 TO DO : save crop sub photo not yet done ! save time : 0.14673852920532227 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.06446647644042969 time for calcul the mask position with numpy : 0.024090290069580078 nb_pixel_total : 2030756 time to create 1 rle with new method : 0.0405879020690918 time for calcul the mask position with numpy : 0.007289886474609375 nb_pixel_total : 21079 time to create 1 rle with old method : 0.02799057960510254 time for calcul the mask position with numpy : 0.008507490158081055 nb_pixel_total : 2792 time to create 1 rle with old method : 0.004819631576538086 time for calcul the mask position with numpy : 0.0077610015869140625 nb_pixel_total : 11849 time to create 1 rle with old method : 0.02750539779663086 time for calcul the mask position with numpy : 0.0070400238037109375 nb_pixel_total : 7124 time to create 1 rle with old method : 0.008099079132080078 create new chi : 0.16780710220336914 time to delete rle : 0.0003814697265625 batch 1 Loaded 9 chid ids of type : 3594 +++++++Number RLEs to save : 2102 TO DO : save crop sub photo not yet done ! save time : 0.2175912857055664 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.033048391342163086 time for calcul the mask position with numpy : 0.01999211311340332 nb_pixel_total : 2058827 time to create 1 rle with new method : 0.03197646141052246 time for calcul the mask position with numpy : 0.006162881851196289 nb_pixel_total : 14773 time to create 1 rle with old method : 0.01714348793029785 create new chi : 0.07552671432495117 time to delete rle : 0.0002884864807128906 batch 1 Loaded 3 chid ids of type : 3594 +++++Number RLEs to save : 1550 TO DO : save crop sub photo not yet done ! save time : 0.1282942295074463 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.037984609603881836 time for calcul the mask position with numpy : 0.02431941032409668 nb_pixel_total : 2063121 time to create 1 rle with new method : 0.04082846641540527 time for calcul the mask position with numpy : 0.006323814392089844 nb_pixel_total : 10479 time to create 1 rle with old method : 0.012123346328735352 create new chi : 0.0881052017211914 time to delete rle : 0.0002593994140625 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1440 TO DO : save crop sub photo not yet done ! save time : 0.12156438827514648 map_output_result : {1377008661: (0.0, 'Should be the crop_list due to order', 0), 1377008606: (0.0, 'Should be the crop_list due to order', 0), 1377008576: (0.0, 'Should be the crop_list due to order', 0), 1377008546: (0.0, 'Should be the crop_list due to order', 0), 1377008517: (0.0, 'Should be the crop_list due to order', 0), 1377008514: (0.0, 'Should be the crop_list due to order', 0), 1377008511: (0.0, 'Should be the crop_list due to order', 0), 1377008459: (0.0, 'Should be the crop_list due to order', 0), 1377008455: (0.0, 'Should be the crop_list due to order', 0), 1377008447: (0.0, 'Should be the crop_list due to order', 0), 1377008416: (0.0, 'Should be the crop_list due to order', 0), 1377008407: (0.0, 'Should be the crop_list due to order', 0), 1377008403: (0.0, 'Should be the crop_list due to order', 0), 1377008247: (0.0, 'Should be the crop_list due to order', 0), 1377008246: (0.0, 'Should be the crop_list due to order', 0), 1377008244: (0.0, 'Should be the crop_list due to order', 0), 1377008242: (0.0, 'Should be the crop_list due to order', 0), 1377008239: (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 [1377008661, 1377008606, 1377008576, 1377008546, 1377008517, 1377008514, 1377008511, 1377008459, 1377008455, 1377008447, 1377008416, 1377008407, 1377008403, 1377008247, 1377008246, 1377008244, 1377008242, 1377008239] Looping around the photos to save general results len do output : 18 /1377008661.Didn't retrieve data . /1377008606.Didn't retrieve data . /1377008576.Didn't retrieve data . /1377008546.Didn't retrieve data . /1377008517.Didn't retrieve data . /1377008514.Didn't retrieve data . /1377008511.Didn't retrieve data . /1377008459.Didn't retrieve data . /1377008455.Didn't retrieve data . /1377008447.Didn't retrieve data . /1377008416.Didn't retrieve data . /1377008407.Didn't retrieve data . /1377008403.Didn't retrieve data . /1377008247.Didn't retrieve data . /1377008246.Didn't retrieve data . /1377008244.Didn't retrieve data . /1377008242.Didn't retrieve data . /1377008239.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, '3535195') ('3318', '25983542', '1377008661', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008606', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008576', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008546', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008517', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008514', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008511', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008459', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008455', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008447', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008416', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008407', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008403', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008247', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008246', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008244', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008242', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008239', None, None, None, None, None, '3535195') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 54 time used for this insertion : 0.015080451965332031 save_final save missing photos in datou_result : time spend for datou_step_exec : 8.80081033706665 time spend to save output : 0.016030311584472656 total time spend for step 3 : 8.816840648651123 step4:ventilate_hashtags_in_portfolio Thu Aug 14 14:21:33 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 : 25983542 get user id for portfolio 25983542 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`=25983542 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','mal_croppe','papier','flou','pet_clair','pehd','environnement','autre','metal','pet_fonce','background')) 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`=25983542 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','mal_croppe','papier','flou','pet_clair','pehd','environnement','autre','metal','pet_fonce','background')) 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`=25983542 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','mal_croppe','papier','flou','pet_clair','pehd','environnement','autre','metal','pet_fonce','background')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/25984430,25984431,25984432,25984433,25984434,25984435,25984436,25984437,25984438,25984439,25984440?tags=carton,mal_croppe,papier,flou,pet_clair,pehd,environnement,autre,metal,pet_fonce,background Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1377008661, 1377008606, 1377008576, 1377008546, 1377008517, 1377008514, 1377008511, 1377008459, 1377008455, 1377008447, 1377008416, 1377008407, 1377008403, 1377008247, 1377008246, 1377008244, 1377008242, 1377008239] Looping around the photos to save general results len do output : 1 /25983542. 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, '3535195') ('3318', '25983542', '1377008661', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008606', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008576', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008546', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008517', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008514', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008511', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008459', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008455', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008447', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008416', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008407', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008403', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008247', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008246', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008244', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008242', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008239', None, None, None, None, None, '3535195') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 19 time used for this insertion : 0.020140886306762695 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.732846975326538 time spend to save output : 0.020497798919677734 total time spend for step 4 : 1.7533447742462158 step5:final Thu Aug 14 14:21:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! Inside saveOutput : final : False verbose : 0 original output for save of step final : {1377008661: ('0.03430676118827159',), 1377008606: ('0.03430676118827159',), 1377008576: ('0.03430676118827159',), 1377008546: ('0.03430676118827159',), 1377008517: ('0.03430676118827159',), 1377008514: ('0.03430676118827159',), 1377008511: ('0.03430676118827159',), 1377008459: ('0.03430676118827159',), 1377008455: ('0.03430676118827159',), 1377008447: ('0.03430676118827159',), 1377008416: ('0.03430676118827159',), 1377008407: ('0.03430676118827159',), 1377008403: ('0.03430676118827159',), 1377008247: ('0.03430676118827159',), 1377008246: ('0.03430676118827159',), 1377008244: ('0.03430676118827159',), 1377008242: ('0.03430676118827159',), 1377008239: ('0.03430676118827159',)} new output for save of step final : {1377008661: ('0.03430676118827159',), 1377008606: ('0.03430676118827159',), 1377008576: ('0.03430676118827159',), 1377008546: ('0.03430676118827159',), 1377008517: ('0.03430676118827159',), 1377008514: ('0.03430676118827159',), 1377008511: ('0.03430676118827159',), 1377008459: ('0.03430676118827159',), 1377008455: ('0.03430676118827159',), 1377008447: ('0.03430676118827159',), 1377008416: ('0.03430676118827159',), 1377008407: ('0.03430676118827159',), 1377008403: ('0.03430676118827159',), 1377008247: ('0.03430676118827159',), 1377008246: ('0.03430676118827159',), 1377008244: ('0.03430676118827159',), 1377008242: ('0.03430676118827159',), 1377008239: ('0.03430676118827159',)} [1377008661, 1377008606, 1377008576, 1377008546, 1377008517, 1377008514, 1377008511, 1377008459, 1377008455, 1377008447, 1377008416, 1377008407, 1377008403, 1377008247, 1377008246, 1377008244, 1377008242, 1377008239] Looping around the photos to save general results len do output : 18 /1377008661.Didn't retrieve data . /1377008606.Didn't retrieve data . /1377008576.Didn't retrieve data . /1377008546.Didn't retrieve data . /1377008517.Didn't retrieve data . /1377008514.Didn't retrieve data . /1377008511.Didn't retrieve data . /1377008459.Didn't retrieve data . /1377008455.Didn't retrieve data . /1377008447.Didn't retrieve data . /1377008416.Didn't retrieve data . /1377008407.Didn't retrieve data . /1377008403.Didn't retrieve data . /1377008247.Didn't retrieve data . /1377008246.Didn't retrieve data . /1377008244.Didn't retrieve data . /1377008242.Didn't retrieve data . /1377008239.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, '3535195') ('3318', '25983542', '1377008661', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008606', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008576', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008546', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008517', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008514', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008511', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008459', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008455', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008447', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008416', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008407', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008403', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008247', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008246', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008244', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008242', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008239', None, None, None, None, None, '3535195') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 54 time used for this insertion : 0.019713640213012695 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.13573431968688965 time spend to save output : 0.02056741714477539 total time spend for step 5 : 0.15630173683166504 step6:blur_detection Thu Aug 14 14:21:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1755174028_2784332_1377008661_a463fb1a1170061e56e0f6ac8bea4dd7.jpg resize: (1080, 1920) 1377008661 -0.11236177760696357 treat image : temp/1755174028_2784332_1377008606_b927c9719c3d64f5db82811929f45cd5.jpg resize: (1080, 1920) 1377008606 0.9265077689562654 treat image : temp/1755174028_2784332_1377008576_9a1cb6283dfbd086512d1440165ac67d.jpg resize: (1080, 1920) 1377008576 0.9933447783556232 treat image : temp/1755174028_2784332_1377008546_067dd98f8ed9c5abe4d5a6dce8b80523.jpg resize: (1080, 1920) 1377008546 0.3978329889635498 treat image : temp/1755174028_2784332_1377008517_443713fd05e2e01336d7624d12f47cba.jpg resize: (1080, 1920) 1377008517 0.5038753493991788 treat image : temp/1755174028_2784332_1377008514_ef7a06e387c316da9ccb642fbb59ce2a.jpg resize: (1080, 1920) 1377008514 0.7812720482472401 treat image : temp/1755174028_2784332_1377008511_eda9c7e78490e2a682d508ed1879105b.jpg resize: (1080, 1920) 1377008511 0.22118003038030645 treat image : temp/1755174028_2784332_1377008459_cc3eac57b22d7d2b508264febdb3afb0.jpg resize: (1080, 1920) 1377008459 -0.17307850481648795 treat image : temp/1755174028_2784332_1377008455_6c8375bb9ce958bce66379ffa7f64adb.jpg resize: (1080, 1920) 1377008455 -0.6934817499190951 treat image : temp/1755174028_2784332_1377008447_c1770afd4465c4ed2bfef975a3647f3b.jpg resize: (1080, 1920) 1377008447 -0.05550462528498291 treat image : temp/1755174028_2784332_1377008416_1de8a5edf3c4b5d971750de231fe9c19.jpg resize: (1080, 1920) 1377008416 1.7471945058228648 treat image : temp/1755174028_2784332_1377008407_93852e9f85cd2b1837a61530745cb344.jpg resize: (1080, 1920) 1377008407 0.988869154376279 treat image : temp/1755174028_2784332_1377008403_3d5135dd49f462f9e518ee32f6fb2d14.jpg resize: (1080, 1920) 1377008403 -0.2888317116632248 treat image : temp/1755174028_2784332_1377008247_38c189d5a8c0b5a46a091bf7df9cb3df.jpg resize: (1080, 1920) 1377008247 0.4664764937728825 treat image : temp/1755174028_2784332_1377008246_6e53110cc645a71ced3e28c285384a5f.jpg resize: (1080, 1920) 1377008246 0.862564543091031 treat image : temp/1755174028_2784332_1377008244_4fd42c6368e29070f57f20dd884f4282.jpg resize: (1080, 1920) 1377008244 -0.7296606469419912 treat image : temp/1755174028_2784332_1377008242_56b10708927ed0d324d03c55b0b05fd1.jpg resize: (1080, 1920) 1377008242 1.4682429133282304 treat image : temp/1755174028_2784332_1377008239_61c8b3dada7af6ca49a845ddd31042b1.jpg resize: (1080, 1920) 1377008239 0.5328937175184386 treat image : temp/1755174028_2784332_1377008661_a463fb1a1170061e56e0f6ac8bea4dd7_rle_crop_3914802148_0.png resize: (85, 101) 1377019343 -2.084481570572022 treat image : temp/1755174028_2784332_1377008661_a463fb1a1170061e56e0f6ac8bea4dd7_rle_crop_3914802150_0.png resize: (92, 118) 1377019344 -1.3427309139955812 treat image : temp/1755174028_2784332_1377008606_b927c9719c3d64f5db82811929f45cd5_rle_crop_3914802156_0.png resize: (167, 114) 1377019345 -0.8838817606478818 treat image : temp/1755174028_2784332_1377008606_b927c9719c3d64f5db82811929f45cd5_rle_crop_3914802157_0.png resize: (80, 62) 1377019346 -2.7741636890935233 treat image : temp/1755174028_2784332_1377008606_b927c9719c3d64f5db82811929f45cd5_rle_crop_3914802158_0.png resize: (57, 84) 1377019347 0.051290614338266774 treat image : temp/1755174028_2784332_1377008606_b927c9719c3d64f5db82811929f45cd5_rle_crop_3914802159_0.png resize: (67, 77) 1377019348 1.7153640792819589 treat image : temp/1755174028_2784332_1377008576_9a1cb6283dfbd086512d1440165ac67d_rle_crop_3914802160_0.png resize: (281, 129) 1377019349 -1.9298253106556524 treat image : temp/1755174028_2784332_1377008576_9a1cb6283dfbd086512d1440165ac67d_rle_crop_3914802161_0.png resize: (125, 41) 1377019350 0.4379558671223456 treat image : 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temp/1755174028_2784332_1377008403_3d5135dd49f462f9e518ee32f6fb2d14_rle_crop_3914802205_0.png resize: (112, 99) 1377019425 -0.5637786213225117 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 : 87 time used for this insertion : 0.018012046813964844 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 87 time used for this insertion : 0.01812577247619629 save missing photos in datou_result : time spend for datou_step_exec : 14.412533521652222 time spend to save output : 0.04117631912231445 total time spend for step 6 : 14.453709840774536 step7:brightness Thu Aug 14 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 ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1755174028_2784332_1377008661_a463fb1a1170061e56e0f6ac8bea4dd7.jpg treat image : temp/1755174028_2784332_1377008606_b927c9719c3d64f5db82811929f45cd5.jpg treat image : temp/1755174028_2784332_1377008576_9a1cb6283dfbd086512d1440165ac67d.jpg treat image : temp/1755174028_2784332_1377008546_067dd98f8ed9c5abe4d5a6dce8b80523.jpg treat image : temp/1755174028_2784332_1377008517_443713fd05e2e01336d7624d12f47cba.jpg treat image : temp/1755174028_2784332_1377008514_ef7a06e387c316da9ccb642fbb59ce2a.jpg treat image : temp/1755174028_2784332_1377008511_eda9c7e78490e2a682d508ed1879105b.jpg treat image : temp/1755174028_2784332_1377008459_cc3eac57b22d7d2b508264febdb3afb0.jpg treat image : temp/1755174028_2784332_1377008455_6c8375bb9ce958bce66379ffa7f64adb.jpg treat image : temp/1755174028_2784332_1377008447_c1770afd4465c4ed2bfef975a3647f3b.jpg treat image : 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temp/1755174028_2784332_1377008459_cc3eac57b22d7d2b508264febdb3afb0_rle_crop_3914802188_0.png treat image : temp/1755174028_2784332_1377008447_c1770afd4465c4ed2bfef975a3647f3b_rle_crop_3914802197_0.png treat image : temp/1755174028_2784332_1377008416_1de8a5edf3c4b5d971750de231fe9c19_rle_crop_3914802199_0.png treat image : temp/1755174028_2784332_1377008416_1de8a5edf3c4b5d971750de231fe9c19_rle_crop_3914802200_0.png treat image : temp/1755174028_2784332_1377008416_1de8a5edf3c4b5d971750de231fe9c19_rle_crop_3914802201_0.png treat image : temp/1755174028_2784332_1377008416_1de8a5edf3c4b5d971750de231fe9c19_rle_crop_3914802202_0.png treat image : temp/1755174028_2784332_1377008407_93852e9f85cd2b1837a61530745cb344_rle_crop_3914802204_0.png treat image : temp/1755174028_2784332_1377008247_38c189d5a8c0b5a46a091bf7df9cb3df_rle_crop_3914802206_0.png treat image : temp/1755174028_2784332_1377008247_38c189d5a8c0b5a46a091bf7df9cb3df_rle_crop_3914802207_0.png treat image : temp/1755174028_2784332_1377008246_6e53110cc645a71ced3e28c285384a5f_rle_crop_3914802208_0.png treat image : temp/1755174028_2784332_1377008246_6e53110cc645a71ced3e28c285384a5f_rle_crop_3914802210_0.png treat image : temp/1755174028_2784332_1377008244_4fd42c6368e29070f57f20dd884f4282_rle_crop_3914802211_0.png treat image : temp/1755174028_2784332_1377008244_4fd42c6368e29070f57f20dd884f4282_rle_crop_3914802214_0.png treat image : temp/1755174028_2784332_1377008239_61c8b3dada7af6ca49a845ddd31042b1_rle_crop_3914802216_0.png treat image : temp/1755174028_2784332_1377008514_ef7a06e387c316da9ccb642fbb59ce2a_rle_crop_3914802181_0.png treat image : temp/1755174028_2784332_1377008455_6c8375bb9ce958bce66379ffa7f64adb_rle_crop_3914802195_0.png treat image : temp/1755174028_2784332_1377008661_a463fb1a1170061e56e0f6ac8bea4dd7_rle_crop_3914802151_0.png treat image : temp/1755174028_2784332_1377008661_a463fb1a1170061e56e0f6ac8bea4dd7_rle_crop_3914802152_0.png treat image : temp/1755174028_2784332_1377008606_b927c9719c3d64f5db82811929f45cd5_rle_crop_3914802154_0.png treat image : temp/1755174028_2784332_1377008576_9a1cb6283dfbd086512d1440165ac67d_rle_crop_3914802162_0.png treat image : temp/1755174028_2784332_1377008576_9a1cb6283dfbd086512d1440165ac67d_rle_crop_3914802163_0.png treat image : temp/1755174028_2784332_1377008576_9a1cb6283dfbd086512d1440165ac67d_rle_crop_3914802164_0.png treat image : temp/1755174028_2784332_1377008576_9a1cb6283dfbd086512d1440165ac67d_rle_crop_3914802165_0.png treat image : temp/1755174028_2784332_1377008546_067dd98f8ed9c5abe4d5a6dce8b80523_rle_crop_3914802166_0.png treat image : temp/1755174028_2784332_1377008546_067dd98f8ed9c5abe4d5a6dce8b80523_rle_crop_3914802170_0.png treat image : temp/1755174028_2784332_1377008546_067dd98f8ed9c5abe4d5a6dce8b80523_rle_crop_3914802171_0.png treat image : temp/1755174028_2784332_1377008546_067dd98f8ed9c5abe4d5a6dce8b80523_rle_crop_3914802172_0.png treat image : 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temp/1755174028_2784332_1377008455_6c8375bb9ce958bce66379ffa7f64adb_rle_crop_3914802194_0.png treat image : temp/1755174028_2784332_1377008447_c1770afd4465c4ed2bfef975a3647f3b_rle_crop_3914802196_0.png treat image : temp/1755174028_2784332_1377008447_c1770afd4465c4ed2bfef975a3647f3b_rle_crop_3914802198_0.png treat image : temp/1755174028_2784332_1377008246_6e53110cc645a71ced3e28c285384a5f_rle_crop_3914802209_0.png treat image : temp/1755174028_2784332_1377008244_4fd42c6368e29070f57f20dd884f4282_rle_crop_3914802212_0.png treat image : temp/1755174028_2784332_1377008244_4fd42c6368e29070f57f20dd884f4282_rle_crop_3914802213_0.png treat image : temp/1755174028_2784332_1377008661_a463fb1a1170061e56e0f6ac8bea4dd7_rle_crop_3914802149_0.png treat image : temp/1755174028_2784332_1377008606_b927c9719c3d64f5db82811929f45cd5_rle_crop_3914802153_0.png treat image : temp/1755174028_2784332_1377008606_b927c9719c3d64f5db82811929f45cd5_rle_crop_3914802155_0.png treat image : temp/1755174028_2784332_1377008416_1de8a5edf3c4b5d971750de231fe9c19_rle_crop_3914802203_0.png treat image : temp/1755174028_2784332_1377008242_56b10708927ed0d324d03c55b0b05fd1_rle_crop_3914802215_0.png treat image : temp/1755174028_2784332_1377008403_3d5135dd49f462f9e518ee32f6fb2d14_rle_crop_3914802205_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 : 87 time used for this insertion : 0.01705145835876465 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 87 time used for this insertion : 0.01700568199157715 save missing photos in datou_result : time spend for datou_step_exec : 4.523160696029663 time spend to save output : 0.039816856384277344 total time spend for step 7 : 4.56297755241394 step8:velours_tree Thu Aug 14 14:21:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 0.1398937702178955 time spend to save output : 4.363059997558594e-05 total time spend for step 8 : 0.1399374008178711 step9:send_mail_cod Thu Aug 14 14:21:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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_P25983542_14-08-2025_14_21_54.pdf 25984430 change filename to text .imagette259844301755174114 25984431 imagette259844311755174114 25984432 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 .imagette259844321755174114 25984433 imagette259844331755174115 25984434 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 .imagette259844341755174115 25984435 change filename to text .imagette259844351755174117 25984437 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette259844371755174117 25984438 change filename to text .imagette259844381755174117 25984439 imagette259844391755174117 25984440 imagette259844401755174117 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=25983542 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/25984430,25984431,25984432,25984433,25984434,25984435,25984436,25984437,25984438,25984439,25984440?tags=carton,mal_croppe,papier,flou,pet_clair,pehd,environnement,autre,metal,pet_fonce,background args[1377008661] : ((1377008661, -0.11236177760696357, 492688767), (1377008661, 0.5283770279744033, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008606] : ((1377008606, 0.9265077689562654, 492688767), (1377008606, 0.3276657176013172, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008576] : ((1377008576, 0.9933447783556232, 492688767), (1377008576, 0.620893993075452, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008546] : ((1377008546, 0.3978329889635498, 492688767), (1377008546, 0.508905982470112, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008517] : ((1377008517, 0.5038753493991788, 492688767), (1377008517, 0.5019747504609186, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008514] : ((1377008514, 0.7812720482472401, 492688767), (1377008514, 0.4013151925045437, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008511] : ((1377008511, 0.22118003038030645, 492688767), (1377008511, 0.41551500422729054, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008459] : ((1377008459, -0.17307850481648795, 492688767), (1377008459, 0.6137251989256995, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008455] : ((1377008455, -0.6934817499190951, 492688767), (1377008455, 0.8064165200328577, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008447] : ((1377008447, -0.05550462528498291, 492688767), (1377008447, 0.5052472072663143, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008416] : ((1377008416, 1.7471945058228648, 492688767), (1377008416, 0.7399736260396886, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008407] : ((1377008407, 0.988869154376279, 492688767), (1377008407, 0.5063377899757716, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008403] : ((1377008403, -0.2888317116632248, 492688767), (1377008403, 0.5094629426160182, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008247] : ((1377008247, 0.4664764937728825, 492688767), (1377008247, 0.7048516334422802, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008246] : ((1377008246, 0.862564543091031, 492688767), (1377008246, 0.5525524617207647, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008244] : ((1377008244, -0.7296606469419912, 492688767), (1377008244, 0.5210996767311998, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008242] : ((1377008242, 1.4682429133282304, 492688767), (1377008242, 0.47760118555168884, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com args[1377008239] : ((1377008239, 0.5328937175184386, 492688767), (1377008239, 0.6403448840968294, 2107752395), '0.03430676118827159') We are sending mail with results at report@fotonower.com refus_total : 0.03430676118827159 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=25983542 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_P25983542_14-08-2025_14_21_54.pdf results_Auto_P25983542_14-08-2025_14_21_54.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P25983542_14-08-2025_14_21_54.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','25983542','results_Auto_P25983542_14-08-2025_14_21_54.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P25983542_14-08-2025_14_21_54.pdf','pdf','','0.27','0.03430676118827159') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/25983542

https://www.fotonower.com/image?json=false&list_photos_id=1377008661
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008606
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008576
La photo est trop floue, merci de reprendre une photo.(avec le score = 0.9933447783556232)
https://www.fotonower.com/image?json=false&list_photos_id=1377008546
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008517
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008514
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008511
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008459
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008455
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008447
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008416
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.7471945058228648)
https://www.fotonower.com/image?json=false&list_photos_id=1377008407
La photo est trop floue, merci de reprendre une photo.(avec le score = 0.988869154376279)
https://www.fotonower.com/image?json=false&list_photos_id=1377008403
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008247
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008246
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008244
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1377008242
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.4682429133282304)
https://www.fotonower.com/image?json=false&list_photos_id=1377008239
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 3.43%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/25984430?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/25984432?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/25984434?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/25984435?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/25984437?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/25984438?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P25983542_14-08-2025_14_21_54.pdf.

Lien vers velours :https://www.fotonower.com/velours/25984430,25984431,25984432,25984433,25984434,25984435,25984436,25984437,25984438,25984439,25984440?tags=carton,mal_croppe,papier,flou,pet_clair,pehd,environnement,autre,metal,pet_fonce,background.


L'équipe Fotonower 202 b'' Server: nginx Date: Thu, 14 Aug 2025 12:21:59 GMT Content-Length: 0 Connection: close X-Message-Id: DI1dMvQPQo-fKsuOhA8eXQ 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 [1377008661, 1377008606, 1377008576, 1377008546, 1377008517, 1377008514, 1377008511, 1377008459, 1377008455, 1377008447, 1377008416, 1377008407, 1377008403, 1377008247, 1377008246, 1377008244, 1377008242, 1377008239] 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, '3535195') ('3318', '25983542', '1377008661', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008606', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008576', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008546', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008517', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008514', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008511', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008459', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008455', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008447', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008416', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008407', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008403', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008247', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008246', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008244', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008242', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008239', None, None, None, None, None, '3535195') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 18 time used for this insertion : 0.01986861228942871 save_final save missing photos in datou_result : time spend for datou_step_exec : 4.585916757583618 time spend to save output : 0.020102977752685547 total time spend for step 9 : 4.606019735336304 step10:split_time_score Thu Aug 14 14:21:59 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 {} 14082025 25983542 Nombre de photos uploadées : 18 / 23040 (0%) 14082025 25983542 Nombre de photos taguées (types de déchets): 0 / 18 (0%) 14082025 25983542 Nombre de photos taguées (volume) : 0 / 18 (0%) elapsed_time : load_data_split_time_score 1.6689300537109375e-06 elapsed_time : order_list_meta_photo_and_scores 4.0531158447265625e-06 ?????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0007865428924560547 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.20562338829040527 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 : 0.1280742026748971 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P25974582_14-08-2025_10_11_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 25974582 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`=25974582 AND mptpi.`type`=3594 To do Qualite : 0.015116423932613166 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P25974586_14-08-2025_10_01_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 25974586 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`=25974586 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 25980071 order by id desc limit 1 Qualite : 0.12013578869047618 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P25980101_14-08-2025_13_01_43.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 25980101 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`=25980101 AND mptpi.`type`=3594 To do Qualite : 0.1582214988425926 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P25980105_14-08-2025_12_51_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 25980105 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`=25980105 AND mptpi.`type`=3594 To do Qualite : 0.16527360973324515 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P25980109_14-08-2025_12_41_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 25980109 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`=25980109 AND mptpi.`type`=3594 To do Qualite : 0.03430676118827159 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P25983542_14-08-2025_14_21_54.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 25983542 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`=25983542 AND mptpi.`type`=3594 To do Qualite : 0.08205182613168727 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P25983560_14-08-2025_14_11_41.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 25983560 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`=25983560 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'14082025': {'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 [1377008661, 1377008606, 1377008576, 1377008546, 1377008517, 1377008514, 1377008511, 1377008459, 1377008455, 1377008447, 1377008416, 1377008407, 1377008403, 1377008247, 1377008246, 1377008244, 1377008242, 1377008239] Looping around the photos to save general results len do output : 1 /25983542Didn'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, '3535195') ('3318', '25983542', '1377008661', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008606', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008576', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008546', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008517', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008514', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008511', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008459', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008455', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008447', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008416', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008407', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008403', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008247', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008246', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008244', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008242', None, None, None, None, None, '3535195') ('3318', None, None, None, None, None, None, None, '3535195') ('3318', '25983542', '1377008239', None, None, None, None, None, '3535195') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 19 time used for this insertion : 0.01639246940612793 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.7485642433166504 time spend to save output : 0.017002344131469727 total time spend for step 10 : 1.7655665874481201 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 52.85user 16.91system 1:35.14elapsed 73%CPU (0avgtext+0avgdata 3221424maxresident)k 532832inputs+20392outputs (152major+1492680minor)pagefaults 0swaps