python /home/admin/mtr/script_for_cron.py -j default -m 20 -a 'python3 ~/workarea/git/Velours/python/prod/datou.py -j batch_current -C 3777379' -s carac_3318 -M 0 -S 0 -U 100,80,95 import MySQLdb succeeded Import error (python version) ['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/darknet', '/home/admin/workarea/git/Velours/python', '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python', '/home/admin/mtr/.credentials', '/home/admin/workarea/install/caffe/python', '/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools', '/home/admin/workarea/git/fotonowerpip', '/home/admin/workarea/install/segment-anything', '/home/admin/workarea/git/pyfvs', '/home/admin/workarea/git/apy', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages'] process id : 361684 load datou : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! Unexpected type for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : (photo_id, hashtag_id, score_max) was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? load thcls load pdts Running datou job : batch_current TODO datou_current to load to do maybe to take outside batchDatouExec updating current state to 1 list_input_json: [] Current got : datou_id : 3318, datou_cur_ids : ['3777379'] with mtr_portfolio_ids : ['27224634'] and first list_photo_ids : [] new path : /proc/361684/ 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 , BFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 11 ; length of list_pids : 11 ; length of list_args : 11 time to download the photos : 2.0011332035064697 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Tue Sep 30 17:01:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10582 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-09-30 17:01:06.681784: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-09-30 17:01:06.716591: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-09-30 17:01:06.718719: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f37a8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-09-30 17:01:06.718782: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-09-30 17:01:06.725056: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-09-30 17:01:06.930275: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x26dcb0d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-09-30 17:01:06.930337: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-09-30 17:01:06.931723: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-30 17:01:06.933633: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-30 17:01:06.964529: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-30 17:01:06.983434: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-30 17:01:06.987274: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-30 17:01:07.015917: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-30 17:01:07.020279: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-30 17:01:07.065632: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-30 17:01:07.068015: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-30 17:01:07.068735: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-30 17:01:07.069984: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-30 17:01:07.070004: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-30 17:01:07.070016: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-30 17:01:07.072034: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9801 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-09-30 17:01:07.535426: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-30 17:01:07.535520: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-30 17:01:07.535541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-30 17:01:07.535560: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-30 17:01:07.535579: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-30 17:01:07.535597: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-30 17:01:07.535614: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-30 17:01:07.535632: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-30 17:01:07.537245: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-30 17:01:07.538642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-30 17:01:07.538674: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-30 17:01:07.538690: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-30 17:01:07.538705: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-30 17:01:07.538718: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-30 17:01:07.538732: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-30 17:01:07.538746: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-30 17:01:07.538760: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-30 17:01:07.540039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-30 17:01:07.540064: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-30 17:01:07.540073: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-30 17:01:07.540081: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-30 17:01:07.541430: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9801 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-09-30 17:01:16.423739: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-30 17:01:16.796587: 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 : 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 : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 44.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 39.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 46.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 56.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 68.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 49.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 8 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 : 12 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 60.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 47.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 63.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 1920.00000 nb d'objets trouves : 3 Detection mask done ! Trying to reset tf kernel 361866 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5290 tf kernel not reseted sub process len(results) : 11 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 11 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10582 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.00041413307189941406 nb_pixel_total : 4438 time to create 1 rle with old method : 0.005731344223022461 length of segment : 82 time for calcul the mask position with numpy : 0.0003333091735839844 nb_pixel_total : 7145 time to create 1 rle with old method : 0.009670734405517578 length of segment : 74 time for calcul the mask position with numpy : 0.0005369186401367188 nb_pixel_total : 12009 time to create 1 rle with old method : 0.015612363815307617 length of segment : 145 time for calcul the mask position with numpy : 0.0002713203430175781 nb_pixel_total : 13267 time to create 1 rle with old method : 0.017713069915771484 length of segment : 149 time for calcul the mask position with numpy : 0.0003101825714111328 nb_pixel_total : 10053 time to create 1 rle with old method : 0.013211250305175781 length of segment : 192 time for calcul the mask position with numpy : 0.0004284381866455078 nb_pixel_total : 24780 time to create 1 rle with old method : 0.02902531623840332 length of segment : 339 time for calcul the mask position with numpy : 0.00013136863708496094 nb_pixel_total : 5264 time to create 1 rle with old method : 0.006138324737548828 length of segment : 94 time for calcul the mask position with numpy : 0.00014662742614746094 nb_pixel_total : 6759 time to create 1 rle with old method : 0.008115530014038086 length of segment : 87 time for calcul the mask position with numpy : 0.0001652240753173828 nb_pixel_total : 7189 time to create 1 rle with old method : 0.008821725845336914 length of segment : 94 time for calcul the mask position with numpy : 0.000209808349609375 nb_pixel_total : 7031 time to create 1 rle with old method : 0.008224010467529297 length of segment : 162 time for calcul the mask position with numpy : 0.00012612342834472656 nb_pixel_total : 4353 time to create 1 rle with old method : 0.005166292190551758 length of segment : 113 time for calcul the mask position with numpy : 0.0006175041198730469 nb_pixel_total : 37084 time to create 1 rle with old method : 0.04247403144836426 length of segment : 181 time for calcul the mask position with numpy : 0.0004737377166748047 nb_pixel_total : 28942 time to create 1 rle with old method : 0.03473544120788574 length of segment : 184 time for calcul the mask position with numpy : 0.0003218650817871094 nb_pixel_total : 16339 time to create 1 rle with old method : 0.018996477127075195 length of segment : 163 time for calcul the mask position with numpy : 0.0018150806427001953 nb_pixel_total : 101770 time to create 1 rle with old method : 0.11385965347290039 length of segment : 531 time for calcul the mask position with numpy : 0.0006725788116455078 nb_pixel_total : 44688 time to create 1 rle with old method : 0.049772024154663086 length of segment : 183 time for calcul the mask position with numpy : 0.0005373954772949219 nb_pixel_total : 26376 time to create 1 rle with old method : 0.02995610237121582 length of segment : 223 time for calcul the mask position with numpy : 0.0018270015716552734 nb_pixel_total : 95234 time to create 1 rle with old method : 0.1331794261932373 length of segment : 448 time for calcul the mask position with numpy : 0.0003631114959716797 nb_pixel_total : 12071 time to create 1 rle with old method : 0.014121055603027344 length of segment : 164 time for calcul the mask position with numpy : 0.00020503997802734375 nb_pixel_total : 9091 time to create 1 rle with old method : 0.01035451889038086 length of segment : 193 time for calcul the mask position with numpy : 0.0011949539184570312 nb_pixel_total : 69941 time to create 1 rle with old method : 0.08167099952697754 length of segment : 439 time for calcul the mask position with numpy : 0.0002422332763671875 nb_pixel_total : 9422 time to create 1 rle with old method : 0.010982751846313477 length of segment : 170 time for calcul the mask position with numpy : 0.00016832351684570312 nb_pixel_total : 11254 time to create 1 rle with old method : 0.013185739517211914 length of segment : 92 time for calcul the mask position with numpy : 0.00012302398681640625 nb_pixel_total : 5642 time to create 1 rle with old method : 0.006731271743774414 length of segment : 116 time for calcul the mask position with numpy : 0.010156869888305664 nb_pixel_total : 606327 time to create 1 rle with new method : 0.04053092002868652 length of segment : 945 time for calcul the mask position with numpy : 0.000888824462890625 nb_pixel_total : 57920 time to create 1 rle with old method : 0.07005095481872559 length of segment : 298 time spent for convertir_results : 2.392218828201294 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 26 chid ids of type : 3594 Number RLEs to save : 5861 save missing photos in datou_result : time spend for datou_step_exec : 28.396320343017578 time spend to save output : 0.8120734691619873 total time spend for step 1 : 29.208393812179565 step2:crop_condition Tue Sep 30 17:01:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 11 ! batch 1 Loaded 26 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 ! map_result returned by crop_photo_return_map_crop : length : 14 About to insert : list_path_to_insert length 14 new photo from crops ! About to upload 14 photos upload in portfolio : 3736932 init cache_photo without model_param we have 14 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1759244494_361684 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff_rle_crop_3981096171_0.png', 0, 83, 82, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076454_dc364a9cb437f1b7d2b5e195cb425189_rle_crop_3981096175_0.png', 0, 122, 192, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076402_442091123492116276dd3e3967317e8c_rle_crop_3981096176_0.png', 0, 210, 202, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076288_8eb4f6d4958332a8df161bab380a8488_rle_crop_3981096180_0.png', 0, 104, 162, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076288_8eb4f6d4958332a8df161bab380a8488_rle_crop_3981096179_0.png', 0, 128, 94, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076256_12606bb94888de66e58fb12cfb73264e_rle_crop_3981096182_0.png', 0, 271, 180, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096186_0.png', 0, 382, 172, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f_rle_crop_3981096187_0.png', 0, 203, 223, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f_rle_crop_3981096188_0.png', 0, 454, 381, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f_rle_crop_3981096189_0.png', 0, 279, 113, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076106_82d24af9213333cde2cd3b619d39fe83_rle_crop_3981096190_0.png', 0, 112, 188, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a_rle_crop_3981096192_0.png', 0, 114, 170, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a_rle_crop_3981096193_0.png', 0, 153, 88, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244497), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a_rle_crop_3981096194_0.png', 0, 98, 88, 0, 1759244497,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 14 photos in the portfolio 3736932 time of upload the photos Elapsed time : 5.207727432250977 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/1759244499_361684 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244500), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff_rle_crop_3981096173_0.png', 0, 125, 144, 0, 1759244500,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.7268874645233154 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 11 About to insert : list_path_to_insert length 11 new photo from crops ! About to upload 11 photos upload in portfolio : 3736932 init cache_photo without model_param we have 11 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1759244504_361684 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff_rle_crop_3981096172_0.png', 0, 126, 72, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076454_dc364a9cb437f1b7d2b5e195cb425189_rle_crop_3981096174_0.png', 0, 118, 149, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076351_053f260d4ea3e17903e85d45fddc3171_rle_crop_3981096177_0.png', 0, 81, 92, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076351_053f260d4ea3e17903e85d45fddc3171_rle_crop_3981096178_0.png', 0, 113, 86, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076256_12606bb94888de66e58fb12cfb73264e_rle_crop_3981096181_0.png', 0, 73, 107, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096184_0.png', 0, 151, 163, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096183_0.png', 0, 250, 180, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096185_0.png', 0, 324, 531, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076106_82d24af9213333cde2cd3b619d39fe83_rle_crop_3981096191_0.png', 0, 300, 425, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076048_38746d9f585a9dc9f592b6de5d8d4a29_rle_crop_3981096195_0.png', 0, 914, 941, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244506), 0.0, 0.0, 14, '', 0, 0, '1759244460_361684_1386076048_38746d9f585a9dc9f592b6de5d8d4a29_rle_crop_3981096196_0.png', 0, 273, 294, 0, 1759244506,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 11 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.8561007976531982 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 begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1386076647, 1386076454, 1386076402, 1386076351, 1386076288, 1386076256, 1386076231, 1386076119, 1386076106, 1386076075, 1386076048] Looping around the photos to save general results len do output : 26 /1386982844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982861Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386982871Didn'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, '3777379') ('3318', '27224634', '1386076647', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076454', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076402', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076351', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076288', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076256', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076231', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076119', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076106', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076075', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076048', None, None, None, None, None, '3777379') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 89 time used for this insertion : 0.03942728042602539 save_final save missing photos in datou_result : time spend for datou_step_exec : 15.796886920928955 time spend to save output : 0.0408635139465332 total time spend for step 2 : 15.837750434875488 step3:rle_unique_nms_with_priority Tue Sep 30 17:01:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 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 26 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++nb_obj : 3 nb_hashtags : 3 time to prepare the origin masks : 0.35709309577941895 time for calcul the mask position with numpy : 0.12324237823486328 nb_pixel_total : 2050008 time to create 1 rle with new method : 0.15912842750549316 time for calcul the mask position with numpy : 0.006261587142944336 nb_pixel_total : 12009 time to create 1 rle with old method : 0.014487028121948242 time for calcul the mask position with numpy : 0.005938053131103516 nb_pixel_total : 7145 time to create 1 rle with old method : 0.007613658905029297 time for calcul the mask position with numpy : 0.00593113899230957 nb_pixel_total : 4438 time to create 1 rle with old method : 0.00499415397644043 create new chi : 0.3382132053375244 time to delete rle : 0.1203758716583252 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1682 TO DO : save crop sub photo not yet done ! save time : 0.341050386428833 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.2822434902191162 time for calcul the mask position with numpy : 0.042649030685424805 nb_pixel_total : 2050280 time to create 1 rle with new method : 0.1692523956298828 time for calcul the mask position with numpy : 0.005906105041503906 nb_pixel_total : 10053 time to create 1 rle with old method : 0.010671615600585938 time for calcul the mask position with numpy : 0.0058231353759765625 nb_pixel_total : 13267 time to create 1 rle with old method : 0.01356959342956543 create new chi : 0.2543797492980957 time to delete rle : 0.0002758502960205078 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1762 TO DO : save crop sub photo not yet done ! save time : 0.3411707878112793 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03240036964416504 time for calcul the mask position with numpy : 0.018393754959106445 nb_pixel_total : 2048820 time to create 1 rle with new method : 0.08414936065673828 time for calcul the mask position with numpy : 0.005822658538818359 nb_pixel_total : 24780 time to create 1 rle with old method : 0.026137113571166992 create new chi : 0.14252734184265137 time to delete rle : 0.00026798248291015625 batch 1 Loaded 3 chid ids of type : 3594 +++++Number RLEs to save : 1758 TO DO : save crop sub photo not yet done ! save time : 0.3263130187988281 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.04609394073486328 time for calcul the mask position with numpy : 0.13134765625 nb_pixel_total : 2061577 time to create 1 rle with new method : 0.1517806053161621 time for calcul the mask position with numpy : 0.005816221237182617 nb_pixel_total : 6759 time to create 1 rle with old method : 0.007060050964355469 time for calcul the mask position with numpy : 0.005987644195556641 nb_pixel_total : 5264 time to create 1 rle with old method : 0.00576329231262207 create new chi : 0.31777000427246094 time to delete rle : 0.00026154518127441406 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1442 TO DO : save crop sub photo not yet done ! save time : 0.27829647064208984 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.042142629623413086 time for calcul the mask position with numpy : 0.06396198272705078 nb_pixel_total : 2059380 time to create 1 rle with new method : 0.14687395095825195 time for calcul the mask position with numpy : 0.0059545040130615234 nb_pixel_total : 7031 time to create 1 rle with old method : 0.007647275924682617 time for calcul the mask position with numpy : 0.006099224090576172 nb_pixel_total : 7189 time to create 1 rle with old method : 0.008111238479614258 create new chi : 0.24875903129577637 time to delete rle : 0.00031566619873046875 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 : 0.31891489028930664 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.06467580795288086 time for calcul the mask position with numpy : 0.13728022575378418 nb_pixel_total : 2032163 time to create 1 rle with new method : 0.08418583869934082 time for calcul the mask position with numpy : 0.006142139434814453 nb_pixel_total : 37084 time to create 1 rle with old method : 0.03905487060546875 time for calcul the mask position with numpy : 0.005815744400024414 nb_pixel_total : 4353 time to create 1 rle with old method : 0.004839420318603516 create new chi : 0.28771281242370605 time to delete rle : 0.0002651214599609375 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1668 TO DO : save crop sub photo not yet done ! save time : 0.32703232765197754 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.24737119674682617 time for calcul the mask position with numpy : 0.06334996223449707 nb_pixel_total : 1881861 time to create 1 rle with new method : 0.10808658599853516 time for calcul the mask position with numpy : 0.006000041961669922 nb_pixel_total : 44688 time to create 1 rle with old method : 0.04701662063598633 time for calcul the mask position with numpy : 0.006666421890258789 nb_pixel_total : 101770 time to create 1 rle with old method : 0.10766148567199707 time for calcul the mask position with numpy : 0.006136655807495117 nb_pixel_total : 16339 time to create 1 rle with old method : 0.017777204513549805 time for calcul the mask position with numpy : 0.006194114685058594 nb_pixel_total : 28942 time to create 1 rle with old method : 0.030850648880004883 create new chi : 0.4102613925933838 time to delete rle : 0.0004169940948486328 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 3202 TO DO : save crop sub photo not yet done ! save time : 0.495572566986084 nb_obj : 3 nb_hashtags : 1 time to prepare the origin masks : 0.06464409828186035 time for calcul the mask position with numpy : 0.0184938907623291 nb_pixel_total : 1939919 time to create 1 rle with new method : 0.1801466941833496 time for calcul the mask position with numpy : 0.006038665771484375 nb_pixel_total : 12071 time to create 1 rle with old method : 0.01300668716430664 time for calcul the mask position with numpy : 0.00634765625 nb_pixel_total : 95234 time to create 1 rle with old method : 0.1012108325958252 time for calcul the mask position with numpy : 0.006248950958251953 nb_pixel_total : 26376 time to create 1 rle with old method : 0.028325557708740234 create new chi : 0.36736416816711426 time to delete rle : 0.00027823448181152344 batch 1 Loaded 7 chid ids of type : 3594 +++++Number RLEs to save : 2750 TO DO : save crop sub photo not yet done ! save time : 0.4938390254974365 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.13555121421813965 time for calcul the mask position with numpy : 0.06548261642456055 nb_pixel_total : 1994568 time to create 1 rle with new method : 0.2229146957397461 time for calcul the mask position with numpy : 0.006360769271850586 nb_pixel_total : 69941 time to create 1 rle with old method : 0.07506537437438965 time for calcul the mask position with numpy : 0.005762815475463867 nb_pixel_total : 9091 time to create 1 rle with old method : 0.009571075439453125 create new chi : 0.3956449031829834 time to delete rle : 0.00033211708068847656 batch 1 Loaded 5 chid ids of type : 3594 ++++Number RLEs to save : 2344 TO DO : save crop sub photo not yet done ! save time : 0.41002535820007324 nb_obj : 3 nb_hashtags : 1 time to prepare the origin masks : 0.05957150459289551 time for calcul the mask position with numpy : 0.16303730010986328 nb_pixel_total : 2047282 time to create 1 rle with new method : 0.08266353607177734 time for calcul the mask position with numpy : 0.0060520172119140625 nb_pixel_total : 5642 time to create 1 rle with old method : 0.006191253662109375 time for calcul the mask position with numpy : 0.005709409713745117 nb_pixel_total : 11254 time to create 1 rle with old method : 0.012206077575683594 time for calcul the mask position with numpy : 0.006159067153930664 nb_pixel_total : 9422 time to create 1 rle with old method : 0.010616779327392578 create new chi : 0.3030076026916504 time to delete rle : 0.00028967857360839844 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1836 TO DO : save crop sub photo not yet done ! save time : 0.32642459869384766 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.046498775482177734 time for calcul the mask position with numpy : 0.15755057334899902 nb_pixel_total : 1409353 time to create 1 rle with new method : 0.08127641677856445 time for calcul the mask position with numpy : 0.006371021270751953 nb_pixel_total : 57920 time to create 1 rle with old method : 0.06048893928527832 time for calcul the mask position with numpy : 0.010033130645751953 nb_pixel_total : 606327 time to create 1 rle with new method : 0.15706276893615723 create new chi : 0.4809916019439697 time to delete rle : 0.0003387928009033203 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 3566 TO DO : save crop sub photo not yet done ! save time : 0.5568463802337646 map_output_result : {1386076647: (0.0, 'Should be the crop_list due to order', 0), 1386076454: (0.0, 'Should be the crop_list due to order', 0), 1386076402: (0.0, 'Should be the crop_list due to order', 0), 1386076351: (0.0, 'Should be the crop_list due to order', 0), 1386076288: (0.0, 'Should be the crop_list due to order', 0), 1386076256: (0.0, 'Should be the crop_list due to order', 0), 1386076231: (0.0, 'Should be the crop_list due to order', 0), 1386076119: (0.0, 'Should be the crop_list due to order', 0), 1386076106: (0.0, 'Should be the crop_list due to order', 0), 1386076075: (0.0, 'Should be the crop_list due to order', 0), 1386076048: (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 [1386076647, 1386076454, 1386076402, 1386076351, 1386076288, 1386076256, 1386076231, 1386076119, 1386076106, 1386076075, 1386076048] Looping around the photos to save general results len do output : 11 /1386076647.Didn't retrieve data . /1386076454.Didn't retrieve data . /1386076402.Didn't retrieve data . /1386076351.Didn't retrieve data . /1386076288.Didn't retrieve data . /1386076256.Didn't retrieve data . /1386076231.Didn't retrieve data . /1386076119.Didn't retrieve data . /1386076106.Didn't retrieve data . /1386076075.Didn't retrieve data . /1386076048.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, '3777379') ('3318', '27224634', '1386076647', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076454', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076402', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076351', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076288', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076256', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076231', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076119', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076106', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076075', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076048', None, None, None, None, None, '3777379') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.03746652603149414 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.783666133880615 time spend to save output : 0.03789234161376953 total time spend for step 3 : 9.821558475494385 step4:ventilate_hashtags_in_portfolio Tue Sep 30 17:01:57 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 ! 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 : 27224634 get user id for portfolio 27224634 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`=27224634 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('mal_croppe','metal','environnement','flou','pehd','pet_clair','background','autre','carton','papier','pet_fonce')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=27224634 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('mal_croppe','metal','environnement','flou','pehd','pet_clair','background','autre','carton','papier','pet_fonce')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=27224634 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('mal_croppe','metal','environnement','flou','pehd','pet_clair','background','autre','carton','papier','pet_fonce')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/27356710,27356711,27356712,27356713,27356714,27356715,27356716,27356717,27356718,27356719,27356720?tags=mal_croppe,metal,environnement,flou,pehd,pet_clair,background,autre,carton,papier,pet_fonce Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1386076647, 1386076454, 1386076402, 1386076351, 1386076288, 1386076256, 1386076231, 1386076119, 1386076106, 1386076075, 1386076048] Looping around the photos to save general results len do output : 1 /27224634. 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, '3777379') ('3318', '27224634', '1386076647', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076454', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076402', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076351', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076288', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076256', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076231', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076119', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076106', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076075', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076048', None, None, None, None, None, '3777379') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.03836703300476074 save_final save missing photos in datou_result : time spend for datou_step_exec : 5.529488801956177 time spend to save output : 0.03876543045043945 total time spend for step 4 : 5.568254232406616 step5:final Tue Sep 30 17:02:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : {1386076647: ('0.054117082281144785',), 1386076454: ('0.054117082281144785',), 1386076402: ('0.054117082281144785',), 1386076351: ('0.054117082281144785',), 1386076288: ('0.054117082281144785',), 1386076256: ('0.054117082281144785',), 1386076231: ('0.054117082281144785',), 1386076119: ('0.054117082281144785',), 1386076106: ('0.054117082281144785',), 1386076075: ('0.054117082281144785',), 1386076048: ('0.054117082281144785',)} new output for save of step final : {1386076647: ('0.054117082281144785',), 1386076454: ('0.054117082281144785',), 1386076402: ('0.054117082281144785',), 1386076351: ('0.054117082281144785',), 1386076288: ('0.054117082281144785',), 1386076256: ('0.054117082281144785',), 1386076231: ('0.054117082281144785',), 1386076119: ('0.054117082281144785',), 1386076106: ('0.054117082281144785',), 1386076075: ('0.054117082281144785',), 1386076048: ('0.054117082281144785',)} [1386076647, 1386076454, 1386076402, 1386076351, 1386076288, 1386076256, 1386076231, 1386076119, 1386076106, 1386076075, 1386076048] Looping around the photos to save general results len do output : 11 /1386076647.Didn't retrieve data . /1386076454.Didn't retrieve data . /1386076402.Didn't retrieve data . /1386076351.Didn't retrieve data . /1386076288.Didn't retrieve data . /1386076256.Didn't retrieve data . /1386076231.Didn't retrieve data . /1386076119.Didn't retrieve data . /1386076106.Didn't retrieve data . /1386076075.Didn't retrieve data . /1386076048.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, '3777379') ('3318', '27224634', '1386076647', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076454', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076402', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076351', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076288', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076256', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076231', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076119', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076106', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076075', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076048', None, None, None, None, None, '3777379') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.04009079933166504 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.3766899108886719 time spend to save output : 0.04069185256958008 total time spend for step 5 : 0.41738176345825195 step6:blur_detection Tue Sep 30 17:02:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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/1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff.jpg resize: (1080, 1920) 1386076647 -0.28785624876082866 treat image : temp/1759244460_361684_1386076454_dc364a9cb437f1b7d2b5e195cb425189.jpg resize: (1080, 1920) 1386076454 1.3803468236400394 treat image : temp/1759244460_361684_1386076402_442091123492116276dd3e3967317e8c.jpg resize: (1080, 1920) 1386076402 -0.7390894649759836 treat image : temp/1759244460_361684_1386076351_053f260d4ea3e17903e85d45fddc3171.jpg resize: (1080, 1920) 1386076351 0.9695771269947767 treat image : temp/1759244460_361684_1386076288_8eb4f6d4958332a8df161bab380a8488.jpg resize: (1080, 1920) 1386076288 0.25485453783049244 treat image : temp/1759244460_361684_1386076256_12606bb94888de66e58fb12cfb73264e.jpg resize: (1080, 1920) 1386076256 0.4357175888821193 treat image : temp/1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1.jpg resize: (1080, 1920) 1386076231 -3.0529958575017644 treat image : temp/1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f.jpg resize: (1080, 1920) 1386076119 1.7395686512883917 treat image : temp/1759244460_361684_1386076106_82d24af9213333cde2cd3b619d39fe83.jpg resize: (1080, 1920) 1386076106 -0.7399043788650919 treat image : temp/1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a.jpg resize: (1080, 1920) 1386076075 -0.1003320025771452 treat image : temp/1759244460_361684_1386076048_38746d9f585a9dc9f592b6de5d8d4a29.jpg resize: (1080, 1920) 1386076048 -1.0155108866718328 treat image : temp/1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff_rle_crop_3981096171_0.png resize: (82, 83) 1386982844 -0.044557720158976515 treat image : temp/1759244460_361684_1386076454_dc364a9cb437f1b7d2b5e195cb425189_rle_crop_3981096175_0.png resize: (192, 122) 1386982845 -1.7657859678764847 treat image : temp/1759244460_361684_1386076402_442091123492116276dd3e3967317e8c_rle_crop_3981096176_0.png resize: (202, 210) 1386982846 -1.443499317824841 treat image : temp/1759244460_361684_1386076288_8eb4f6d4958332a8df161bab380a8488_rle_crop_3981096180_0.png resize: (162, 104) 1386982847 -2.0306129358294847 treat image : temp/1759244460_361684_1386076288_8eb4f6d4958332a8df161bab380a8488_rle_crop_3981096179_0.png resize: (94, 128) 1386982848 -1.692362456200479 treat image : temp/1759244460_361684_1386076256_12606bb94888de66e58fb12cfb73264e_rle_crop_3981096182_0.png resize: (180, 271) 1386982849 -2.2419056032201072 treat image : temp/1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096186_0.png resize: (172, 382) 1386982850 -2.108019437018013 treat image : temp/1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f_rle_crop_3981096187_0.png resize: (223, 203) 1386982851 -1.610864096991758 treat image : temp/1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f_rle_crop_3981096188_0.png resize: (381, 454) 1386982852 -1.2087961869925046 treat image : temp/1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f_rle_crop_3981096189_0.png resize: (113, 279) 1386982853 -2.4990344198547154 treat image : temp/1759244460_361684_1386076106_82d24af9213333cde2cd3b619d39fe83_rle_crop_3981096190_0.png resize: (188, 112) 1386982854 -1.3176167383715052 treat image : temp/1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a_rle_crop_3981096192_0.png resize: (170, 114) 1386982855 -1.2201419757477732 treat image : temp/1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a_rle_crop_3981096193_0.png resize: (88, 153) 1386982856 3.531487550366369 treat image : temp/1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a_rle_crop_3981096194_0.png resize: (88, 98) 1386982857 -1.2600901863479204 treat image : temp/1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff_rle_crop_3981096173_0.png resize: (144, 125) 1386982859 -1.4433366770639482 treat image : temp/1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff_rle_crop_3981096172_0.png resize: (72, 126) 1386982861 -2.0787310989147976 treat image : temp/1759244460_361684_1386076454_dc364a9cb437f1b7d2b5e195cb425189_rle_crop_3981096174_0.png resize: (149, 118) 1386982862 -1.1322656968076552 treat image : temp/1759244460_361684_1386076351_053f260d4ea3e17903e85d45fddc3171_rle_crop_3981096177_0.png resize: (92, 81) 1386982863 -1.8407598481488996 treat image : temp/1759244460_361684_1386076351_053f260d4ea3e17903e85d45fddc3171_rle_crop_3981096178_0.png resize: (86, 113) 1386982864 -1.117795488229906 treat image : temp/1759244460_361684_1386076256_12606bb94888de66e58fb12cfb73264e_rle_crop_3981096181_0.png resize: (107, 73) 1386982865 -2.4246502304850908 treat image : temp/1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096184_0.png resize: (163, 151) 1386982866 -0.8786138106494901 treat image : temp/1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096183_0.png resize: (180, 250) 1386982867 -2.0038173712363094 treat image : temp/1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096185_0.png resize: (531, 324) 1386982868 0.40594867873982177 treat image : temp/1759244460_361684_1386076106_82d24af9213333cde2cd3b619d39fe83_rle_crop_3981096191_0.png resize: (425, 300) 1386982869 -0.16561001230674474 treat image : temp/1759244460_361684_1386076048_38746d9f585a9dc9f592b6de5d8d4a29_rle_crop_3981096195_0.png resize: (941, 914) 1386982870 1.3928422465044275 treat image : temp/1759244460_361684_1386076048_38746d9f585a9dc9f592b6de5d8d4a29_rle_crop_3981096196_0.png resize: (294, 273) 1386982871 1.022018102107285 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 : 37 time used for this insertion : 0.03760957717895508 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 37 time used for this insertion : 0.03725004196166992 save missing photos in datou_result : time spend for datou_step_exec : 9.376645565032959 time spend to save output : 0.09251570701599121 total time spend for step 6 : 9.46916127204895 step7:brightness Tue Sep 30 17:02:13 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/1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff.jpg treat image : temp/1759244460_361684_1386076454_dc364a9cb437f1b7d2b5e195cb425189.jpg treat image : temp/1759244460_361684_1386076402_442091123492116276dd3e3967317e8c.jpg treat image : temp/1759244460_361684_1386076351_053f260d4ea3e17903e85d45fddc3171.jpg treat image : temp/1759244460_361684_1386076288_8eb4f6d4958332a8df161bab380a8488.jpg treat image : temp/1759244460_361684_1386076256_12606bb94888de66e58fb12cfb73264e.jpg treat image : temp/1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1.jpg treat image : temp/1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f.jpg treat image : temp/1759244460_361684_1386076106_82d24af9213333cde2cd3b619d39fe83.jpg treat image : temp/1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a.jpg treat image : temp/1759244460_361684_1386076048_38746d9f585a9dc9f592b6de5d8d4a29.jpg treat image : temp/1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff_rle_crop_3981096171_0.png treat image : temp/1759244460_361684_1386076454_dc364a9cb437f1b7d2b5e195cb425189_rle_crop_3981096175_0.png treat image : temp/1759244460_361684_1386076402_442091123492116276dd3e3967317e8c_rle_crop_3981096176_0.png treat image : temp/1759244460_361684_1386076288_8eb4f6d4958332a8df161bab380a8488_rle_crop_3981096180_0.png treat image : temp/1759244460_361684_1386076288_8eb4f6d4958332a8df161bab380a8488_rle_crop_3981096179_0.png treat image : temp/1759244460_361684_1386076256_12606bb94888de66e58fb12cfb73264e_rle_crop_3981096182_0.png treat image : temp/1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096186_0.png treat image : temp/1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f_rle_crop_3981096187_0.png treat image : temp/1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f_rle_crop_3981096188_0.png treat image : temp/1759244460_361684_1386076119_9b707a17525bcb888c7f2b040a44705f_rle_crop_3981096189_0.png treat image : temp/1759244460_361684_1386076106_82d24af9213333cde2cd3b619d39fe83_rle_crop_3981096190_0.png treat image : temp/1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a_rle_crop_3981096192_0.png treat image : temp/1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a_rle_crop_3981096193_0.png treat image : temp/1759244460_361684_1386076075_2c913bca0ef44872e804cb4b8e12d89a_rle_crop_3981096194_0.png treat image : temp/1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff_rle_crop_3981096173_0.png treat image : temp/1759244460_361684_1386076647_a11ffb3f9b8319a1072ff7b1fad84eff_rle_crop_3981096172_0.png treat image : temp/1759244460_361684_1386076454_dc364a9cb437f1b7d2b5e195cb425189_rle_crop_3981096174_0.png treat image : temp/1759244460_361684_1386076351_053f260d4ea3e17903e85d45fddc3171_rle_crop_3981096177_0.png treat image : temp/1759244460_361684_1386076351_053f260d4ea3e17903e85d45fddc3171_rle_crop_3981096178_0.png treat image : temp/1759244460_361684_1386076256_12606bb94888de66e58fb12cfb73264e_rle_crop_3981096181_0.png treat image : temp/1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096184_0.png treat image : temp/1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096183_0.png treat image : temp/1759244460_361684_1386076231_159ed2668915f9895a8e375555e640a1_rle_crop_3981096185_0.png treat image : temp/1759244460_361684_1386076106_82d24af9213333cde2cd3b619d39fe83_rle_crop_3981096191_0.png treat image : temp/1759244460_361684_1386076048_38746d9f585a9dc9f592b6de5d8d4a29_rle_crop_3981096195_0.png treat image : temp/1759244460_361684_1386076048_38746d9f585a9dc9f592b6de5d8d4a29_rle_crop_3981096196_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 : 37 time used for this insertion : 0.03686642646789551 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 37 time used for this insertion : 0.03798937797546387 save missing photos in datou_result : time spend for datou_step_exec : 2.7276358604431152 time spend to save output : 0.09258842468261719 total time spend for step 7 : 2.8202242851257324 step8:velours_tree Tue Sep 30 17:02:16 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 : 0.26239776611328125 time spend to save output : 4.553794860839844e-05 total time spend for step 8 : 0.26244330406188965 step9:send_mail_cod Tue Sep 30 17:02:16 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin in order to get the selector url, please entre the license of selector results_Auto_P27224634_30-09-2025_17_02_16.pdf 27356710 imagette273567101759244536 27356711 imagette273567111759244536 27356713 imagette273567131759244536 27356714 imagette273567141759244536 27356715 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 .imagette273567151759244536 27356716 imagette273567161759244537 27356717 imagette273567171759244537 27356718 change filename to text .imagette273567181759244537 27356719 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 .imagette273567191759244537 27356720 imagette273567201759244538 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=27224634 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/27356710,27356711,27356712,27356713,27356714,27356715,27356716,27356717,27356718,27356719,27356720?tags=mal_croppe,metal,environnement,flou,pehd,pet_clair,background,autre,carton,papier,pet_fonce args[1386076647] : ((1386076647, -0.28785624876082866, 492688767), (1386076647, 0.37746834426847864, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com args[1386076454] : ((1386076454, 1.3803468236400394, 492688767), (1386076454, 0.48728114189847915, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com args[1386076402] : ((1386076402, -0.7390894649759836, 492688767), (1386076402, 0.3573436025412682, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com args[1386076351] : ((1386076351, 0.9695771269947767, 492688767), (1386076351, 0.7817643548556314, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com args[1386076288] : ((1386076288, 0.25485453783049244, 492688767), (1386076288, 1.0331887002580853, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com args[1386076256] : ((1386076256, 0.4357175888821193, 492688767), (1386076256, 1.1483359677629028, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com args[1386076231] : ((1386076231, -3.0529958575017644, 492609224), (1386076231, 0.635841645541728, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com args[1386076119] : ((1386076119, 1.7395686512883917, 492688767), (1386076119, 0.41445248008082214, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com args[1386076106] : ((1386076106, -0.7399043788650919, 492688767), (1386076106, 0.9346923972687862, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com args[1386076075] : ((1386076075, -0.1003320025771452, 492688767), (1386076075, 0.8260811838946883, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com args[1386076048] : ((1386076048, -1.0155108866718328, 492688767), (1386076048, 1.4304022395579192, 2107752395), '0.054117082281144785') We are sending mail with results at report@fotonower.com refus_total : 0.054117082281144785 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=27224634 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_P27224634_30-09-2025_17_02_16.pdf results_Auto_P27224634_30-09-2025_17_02_16.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27224634_30-09-2025_17_02_16.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','27224634','results_Auto_P27224634_30-09-2025_17_02_16.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27224634_30-09-2025_17_02_16.pdf','pdf','','0.22','0.054117082281144785') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/27224634

https://www.fotonower.com/image?json=false&list_photos_id=1386076647
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1386076454
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.3803468236400394)
https://www.fotonower.com/image?json=false&list_photos_id=1386076402
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1386076351
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1386076288
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1386076256
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1386076231
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1386076119
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.7395686512883917)
https://www.fotonower.com/image?json=false&list_photos_id=1386076106
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1386076075
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1386076048
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 5.41%
Veuillez trouver les photos des contaminants.

exemples de contaminants: pet_clair: https://www.fotonower.com/view/27356715?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/27356718?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/27356719?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27224634_30-09-2025_17_02_16.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/27356710,27356711,27356712,27356713,27356714,27356715,27356716,27356717,27356718,27356719,27356720?tags=mal_croppe,metal,environnement,flou,pehd,pet_clair,background,autre,carton,papier,pet_fonce.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 30 Sep 2025 15:02:20 GMT Content-Length: 0 Connection: close X-Message-Id: A2gXmCbcQ2KKk_RhJh0s2w 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 [1386076647, 1386076454, 1386076402, 1386076351, 1386076288, 1386076256, 1386076231, 1386076119, 1386076106, 1386076075, 1386076048] 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, '3777379') ('3318', '27224634', '1386076647', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076454', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076402', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076351', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076288', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076256', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076231', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076119', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076106', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076075', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076048', None, None, None, None, None, '3777379') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.04181051254272461 save_final save missing photos in datou_result : time spend for datou_step_exec : 4.483489036560059 time spend to save output : 0.04207181930541992 total time spend for step 9 : 4.5255608558654785 step10:split_time_score Tue Sep 30 17:02:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('11', 11),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 25092025 27224634 Nombre de photos uploadées : 11 / 23040 (0%) 25092025 27224634 Nombre de photos taguées (types de déchets): 0 / 11 (0%) 25092025 27224634 Nombre de photos taguées (volume) : 0 / 11 (0%) Catched exception ! (1213, 'Deadlock found when trying to get lock; try restarting transaction') Connect or reconnect ! elapsed_time : load_data_split_time_score 2.86102294921875e-06 elapsed_time : order_list_meta_photo_and_scores 5.9604644775390625e-06 ??????????? elapsed_time : fill_and_build_computed_from_old_data 0.0006318092346191406 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.654193639755249 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 : 0.09845333397633747 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27221477_30-09-2025_16_57_19.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27221477 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`=27221477 AND mptpi.`type`=3594 To do Qualite : 0.13090677358906527 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27223364_30-09-2025_17_01_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27223364 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`=27223364 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27223367 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27223369 order by id desc limit 1 Qualite : 0.054117082281144785 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27224634_30-09-2025_17_02_16.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27224634 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`=27224634 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27224635 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27225428 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27228443 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27253365 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27236083 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27236085 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27236088 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27236093 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27236096 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27236099 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27241406 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27241410 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27241422 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27247505 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27247506 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'25092025': {'nb_upload': 11, '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 [1386076647, 1386076454, 1386076402, 1386076351, 1386076288, 1386076256, 1386076231, 1386076119, 1386076106, 1386076075, 1386076048] Looping around the photos to save general results len do output : 1 /27224634Didn'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, '3777379') ('3318', '27224634', '1386076647', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076454', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076402', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076351', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076288', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076256', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076231', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076119', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076106', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076075', None, None, None, None, None, '3777379') ('3318', None, None, None, None, None, None, None, '3777379') ('3318', '27224634', '1386076048', None, None, None, None, None, '3777379') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.035689353942871094 save_final save missing photos in datou_result : time spend for datou_step_exec : 13.448470830917358 time spend to save output : 0.03599095344543457 total time spend for step 10 : 13.484461784362793 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 11 set_done_treatment 35.95user 20.88system 1:35.37elapsed 59%CPU (0avgtext+0avgdata 2808144maxresident)k 1839008inputs+15304outputs (8410major+1502357minor)pagefaults 0swaps