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 2502549' -s traitement_3459 -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 : 4167303 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 : 3459, datou_cur_ids : ['2502549'] with mtr_portfolio_ids : ['19816777'] and first list_photo_ids : [] new path : /proc/4167303/ 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 ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, thcl, merge_mask_thcl_custom, rle_unique_nms_with_priority, crop_condition, ventilate_hashtags_in_portfolio, final, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 20 list_input_json : [] origin We have 1 , WARNING: data may be incomplete, need to offset and complete ! BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 20 ; length of list_pids : 20 ; length of list_args : 20 time to download the photos : 4.089690685272217 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 : 11 step1:mask_detect Tue Feb 4 14:19:30 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 : 10332 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-04 14:19:33.421438: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-04 14:19:33.447109: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-04 14:19:33.449534: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7ffa38000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-04 14:19:33.449592: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-04 14:19:33.453939: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-04 14:19:33.688719: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2e6559c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-04 14:19:33.688783: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-04 14:19:33.689848: 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 2025-02-04 14:19:33.690196: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 14:19:33.692247: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 14:19:33.694350: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-04 14:19:33.694683: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-04 14:19:33.696996: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-04 14:19:33.698032: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-04 14:19:33.702513: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-04 14:19:33.704101: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-04 14:19:33.704198: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 14:19:33.704979: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-04 14:19:33.704996: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-04 14:19:33.705022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-04 14:19:33.706378: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9570 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. 2025-02-04 14:19:33.988950: 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 2025-02-04 14:19:33.989128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 14:19:33.989159: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 14:19:33.989185: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-04 14:19:33.989209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-04 14:19:33.989233: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-04 14:19:33.989258: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-04 14:19:33.989283: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-04 14:19:33.991365: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-04 14:19:33.992739: 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 2025-02-04 14:19:33.992777: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-04 14:19:33.992792: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 14:19:33.992810: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-04 14:19:33.992823: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-04 14:19:33.992837: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-04 14:19:33.992850: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-04 14:19:33.992864: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-04 14:19:33.994014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-04 14:19:33.994062: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-04 14:19:33.994070: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-04 14:19:33.994078: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-04 14:19:33.995322: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9570 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 : thcl2896 thcls : [{'id': 2896, 'mtr_user_id': 31, 'name': 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'photo_desc_type': 5309, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 2896, 'mtr_user_id': 31, 'name': 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'photo_desc_type': 5309, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5309 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5309, 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 16384, 25088, 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 5, 10, 19, 20, 46), datetime.datetime(2021, 5, 10, 19, 20, 46)) {'thcl': {'id': 2896, 'mtr_user_id': 31, 'name': 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'photo_desc_type': 5309, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'carton_brun', 'carton_gris', 'cartonnette', 'kraft', 'autre_refus', 'metal', 'plastique', 'teint_dans_la_masse', 'environnement'], 'list_hashtags_csv': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'svm_hashtag_type_desc': 5309, 'photo_desc_type': 5309, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'carton_brun', 'carton_gris', 'cartonnette', 'kraft', 'autre_refus', 'metal', 'plastique', 'teint_dans_la_masse', '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_convoyeur_qualipapia_nantes_poly_100521_1 NUM_CLASSES 10 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_convoyeur_qualipapia_nantes_poly_100521_1 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-02-04 14:19:43.171239: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-04 14:19:43.362722: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_convoyeur_qualipapia_nantes_poly_100521_1 /data/models_weight/learn_convoyeur_qualipapia_nantes_poly_100521_1/mask_model.h5 size_local : 256031040 size in s3 : 256031040 create time local : 2021-08-09 05:45:48 create time in s3 : 2021-08-06 18:59:51 mask_model.h5 already exist and didn't need to update list_images length : 20 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 30 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 18 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 18 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 25 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 25 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 14 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 24 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 29 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 25 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 18) min: 0.00000 max: 3264.00000 nb d'objets trouves : 16 Detection mask done ! Trying to reset tf kernel 4167493 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5264 tf kernel not reseted sub process len(results) : 20 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 20 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 : 10553 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2896 Catched exception ! Connect or reconnect ! thcls : [{'id': 2896, 'mtr_user_id': 31, 'name': 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'photo_desc_type': 5309, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 2896, 'mtr_user_id': 31, 'name': 'learn_convoyeur_qualipapia_nantes_poly_100521_1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,carton_brun,carton_gris,cartonnette,kraft,autre_refus,metal,plastique,teint_dans_la_masse,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3663, 'photo_desc_type': 5309, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5309 ['background', 'carton_brun', 'carton_gris', 'cartonnette', 'kraft', 'autre_refus', 'metal', 'plastique', 'teint_dans_la_masse', 'environnement'] time for calcul the mask position with numpy : 0.0034093856811523438 nb_pixel_total : 115464 time to create 1 rle with old method : 0.1376628875732422 length of segment : 471 time for calcul the mask position with numpy : 0.00016736984252929688 nb_pixel_total : 3607 time to create 1 rle with old method : 0.0061490535736083984 length of segment : 73 time for calcul the mask position with numpy : 0.0017731189727783203 nb_pixel_total : 90158 time to create 1 rle with old method : 0.10629129409790039 length of segment : 489 time for calcul the mask position with numpy : 0.0003876686096191406 nb_pixel_total : 16348 time to create 1 rle with old method : 0.020302534103393555 length of segment : 133 time for calcul the mask position with numpy : 0.00014734268188476562 nb_pixel_total : 2301 time to create 1 rle with old method : 0.004899740219116211 length of segment : 62 time for calcul the mask position with numpy : 0.001255035400390625 nb_pixel_total : 25584 time to create 1 rle with old method : 0.03075718879699707 length of segment : 238 time for calcul the mask position with numpy : 0.0026590824127197266 nb_pixel_total : 83908 time to create 1 rle with old method : 0.1020655632019043 length of segment : 666 time for calcul the mask position with numpy : 0.0014700889587402344 nb_pixel_total : 41566 time to create 1 rle with old method : 0.05220961570739746 length of segment : 287 time for calcul the mask position with numpy : 0.0001709461212158203 nb_pixel_total : 2136 time to create 1 rle with old method : 0.0028870105743408203 length of segment : 46 time for calcul the mask position with numpy : 0.002235889434814453 nb_pixel_total : 101271 time to create 1 rle with old method : 0.1197052001953125 length of segment : 491 time for calcul the mask position with numpy : 0.0007898807525634766 nb_pixel_total : 36636 time to create 1 rle with old method : 0.04802536964416504 length of segment : 289 time for calcul the mask position with numpy : 0.005847930908203125 nb_pixel_total : 282843 time to create 1 rle with new method : 0.016339778900146484 length of segment : 984 time for calcul the mask position with numpy : 0.004049062728881836 nb_pixel_total : 188033 time to create 1 rle with new method : 0.01235198974609375 length of segment : 340 time for calcul the mask position with numpy : 0.003812074661254883 nb_pixel_total : 171348 time to create 1 rle with new method : 0.011465787887573242 length of segment : 549 time for calcul the mask position with numpy : 0.0007865428924560547 nb_pixel_total : 30948 time to create 1 rle with old method : 0.036649227142333984 length of segment : 228 time for calcul the mask position with numpy : 0.0018613338470458984 nb_pixel_total : 94444 time to create 1 rle with old method : 0.11280965805053711 length of segment : 323 time for calcul the mask position with numpy : 0.00011348724365234375 nb_pixel_total : 2369 time to create 1 rle with old method : 0.0032629966735839844 length of segment : 41 time for calcul the mask position with numpy : 0.0005917549133300781 nb_pixel_total : 26485 time to create 1 rle with old method : 0.03244447708129883 length of segment : 226 time for calcul the mask position with numpy : 0.007254362106323242 nb_pixel_total : 153851 time to create 1 rle with new method : 0.013281822204589844 length of segment : 536 time for calcul the mask position with numpy : 0.0002281665802001953 nb_pixel_total : 3960 time to create 1 rle with old method : 0.005202531814575195 length of segment : 89 time for calcul the mask position with numpy : 0.0007207393646240234 nb_pixel_total : 46614 time to create 1 rle with old method : 0.05890083312988281 length of segment : 246 time for calcul the mask position with numpy : 9.584426879882812e-05 nb_pixel_total : 2281 time to create 1 rle with old method : 0.0030508041381835938 length of segment : 69 time for calcul the mask position with numpy : 0.002170085906982422 nb_pixel_total : 104460 time to create 1 rle with old method : 0.1304783821105957 length of segment : 572 time for calcul the mask position with numpy : 0.005095243453979492 nb_pixel_total : 281476 time to create 1 rle with new method : 0.024266481399536133 length of segment : 310 time for calcul the mask position with numpy : 0.0051877498626708984 nb_pixel_total : 164181 time to create 1 rle with new method : 0.009686470031738281 length of segment : 553 time for calcul the mask position with numpy : 0.0006339550018310547 nb_pixel_total : 17836 time to create 1 rle with old method : 0.021947145462036133 length of segment : 187 time for calcul the mask position with numpy : 0.005550861358642578 nb_pixel_total : 207982 time to create 1 rle with new method : 0.015517234802246094 length of segment : 742 time for calcul the mask position with numpy : 0.060315608978271484 nb_pixel_total : 2138985 time to create 1 rle with new method : 0.11718416213989258 length of segment : 3759 time for calcul the mask position with numpy : 0.022510766983032227 nb_pixel_total : 811044 time to create 1 rle with new method : 0.04222822189331055 length of segment : 1636 time for calcul the mask position with numpy : 0.00677943229675293 nb_pixel_total : 302928 time to create 1 rle with new method : 0.011858940124511719 length of segment : 955 time for calcul the mask position with numpy : 0.002318143844604492 nb_pixel_total : 115839 time to create 1 rle with old method : 0.18314242362976074 length of segment : 305 time for calcul the mask position with numpy : 0.00026535987854003906 nb_pixel_total : 2730 time to create 1 rle with old method : 0.005007743835449219 length of segment : 73 time for calcul the mask position with numpy : 0.0029370784759521484 nb_pixel_total : 96331 time to create 1 rle with old method : 0.11740469932556152 length of segment : 426 time for calcul the mask position with numpy : 0.0025453567504882812 nb_pixel_total : 102541 time to create 1 rle with old method : 0.12634038925170898 length of segment : 445 time for calcul the mask position with numpy : 0.0009853839874267578 nb_pixel_total : 23085 time to create 1 rle with old method : 0.03080272674560547 length of segment : 269 time for calcul the mask position with numpy : 0.004020214080810547 nb_pixel_total : 154878 time to create 1 rle with new method : 0.00781869888305664 length of segment : 546 time for calcul the mask position with numpy : 0.0019028186798095703 nb_pixel_total : 69543 time to create 1 rle with old method : 0.08422446250915527 length of segment : 564 time for calcul the mask position with numpy : 0.0024271011352539062 nb_pixel_total : 64778 time to create 1 rle with old method : 0.0782928466796875 length of segment : 409 time for calcul the mask position with numpy : 0.0011174678802490234 nb_pixel_total : 29680 time to create 1 rle with old method : 0.0372462272644043 length of segment : 336 time for calcul the mask position with numpy : 0.006693363189697266 nb_pixel_total : 248983 time to create 1 rle with new method : 0.008529186248779297 length of segment : 691 time for calcul the mask position with numpy : 0.005400896072387695 nb_pixel_total : 125435 time to create 1 rle with old method : 0.15263748168945312 length of segment : 561 time for calcul the mask position with numpy : 0.0012996196746826172 nb_pixel_total : 31107 time to create 1 rle with old method : 0.038038015365600586 length of segment : 247 time for calcul the mask position with numpy : 0.0003082752227783203 nb_pixel_total : 6012 time to create 1 rle with old method : 0.007557392120361328 length of segment : 115 time for calcul the mask position with numpy : 0.0037004947662353516 nb_pixel_total : 28843 time to create 1 rle with old method : 0.03528285026550293 length of segment : 282 time for calcul the mask position with numpy : 0.0009474754333496094 nb_pixel_total : 36030 time to create 1 rle with old method : 0.04521059989929199 length of segment : 172 time for calcul the mask position with numpy : 0.0006928443908691406 nb_pixel_total : 35253 time to create 1 rle with old method : 0.04416322708129883 length of segment : 159 time for calcul the mask position with numpy : 0.003147602081298828 nb_pixel_total : 120589 time to create 1 rle with old method : 0.15385103225708008 length of segment : 372 time for calcul the mask position with numpy : 0.0022232532501220703 nb_pixel_total : 124426 time to create 1 rle with old method : 0.14716076850891113 length of segment : 363 time for calcul the mask position with numpy : 0.004788398742675781 nb_pixel_total : 161063 time to create 1 rle with new method : 0.007040977478027344 length of segment : 537 time for calcul the mask position with numpy : 0.0004649162292480469 nb_pixel_total : 9672 time to create 1 rle with old method : 0.011510610580444336 length of segment : 174 time for calcul the mask position with numpy : 0.0009126663208007812 nb_pixel_total : 35211 time to create 1 rle with old method : 0.04233717918395996 length of segment : 265 time for calcul the mask position with numpy : 0.00823521614074707 nb_pixel_total : 258674 time to create 1 rle with new method : 0.010732173919677734 length of segment : 802 time for calcul the mask position with numpy : 0.00023174285888671875 nb_pixel_total : 2730 time to create 1 rle with old method : 0.0036106109619140625 length of segment : 73 time for calcul the mask position with numpy : 0.0029180049896240234 nb_pixel_total : 96334 time to create 1 rle with old method : 0.11805844306945801 length of segment : 426 time for calcul the mask position with numpy : 0.0031752586364746094 nb_pixel_total : 102540 time to create 1 rle with old method : 0.12500762939453125 length of segment : 446 time for calcul the mask position with numpy : 0.001184701919555664 nb_pixel_total : 23084 time to create 1 rle with old method : 0.028176069259643555 length of segment : 269 time for calcul the mask position with numpy : 0.004301548004150391 nb_pixel_total : 154877 time to create 1 rle with new method : 0.008762121200561523 length of segment : 546 time for calcul the mask position with numpy : 0.0024771690368652344 nb_pixel_total : 69546 time to create 1 rle with old method : 0.10481548309326172 length of segment : 564 time for calcul the mask position with numpy : 0.0031642913818359375 nb_pixel_total : 64820 time to create 1 rle with old method : 0.10940861701965332 length of segment : 408 time for calcul the mask position with numpy : 0.0012042522430419922 nb_pixel_total : 29680 time to create 1 rle with old method : 0.03560614585876465 length of segment : 336 time for calcul the mask position with numpy : 0.005919218063354492 nb_pixel_total : 249010 time to create 1 rle with new method : 0.00869297981262207 length of segment : 691 time for calcul the mask position with numpy : 0.0048182010650634766 nb_pixel_total : 162936 time to create 1 rle with new method : 0.0081939697265625 length of segment : 543 time for calcul the mask position with numpy : 0.005202054977416992 nb_pixel_total : 125531 time to create 1 rle with old method : 0.14844083786010742 length of segment : 562 time for calcul the mask position with numpy : 0.0009837150573730469 nb_pixel_total : 31105 time to create 1 rle with old method : 0.03748464584350586 length of segment : 247 time for calcul the mask position with numpy : 0.0002276897430419922 nb_pixel_total : 6011 time to create 1 rle with old method : 0.007637500762939453 length of segment : 115 time for calcul the mask position with numpy : 0.0020346641540527344 nb_pixel_total : 28850 time to create 1 rle with old method : 0.03532743453979492 length of segment : 282 time for calcul the mask position with numpy : 0.0009999275207519531 nb_pixel_total : 36035 time to create 1 rle with old method : 0.044762611389160156 length of segment : 172 time for calcul the mask position with numpy : 0.0005974769592285156 nb_pixel_total : 35261 time to create 1 rle with old method : 0.043395280838012695 length of segment : 158 time for calcul the mask position with numpy : 0.003156900405883789 nb_pixel_total : 120601 time to create 1 rle with old method : 0.14654326438903809 length of segment : 372 time for calcul the mask position with numpy : 0.0020852088928222656 nb_pixel_total : 124434 time to create 1 rle with old method : 0.17380857467651367 length of segment : 363 time for calcul the mask position with numpy : 0.0007867813110351562 nb_pixel_total : 9691 time to create 1 rle with old method : 0.014696598052978516 length of segment : 174 time for calcul the mask position with numpy : 0.0016274452209472656 nb_pixel_total : 35208 time to create 1 rle with old method : 0.04334092140197754 length of segment : 265 time for calcul the mask position with numpy : 0.005532026290893555 nb_pixel_total : 258595 time to create 1 rle with new method : 0.010880470275878906 length of segment : 804 time for calcul the mask position with numpy : 0.0020813941955566406 nb_pixel_total : 70687 time to create 1 rle with old method : 0.09193682670593262 length of segment : 319 time for calcul the mask position with numpy : 0.0013682842254638672 nb_pixel_total : 61401 time to create 1 rle with old method : 0.07800817489624023 length of segment : 324 time for calcul the mask position with numpy : 0.00020885467529296875 nb_pixel_total : 3879 time to create 1 rle with old method : 0.0051729679107666016 length of segment : 53 time for calcul the mask position with numpy : 0.0003554821014404297 nb_pixel_total : 2419 time to create 1 rle with old method : 0.003628969192504883 length of segment : 53 time for calcul the mask position with numpy : 0.0005438327789306641 nb_pixel_total : 11992 time to create 1 rle with old method : 0.015139341354370117 length of segment : 225 time for calcul the mask position with numpy : 0.0002205371856689453 nb_pixel_total : 4311 time to create 1 rle with old method : 0.005521535873413086 length of segment : 84 time for calcul the mask position with numpy : 0.0005154609680175781 nb_pixel_total : 9038 time to create 1 rle with old method : 0.012736320495605469 length of segment : 88 time for calcul the mask position with numpy : 0.000274658203125 nb_pixel_total : 3495 time to create 1 rle with old method : 0.004498720169067383 length of segment : 86 time for calcul the mask position with numpy : 0.0005886554718017578 nb_pixel_total : 7343 time to create 1 rle with old method : 0.009108781814575195 length of segment : 237 time for calcul the mask position with numpy : 0.0009381771087646484 nb_pixel_total : 32276 time to create 1 rle with old method : 0.04131197929382324 length of segment : 227 time for calcul the mask position with numpy : 0.001795053482055664 nb_pixel_total : 22923 time to create 1 rle with old method : 0.028838396072387695 length of segment : 407 time for calcul the mask position with numpy : 0.003488779067993164 nb_pixel_total : 122885 time to create 1 rle with old method : 0.14778566360473633 length of segment : 502 time for calcul the mask position with numpy : 0.000843048095703125 nb_pixel_total : 28816 time to create 1 rle with old method : 0.03645920753479004 length of segment : 181 time for calcul the mask position with numpy : 0.0013082027435302734 nb_pixel_total : 57027 time to create 1 rle with old method : 0.08990311622619629 length of segment : 293 time for calcul the mask position with numpy : 0.002218008041381836 nb_pixel_total : 70687 time to create 1 rle with old method : 0.08626484870910645 length of segment : 319 time for calcul the mask position with numpy : 0.0015063285827636719 nb_pixel_total : 61383 time to create 1 rle with old method : 0.0738372802734375 length of segment : 323 time for calcul the mask position with numpy : 0.0002803802490234375 nb_pixel_total : 3880 time to create 1 rle with old method : 0.00562596321105957 length of segment : 53 time for calcul the mask position with numpy : 0.0003764629364013672 nb_pixel_total : 2419 time to create 1 rle with old method : 0.0037322044372558594 length of segment : 53 time for calcul the mask position with numpy : 0.0005731582641601562 nb_pixel_total : 11994 time to create 1 rle with old method : 0.015046358108520508 length of segment : 225 time for calcul the mask position with numpy : 0.00017070770263671875 nb_pixel_total : 4316 time to create 1 rle with old method : 0.005456209182739258 length of segment : 84 time for calcul the mask position with numpy : 0.0003974437713623047 nb_pixel_total : 9038 time to create 1 rle with old method : 0.01213979721069336 length of segment : 88 time for calcul the mask position with numpy : 0.0010330677032470703 nb_pixel_total : 32251 time to create 1 rle with old method : 0.03914642333984375 length of segment : 225 time for calcul the mask position with numpy : 0.00024819374084472656 nb_pixel_total : 3494 time to create 1 rle with old method : 0.004495859146118164 length of segment : 86 time for calcul the mask position with numpy : 0.0005207061767578125 nb_pixel_total : 7338 time to create 1 rle with old method : 0.009002923965454102 length of segment : 237 time for calcul the mask position with numpy : 0.0017824172973632812 nb_pixel_total : 22932 time to create 1 rle with old method : 0.0283205509185791 length of segment : 407 time for calcul the mask position with numpy : 0.0033359527587890625 nb_pixel_total : 123096 time to create 1 rle with old method : 0.1440291404724121 length of segment : 502 time for calcul the mask position with numpy : 0.0008883476257324219 nb_pixel_total : 28824 time to create 1 rle with old method : 0.03594017028808594 length of segment : 181 time for calcul the mask position with numpy : 0.0012292861938476562 nb_pixel_total : 57014 time to create 1 rle with old method : 0.06843161582946777 length of segment : 293 time for calcul the mask position with numpy : 0.00047779083251953125 nb_pixel_total : 11633 time to create 1 rle with old method : 0.014028072357177734 length of segment : 205 time for calcul the mask position with numpy : 0.00024271011352539062 nb_pixel_total : 4503 time to create 1 rle with old method : 0.00575566291809082 length of segment : 85 time for calcul the mask position with numpy : 0.002012968063354492 nb_pixel_total : 65880 time to create 1 rle with old method : 0.0780649185180664 length of segment : 235 time for calcul the mask position with numpy : 0.001499176025390625 nb_pixel_total : 51237 time to create 1 rle with old method : 0.06462526321411133 length of segment : 300 time for calcul the mask position with numpy : 0.0011708736419677734 nb_pixel_total : 42171 time to create 1 rle with old method : 0.05039072036743164 length of segment : 187 time for calcul the mask position with numpy : 0.0010113716125488281 nb_pixel_total : 32259 time to create 1 rle with old method : 0.03945279121398926 length of segment : 172 time for calcul the mask position with numpy : 0.0004267692565917969 nb_pixel_total : 6562 time to create 1 rle with old method : 0.008314847946166992 length of segment : 124 time for calcul the mask position with numpy : 0.001224517822265625 nb_pixel_total : 40504 time to create 1 rle with old method : 0.0493316650390625 length of segment : 179 time for calcul the mask position with numpy : 0.0002880096435546875 nb_pixel_total : 3862 time to create 1 rle with old method : 0.0049097537994384766 length of segment : 107 time for calcul the mask position with numpy : 0.0022225379943847656 nb_pixel_total : 84207 time to create 1 rle with old method : 0.09835410118103027 length of segment : 445 time for calcul the mask position with numpy : 0.0009262561798095703 nb_pixel_total : 44685 time to create 1 rle with old method : 0.053708791732788086 length of segment : 200 time for calcul the mask position with numpy : 0.0010929107666015625 nb_pixel_total : 23979 time to create 1 rle with old method : 0.028925657272338867 length of segment : 268 time for calcul the mask position with numpy : 0.0019483566284179688 nb_pixel_total : 52251 time to create 1 rle with old method : 0.06219124794006348 length of segment : 500 time for calcul the mask position with numpy : 0.005497932434082031 nb_pixel_total : 250675 time to create 1 rle with new method : 0.008429288864135742 length of segment : 522 time for calcul the mask position with numpy : 0.0016558170318603516 nb_pixel_total : 47221 time to create 1 rle with old method : 0.055814504623413086 length of segment : 428 time for calcul the mask position with numpy : 0.0005352497100830078 nb_pixel_total : 11634 time to create 1 rle with old method : 0.014288663864135742 length of segment : 205 time for calcul the mask position with numpy : 0.0002765655517578125 nb_pixel_total : 4503 time to create 1 rle with old method : 0.005879878997802734 length of segment : 85 time for calcul the mask position with numpy : 0.0021173954010009766 nb_pixel_total : 65873 time to create 1 rle with old method : 0.07817578315734863 length of segment : 234 time for calcul the mask position with numpy : 0.0011985301971435547 nb_pixel_total : 51241 time to create 1 rle with old method : 0.058806419372558594 length of segment : 299 time for calcul the mask position with numpy : 0.0010876655578613281 nb_pixel_total : 42172 time to create 1 rle with old method : 0.050760507583618164 length of segment : 187 time for calcul the mask position with numpy : 0.0010075569152832031 nb_pixel_total : 32256 time to create 1 rle with old method : 0.040979862213134766 length of segment : 172 time for calcul the mask position with numpy : 0.00038504600524902344 nb_pixel_total : 6561 time to create 1 rle with old method : 0.008182287216186523 length of segment : 124 time for calcul the mask position with numpy : 0.0011749267578125 nb_pixel_total : 40496 time to create 1 rle with old method : 0.05015826225280762 length of segment : 179 time for calcul the mask position with numpy : 0.00028443336486816406 nb_pixel_total : 3860 time to create 1 rle with old method : 0.0050847530364990234 length of segment : 107 time for calcul the mask position with numpy : 0.0022821426391601562 nb_pixel_total : 84203 time to create 1 rle with old method : 0.1010897159576416 length of segment : 445 time for calcul the mask position with numpy : 0.0009047985076904297 nb_pixel_total : 44684 time to create 1 rle with old method : 0.05481743812561035 length of segment : 200 time for calcul the mask position with numpy : 0.0009765625 nb_pixel_total : 23979 time to create 1 rle with old method : 0.029244184494018555 length of segment : 268 time for calcul the mask position with numpy : 0.0019385814666748047 nb_pixel_total : 52251 time to create 1 rle with old method : 0.0849916934967041 length of segment : 500 time for calcul the mask position with numpy : 0.005511045455932617 nb_pixel_total : 250661 time to create 1 rle with new method : 0.00819087028503418 length of segment : 522 time for calcul the mask position with numpy : 0.001781463623046875 nb_pixel_total : 47229 time to create 1 rle with old method : 0.0599820613861084 length of segment : 427 time for calcul the mask position with numpy : 0.0006277561187744141 nb_pixel_total : 11755 time to create 1 rle with old method : 0.017314910888671875 length of segment : 238 time for calcul the mask position with numpy : 0.00030684471130371094 nb_pixel_total : 4439 time to create 1 rle with old method : 0.0056531429290771484 length of segment : 86 time for calcul the mask position with numpy : 0.00189971923828125 nb_pixel_total : 59514 time to create 1 rle with old method : 0.07006978988647461 length of segment : 227 time for calcul the mask position with numpy : 0.0013785362243652344 nb_pixel_total : 52259 time to create 1 rle with old method : 0.062229156494140625 length of segment : 308 time for calcul the mask position with numpy : 0.0028367042541503906 nb_pixel_total : 82862 time to create 1 rle with old method : 0.10250043869018555 length of segment : 439 time for calcul the mask position with numpy : 0.0003840923309326172 nb_pixel_total : 4347 time to create 1 rle with old method : 0.005731344223022461 length of segment : 100 time for calcul the mask position with numpy : 0.0003790855407714844 nb_pixel_total : 6257 time to create 1 rle with old method : 0.009490251541137695 length of segment : 128 time for calcul the mask position with numpy : 0.0016183853149414062 nb_pixel_total : 48295 time to create 1 rle with old method : 0.05881810188293457 length of segment : 466 time for calcul the mask position with numpy : 0.0021839141845703125 nb_pixel_total : 53878 time to create 1 rle with old method : 0.07356643676757812 length of segment : 464 time for calcul the mask position with numpy : 0.007582426071166992 nb_pixel_total : 263531 time to create 1 rle with new method : 0.013086795806884766 length of segment : 658 time for calcul the mask position with numpy : 0.0058634281158447266 nb_pixel_total : 248338 time to create 1 rle with new method : 0.008933544158935547 length of segment : 654 time for calcul the mask position with numpy : 0.0008974075317382812 nb_pixel_total : 51553 time to create 1 rle with old method : 0.06219744682312012 length of segment : 314 time for calcul the mask position with numpy : 0.0011148452758789062 nb_pixel_total : 42955 time to create 1 rle with old method : 0.05188775062561035 length of segment : 191 time for calcul the mask position with numpy : 0.002151012420654297 nb_pixel_total : 88489 time to create 1 rle with old method : 0.12992644309997559 length of segment : 335 time for calcul the mask position with numpy : 0.0008945465087890625 nb_pixel_total : 49658 time to create 1 rle with old method : 0.06294393539428711 length of segment : 432 time for calcul the mask position with numpy : 0.000682830810546875 nb_pixel_total : 11755 time to create 1 rle with old method : 0.014545440673828125 length of segment : 238 time for calcul the mask position with numpy : 0.0002837181091308594 nb_pixel_total : 4437 time to create 1 rle with old method : 0.006639957427978516 length of segment : 86 time for calcul the mask position with numpy : 0.0015475749969482422 nb_pixel_total : 59511 time to create 1 rle with old method : 0.0758047103881836 length of segment : 227 time for calcul the mask position with numpy : 0.0013782978057861328 nb_pixel_total : 52259 time to create 1 rle with old method : 0.06235074996948242 length of segment : 308 time for calcul the mask position with numpy : 0.002908945083618164 nb_pixel_total : 82866 time to create 1 rle with old method : 0.10324764251708984 length of segment : 439 time for calcul the mask position with numpy : 0.0003342628479003906 nb_pixel_total : 4345 time to create 1 rle with old method : 0.005579710006713867 length of segment : 100 time for calcul the mask position with numpy : 0.00036525726318359375 nb_pixel_total : 6256 time to create 1 rle with old method : 0.00780177116394043 length of segment : 128 time for calcul the mask position with numpy : 0.0014677047729492188 nb_pixel_total : 48303 time to create 1 rle with old method : 0.060280561447143555 length of segment : 466 time for calcul the mask position with numpy : 0.0018427371978759766 nb_pixel_total : 53881 time to create 1 rle with old method : 0.06481289863586426 length of segment : 463 time for calcul the mask position with numpy : 0.0070993900299072266 nb_pixel_total : 263494 time to create 1 rle with new method : 0.01175379753112793 length of segment : 658 time for calcul the mask position with numpy : 0.006539583206176758 nb_pixel_total : 248366 time to create 1 rle with new method : 0.010841608047485352 length of segment : 654 time for calcul the mask position with numpy : 0.0008051395416259766 nb_pixel_total : 51561 time to create 1 rle with old method : 0.06774067878723145 length of segment : 314 time for calcul the mask position with numpy : 0.0009915828704833984 nb_pixel_total : 42956 time to create 1 rle with old method : 0.052120208740234375 length of segment : 191 time for calcul the mask position with numpy : 0.002379179000854492 nb_pixel_total : 88485 time to create 1 rle with old method : 0.10730433464050293 length of segment : 335 time for calcul the mask position with numpy : 0.0008704662322998047 nb_pixel_total : 49691 time to create 1 rle with old method : 0.05983591079711914 length of segment : 432 time for calcul the mask position with numpy : 0.0009081363677978516 nb_pixel_total : 16175 time to create 1 rle with old method : 0.020362377166748047 length of segment : 234 time for calcul the mask position with numpy : 0.0062220096588134766 nb_pixel_total : 235743 time to create 1 rle with new method : 0.008471250534057617 length of segment : 464 time for calcul the mask position with numpy : 0.001779317855834961 nb_pixel_total : 50774 time to create 1 rle with old method : 0.060590505599975586 length of segment : 280 time for calcul the mask position with numpy : 0.0021250247955322266 nb_pixel_total : 60280 time to create 1 rle with old method : 0.08281993865966797 length of segment : 361 time for calcul the mask position with numpy : 0.0036170482635498047 nb_pixel_total : 64690 time to create 1 rle with old method : 0.07942867279052734 length of segment : 424 time for calcul the mask position with numpy : 0.0003228187561035156 nb_pixel_total : 4031 time to create 1 rle with old method : 0.0054035186767578125 length of segment : 88 time for calcul the mask position with numpy : 0.0024492740631103516 nb_pixel_total : 64373 time to create 1 rle with old method : 0.07901978492736816 length of segment : 264 time for calcul the mask position with numpy : 0.001142740249633789 nb_pixel_total : 25136 time to create 1 rle with old method : 0.03537416458129883 length of segment : 195 time for calcul the mask position with numpy : 0.005048990249633789 nb_pixel_total : 168115 time to create 1 rle with new method : 0.008819818496704102 length of segment : 416 time for calcul the mask position with numpy : 0.0005631446838378906 nb_pixel_total : 18057 time to create 1 rle with old method : 0.023007631301879883 length of segment : 170 time for calcul the mask position with numpy : 0.00031256675720214844 nb_pixel_total : 3256 time to create 1 rle with old method : 0.004114389419555664 length of segment : 146 time for calcul the mask position with numpy : 0.0005214214324951172 nb_pixel_total : 7893 time to create 1 rle with old method : 0.009837865829467773 length of segment : 149 time for calcul the mask position with numpy : 0.007841110229492188 nb_pixel_total : 237958 time to create 1 rle with new method : 0.012243509292602539 length of segment : 772 time for calcul the mask position with numpy : 0.0013327598571777344 nb_pixel_total : 46890 time to create 1 rle with old method : 0.05599045753479004 length of segment : 327 time for calcul the mask position with numpy : 0.007099151611328125 nb_pixel_total : 167270 time to create 1 rle with new method : 0.013043642044067383 length of segment : 1042 time for calcul the mask position with numpy : 0.0003345012664794922 nb_pixel_total : 5648 time to create 1 rle with old method : 0.007297992706298828 length of segment : 100 time for calcul the mask position with numpy : 0.0016095638275146484 nb_pixel_total : 52559 time to create 1 rle with old method : 0.06468534469604492 length of segment : 225 time for calcul the mask position with numpy : 0.001249074935913086 nb_pixel_total : 47832 time to create 1 rle with old method : 0.058087825775146484 length of segment : 299 time for calcul the mask position with numpy : 0.0002460479736328125 nb_pixel_total : 4241 time to create 1 rle with old method : 0.005536556243896484 length of segment : 78 time for calcul the mask position with numpy : 0.0053713321685791016 nb_pixel_total : 224848 time to create 1 rle with new method : 0.00821828842163086 length of segment : 599 time for calcul the mask position with numpy : 0.0007450580596923828 nb_pixel_total : 16172 time to create 1 rle with old method : 0.019737958908081055 length of segment : 235 time for calcul the mask position with numpy : 0.005691051483154297 nb_pixel_total : 235750 time to create 1 rle with new method : 0.007542610168457031 length of segment : 464 time for calcul the mask position with numpy : 0.0017049312591552734 nb_pixel_total : 50774 time to create 1 rle with old method : 0.06105613708496094 length of segment : 280 time for calcul the mask position with numpy : 0.0022678375244140625 nb_pixel_total : 60244 time to create 1 rle with old method : 0.07468318939208984 length of segment : 361 time for calcul the mask position with numpy : 0.0032351016998291016 nb_pixel_total : 64697 time to create 1 rle with old method : 0.0813302993774414 length of segment : 424 time for calcul the mask position with numpy : 0.0003349781036376953 nb_pixel_total : 4031 time to create 1 rle with old method : 0.005107879638671875 length of segment : 88 time for calcul the mask position with numpy : 0.002831697463989258 nb_pixel_total : 64375 time to create 1 rle with old method : 0.0786123275756836 length of segment : 264 time for calcul the mask position with numpy : 0.0009529590606689453 nb_pixel_total : 25138 time to create 1 rle with old method : 0.03080129623413086 length of segment : 195 time for calcul the mask position with numpy : 0.0053081512451171875 nb_pixel_total : 168075 time to create 1 rle with new method : 0.007783412933349609 length of segment : 415 time for calcul the mask position with numpy : 0.0006821155548095703 nb_pixel_total : 18056 time to create 1 rle with old method : 0.02360367774963379 length of segment : 170 time for calcul the mask position with numpy : 0.0003345012664794922 nb_pixel_total : 3257 time to create 1 rle with old method : 0.00425410270690918 length of segment : 146 time for calcul the mask position with numpy : 0.0005991458892822266 nb_pixel_total : 8028 time to create 1 rle with old method : 0.010479211807250977 length of segment : 150 time for calcul the mask position with numpy : 0.008625984191894531 nb_pixel_total : 237961 time to create 1 rle with new method : 0.015561103820800781 length of segment : 772 time for calcul the mask position with numpy : 0.0014946460723876953 nb_pixel_total : 46899 time to create 1 rle with old method : 0.06874990463256836 length of segment : 327 time for calcul the mask position with numpy : 0.012717723846435547 nb_pixel_total : 167364 time to create 1 rle with new method : 0.02167820930480957 length of segment : 1040 time for calcul the mask position with numpy : 0.0004513263702392578 nb_pixel_total : 5647 time to create 1 rle with old method : 0.007490873336791992 length of segment : 100 time for calcul the mask position with numpy : 0.0016880035400390625 nb_pixel_total : 52551 time to create 1 rle with old method : 0.09300780296325684 length of segment : 223 time for calcul the mask position with numpy : 0.0012974739074707031 nb_pixel_total : 47833 time to create 1 rle with old method : 0.056946754455566406 length of segment : 299 time for calcul the mask position with numpy : 0.0003066062927246094 nb_pixel_total : 4241 time to create 1 rle with old method : 0.007529497146606445 length of segment : 78 time for calcul the mask position with numpy : 0.005276203155517578 nb_pixel_total : 224838 time to create 1 rle with new method : 0.008258819580078125 length of segment : 599 time for calcul the mask position with numpy : 0.005601406097412109 nb_pixel_total : 237052 time to create 1 rle with new method : 0.008681535720825195 length of segment : 568 time for calcul the mask position with numpy : 0.0003452301025390625 nb_pixel_total : 6002 time to create 1 rle with old method : 0.007848262786865234 length of segment : 94 time for calcul the mask position with numpy : 0.002523183822631836 nb_pixel_total : 67607 time to create 1 rle with old method : 0.08311724662780762 length of segment : 254 time for calcul the mask position with numpy : 0.0006048679351806641 nb_pixel_total : 12360 time to create 1 rle with old method : 0.015346050262451172 length of segment : 259 time for calcul the mask position with numpy : 0.004458427429199219 nb_pixel_total : 169689 time to create 1 rle with new method : 0.006662130355834961 length of segment : 465 time for calcul the mask position with numpy : 0.0032532215118408203 nb_pixel_total : 58442 time to create 1 rle with old method : 0.07788801193237305 length of segment : 399 time for calcul the mask position with numpy : 0.0004513263702392578 nb_pixel_total : 13676 time to create 1 rle with old method : 0.01763319969177246 length of segment : 192 time for calcul the mask position with numpy : 0.0026018619537353516 nb_pixel_total : 86531 time to create 1 rle with old method : 0.12140941619873047 length of segment : 420 time for calcul the mask position with numpy : 0.0023839473724365234 nb_pixel_total : 49740 time to create 1 rle with old method : 0.06433725357055664 length of segment : 267 time for calcul the mask position with numpy : 0.007993936538696289 nb_pixel_total : 112003 time to create 1 rle with old method : 0.17217683792114258 length of segment : 898 time for calcul the mask position with numpy : 0.0013473033905029297 nb_pixel_total : 52056 time to create 1 rle with old method : 0.06517958641052246 length of segment : 304 time for calcul the mask position with numpy : 0.0011260509490966797 nb_pixel_total : 37981 time to create 1 rle with old method : 0.04606461524963379 length of segment : 271 time for calcul the mask position with numpy : 0.009931564331054688 nb_pixel_total : 230939 time to create 1 rle with new method : 0.008203744888305664 length of segment : 572 time for calcul the mask position with numpy : 0.0008554458618164062 nb_pixel_total : 36703 time to create 1 rle with old method : 0.045668601989746094 length of segment : 554 time for calcul the mask position with numpy : 0.0010428428649902344 nb_pixel_total : 25930 time to create 1 rle with old method : 0.03141498565673828 length of segment : 190 time for calcul the mask position with numpy : 0.0003910064697265625 nb_pixel_total : 4756 time to create 1 rle with old method : 0.007630348205566406 length of segment : 76 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 1408 time to create 1 rle with old method : 0.001873016357421875 length of segment : 49 time for calcul the mask position with numpy : 0.0002512931823730469 nb_pixel_total : 5864 time to create 1 rle with old method : 0.007597446441650391 length of segment : 136 time for calcul the mask position with numpy : 0.0011587142944335938 nb_pixel_total : 52151 time to create 1 rle with old method : 0.06325793266296387 length of segment : 396 time for calcul the mask position with numpy : 0.0024716854095458984 nb_pixel_total : 120348 time to create 1 rle with old method : 0.14369702339172363 length of segment : 526 time for calcul the mask position with numpy : 0.0011868476867675781 nb_pixel_total : 27390 time to create 1 rle with old method : 0.034182071685791016 length of segment : 221 time for calcul the mask position with numpy : 0.0054378509521484375 nb_pixel_total : 243832 time to create 1 rle with new method : 0.007737159729003906 length of segment : 593 time for calcul the mask position with numpy : 0.0003502368927001953 nb_pixel_total : 6002 time to create 1 rle with old method : 0.007632017135620117 length of segment : 94 time for calcul the mask position with numpy : 0.0025017261505126953 nb_pixel_total : 67615 time to create 1 rle with old method : 0.08213496208190918 length of segment : 254 time for calcul the mask position with numpy : 0.0007421970367431641 nb_pixel_total : 12359 time to create 1 rle with old method : 0.021840572357177734 length of segment : 259 time for calcul the mask position with numpy : 0.005635499954223633 nb_pixel_total : 169606 time to create 1 rle with new method : 0.006945610046386719 length of segment : 464 time for calcul the mask position with numpy : 0.0026962757110595703 nb_pixel_total : 58435 time to create 1 rle with old method : 0.07508039474487305 length of segment : 399 time for calcul the mask position with numpy : 0.0005075931549072266 nb_pixel_total : 13678 time to create 1 rle with old method : 0.017557859420776367 length of segment : 192 time for calcul the mask position with numpy : 0.0025985240936279297 nb_pixel_total : 86543 time to create 1 rle with old method : 0.10897016525268555 length of segment : 420 time for calcul the mask position with numpy : 0.0017490386962890625 nb_pixel_total : 49737 time to create 1 rle with old method : 0.06176948547363281 length of segment : 267 time for calcul the mask position with numpy : 0.008613348007202148 nb_pixel_total : 110037 time to create 1 rle with old method : 0.18030166625976562 length of segment : 885 time for calcul the mask position with numpy : 0.001729726791381836 nb_pixel_total : 52053 time to create 1 rle with old method : 0.0647890567779541 length of segment : 304 time for calcul the mask position with numpy : 0.0011186599731445312 nb_pixel_total : 46216 time to create 1 rle with old method : 0.05971074104309082 length of segment : 312 time for calcul the mask position with numpy : 0.001117706298828125 nb_pixel_total : 37983 time to create 1 rle with old method : 0.04553484916687012 length of segment : 271 time for calcul the mask position with numpy : 0.007191181182861328 nb_pixel_total : 230977 time to create 1 rle with new method : 0.01079249382019043 length of segment : 572 time for calcul the mask position with numpy : 0.0017230510711669922 nb_pixel_total : 36708 time to create 1 rle with old method : 0.04408764839172363 length of segment : 554 time for calcul the mask position with numpy : 0.0011379718780517578 nb_pixel_total : 25932 time to create 1 rle with old method : 0.03238964080810547 length of segment : 190 time for calcul the mask position with numpy : 0.00035309791564941406 nb_pixel_total : 4753 time to create 1 rle with old method : 0.006094455718994141 length of segment : 75 time for calcul the mask position with numpy : 0.00014925003051757812 nb_pixel_total : 1409 time to create 1 rle with old method : 0.0019073486328125 length of segment : 49 time for calcul the mask position with numpy : 0.0003781318664550781 nb_pixel_total : 5857 time to create 1 rle with old method : 0.007389068603515625 length of segment : 137 time for calcul the mask position with numpy : 0.004213571548461914 nb_pixel_total : 120341 time to create 1 rle with old method : 0.14539813995361328 length of segment : 526 time for calcul the mask position with numpy : 0.0041713714599609375 nb_pixel_total : 27391 time to create 1 rle with old method : 0.038064002990722656 length of segment : 221 time for calcul the mask position with numpy : 0.007267475128173828 nb_pixel_total : 202192 time to create 1 rle with new method : 0.011230230331420898 length of segment : 823 time for calcul the mask position with numpy : 0.0020189285278320312 nb_pixel_total : 71427 time to create 1 rle with old method : 0.08562016487121582 length of segment : 348 time for calcul the mask position with numpy : 0.00047898292541503906 nb_pixel_total : 6847 time to create 1 rle with old method : 0.009192705154418945 length of segment : 92 time for calcul the mask position with numpy : 0.0051915645599365234 nb_pixel_total : 101294 time to create 1 rle with old method : 0.12411236763000488 length of segment : 561 time for calcul the mask position with numpy : 0.0013523101806640625 nb_pixel_total : 36431 time to create 1 rle with old method : 0.04473567008972168 length of segment : 270 time for calcul the mask position with numpy : 0.002882242202758789 nb_pixel_total : 119580 time to create 1 rle with old method : 0.14606189727783203 length of segment : 629 time for calcul the mask position with numpy : 0.0008270740509033203 nb_pixel_total : 27018 time to create 1 rle with old method : 0.03346753120422363 length of segment : 259 time for calcul the mask position with numpy : 0.00017523765563964844 nb_pixel_total : 4460 time to create 1 rle with old method : 0.006024360656738281 length of segment : 84 time for calcul the mask position with numpy : 0.0007178783416748047 nb_pixel_total : 35600 time to create 1 rle with old method : 0.048281192779541016 length of segment : 272 time for calcul the mask position with numpy : 0.0008218288421630859 nb_pixel_total : 25371 time to create 1 rle with old method : 0.031184673309326172 length of segment : 242 time for calcul the mask position with numpy : 0.0014498233795166016 nb_pixel_total : 40882 time to create 1 rle with old method : 0.05126452445983887 length of segment : 313 time for calcul the mask position with numpy : 0.0038330554962158203 nb_pixel_total : 123548 time to create 1 rle with old method : 0.16059160232543945 length of segment : 788 time for calcul the mask position with numpy : 0.0010476112365722656 nb_pixel_total : 21776 time to create 1 rle with old method : 0.02682805061340332 length of segment : 243 time for calcul the mask position with numpy : 0.00022339820861816406 nb_pixel_total : 1928 time to create 1 rle with old method : 0.0025663375854492188 length of segment : 97 time for calcul the mask position with numpy : 0.00435638427734375 nb_pixel_total : 125967 time to create 1 rle with old method : 0.1563434600830078 length of segment : 455 time for calcul the mask position with numpy : 0.00048828125 nb_pixel_total : 9726 time to create 1 rle with old method : 0.018437623977661133 length of segment : 234 time for calcul the mask position with numpy : 0.0006167888641357422 nb_pixel_total : 16764 time to create 1 rle with old method : 0.029764175415039062 length of segment : 106 time for calcul the mask position with numpy : 0.00020599365234375 nb_pixel_total : 1546 time to create 1 rle with old method : 0.0029027462005615234 length of segment : 52 time for calcul the mask position with numpy : 0.0006091594696044922 nb_pixel_total : 13102 time to create 1 rle with old method : 0.01674962043762207 length of segment : 134 time for calcul the mask position with numpy : 0.0017969608306884766 nb_pixel_total : 46754 time to create 1 rle with old method : 0.05648970603942871 length of segment : 352 time for calcul the mask position with numpy : 0.00036597251892089844 nb_pixel_total : 3162 time to create 1 rle with old method : 0.0042383670806884766 length of segment : 57 time for calcul the mask position with numpy : 0.00475001335144043 nb_pixel_total : 78422 time to create 1 rle with old method : 0.10611677169799805 length of segment : 388 time for calcul the mask position with numpy : 0.0013942718505859375 nb_pixel_total : 25936 time to create 1 rle with old method : 0.03150534629821777 length of segment : 281 time for calcul the mask position with numpy : 0.0018014907836914062 nb_pixel_total : 42109 time to create 1 rle with old method : 0.051808834075927734 length of segment : 340 time for calcul the mask position with numpy : 0.0019409656524658203 nb_pixel_total : 38715 time to create 1 rle with old method : 0.05875682830810547 length of segment : 211 time for calcul the mask position with numpy : 0.0027666091918945312 nb_pixel_total : 71487 time to create 1 rle with old method : 0.09218621253967285 length of segment : 246 time for calcul the mask position with numpy : 0.0161895751953125 nb_pixel_total : 387029 time to create 1 rle with new method : 0.019609689712524414 length of segment : 834 time for calcul the mask position with numpy : 0.0011875629425048828 nb_pixel_total : 23153 time to create 1 rle with old method : 0.028337717056274414 length of segment : 219 time for calcul the mask position with numpy : 0.0003039836883544922 nb_pixel_total : 2548 time to create 1 rle with old method : 0.0033376216888427734 length of segment : 86 time for calcul the mask position with numpy : 0.0017011165618896484 nb_pixel_total : 70518 time to create 1 rle with old method : 0.08878278732299805 length of segment : 243 time for calcul the mask position with numpy : 0.0011785030364990234 nb_pixel_total : 9445 time to create 1 rle with old method : 0.012337207794189453 length of segment : 157 time for calcul the mask position with numpy : 0.0045719146728515625 nb_pixel_total : 94125 time to create 1 rle with old method : 0.13705897331237793 length of segment : 365 time for calcul the mask position with numpy : 0.004483222961425781 nb_pixel_total : 110557 time to create 1 rle with old method : 0.15754461288452148 length of segment : 408 time for calcul the mask position with numpy : 0.010010242462158203 nb_pixel_total : 326750 time to create 1 rle with new method : 0.022928476333618164 length of segment : 431 time for calcul the mask position with numpy : 0.0004928112030029297 nb_pixel_total : 6702 time to create 1 rle with old method : 0.008683443069458008 length of segment : 100 time for calcul the mask position with numpy : 0.0010280609130859375 nb_pixel_total : 29107 time to create 1 rle with old method : 0.037755489349365234 length of segment : 195 time for calcul the mask position with numpy : 0.0011801719665527344 nb_pixel_total : 25089 time to create 1 rle with old method : 0.04378390312194824 length of segment : 155 time for calcul the mask position with numpy : 0.002260923385620117 nb_pixel_total : 40394 time to create 1 rle with old method : 0.05271482467651367 length of segment : 638 time for calcul the mask position with numpy : 0.00022125244140625 nb_pixel_total : 2020 time to create 1 rle with old method : 0.0027136802673339844 length of segment : 77 time for calcul the mask position with numpy : 0.0005865097045898438 nb_pixel_total : 17010 time to create 1 rle with old method : 0.021219730377197266 length of segment : 214 time for calcul the mask position with numpy : 0.0017642974853515625 nb_pixel_total : 42388 time to create 1 rle with old method : 0.05070948600769043 length of segment : 364 time for calcul the mask position with numpy : 0.0033693313598632812 nb_pixel_total : 70921 time to create 1 rle with old method : 0.08780670166015625 length of segment : 415 time for calcul the mask position with numpy : 0.0006163120269775391 nb_pixel_total : 7045 time to create 1 rle with old method : 0.009084939956665039 length of segment : 109 time for calcul the mask position with numpy : 0.0005548000335693359 nb_pixel_total : 4550 time to create 1 rle with old method : 0.006694316864013672 length of segment : 102 time for calcul the mask position with numpy : 0.0005412101745605469 nb_pixel_total : 10196 time to create 1 rle with old method : 0.013222455978393555 length of segment : 83 time for calcul the mask position with numpy : 0.0040285587310791016 nb_pixel_total : 100660 time to create 1 rle with old method : 0.12511682510375977 length of segment : 397 time for calcul the mask position with numpy : 0.0013546943664550781 nb_pixel_total : 25868 time to create 1 rle with old method : 0.03140759468078613 length of segment : 229 time for calcul the mask position with numpy : 0.004379749298095703 nb_pixel_total : 130989 time to create 1 rle with old method : 0.15731072425842285 length of segment : 658 time for calcul the mask position with numpy : 0.007712602615356445 nb_pixel_total : 129399 time to create 1 rle with old method : 0.15857291221618652 length of segment : 482 time for calcul the mask position with numpy : 0.0071065425872802734 nb_pixel_total : 160426 time to create 1 rle with new method : 0.013510465621948242 length of segment : 618 time for calcul the mask position with numpy : 0.0006456375122070312 nb_pixel_total : 19949 time to create 1 rle with old method : 0.024236202239990234 length of segment : 235 time for calcul the mask position with numpy : 0.003190755844116211 nb_pixel_total : 110432 time to create 1 rle with old method : 0.13878631591796875 length of segment : 351 time for calcul the mask position with numpy : 0.0015027523040771484 nb_pixel_total : 59772 time to create 1 rle with old method : 0.0799875259399414 length of segment : 339 time for calcul the mask position with numpy : 0.00882720947265625 nb_pixel_total : 131416 time to create 1 rle with old method : 0.16234421730041504 length of segment : 553 time for calcul the mask position with numpy : 0.0019428730010986328 nb_pixel_total : 27363 time to create 1 rle with old method : 0.07935690879821777 length of segment : 225 time for calcul the mask position with numpy : 0.0032253265380859375 nb_pixel_total : 122935 time to create 1 rle with old method : 0.1502518653869629 length of segment : 546 time for calcul the mask position with numpy : 0.001325845718383789 nb_pixel_total : 31835 time to create 1 rle with old method : 0.03969311714172363 length of segment : 158 time for calcul the mask position with numpy : 0.0004291534423828125 nb_pixel_total : 4204 time to create 1 rle with old method : 0.0057642459869384766 length of segment : 90 time for calcul the mask position with numpy : 0.002428770065307617 nb_pixel_total : 41534 time to create 1 rle with old method : 0.054717302322387695 length of segment : 265 time for calcul the mask position with numpy : 0.0009052753448486328 nb_pixel_total : 21568 time to create 1 rle with old method : 0.026462554931640625 length of segment : 211 time for calcul the mask position with numpy : 0.0009534358978271484 nb_pixel_total : 24607 time to create 1 rle with old method : 0.030447006225585938 length of segment : 215 time for calcul the mask position with numpy : 0.005135059356689453 nb_pixel_total : 133006 time to create 1 rle with old method : 0.15828657150268555 length of segment : 459 time for calcul the mask position with numpy : 0.0006544589996337891 nb_pixel_total : 16305 time to create 1 rle with old method : 0.0204007625579834 length of segment : 117 time for calcul the mask position with numpy : 0.0005624294281005859 nb_pixel_total : 9088 time to create 1 rle with old method : 0.011520624160766602 length of segment : 105 time for calcul the mask position with numpy : 0.0009937286376953125 nb_pixel_total : 12141 time to create 1 rle with old method : 0.015215873718261719 length of segment : 205 time for calcul the mask position with numpy : 0.0004782676696777344 nb_pixel_total : 4562 time to create 1 rle with old method : 0.005850076675415039 length of segment : 91 time for calcul the mask position with numpy : 0.0033266544342041016 nb_pixel_total : 149992 time to create 1 rle with old method : 0.18509459495544434 length of segment : 587 time for calcul the mask position with numpy : 0.0009996891021728516 nb_pixel_total : 33217 time to create 1 rle with old method : 0.04247641563415527 length of segment : 232 time for calcul the mask position with numpy : 0.0047147274017333984 nb_pixel_total : 161112 time to create 1 rle with new method : 0.01492166519165039 length of segment : 606 time for calcul the mask position with numpy : 0.003712177276611328 nb_pixel_total : 90823 time to create 1 rle with old method : 0.11679601669311523 length of segment : 352 time for calcul the mask position with numpy : 0.0008077621459960938 nb_pixel_total : 7047 time to create 1 rle with old method : 0.01392364501953125 length of segment : 145 time for calcul the mask position with numpy : 0.002687692642211914 nb_pixel_total : 43126 time to create 1 rle with old method : 0.06750965118408203 length of segment : 346 time for calcul the mask position with numpy : 0.0002460479736328125 nb_pixel_total : 2516 time to create 1 rle with old method : 0.0032286643981933594 length of segment : 80 time for calcul the mask position with numpy : 0.00446772575378418 nb_pixel_total : 106358 time to create 1 rle with old method : 0.12606096267700195 length of segment : 406 time for calcul the mask position with numpy : 0.0077054500579833984 nb_pixel_total : 94884 time to create 1 rle with old method : 0.15985560417175293 length of segment : 470 time for calcul the mask position with numpy : 0.00040268898010253906 nb_pixel_total : 5496 time to create 1 rle with old method : 0.006944179534912109 length of segment : 171 time for calcul the mask position with numpy : 0.00019240379333496094 nb_pixel_total : 3623 time to create 1 rle with old method : 0.004874229431152344 length of segment : 58 time for calcul the mask position with numpy : 0.007139444351196289 nb_pixel_total : 305831 time to create 1 rle with new method : 0.009066581726074219 length of segment : 531 time for calcul the mask position with numpy : 0.009133338928222656 nb_pixel_total : 119948 time to create 1 rle with old method : 0.15943074226379395 length of segment : 732 time for calcul the mask position with numpy : 0.0017826557159423828 nb_pixel_total : 43771 time to create 1 rle with old method : 0.052443504333496094 length of segment : 248 time for calcul the mask position with numpy : 0.0020258426666259766 nb_pixel_total : 51971 time to create 1 rle with old method : 0.06397366523742676 length of segment : 276 time for calcul the mask position with numpy : 0.0030150413513183594 nb_pixel_total : 98453 time to create 1 rle with old method : 0.11941099166870117 length of segment : 416 time for calcul the mask position with numpy : 0.0274507999420166 nb_pixel_total : 351939 time to create 1 rle with new method : 0.02150869369506836 length of segment : 746 time for calcul the mask position with numpy : 0.0019266605377197266 nb_pixel_total : 58508 time to create 1 rle with old method : 0.07413983345031738 length of segment : 305 time for calcul the mask position with numpy : 0.007345676422119141 nb_pixel_total : 107450 time to create 1 rle with old method : 0.13472771644592285 length of segment : 679 time for calcul the mask position with numpy : 0.0002510547637939453 nb_pixel_total : 2129 time to create 1 rle with old method : 0.002828836441040039 length of segment : 64 time for calcul the mask position with numpy : 0.00022172927856445312 nb_pixel_total : 5608 time to create 1 rle with old method : 0.007097959518432617 length of segment : 89 time for calcul the mask position with numpy : 0.0007758140563964844 nb_pixel_total : 12816 time to create 1 rle with old method : 0.015984535217285156 length of segment : 200 time for calcul the mask position with numpy : 0.0005764961242675781 nb_pixel_total : 5301 time to create 1 rle with old method : 0.006440877914428711 length of segment : 156 time for calcul the mask position with numpy : 0.00043487548828125 nb_pixel_total : 3367 time to create 1 rle with old method : 0.006050586700439453 length of segment : 98 time for calcul the mask position with numpy : 0.0008022785186767578 nb_pixel_total : 10464 time to create 1 rle with old method : 0.013367652893066406 length of segment : 139 time for calcul the mask position with numpy : 0.0006368160247802734 nb_pixel_total : 10752 time to create 1 rle with old method : 0.01355290412902832 length of segment : 149 time for calcul the mask position with numpy : 0.0013012886047363281 nb_pixel_total : 24380 time to create 1 rle with old method : 0.0298311710357666 length of segment : 234 time for calcul the mask position with numpy : 0.0002548694610595703 nb_pixel_total : 2362 time to create 1 rle with old method : 0.0030417442321777344 length of segment : 76 time for calcul the mask position with numpy : 0.0002319812774658203 nb_pixel_total : 2366 time to create 1 rle with old method : 0.0031151771545410156 length of segment : 61 time for calcul the mask position with numpy : 0.001569509506225586 nb_pixel_total : 41433 time to create 1 rle with old method : 0.04866600036621094 length of segment : 226 time for calcul the mask position with numpy : 0.0005049705505371094 nb_pixel_total : 9608 time to create 1 rle with old method : 0.011505365371704102 length of segment : 73 time for calcul the mask position with numpy : 0.0009725093841552734 nb_pixel_total : 11124 time to create 1 rle with old method : 0.014080286026000977 length of segment : 166 time for calcul the mask position with numpy : 0.0005362033843994141 nb_pixel_total : 7442 time to create 1 rle with old method : 0.009069442749023438 length of segment : 87 time for calcul the mask position with numpy : 0.000370025634765625 nb_pixel_total : 4395 time to create 1 rle with old method : 0.005322694778442383 length of segment : 87 time for calcul the mask position with numpy : 0.0038504600524902344 nb_pixel_total : 117893 time to create 1 rle with old method : 0.1340322494506836 length of segment : 435 time for calcul the mask position with numpy : 0.0031273365020751953 nb_pixel_total : 98314 time to create 1 rle with old method : 0.11680722236633301 length of segment : 505 time for calcul the mask position with numpy : 0.0037412643432617188 nb_pixel_total : 115099 time to create 1 rle with old method : 0.1378920078277588 length of segment : 435 time for calcul the mask position with numpy : 0.00472259521484375 nb_pixel_total : 211852 time to create 1 rle with new method : 0.008226156234741211 length of segment : 681 time for calcul the mask position with numpy : 0.005860567092895508 nb_pixel_total : 297225 time to create 1 rle with new method : 0.00914764404296875 length of segment : 401 time for calcul the mask position with numpy : 0.0003323554992675781 nb_pixel_total : 4430 time to create 1 rle with old method : 0.0058362483978271484 length of segment : 69 time for calcul the mask position with numpy : 0.003378152847290039 nb_pixel_total : 95935 time to create 1 rle with old method : 0.11856317520141602 length of segment : 499 time for calcul the mask position with numpy : 0.01088571548461914 nb_pixel_total : 272384 time to create 1 rle with new method : 0.016512632369995117 length of segment : 395 time spent for convertir_results : 35.93153977394104 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 395 chid ids of type : 3663 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 115408 save missing photos in datou_result : time spend for datou_step_exec : 94.39850950241089 time spend to save output : 6.720041513442993 total time spend for step 1 : 101.11855101585388 step2:crop_condition Tue Feb 4 14:21:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 3663 Loading chi in step crop for list_pids : 20 ! batch 1 Loaded 395 chid ids of type : 3663 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : teint_dans_la_masse param for this class : {'min_score': 0.7} filtre for class : teint_dans_la_masse hashtag_id of this class : 2107752385 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 18 About to insert : list_path_to_insert length 18 new photo from crops ! we have finished the crop for the class : teint_dans_la_masse begin to crop the class : autre_refus param for this class : {'min_score': 0.5} filtre for class : autre_refus hashtag_id of this class : 2107752406 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 35 About to insert : list_path_to_insert length 35 new photo from crops ! we have finished the crop for the class : autre_refus begin to crop the class : carton_gris param for this class : {'min_score': 0.5} filtre for class : carton_gris hashtag_id of this class : 2107753020 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 19 About to insert : list_path_to_insert length 19 new photo from crops ! we have finished the crop for the class : carton_gris begin to crop the class : cartonnette param for this class : {'min_score': 0.5} filtre for class : cartonnette hashtag_id of this class : 702398920 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 121 About to insert : list_path_to_insert length 121 new photo from crops ! we have finished the crop for the class : cartonnette begin to crop the class : carton_brun param for this class : {'min_score': 0.7} filtre for class : carton_brun hashtag_id of this class : 2107753024 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 17 About to insert : list_path_to_insert length 17 new photo from crops ! we have finished the crop for the class : carton_brun begin to crop the class : plastique param for this class : {'min_score': 0.5} filtre for class : plastique hashtag_id of this class : 492725882 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 ! we have finished the crop for the class : plastique begin to crop the class : kraft param for this class : {'min_score': 0.5} filtre for class : kraft hashtag_id of this class : 493202403 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 ! we have finished the crop for the class : kraft begin to crop the class : metal param for this class : {'min_score': 0.5} filtre for class : metal hashtag_id of this class : 492628673 delete rles for these photos Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1330514538, 1330514533, 1330501519, 1330501508, 1330501454, 1330501453, 1330501377, 1330501376, 1330501258, 1330501257, 1330501208, 1330501207, 1330501140, 1330501137, 1330500943, 1330500930, 1330500898, 1330500895, 1330500893, 1330500891] Looping around the photos to save general results len do output : 225 /-3653468771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468878Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469015Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469082Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468909Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468939Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468935Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468947Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468959Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469024Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469091Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468875Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468905Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468934Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468954Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468979Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469081Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469066Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469101Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468911Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468915Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468913Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468944Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468933Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468945Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468938Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468949Didn't 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data . /-3653468978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468973Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468999Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469023Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469054Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469059Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469063Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469074Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469064Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469080Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469094Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469095Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469093Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469097Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469084Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469090Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469096Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469108Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468903Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468937Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469069Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469109Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469111Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468960Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468975Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653468996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653589671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469092Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /-3653469104Didn'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 ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514538', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514533', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501519', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501508', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501454', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501453', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501377', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501376', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501258', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501257', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501208', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501207', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501140', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501137', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500943', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500930', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500898', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500895', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500893', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500891', None, None, None, None, None, '2502549') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 695 time used for this insertion : 0.11923742294311523 save_final save missing photos in datou_result : time spend for datou_step_exec : 109.65134787559509 time spend to save output : 0.12553930282592773 total time spend for step 2 : 109.77688717842102 step3:thcl Tue Feb 4 14:23:01 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 Beginning of datou step Thcl ! nombre de thcls : 2 we are using the classfication for multi_thcl [2456, 2868] time to import caffe and check if the image exist : 0.05411577224731445 time to convert the images to numpy array : 0.24275636672973633 time to import caffe and check if the image exist : 0.05683493614196777 time to convert the images to numpy array : 0.24890518188476562 time to import caffe and check if the image exist : 0.056533098220825195 time to convert the images to numpy array : 0.2908310890197754 time to import caffe and check if the image exist : 0.05865168571472168 time to convert the images to numpy array : 0.2941009998321533 time to import caffe and check if the image exist : 0.015260934829711914 time to convert the images to numpy array : 0.3420381546020508 time to import caffe and check if the image exist : 0.050577402114868164 time to convert the images to numpy array : 0.318981409072876 time to import caffe and check if the image exist : 0.055170536041259766 time to convert the images to numpy array : 0.32399892807006836 time to import caffe and check if the image exist : 0.055687665939331055 time to convert the images to numpy array : 0.32889270782470703 time to import caffe and check if the image exist : 0.05201578140258789 time to convert the images to numpy array : 0.34833741188049316 time to import caffe and check if the image exist : 0.05485033988952637 time to convert the images to numpy array : 0.36427736282348633 total time to convert the images to numpy array : 0.6046037673950195 list photo_ids error: [] list photo_ids correct : [-3653468771, -3653468777, -3653468776, -3653468814, -3653468804, -3653468834, -3653468825, -3653468846, -3653468854, -3653468859, -3653468867, -3653468878, -3653468893, -3653468908, -3653468923, -3653469015, -3653469082, -3653469105, -3653468792, -3653589635, -3653589634, -3653468779, -3653468819, -3653469090, -3653589670, -3653469096, -3653469108, -3653589674, -3653469116, -3653589675, -3653589672, -3653589673, -3653468774, -3653468785, -3653468797, -3653468850, -3653468863, -3653468876, -3653468873, -3653468891, -3653468888, -3653468903, -3653468918, -3653468937, -3653468957, -3653469013, -3653469011, -3653589660, -3653469024, -3653469045, -3653469068, -3653469091, -3653589676, -3653468794, -3653468882, -3653468875, -3653468897, -3653468890, -3653468905, -3653468920, -3653468934, -3653468954, -3653468979, -3653468981, -3653469000, -3653469002, -3653589663, -3653469034, -3653469081, -3653469037, -3653469036, -3653469054, -3653589669, -3653469053, -3653469051, -3653469059, -3653469062, -3653469063, -3653469074, -3653469071, -3653469058, -3653469061, -3653469067, -3653469075, -3653469064, -3653469060, -3653469080, -3653469094, -3653469095, -3653469093, -3653469097, -3653469084, -3653589644, -3653468828, -3653468843, -3653468826, -3653468829, -3653589648, -3653589646, -3653468871, -3653468858, -3653468881, -3653468874, -3653468886, -3653468896, -3653468889, -3653468901, -3653468911, -3653468915, -3653468912, -3653468904, -3653468913, -3653468910, -3653468926, -3653589651, -3653469069, -3653469109, -3653469111, -3653468811, -3653468820, -3653468832, -3653468840, -3653589647, -3653589649, -3653468940, -3653468960, -3653468975, -3653468991, -3653468996, -3653469012, -3653589671, -3653469092, -3653469104, -3653468823, -3653468839, -3653468844, -3653468855, -3653468868, -3653468884, -3653468885, -3653468899, -3653468900, -3653468909, -3653468924, -3653468939, -3653468935, -3653468947, -3653468959, -3653589652, -3653468966, -3653468986, -3653468983, -3653468990, -3653589657, -3653469006, -3653469003, -3653468927, -3653468919, -3653468928, -3653468925, -3653468936, -3653468951, -3653468948, -3653468944, -3653468933, -3653468945, -3653468938, -3653468949, -3653468943, -3653589653, -3653468970, -3653468968, -3653468964, -3653468953, -3653468965, -3653468958, -3653468969, -3653589654, -3653468977, -3653468984, -3653468978, -3653468971, -3653468973, -3653589658, -3653468997, -3653469004, -3653468999, -3653468992, -3653589659, -3653589661, -3653589662, -3653469017, -3653469025, -3653469022, -3653589664, -3653589665, -3653469023, -3653469039, -3653469046, -3653589667, -3653589668, -3653469035, -3653469073, -3653469066, -3653469101, -3653468784, -3653468783, -3653468787, -3653589637, -3653589638, -3653589633, -3653468790, -3653468782, -3653468802, -3653589641, -3653589639, -3653589642, -3653468809, -3653589643, -3653468807, -3653468822, -3653468805, -3653468808, -3653589645, -3653468830] number of photos to traite : 225 try to delete the photos incorrect in DB tagging for thcl : 2456 To do loadFromThcl(), then load ParamDescType : thcl2456 thcls : [{'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'}] thcl {'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'} Update svm_hashtag_type_desc : 4999 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4999, 'learn_qualipapia_papier_refus_from_vlg_data_aug', 16384, 25088, 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2020, 10, 23, 14, 27, 22), datetime.datetime(2020, 10, 23, 14, 27, 22)) To loadFromThcl() : net_4999 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10332 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4999, 'learn_qualipapia_papier_refus_from_vlg_data_aug', 16384, 25088, 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2020, 10, 23, 14, 27, 22), datetime.datetime(2020, 10, 23, 14, 27, 22)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_qualipapia_papier_refus_from_vlg_data_aug Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_qualipapia_papier_refus_from_vlg_data_aug model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/caffemodel size_local : 44972172 size in s3 : 44972172 create time local : 2021-08-09 05:55:48 create time in s3 : 2021-08-06 19:28:49 caffemodel already exist and didn't need to update /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/deploy.prototxt size_local : 17311 size in s3 : 17311 create time local : 2021-08-09 05:55:48 create time in s3 : 2021-08-06 19:28:49 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:55:48 create time in s3 : 2021-08-06 19:28:51 mean.npy already exist and didn't need to update /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/synset_words.txt size_local : 57 size in s3 : 57 create time local : 2021-08-09 05:55:48 create time in s3 : 2021-08-06 19:28:49 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /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/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/deploy.prototxt caffemodel_filename : /data/models_weight/learn_qualipapia_papier_refus_from_vlg_data_aug/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10113 max_wait_temp : 1 max_wait : 0 dict_keys(['prob']) time used to do the prepocess of the images : 3.031003952026367 time used to do the prediction : 0.4012610912322998 we don't save the descriptors for this thcl 2456 tagging for thcl : 2868 To do loadFromThcl(), then load ParamDescType : thcl2868 thcls : [{'id': 2868, 'mtr_user_id': 31, 'name': 'learn_papier_nantes_300421', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,environnement,kraft,metal,papier,plastique', 'svm_portfolios_learning': '3752117,3752118,3752123,3752106,3752116,3752124,3752119,3581575,3486029,3752122', 'photo_hashtag_type': 3632, 'photo_desc_type': 5288, 'type_classification': 'caffe', 'hashtag_id_list': '2107753024,2107753020,2107752385,2107752406,702398920,493012381,493202403,492628673,492668766,492725882'}] thcl {'id': 2868, 'mtr_user_id': 31, 'name': 'learn_papier_nantes_300421', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,environnement,kraft,metal,papier,plastique', 'svm_portfolios_learning': '3752117,3752118,3752123,3752106,3752116,3752124,3752119,3581575,3486029,3752122', 'photo_hashtag_type': 3632, 'photo_desc_type': 5288, 'type_classification': 'caffe', 'hashtag_id_list': '2107753024,2107753020,2107752385,2107752406,702398920,493012381,493202403,492628673,492668766,492725882'} Update svm_hashtag_type_desc : 5288 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5288, 'learn_papier_nantes_300421', 512, 512, 'learn_papier_nantes_300421', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 30, 17, 9, 41), datetime.datetime(2021, 4, 30, 17, 9, 41)) To loadFromThcl() : net_5288 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10111 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5288, 'learn_papier_nantes_300421', 512, 512, 'learn_papier_nantes_300421', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 30, 17, 9, 41), datetime.datetime(2021, 4, 30, 17, 9, 41)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_papier_nantes_300421 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_papier_nantes_300421 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_papier_nantes_300421 /data/models_weight/learn_papier_nantes_300421/caffemodel size_local : 44791983 size in s3 : 44791983 create time local : 2021-08-09 05:55:59 create time in s3 : 2021-08-06 19:22:13 caffemodel already exist and didn't need to update /data/models_weight/learn_papier_nantes_300421/deploy.prototxt size_local : 17255 size in s3 : 17255 create time local : 2021-08-09 05:55:59 create time in s3 : 2021-08-06 19:22:12 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_papier_nantes_300421/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:55:59 create time in s3 : 2021-08-06 19:22:14 mean.npy already exist and didn't need to update /data/models_weight/learn_papier_nantes_300421/synset_words.txt size_local : 331 size in s3 : 331 create time local : 2021-08-09 05:56:00 create time in s3 : 2021-08-06 19:22:12 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /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/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_papier_nantes_300421/deploy.prototxt caffemodel_filename : /data/models_weight/learn_papier_nantes_300421/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10111 max_wait_temp : 1 max_wait : 0 dict_keys(['prob']) time used to do the prepocess of the images : 2.766291379928589 time used to do the prediction : 0.34705567359924316 we don't save the descriptors for this thcl 2868 Inside saveOutput : final : False verbose : 0 time used to find the portfolios of the photos test new format of the output of the step_thcl begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 time used for this insertion : 7.152557373046875e-06 save missing photos in datou_result : time spend for datou_step_exec : 14.103656530380249 time spend to save output : 0.0015499591827392578 total time spend for step 3 : 14.105206489562988 step4:merge_mask_thcl_custom Tue Feb 4 14:23:15 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 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step merge_mask_thcl_custom batch 1 Loaded 395 chid ids of type : 3663 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++As expected we have just one thcl present End of step merge_mask_thcl_custom Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : merge_mask_thcl_custom we use saveGeneral [1330514538, 1330514533, 1330501519, 1330501508, 1330501454, 1330501453, 1330501377, 1330501376, 1330501258, 1330501257, 1330501208, 1330501207, 1330501140, 1330501137, 1330500943, 1330500930, 1330500898, 1330500895, 1330500893, 1330500891] Looping around the photos to save general results len do output : 20 /1330514538Didn't retrieve data .Didn't retrieve data . /1330514533Didn't retrieve data .Didn't retrieve data . /1330501519Didn't retrieve data .Didn't retrieve data . /1330501508Didn't retrieve data .Didn't retrieve data . /1330501454Didn't retrieve data .Didn't retrieve data . /1330501453Didn't retrieve data .Didn't retrieve data . /1330501377Didn't retrieve data .Didn't retrieve data . /1330501376Didn't retrieve data .Didn't retrieve data . /1330501258Didn't retrieve data .Didn't retrieve data . /1330501257Didn't retrieve data .Didn't retrieve data . /1330501208Didn't retrieve data .Didn't retrieve data . /1330501207Didn't retrieve data .Didn't retrieve data . /1330501140Didn't retrieve data .Didn't retrieve data . /1330501137Didn't retrieve data .Didn't retrieve data . /1330500943Didn't retrieve data .Didn't retrieve data . /1330500930Didn't retrieve data .Didn't retrieve data . /1330500898Didn't retrieve data .Didn't retrieve data . /1330500895Didn't retrieve data .Didn't retrieve data . /1330500893Didn't retrieve data .Didn't retrieve data . /1330500891Didn'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 : Managing all output in save final without adding information in the mtr_datou_result ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514538', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514533', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501519', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501508', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501454', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501453', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501377', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501376', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501258', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501257', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501208', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501207', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501140', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501137', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500943', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500930', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500898', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500895', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500893', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500891', None, None, None, None, None, '2502549') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 60 time used for this insertion : 0.013900995254516602 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.10407304763793945 time spend to save output : 0.01439523696899414 total time spend for step 4 : 0.1184682846069336 step5:rle_unique_nms_with_priority Tue Feb 4 14:23:15 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 Begin step rle-unique-nms nb_obj : 26 nb_hashtags : 8 time to prepare the origin masks : 11.200013399124146 time for calcul the mask position with numpy : 0.07092142105102539 nb_pixel_total : 5246192 time to create 1 rle with new method : 0.1423177719116211 time for calcul the mask position with numpy : 0.02898383140563965 nb_pixel_total : 2293 time to create 1 rle with old method : 0.0029201507568359375 time for calcul the mask position with numpy : 0.02988147735595703 nb_pixel_total : 46552 time to create 1 rle with old method : 0.0550379753112793 time for calcul the mask position with numpy : 0.029595613479614258 nb_pixel_total : 101160 time to create 1 rle with old method : 0.12058734893798828 time for calcul the mask position with numpy : 0.030538082122802734 nb_pixel_total : 42478 time to create 1 rle with old method : 0.0786135196685791 time for calcul the mask position with numpy : 0.029371976852416992 nb_pixel_total : 115402 time to create 1 rle with old method : 0.1364433765411377 time for calcul the mask position with numpy : 0.0293731689453125 nb_pixel_total : 1924 time to create 1 rle with old method : 0.0026578903198242188 time for calcul the mask position with numpy : 0.029332637786865234 nb_pixel_total : 25553 time to create 1 rle with old method : 0.030639171600341797 time for calcul the mask position with numpy : 0.030316829681396484 nb_pixel_total : 203113 time to create 1 rle with new method : 0.1253511905670166 time for calcul the mask position with numpy : 0.029036521911621094 nb_pixel_total : 83037 time to create 1 rle with old method : 0.10392117500305176 time for calcul the mask position with numpy : 0.029260873794555664 nb_pixel_total : 3904 time to create 1 rle with old method : 0.004886150360107422 time for calcul the mask position with numpy : 0.0294034481048584 nb_pixel_total : 3561 time to create 1 rle with old method : 0.004382133483886719 time for calcul the mask position with numpy : 0.029787540435791016 nb_pixel_total : 94206 time to create 1 rle with old method : 0.11121654510498047 time for calcul the mask position with numpy : 0.03004169464111328 nb_pixel_total : 16285 time to create 1 rle with old method : 0.02094864845275879 time for calcul the mask position with numpy : 0.030646085739135742 nb_pixel_total : 170614 time to create 1 rle with new method : 0.1237947940826416 time for calcul the mask position with numpy : 0.029879093170166016 nb_pixel_total : 2345 time to create 1 rle with old method : 0.0031197071075439453 time for calcul the mask position with numpy : 0.033986568450927734 nb_pixel_total : 2115 time to create 1 rle with old method : 0.0027723312377929688 time for calcul the mask position with numpy : 0.029238462448120117 nb_pixel_total : 3536 time to create 1 rle with old method : 0.004967927932739258 time for calcul the mask position with numpy : 0.029253005981445312 nb_pixel_total : 36460 time to create 1 rle with old method : 0.04333925247192383 time for calcul the mask position with numpy : 0.030446290969848633 nb_pixel_total : 187337 time to create 1 rle with new method : 0.1280980110168457 time for calcul the mask position with numpy : 0.029796600341796875 nb_pixel_total : 90161 time to create 1 rle with old method : 0.10673046112060547 time for calcul the mask position with numpy : 0.03039240837097168 nb_pixel_total : 153672 time to create 1 rle with new method : 0.12920022010803223 time for calcul the mask position with numpy : 0.02920079231262207 nb_pixel_total : 2485 time to create 1 rle with old method : 0.003274202346801758 time for calcul the mask position with numpy : 0.029636621475219727 nb_pixel_total : 55 time to create 1 rle with old method : 0.00023293495178222656 time for calcul the mask position with numpy : 0.03198647499084473 nb_pixel_total : 103941 time to create 1 rle with old method : 0.13245749473571777 time for calcul the mask position with numpy : 0.0316162109375 nb_pixel_total : 281007 time to create 1 rle with new method : 0.13188672065734863 time for calcul the mask position with numpy : 0.02936100959777832 nb_pixel_total : 30852 time to create 1 rle with old method : 0.036847829818725586 create new chi : 2.7113332748413086 time to delete rle : 0.010337352752685547 batch 1 Loaded 27 chid ids of type : 3726 Number RLEs to save : 17410 TO DO : save crop sub photo not yet done ! save time : 1.311218500137329 nb_obj : 7 nb_hashtags : 4 time to prepare the origin masks : 2.4140214920043945 time for calcul the mask position with numpy : 0.06529355049133301 nb_pixel_total : 3584975 time to create 1 rle with new method : 0.15956950187683105 time for calcul the mask position with numpy : 0.025063514709472656 nb_pixel_total : 191460 time to create 1 rle with new method : 0.14991331100463867 time for calcul the mask position with numpy : 0.02392864227294922 nb_pixel_total : 178846 time to create 1 rle with new method : 0.14716887474060059 time for calcul the mask position with numpy : 0.021215200424194336 nb_pixel_total : 17792 time to create 1 rle with old method : 0.02101922035217285 time for calcul the mask position with numpy : 0.02075982093811035 nb_pixel_total : 23791 time to create 1 rle with old method : 0.03067922592163086 time for calcul the mask position with numpy : 0.04105973243713379 nb_pixel_total : 2635080 time to create 1 rle with new method : 0.13769769668579102 time for calcul the mask position with numpy : 0.02259230613708496 nb_pixel_total : 302663 time to create 1 rle with new method : 0.1480112075805664 time for calcul the mask position with numpy : 0.022263765335083008 nb_pixel_total : 115633 time to create 1 rle with old method : 0.1375575065612793 create new chi : 1.2392444610595703 time to delete rle : 0.0013151168823242188 batch 1 Loaded 8 chid ids of type : 3726 Number RLEs to save : 15677 TO DO : save crop sub photo not yet done ! save time : 1.1066794395446777 nb_obj : 16 nb_hashtags : 7 time to prepare the origin masks : 5.558063983917236 time for calcul the mask position with numpy : 0.07712507247924805 nb_pixel_total : 5725160 time to create 1 rle with new method : 0.1484847068786621 time for calcul the mask position with numpy : 0.02472829818725586 nb_pixel_total : 248928 time to create 1 rle with new method : 0.13950657844543457 time for calcul the mask position with numpy : 0.02232503890991211 nb_pixel_total : 125318 time to create 1 rle with old method : 0.15712499618530273 time for calcul the mask position with numpy : 0.02180957794189453 nb_pixel_total : 1 time to create 1 rle with old method : 2.5510787963867188e-05 time for calcul the mask position with numpy : 0.023380279541015625 nb_pixel_total : 185191 time to create 1 rle with new method : 0.13481569290161133 time for calcul the mask position with numpy : 0.021578311920166016 nb_pixel_total : 160952 time to create 1 rle with new method : 0.14590740203857422 time for calcul the mask position with numpy : 0.026113033294677734 nb_pixel_total : 63962 time to create 1 rle with old method : 0.08925509452819824 time for calcul the mask position with numpy : 0.021370649337768555 nb_pixel_total : 35011 time to create 1 rle with old method : 0.04115033149719238 time for calcul the mask position with numpy : 0.021718978881835938 nb_pixel_total : 64500 time to create 1 rle with old method : 0.07938075065612793 time for calcul the mask position with numpy : 0.023009300231933594 nb_pixel_total : 29537 time to create 1 rle with old method : 0.03652787208557129 time for calcul the mask position with numpy : 0.022198200225830078 nb_pixel_total : 154194 time to create 1 rle with new method : 0.13745379447937012 time for calcul the mask position with numpy : 0.02307581901550293 nb_pixel_total : 755 time to create 1 rle with old method : 0.0012254714965820312 time for calcul the mask position with numpy : 0.023852825164794922 nb_pixel_total : 22932 time to create 1 rle with old method : 0.03502058982849121 time for calcul the mask position with numpy : 0.02270221710205078 nb_pixel_total : 102355 time to create 1 rle with old method : 0.12212491035461426 time for calcul the mask position with numpy : 0.023308277130126953 nb_pixel_total : 2718 time to create 1 rle with old method : 0.0034651756286621094 time for calcul the mask position with numpy : 0.02162027359008789 nb_pixel_total : 8308 time to create 1 rle with old method : 0.010248422622680664 time for calcul the mask position with numpy : 0.0217437744140625 nb_pixel_total : 120418 time to create 1 rle with old method : 0.1433103084564209 create new chi : 1.9385771751403809 time to delete rle : 0.0018432140350341797 batch 1 Loaded 17 chid ids of type : 3726 Number RLEs to save : 13405 TO DO : save crop sub photo not yet done ! save time : 0.7605619430541992 nb_obj : 16 nb_hashtags : 7 time to prepare the origin masks : 4.47657585144043 time for calcul the mask position with numpy : 0.06893420219421387 nb_pixel_total : 5724284 time to create 1 rle with new method : 0.13527679443359375 time for calcul the mask position with numpy : 0.021166086196899414 nb_pixel_total : 249118 time to create 1 rle with new method : 0.12868905067443848 time for calcul the mask position with numpy : 0.02266860008239746 nb_pixel_total : 1 time to create 1 rle with old method : 3.0517578125e-05 time for calcul the mask position with numpy : 0.0228273868560791 nb_pixel_total : 125498 time to create 1 rle with old method : 0.14719104766845703 time for calcul the mask position with numpy : 0.020911216735839844 nb_pixel_total : 183905 time to create 1 rle with new method : 0.1332240104675293 time for calcul the mask position with numpy : 0.02156376838684082 nb_pixel_total : 162596 time to create 1 rle with new method : 0.13730955123901367 time for calcul the mask position with numpy : 0.0225374698638916 nb_pixel_total : 63856 time to create 1 rle with old method : 0.09657716751098633 time for calcul the mask position with numpy : 0.02248549461364746 nb_pixel_total : 35004 time to create 1 rle with old method : 0.041420936584472656 time for calcul the mask position with numpy : 0.023071765899658203 nb_pixel_total : 64585 time to create 1 rle with old method : 0.07608985900878906 time for calcul the mask position with numpy : 0.023586273193359375 nb_pixel_total : 29439 time to create 1 rle with old method : 0.0419766902923584 time for calcul the mask position with numpy : 0.02291274070739746 nb_pixel_total : 154387 time to create 1 rle with new method : 0.14705538749694824 time for calcul the mask position with numpy : 0.02219223976135254 nb_pixel_total : 726 time to create 1 rle with old method : 0.0011856555938720703 time for calcul the mask position with numpy : 0.022252559661865234 nb_pixel_total : 22932 time to create 1 rle with old method : 0.02757740020751953 time for calcul the mask position with numpy : 0.021663427352905273 nb_pixel_total : 102481 time to create 1 rle with old method : 0.12575531005859375 time for calcul the mask position with numpy : 0.020366668701171875 nb_pixel_total : 2718 time to create 1 rle with old method : 0.003469705581665039 time for calcul the mask position with numpy : 0.020506620407104492 nb_pixel_total : 8302 time to create 1 rle with old method : 0.010223388671875 time for calcul the mask position with numpy : 0.021084308624267578 nb_pixel_total : 120408 time to create 1 rle with old method : 0.14483141899108887 create new chi : 1.8845152854919434 time to delete rle : 0.0015866756439208984 batch 1 Loaded 17 chid ids of type : 3726 Number RLEs to save : 13404 TO DO : save crop sub photo not yet done ! save time : 0.7298991680145264 nb_obj : 13 nb_hashtags : 5 time to prepare the origin masks : 4.023344278335571 time for calcul the mask position with numpy : 0.07949614524841309 nb_pixel_total : 6641781 time to create 1 rle with new method : 0.14305782318115234 time for calcul the mask position with numpy : 0.020715951919555664 nb_pixel_total : 4317 time to create 1 rle with old method : 0.005314350128173828 time for calcul the mask position with numpy : 0.020598888397216797 nb_pixel_total : 32248 time to create 1 rle with old method : 0.03789949417114258 time for calcul the mask position with numpy : 0.021157026290893555 nb_pixel_total : 54619 time to create 1 rle with old method : 0.0647120475769043 time for calcul the mask position with numpy : 0.02000594139099121 nb_pixel_total : 7332 time to create 1 rle with old method : 0.008519411087036133 time for calcul the mask position with numpy : 0.019635438919067383 nb_pixel_total : 3474 time to create 1 rle with old method : 0.004202604293823242 time for calcul the mask position with numpy : 0.020483016967773438 nb_pixel_total : 122602 time to create 1 rle with old method : 0.1405034065246582 time for calcul the mask position with numpy : 0.019851207733154297 nb_pixel_total : 8880 time to create 1 rle with old method : 0.011126518249511719 time for calcul the mask position with numpy : 0.02123856544494629 nb_pixel_total : 11937 time to create 1 rle with old method : 0.019624710083007812 time for calcul the mask position with numpy : 0.022081375122070312 nb_pixel_total : 63511 time to create 1 rle with old method : 0.0902717113494873 time for calcul the mask position with numpy : 0.019886493682861328 nb_pixel_total : 2399 time to create 1 rle with old method : 0.002954244613647461 time for calcul the mask position with numpy : 0.01972031593322754 nb_pixel_total : 3858 time to create 1 rle with old method : 0.004654407501220703 time for calcul the mask position with numpy : 0.019656658172607422 nb_pixel_total : 70520 time to create 1 rle with old method : 0.0809328556060791 time for calcul the mask position with numpy : 0.020259618759155273 nb_pixel_total : 22762 time to create 1 rle with old method : 0.0264737606048584 create new chi : 0.9996449947357178 time to delete rle : 0.0007989406585693359 batch 1 Loaded 14 chid ids of type : 3726 Number RLEs to save : 7732 TO DO : save crop sub photo not yet done ! save time : 0.45522427558898926 nb_obj : 13 nb_hashtags : 5 time to prepare the origin masks : 3.4747486114501953 time for calcul the mask position with numpy : 0.07884836196899414 nb_pixel_total : 6641676 time to create 1 rle with new method : 0.13153719902038574 time for calcul the mask position with numpy : 0.019914865493774414 nb_pixel_total : 4287 time to create 1 rle with old method : 0.0050923824310302734 time for calcul the mask position with numpy : 0.019933223724365234 nb_pixel_total : 32195 time to create 1 rle with old method : 0.03658175468444824 time for calcul the mask position with numpy : 0.021116971969604492 nb_pixel_total : 54510 time to create 1 rle with old method : 0.06284403800964355 time for calcul the mask position with numpy : 0.022197246551513672 nb_pixel_total : 7330 time to create 1 rle with old method : 0.008786678314208984 time for calcul the mask position with numpy : 0.022876977920532227 nb_pixel_total : 3475 time to create 1 rle with old method : 0.0044727325439453125 time for calcul the mask position with numpy : 0.02354741096496582 nb_pixel_total : 122879 time to create 1 rle with old method : 0.14365530014038086 time for calcul the mask position with numpy : 0.0214383602142334 nb_pixel_total : 8863 time to create 1 rle with old method : 0.010504484176635742 time for calcul the mask position with numpy : 0.020387887954711914 nb_pixel_total : 11937 time to create 1 rle with old method : 0.01424860954284668 time for calcul the mask position with numpy : 0.021474123001098633 nb_pixel_total : 63503 time to create 1 rle with old method : 0.07653236389160156 time for calcul the mask position with numpy : 0.02057480812072754 nb_pixel_total : 2397 time to create 1 rle with old method : 0.0031113624572753906 time for calcul the mask position with numpy : 0.020553112030029297 nb_pixel_total : 3846 time to create 1 rle with old method : 0.004807233810424805 time for calcul the mask position with numpy : 0.020418405532836914 nb_pixel_total : 70496 time to create 1 rle with old method : 0.08155441284179688 time for calcul the mask position with numpy : 0.01992344856262207 nb_pixel_total : 22846 time to create 1 rle with old method : 0.026409149169921875 create new chi : 0.9776976108551025 time to delete rle : 0.0009472370147705078 batch 1 Loaded 14 chid ids of type : 3726 Number RLEs to save : 7745 TO DO : save crop sub photo not yet done ! save time : 0.48854708671569824 nb_obj : 14 nb_hashtags : 6 time to prepare the origin masks : 3.6237294673919678 time for calcul the mask position with numpy : 0.07277727127075195 nb_pixel_total : 6385435 time to create 1 rle with new method : 0.13225245475769043 time for calcul the mask position with numpy : 0.02132129669189453 nb_pixel_total : 52186 time to create 1 rle with old method : 0.0616915225982666 time for calcul the mask position with numpy : 0.020356178283691406 nb_pixel_total : 707 time to create 1 rle with old method : 0.001104593276977539 time for calcul the mask position with numpy : 0.020351409912109375 nb_pixel_total : 51165 time to create 1 rle with old method : 0.05930185317993164 time for calcul the mask position with numpy : 0.01977229118347168 nb_pixel_total : 4481 time to create 1 rle with old method : 0.005230903625488281 time for calcul the mask position with numpy : 0.020029783248901367 nb_pixel_total : 44572 time to create 1 rle with old method : 0.051854848861694336 time for calcul the mask position with numpy : 0.019765615463256836 nb_pixel_total : 3820 time to create 1 rle with old method : 0.004549741744995117 time for calcul the mask position with numpy : 0.02026081085205078 nb_pixel_total : 40147 time to create 1 rle with old method : 0.046854496002197266 time for calcul the mask position with numpy : 0.021251440048217773 nb_pixel_total : 6545 time to create 1 rle with old method : 0.008207082748413086 time for calcul the mask position with numpy : 0.023020267486572266 nb_pixel_total : 250322 time to create 1 rle with new method : 0.1418285369873047 time for calcul the mask position with numpy : 0.021299123764038086 nb_pixel_total : 2382 time to create 1 rle with old method : 0.003179311752319336 time for calcul the mask position with numpy : 0.02259540557861328 nb_pixel_total : 84090 time to create 1 rle with old method : 0.0981144905090332 time for calcul the mask position with numpy : 0.02204728126525879 nb_pixel_total : 65641 time to create 1 rle with old method : 0.0797891616821289 time for calcul the mask position with numpy : 0.02223491668701172 nb_pixel_total : 47170 time to create 1 rle with old method : 0.05545783042907715 time for calcul the mask position with numpy : 0.021021127700805664 nb_pixel_total : 11577 time to create 1 rle with old method : 0.01399540901184082 create new chi : 1.157160758972168 time to delete rle : 0.0009214878082275391 batch 1 Loaded 15 chid ids of type : 3726 Number RLEs to save : 9115 TO DO : save crop sub photo not yet done ! save time : 0.5387539863586426 nb_obj : 14 nb_hashtags : 6 time to prepare the origin masks : 3.1356749534606934 time for calcul the mask position with numpy : 0.07161593437194824 nb_pixel_total : 6385401 time to create 1 rle with new method : 0.1355602741241455 time for calcul the mask position with numpy : 0.020264625549316406 nb_pixel_total : 52199 time to create 1 rle with old method : 0.059607744216918945 time for calcul the mask position with numpy : 0.019777774810791016 nb_pixel_total : 707 time to create 1 rle with old method : 0.0010790824890136719 time for calcul the mask position with numpy : 0.020364046096801758 nb_pixel_total : 51189 time to create 1 rle with old method : 0.05937623977661133 time for calcul the mask position with numpy : 0.020304203033447266 nb_pixel_total : 4484 time to create 1 rle with old method : 0.0056040287017822266 time for calcul the mask position with numpy : 0.020212650299072266 nb_pixel_total : 44572 time to create 1 rle with old method : 0.05366182327270508 time for calcul the mask position with numpy : 0.02171802520751953 nb_pixel_total : 3825 time to create 1 rle with old method : 0.004679441452026367 time for calcul the mask position with numpy : 0.021265029907226562 nb_pixel_total : 40273 time to create 1 rle with old method : 0.046735525131225586 time for calcul the mask position with numpy : 0.01997089385986328 nb_pixel_total : 6543 time to create 1 rle with old method : 0.008030176162719727 time for calcul the mask position with numpy : 0.02126908302307129 nb_pixel_total : 250156 time to create 1 rle with new method : 0.141265869140625 time for calcul the mask position with numpy : 0.020786762237548828 nb_pixel_total : 2362 time to create 1 rle with old method : 0.0030074119567871094 time for calcul the mask position with numpy : 0.020710468292236328 nb_pixel_total : 84084 time to create 1 rle with old method : 0.09641313552856445 time for calcul the mask position with numpy : 0.021124601364135742 nb_pixel_total : 65678 time to create 1 rle with old method : 0.07576203346252441 time for calcul the mask position with numpy : 0.020054340362548828 nb_pixel_total : 47190 time to create 1 rle with old method : 0.05678868293762207 time for calcul the mask position with numpy : 0.020343542098999023 nb_pixel_total : 11577 time to create 1 rle with old method : 0.014050483703613281 create new chi : 1.1478610038757324 time to delete rle : 0.0007710456848144531 batch 1 Loaded 15 chid ids of type : 3726 Number RLEs to save : 9139 TO DO : save crop sub photo not yet done ! save time : 0.6238181591033936 nb_obj : 13 nb_hashtags : 6 time to prepare the origin masks : 3.476292133331299 time for calcul the mask position with numpy : 0.06977033615112305 nb_pixel_total : 6127736 time to create 1 rle with new method : 0.14168810844421387 time for calcul the mask position with numpy : 0.02203822135925293 nb_pixel_total : 1101 time to create 1 rle with old method : 0.002362966537475586 time for calcul the mask position with numpy : 0.022621631622314453 nb_pixel_total : 48116 time to create 1 rle with old method : 0.06787395477294922 time for calcul the mask position with numpy : 0.019960641860961914 nb_pixel_total : 51527 time to create 1 rle with old method : 0.06159162521362305 time for calcul the mask position with numpy : 0.024580717086791992 nb_pixel_total : 4421 time to create 1 rle with old method : 0.0054891109466552734 time for calcul the mask position with numpy : 0.020633935928344727 nb_pixel_total : 59237 time to create 1 rle with old method : 0.06792449951171875 time for calcul the mask position with numpy : 0.021946191787719727 nb_pixel_total : 248184 time to create 1 rle with new method : 0.14190101623535156 time for calcul the mask position with numpy : 0.020992517471313477 nb_pixel_total : 4324 time to create 1 rle with old method : 0.0051686763763427734 time for calcul the mask position with numpy : 0.021398544311523438 nb_pixel_total : 82568 time to create 1 rle with old method : 0.09668231010437012 time for calcul the mask position with numpy : 0.021427154541015625 nb_pixel_total : 88074 time to create 1 rle with old method : 0.10564446449279785 time for calcul the mask position with numpy : 0.021184682846069336 nb_pixel_total : 6244 time to create 1 rle with old method : 0.007995843887329102 time for calcul the mask position with numpy : 0.02059483528137207 nb_pixel_total : 53763 time to create 1 rle with old method : 0.06193351745605469 time for calcul the mask position with numpy : 0.020238637924194336 nb_pixel_total : 11733 time to create 1 rle with old method : 0.014107227325439453 time for calcul the mask position with numpy : 0.02491140365600586 nb_pixel_total : 263212 time to create 1 rle with new method : 0.12738871574401855 create new chi : 1.2988290786743164 time to delete rle : 0.0011038780212402344 batch 1 Loaded 14 chid ids of type : 3726 Number RLEs to save : 10748 TO DO : save crop sub photo not yet done ! save time : 0.6484389305114746 nb_obj : 14 nb_hashtags : 6 time to prepare the origin masks : 3.475691556930542 time for calcul the mask position with numpy : 0.0674123764038086 nb_pixel_total : 6127799 time to create 1 rle with new method : 0.13549304008483887 time for calcul the mask position with numpy : 0.019549846649169922 nb_pixel_total : 1320 time to create 1 rle with old method : 0.001917123794555664 time for calcul the mask position with numpy : 0.019902944564819336 nb_pixel_total : 48168 time to create 1 rle with old method : 0.05575084686279297 time for calcul the mask position with numpy : 0.019587039947509766 nb_pixel_total : 51488 time to create 1 rle with old method : 0.059873342514038086 time for calcul the mask position with numpy : 0.02050948143005371 nb_pixel_total : 4423 time to create 1 rle with old method : 0.005586862564086914 time for calcul the mask position with numpy : 0.02075958251953125 nb_pixel_total : 59214 time to create 1 rle with old method : 0.06900215148925781 time for calcul the mask position with numpy : 0.021224021911621094 nb_pixel_total : 247576 time to create 1 rle with new method : 0.12717437744140625 time for calcul the mask position with numpy : 0.02026844024658203 nb_pixel_total : 264 time to create 1 rle with old method : 0.00038743019104003906 time for calcul the mask position with numpy : 0.020082712173461914 nb_pixel_total : 4182 time to create 1 rle with old method : 0.0052564144134521484 time for calcul the mask position with numpy : 0.02092432975769043 nb_pixel_total : 82479 time to create 1 rle with old method : 0.0977632999420166 time for calcul the mask position with numpy : 0.020190715789794922 nb_pixel_total : 6231 time to create 1 rle with old method : 0.007488250732421875 time for calcul the mask position with numpy : 0.02057194709777832 nb_pixel_total : 88158 time to create 1 rle with old method : 0.10272908210754395 time for calcul the mask position with numpy : 0.020846843719482422 nb_pixel_total : 53980 time to create 1 rle with old method : 0.06300663948059082 time for calcul the mask position with numpy : 0.020510196685791016 nb_pixel_total : 11733 time to create 1 rle with old method : 0.013822793960571289 time for calcul the mask position with numpy : 0.022178173065185547 nb_pixel_total : 263225 time to create 1 rle with new method : 0.1306743621826172 create new chi : 1.2701687812805176 time to delete rle : 0.0010340213775634766 batch 1 Loaded 15 chid ids of type : 3726 Number RLEs to save : 10892 TO DO : save crop sub photo not yet done ! save time : 0.7723147869110107 nb_obj : 19 nb_hashtags : 6 time to prepare the origin masks : 5.1295554637908936 time for calcul the mask position with numpy : 0.09346222877502441 nb_pixel_total : 5552735 time to create 1 rle with new method : 0.8953437805175781 time for calcul the mask position with numpy : 0.021701335906982422 nb_pixel_total : 46738 time to create 1 rle with old method : 0.058766841888427734 time for calcul the mask position with numpy : 0.021676301956176758 nb_pixel_total : 1815 time to create 1 rle with old method : 0.0023250579833984375 time for calcul the mask position with numpy : 0.023520946502685547 nb_pixel_total : 237511 time to create 1 rle with new method : 0.1409013271331787 time for calcul the mask position with numpy : 0.021085262298583984 nb_pixel_total : 60055 time to create 1 rle with old method : 0.07091236114501953 time for calcul the mask position with numpy : 0.021569013595581055 nb_pixel_total : 5629 time to create 1 rle with old method : 0.006727695465087891 time for calcul the mask position with numpy : 0.02125239372253418 nb_pixel_total : 64243 time to create 1 rle with old method : 0.07517051696777344 time for calcul the mask position with numpy : 0.023512840270996094 nb_pixel_total : 52242 time to create 1 rle with old method : 0.061385154724121094 time for calcul the mask position with numpy : 0.021069765090942383 nb_pixel_total : 7882 time to create 1 rle with old method : 0.00952458381652832 time for calcul the mask position with numpy : 0.021911144256591797 nb_pixel_total : 3236 time to create 1 rle with old method : 0.004123687744140625 time for calcul the mask position with numpy : 0.020922422409057617 nb_pixel_total : 17878 time to create 1 rle with old method : 0.021757125854492188 time for calcul the mask position with numpy : 0.02175593376159668 nb_pixel_total : 235505 time to create 1 rle with new method : 0.13513731956481934 time for calcul the mask position with numpy : 0.020582914352416992 nb_pixel_total : 46688 time to create 1 rle with old method : 0.05565905570983887 time for calcul the mask position with numpy : 0.02063274383544922 nb_pixel_total : 50702 time to create 1 rle with old method : 0.06003904342651367 time for calcul the mask position with numpy : 0.023730039596557617 nb_pixel_total : 224167 time to create 1 rle with new method : 0.1356337070465088 time for calcul the mask position with numpy : 0.020895004272460938 nb_pixel_total : 25018 time to create 1 rle with old method : 0.02989959716796875 time for calcul the mask position with numpy : 0.021109342575073242 nb_pixel_total : 170394 time to create 1 rle with new method : 0.13449525833129883 time for calcul the mask position with numpy : 0.020911693572998047 nb_pixel_total : 16178 time to create 1 rle with old method : 0.019116878509521484 time for calcul the mask position with numpy : 0.02117609977722168 nb_pixel_total : 64208 time to create 1 rle with old method : 0.07530760765075684 time for calcul the mask position with numpy : 0.02299952507019043 nb_pixel_total : 167416 time to create 1 rle with new method : 0.13505768775939941 create new chi : 2.72200870513916 time to delete rle : 0.001955747604370117 batch 1 Loaded 20 chid ids of type : 3726 Number RLEs to save : 15068 TO DO : save crop sub photo not yet done ! save time : 1.319826364517212 nb_obj : 19 nb_hashtags : 6 time to prepare the origin masks : 4.641643047332764 time for calcul the mask position with numpy : 0.40027689933776855 nb_pixel_total : 5552600 time to create 1 rle with new method : 0.3367936611175537 time for calcul the mask position with numpy : 0.02151799201965332 nb_pixel_total : 46740 time to create 1 rle with old method : 0.05512094497680664 time for calcul the mask position with numpy : 0.02127671241760254 nb_pixel_total : 1815 time to create 1 rle with old method : 0.0024192333221435547 time for calcul the mask position with numpy : 0.023272991180419922 nb_pixel_total : 237728 time to create 1 rle with new method : 0.15642404556274414 time for calcul the mask position with numpy : 0.02201533317565918 nb_pixel_total : 60149 time to create 1 rle with old method : 0.07274365425109863 time for calcul the mask position with numpy : 0.024675607681274414 nb_pixel_total : 8014 time to create 1 rle with old method : 0.009557247161865234 time for calcul the mask position with numpy : 0.021056652069091797 nb_pixel_total : 5607 time to create 1 rle with old method : 0.007851600646972656 time for calcul the mask position with numpy : 0.024173259735107422 nb_pixel_total : 64232 time to create 1 rle with old method : 0.07778263092041016 time for calcul the mask position with numpy : 0.02254939079284668 nb_pixel_total : 3241 time to create 1 rle with old method : 0.004030704498291016 time for calcul the mask position with numpy : 0.021722793579101562 nb_pixel_total : 52332 time to create 1 rle with old method : 0.06328940391540527 time for calcul the mask position with numpy : 0.0212247371673584 nb_pixel_total : 17874 time to create 1 rle with old method : 0.02123260498046875 time for calcul the mask position with numpy : 0.026692628860473633 nb_pixel_total : 235677 time to create 1 rle with new method : 0.1876363754272461 time for calcul the mask position with numpy : 0.02533721923828125 nb_pixel_total : 46713 time to create 1 rle with old method : 0.06821537017822266 time for calcul the mask position with numpy : 0.022284269332885742 nb_pixel_total : 50660 time to create 1 rle with old method : 0.06269693374633789 time for calcul the mask position with numpy : 0.02332305908203125 nb_pixel_total : 224660 time to create 1 rle with new method : 0.13802385330200195 time for calcul the mask position with numpy : 0.02211785316467285 nb_pixel_total : 25035 time to create 1 rle with old method : 0.02970123291015625 time for calcul the mask position with numpy : 0.022797346115112305 nb_pixel_total : 169224 time to create 1 rle with new method : 0.14150547981262207 time for calcul the mask position with numpy : 0.025277137756347656 nb_pixel_total : 16151 time to create 1 rle with old method : 0.01932835578918457 time for calcul the mask position with numpy : 0.02281045913696289 nb_pixel_total : 64161 time to create 1 rle with old method : 0.07579708099365234 time for calcul the mask position with numpy : 0.024958133697509766 nb_pixel_total : 167627 time to create 1 rle with new method : 0.14133834838867188 create new chi : 2.6039609909057617 time to delete rle : 0.0014629364013671875 batch 1 Loaded 20 chid ids of type : 3726 Number RLEs to save : 15036 TO DO : save crop sub photo not yet done ! save time : 1.0815577507019043 nb_obj : 20 nb_hashtags : 5 time to prepare the origin masks : 6.904533624649048 time for calcul the mask position with numpy : 0.09823060035705566 nb_pixel_total : 5786060 time to create 1 rle with new method : 0.5576753616333008 time for calcul the mask position with numpy : 0.024101972579956055 nb_pixel_total : 74856 time to create 1 rle with old method : 0.0915672779083252 time for calcul the mask position with numpy : 0.02107405662536621 nb_pixel_total : 86447 time to create 1 rle with old method : 0.10201597213745117 time for calcul the mask position with numpy : 0.021642208099365234 nb_pixel_total : 1396 time to create 1 rle with old method : 0.0017421245574951172 time for calcul the mask position with numpy : 0.02567887306213379 nb_pixel_total : 1085 time to create 1 rle with old method : 0.0016148090362548828 time for calcul the mask position with numpy : 0.02424335479736328 nb_pixel_total : 53534 time to create 1 rle with old method : 0.06377029418945312 time for calcul the mask position with numpy : 0.022230863571166992 nb_pixel_total : 25854 time to create 1 rle with old method : 0.03070664405822754 time for calcul the mask position with numpy : 0.022223234176635742 nb_pixel_total : 2124 time to create 1 rle with old method : 0.0029206275939941406 time for calcul the mask position with numpy : 0.021271467208862305 nb_pixel_total : 49620 time to create 1 rle with old method : 0.05865812301635742 time for calcul the mask position with numpy : 0.02448749542236328 nb_pixel_total : 235046 time to create 1 rle with new method : 0.15702319145202637 time for calcul the mask position with numpy : 0.022873640060424805 nb_pixel_total : 612 time to create 1 rle with old method : 0.0009770393371582031 time for calcul the mask position with numpy : 0.02265763282775879 nb_pixel_total : 13257 time to create 1 rle with old method : 0.0159304141998291 time for calcul the mask position with numpy : 0.02156805992126465 nb_pixel_total : 27352 time to create 1 rle with old method : 0.03212738037109375 time for calcul the mask position with numpy : 0.022188901901245117 nb_pixel_total : 12360 time to create 1 rle with old method : 0.015058279037475586 time for calcul the mask position with numpy : 0.023133516311645508 nb_pixel_total : 236782 time to create 1 rle with new method : 0.14253783226013184 time for calcul the mask position with numpy : 0.021886110305786133 nb_pixel_total : 37956 time to create 1 rle with old method : 0.04892730712890625 time for calcul the mask position with numpy : 0.0244295597076416 nb_pixel_total : 66692 time to create 1 rle with old method : 0.08294558525085449 time for calcul the mask position with numpy : 0.022750139236450195 nb_pixel_total : 5964 time to create 1 rle with old method : 0.007251739501953125 time for calcul the mask position with numpy : 0.0219879150390625 nb_pixel_total : 52395 time to create 1 rle with old method : 0.06285238265991211 time for calcul the mask position with numpy : 0.02144312858581543 nb_pixel_total : 169038 time to create 1 rle with new method : 0.12875580787658691 time for calcul the mask position with numpy : 0.021178245544433594 nb_pixel_total : 111810 time to create 1 rle with old method : 0.12876391410827637 create new chi : 2.351020336151123 time to delete rle : 0.001352548599243164 batch 1 Loaded 21 chid ids of type : 3726 Number RLEs to save : 14404 TO DO : save crop sub photo not yet done ! save time : 1.0592999458312988 nb_obj : 19 nb_hashtags : 5 time to prepare the origin masks : 4.870589733123779 time for calcul the mask position with numpy : 0.07222557067871094 nb_pixel_total : 5783288 time to create 1 rle with new method : 0.1556715965270996 time for calcul the mask position with numpy : 0.021326065063476562 nb_pixel_total : 75272 time to create 1 rle with old method : 0.08871746063232422 time for calcul the mask position with numpy : 0.022304296493530273 nb_pixel_total : 86357 time to create 1 rle with old method : 0.10126590728759766 time for calcul the mask position with numpy : 0.021226882934570312 nb_pixel_total : 1396 time to create 1 rle with old method : 0.0017542839050292969 time for calcul the mask position with numpy : 0.020882129669189453 nb_pixel_total : 1038 time to create 1 rle with old method : 0.0014922618865966797 time for calcul the mask position with numpy : 0.021111249923706055 nb_pixel_total : 53697 time to create 1 rle with old method : 0.06885600090026855 time for calcul the mask position with numpy : 0.023677587509155273 nb_pixel_total : 575 time to create 1 rle with old method : 0.0009510517120361328 time for calcul the mask position with numpy : 0.02140522003173828 nb_pixel_total : 25825 time to create 1 rle with old method : 0.03077220916748047 time for calcul the mask position with numpy : 0.020244359970092773 nb_pixel_total : 51893 time to create 1 rle with old method : 0.06066632270812988 time for calcul the mask position with numpy : 0.020744800567626953 nb_pixel_total : 49641 time to create 1 rle with old method : 0.0578463077545166 time for calcul the mask position with numpy : 0.022478580474853516 nb_pixel_total : 234809 time to create 1 rle with new method : 0.12906742095947266 time for calcul the mask position with numpy : 0.02167987823486328 nb_pixel_total : 13512 time to create 1 rle with old method : 0.016684532165527344 time for calcul the mask position with numpy : 0.02161693572998047 nb_pixel_total : 27375 time to create 1 rle with old method : 0.03240323066711426 time for calcul the mask position with numpy : 0.021727561950683594 nb_pixel_total : 12321 time to create 1 rle with old method : 0.015047311782836914 time for calcul the mask position with numpy : 0.022076845169067383 nb_pixel_total : 243086 time to create 1 rle with new method : 0.13329553604125977 time for calcul the mask position with numpy : 0.021218061447143555 nb_pixel_total : 37956 time to create 1 rle with old method : 0.04595160484313965 time for calcul the mask position with numpy : 0.02171778678894043 nb_pixel_total : 67437 time to create 1 rle with old method : 0.07924437522888184 time for calcul the mask position with numpy : 0.02107548713684082 nb_pixel_total : 5934 time to create 1 rle with old method : 0.007318258285522461 time for calcul the mask position with numpy : 0.022080421447753906 nb_pixel_total : 169072 time to create 1 rle with new method : 0.13220524787902832 time for calcul the mask position with numpy : 0.021744966506958008 nb_pixel_total : 109756 time to create 1 rle with old method : 0.1301724910736084 create new chi : 1.8270070552825928 time to delete rle : 0.0013887882232666016 batch 1 Loaded 20 chid ids of type : 3726 Number RLEs to save : 14074 TO DO : save crop sub photo not yet done ! save time : 0.8578453063964844 nb_obj : 13 nb_hashtags : 5 time to prepare the origin masks : 4.180667877197266 time for calcul the mask position with numpy : 0.07352423667907715 nb_pixel_total : 6252026 time to create 1 rle with new method : 0.17429590225219727 time for calcul the mask position with numpy : 0.020910978317260742 nb_pixel_total : 25354 time to create 1 rle with old method : 0.029982805252075195 time for calcul the mask position with numpy : 0.020844697952270508 nb_pixel_total : 1262 time to create 1 rle with old method : 0.0022058486938476562 time for calcul the mask position with numpy : 0.02328205108642578 nb_pixel_total : 3637 time to create 1 rle with old method : 0.004487514495849609 time for calcul the mask position with numpy : 0.022816896438598633 nb_pixel_total : 211493 time to create 1 rle with new method : 0.13484525680541992 time for calcul the mask position with numpy : 0.02230548858642578 nb_pixel_total : 71221 time to create 1 rle with old method : 0.08285379409790039 time for calcul the mask position with numpy : 0.02009296417236328 nb_pixel_total : 21719 time to create 1 rle with old method : 0.02541828155517578 time for calcul the mask position with numpy : 0.02025914192199707 nb_pixel_total : 27435 time to create 1 rle with old method : 0.03207087516784668 time for calcul the mask position with numpy : 0.01963496208190918 nb_pixel_total : 6828 time to create 1 rle with old method : 0.008409500122070312 time for calcul the mask position with numpy : 0.02034783363342285 nb_pixel_total : 36386 time to create 1 rle with old method : 0.042716026306152344 time for calcul the mask position with numpy : 0.02094864845275879 nb_pixel_total : 119529 time to create 1 rle with old method : 0.13725709915161133 time for calcul the mask position with numpy : 0.0220794677734375 nb_pixel_total : 100749 time to create 1 rle with old method : 0.14180684089660645 time for calcul the mask position with numpy : 0.02162480354309082 nb_pixel_total : 131762 time to create 1 rle with old method : 0.15215301513671875 time for calcul the mask position with numpy : 0.02059006690979004 nb_pixel_total : 40839 time to create 1 rle with old method : 0.050104379653930664 create new chi : 1.4000818729400635 time to delete rle : 0.0011105537414550781 batch 1 Loaded 14 chid ids of type : 3726 Number RLEs to save : 11033 TO DO : save crop sub photo not yet done ! save time : 0.6071395874023438 nb_obj : 19 nb_hashtags : 4 time to prepare the origin masks : 4.380306959152222 time for calcul the mask position with numpy : 0.08838796615600586 nb_pixel_total : 5794149 time to create 1 rle with new method : 0.15405797958374023 time for calcul the mask position with numpy : 0.02423262596130371 nb_pixel_total : 110123 time to create 1 rle with old method : 0.12304854393005371 time for calcul the mask position with numpy : 0.0224306583404541 nb_pixel_total : 2538 time to create 1 rle with old method : 0.003037691116333008 time for calcul the mask position with numpy : 0.022556066513061523 nb_pixel_total : 25849 time to create 1 rle with old method : 0.029761552810668945 time for calcul the mask position with numpy : 0.02286529541015625 nb_pixel_total : 23066 time to create 1 rle with old method : 0.025891542434692383 time for calcul the mask position with numpy : 0.022783756256103516 nb_pixel_total : 41756 time to create 1 rle with old method : 0.04695248603820801 time for calcul the mask position with numpy : 0.023365497589111328 nb_pixel_total : 78211 time to create 1 rle with old method : 0.09806323051452637 time for calcul the mask position with numpy : 0.024122238159179688 nb_pixel_total : 38503 time to create 1 rle with old method : 0.0435488224029541 time for calcul the mask position with numpy : 0.0231320858001709 nb_pixel_total : 3146 time to create 1 rle with old method : 0.0036177635192871094 time for calcul the mask position with numpy : 0.022101879119873047 nb_pixel_total : 13047 time to create 1 rle with old method : 0.01524209976196289 time for calcul the mask position with numpy : 0.02215576171875 nb_pixel_total : 1538 time to create 1 rle with old method : 0.0019218921661376953 time for calcul the mask position with numpy : 0.022136449813842773 nb_pixel_total : 46666 time to create 1 rle with old method : 0.053252220153808594 time for calcul the mask position with numpy : 0.02331376075744629 nb_pixel_total : 86 time to create 1 rle with old method : 0.00029540061950683594 time for calcul the mask position with numpy : 0.02213597297668457 nb_pixel_total : 4689 time to create 1 rle with old method : 0.0055696964263916016 time for calcul the mask position with numpy : 0.023135662078857422 nb_pixel_total : 16951 time to create 1 rle with old method : 0.019728660583496094 time for calcul the mask position with numpy : 0.022804975509643555 nb_pixel_total : 9397 time to create 1 rle with old method : 0.010713338851928711 time for calcul the mask position with numpy : 0.023629426956176758 nb_pixel_total : 125693 time to create 1 rle with old method : 0.1454150676727295 time for calcul the mask position with numpy : 0.027484416961669922 nb_pixel_total : 1919 time to create 1 rle with old method : 0.002507925033569336 time for calcul the mask position with numpy : 0.029423952102661133 nb_pixel_total : 326296 time to create 1 rle with new method : 0.17554283142089844 time for calcul the mask position with numpy : 0.03237605094909668 nb_pixel_total : 386617 time to create 1 rle with new method : 0.16509270668029785 create new chi : 1.7121801376342773 time to delete rle : 0.0013022422790527344 batch 1 Loaded 20 chid ids of type : 3726 Number RLEs to save : 11721 TO DO : save crop sub photo not yet done ! save time : 0.6501407623291016 nb_obj : 8 nb_hashtags : 4 time to prepare the origin masks : 3.2118642330169678 time for calcul the mask position with numpy : 0.0823819637298584 nb_pixel_total : 6819905 time to create 1 rle with new method : 0.14243721961975098 time for calcul the mask position with numpy : 0.022960901260375977 nb_pixel_total : 13804 time to create 1 rle with old method : 0.01644730567932129 time for calcul the mask position with numpy : 0.021583080291748047 nb_pixel_total : 40439 time to create 1 rle with old method : 0.04854941368103027 time for calcul the mask position with numpy : 0.020657777786254883 nb_pixel_total : 70627 time to create 1 rle with old method : 0.08284235000610352 time for calcul the mask position with numpy : 0.021045446395874023 nb_pixel_total : 2012 time to create 1 rle with old method : 0.0024347305297851562 time for calcul the mask position with numpy : 0.022388219833374023 nb_pixel_total : 24900 time to create 1 rle with old method : 0.03128409385681152 time for calcul the mask position with numpy : 0.020359516143798828 nb_pixel_total : 43001 time to create 1 rle with old method : 0.049849748611450195 time for calcul the mask position with numpy : 0.02048206329345703 nb_pixel_total : 28933 time to create 1 rle with old method : 0.033530235290527344 time for calcul the mask position with numpy : 0.020277023315429688 nb_pixel_total : 6619 time to create 1 rle with old method : 0.0077745914459228516 create new chi : 0.683936595916748 time to delete rle : 0.0006952285766601562 batch 1 Loaded 9 chid ids of type : 3726 Number RLEs to save : 6272 TO DO : save crop sub photo not yet done ! save time : 0.4079322814941406 nb_obj : 24 nb_hashtags : 7 time to prepare the origin masks : 6.529685974121094 time for calcul the mask position with numpy : 0.13687396049499512 nb_pixel_total : 5894313 time to create 1 rle with new method : 0.8339333534240723 time for calcul the mask position with numpy : 0.025989055633544922 nb_pixel_total : 24581 time to create 1 rle with old method : 0.028855562210083008 time for calcul the mask position with numpy : 0.02648186683654785 nb_pixel_total : 33133 time to create 1 rle with old method : 0.03820037841796875 time for calcul the mask position with numpy : 0.02266716957092285 nb_pixel_total : 16188 time to create 1 rle with old method : 0.018955469131469727 time for calcul the mask position with numpy : 0.02355670928955078 nb_pixel_total : 50492 time to create 1 rle with old method : 0.05751180648803711 time for calcul the mask position with numpy : 0.023799896240234375 nb_pixel_total : 100627 time to create 1 rle with old method : 0.11690115928649902 time for calcul the mask position with numpy : 0.023290634155273438 nb_pixel_total : 130662 time to create 1 rle with old method : 0.14993786811828613 time for calcul the mask position with numpy : 0.02368330955505371 nb_pixel_total : 129044 time to create 1 rle with old method : 0.14644265174865723 time for calcul the mask position with numpy : 0.022709131240844727 nb_pixel_total : 27249 time to create 1 rle with old method : 0.032569169998168945 time for calcul the mask position with numpy : 0.02228689193725586 nb_pixel_total : 714 time to create 1 rle with old method : 0.0013513565063476562 time for calcul the mask position with numpy : 0.023143529891967773 nb_pixel_total : 4566 time to create 1 rle with old method : 0.005366325378417969 time for calcul the mask position with numpy : 0.022321701049804688 nb_pixel_total : 9017 time to create 1 rle with old method : 0.010077476501464844 time for calcul the mask position with numpy : 0.022102832794189453 nb_pixel_total : 40762 time to create 1 rle with old method : 0.045697689056396484 time for calcul the mask position with numpy : 0.0237581729888916 nb_pixel_total : 131212 time to create 1 rle with old method : 0.1470940113067627 time for calcul the mask position with numpy : 0.023026704788208008 nb_pixel_total : 19877 time to create 1 rle with old method : 0.023475170135498047 time for calcul the mask position with numpy : 0.023238658905029297 nb_pixel_total : 4181 time to create 1 rle with old method : 0.004927396774291992 time for calcul the mask position with numpy : 0.02384662628173828 nb_pixel_total : 59730 time to create 1 rle with old method : 0.06888270378112793 time for calcul the mask position with numpy : 0.024949312210083008 nb_pixel_total : 132442 time to create 1 rle with old method : 0.1542034149169922 time for calcul the mask position with numpy : 0.02899312973022461 nb_pixel_total : 24892 time to create 1 rle with old method : 0.043059587478637695 time for calcul the mask position with numpy : 0.026381969451904297 nb_pixel_total : 31598 time to create 1 rle with old method : 0.05875420570373535 time for calcul the mask position with numpy : 0.03001856803894043 nb_pixel_total : 2462 time to create 1 rle with old method : 0.0035316944122314453 time for calcul the mask position with numpy : 0.025705337524414062 nb_pixel_total : 10085 time to create 1 rle with old method : 0.012316703796386719 time for calcul the mask position with numpy : 0.02511143684387207 nb_pixel_total : 4507 time to create 1 rle with old method : 0.00568699836730957 time for calcul the mask position with numpy : 0.027059316635131836 nb_pixel_total : 160897 time to create 1 rle with new method : 0.17944884300231934 time for calcul the mask position with numpy : 0.026732683181762695 nb_pixel_total : 7009 time to create 1 rle with old method : 0.00871896743774414 create new chi : 2.9716098308563232 time to delete rle : 0.0016200542449951172 batch 1 Loaded 25 chid ids of type : 3726 Number RLEs to save : 15055 TO DO : save crop sub photo not yet done ! save time : 0.8390653133392334 nb_obj : 22 nb_hashtags : 8 time to prepare the origin masks : 6.112164258956909 time for calcul the mask position with numpy : 0.15179109573364258 nb_pixel_total : 5566086 time to create 1 rle with new method : 0.8120865821838379 time for calcul the mask position with numpy : 0.023553133010864258 nb_pixel_total : 8181 time to create 1 rle with old method : 0.010294437408447266 time for calcul the mask position with numpy : 0.026436805725097656 nb_pixel_total : 106050 time to create 1 rle with old method : 0.1190481185913086 time for calcul the mask position with numpy : 0.02300715446472168 nb_pixel_total : 5302 time to create 1 rle with old method : 0.006306648254394531 time for calcul the mask position with numpy : 0.023752450942993164 nb_pixel_total : 119899 time to create 1 rle with old method : 0.13541293144226074 time for calcul the mask position with numpy : 0.02390742301940918 nb_pixel_total : 43706 time to create 1 rle with old method : 0.05709409713745117 time for calcul the mask position with numpy : 0.025038957595825195 nb_pixel_total : 98259 time to create 1 rle with old method : 0.1214742660522461 time for calcul the mask position with numpy : 0.026421785354614258 nb_pixel_total : 43083 time to create 1 rle with old method : 0.05063438415527344 time for calcul the mask position with numpy : 0.02341628074645996 nb_pixel_total : 3354 time to create 1 rle with old method : 0.004044055938720703 time for calcul the mask position with numpy : 0.023897409439086914 nb_pixel_total : 58133 time to create 1 rle with old method : 0.06918787956237793 time for calcul the mask position with numpy : 0.022777557373046875 nb_pixel_total : 12721 time to create 1 rle with old method : 0.014917850494384766 time for calcul the mask position with numpy : 0.022171974182128906 nb_pixel_total : 5586 time to create 1 rle with old method : 0.006569385528564453 time for calcul the mask position with numpy : 0.022169113159179688 nb_pixel_total : 2099 time to create 1 rle with old method : 0.0025277137756347656 time for calcul the mask position with numpy : 0.02306365966796875 nb_pixel_total : 94733 time to create 1 rle with old method : 0.10755419731140137 time for calcul the mask position with numpy : 0.02359771728515625 nb_pixel_total : 3604 time to create 1 rle with old method : 0.00429534912109375 time for calcul the mask position with numpy : 0.0230257511138916 nb_pixel_total : 90711 time to create 1 rle with old method : 0.10151028633117676 time for calcul the mask position with numpy : 0.024207592010498047 nb_pixel_total : 305257 time to create 1 rle with new method : 0.15736722946166992 time for calcul the mask position with numpy : 0.0242006778717041 nb_pixel_total : 106441 time to create 1 rle with old method : 0.13610529899597168 time for calcul the mask position with numpy : 0.02776503562927246 nb_pixel_total : 351298 time to create 1 rle with new method : 0.17018413543701172 time for calcul the mask position with numpy : 0.02394247055053711 nb_pixel_total : 5486 time to create 1 rle with old method : 0.006902933120727539 time for calcul the mask position with numpy : 0.02534317970275879 nb_pixel_total : 10735 time to create 1 rle with old method : 0.012934207916259766 time for calcul the mask position with numpy : 0.024891138076782227 nb_pixel_total : 2510 time to create 1 rle with old method : 0.0030829906463623047 time for calcul the mask position with numpy : 0.025379657745361328 nb_pixel_total : 7006 time to create 1 rle with old method : 0.008472681045532227 create new chi : 2.8728201389312744 time to delete rle : 0.0015833377838134766 batch 1 Loaded 23 chid ids of type : 3726 Number RLEs to save : 15537 TO DO : save crop sub photo not yet done ! save time : 0.8801424503326416 nb_obj : 15 nb_hashtags : 6 time to prepare the origin masks : 3.9719038009643555 time for calcul the mask position with numpy : 0.07979035377502441 nb_pixel_total : 6107215 time to create 1 rle with new method : 0.15294575691223145 time for calcul the mask position with numpy : 0.020737171173095703 nb_pixel_total : 7395 time to create 1 rle with old method : 0.009233474731445312 time for calcul the mask position with numpy : 0.021997690200805664 nb_pixel_total : 98119 time to create 1 rle with old method : 0.11674642562866211 time for calcul the mask position with numpy : 0.023249387741088867 nb_pixel_total : 750 time to create 1 rle with old method : 0.0010302066802978516 time for calcul the mask position with numpy : 0.02317523956298828 nb_pixel_total : 114907 time to create 1 rle with old method : 0.13656258583068848 time for calcul the mask position with numpy : 0.022511720657348633 nb_pixel_total : 4381 time to create 1 rle with old method : 0.005462169647216797 time for calcul the mask position with numpy : 0.0233767032623291 nb_pixel_total : 9546 time to create 1 rle with old method : 0.017052412033081055 time for calcul the mask position with numpy : 0.024011611938476562 nb_pixel_total : 11004 time to create 1 rle with old method : 0.018875598907470703 time for calcul the mask position with numpy : 0.02388167381286621 nb_pixel_total : 25965 time to create 1 rle with old method : 0.032034873962402344 time for calcul the mask position with numpy : 0.023418903350830078 nb_pixel_total : 271607 time to create 1 rle with new method : 0.14310336112976074 time for calcul the mask position with numpy : 0.021823883056640625 nb_pixel_total : 2357 time to create 1 rle with old method : 0.0029239654541015625 time for calcul the mask position with numpy : 0.02202582359313965 nb_pixel_total : 2349 time to create 1 rle with old method : 0.003052234649658203 time for calcul the mask position with numpy : 0.022189855575561523 nb_pixel_total : 24345 time to create 1 rle with old method : 0.029088497161865234 time for calcul the mask position with numpy : 0.023648977279663086 nb_pixel_total : 41291 time to create 1 rle with old method : 0.06675434112548828 time for calcul the mask position with numpy : 0.022840023040771484 nb_pixel_total : 117370 time to create 1 rle with old method : 0.14557790756225586 time for calcul the mask position with numpy : 0.024156808853149414 nb_pixel_total : 211639 time to create 1 rle with new method : 0.14806079864501953 create new chi : 1.5021347999572754 time to delete rle : 0.0016243457794189453 batch 1 Loaded 16 chid ids of type : 3726 Number RLEs to save : 9813 TO DO : save crop sub photo not yet done ! save time : 0.5558173656463623 map_output_result : {1330514538: (0.0, 'Should be the crop_list due to order', 0.0), 1330514533: (0.0, 'Should be the crop_list due to order', 0.0), 1330501519: (0.0, 'Should be the crop_list due to order', 0.0), 1330501508: (0.0, 'Should be the crop_list due to order', 0.0), 1330501454: (0.0, 'Should be the crop_list due to order', 0.0), 1330501453: (0.0, 'Should be the crop_list due to order', 0.0), 1330501377: (0.0, 'Should be the crop_list due to order', 0.0), 1330501376: (0.0, 'Should be the crop_list due to order', 0.0), 1330501258: (0.0, 'Should be the crop_list due to order', 0.0), 1330501257: (0.0, 'Should be the crop_list due to order', 0.0), 1330501208: (0.0, 'Should be the crop_list due to order', 0.0), 1330501207: (0.0, 'Should be the crop_list due to order', 0.0), 1330501140: (0.0, 'Should be the crop_list due to order', 0.0), 1330501137: (0.0, 'Should be the crop_list due to order', 0.0), 1330500943: (0.0, 'Should be the crop_list due to order', 0.0), 1330500930: (0.0, 'Should be the crop_list due to order', 0.0), 1330500898: (0.0, 'Should be the crop_list due to order', 0.0), 1330500895: (0.0, 'Should be the crop_list due to order', 0.0), 1330500893: (0.0, 'Should be the crop_list due to order', 0.0), 1330500891: (0.0, 'Should be the crop_list due to order', 0.0)} 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 [1330514538, 1330514533, 1330501519, 1330501508, 1330501454, 1330501453, 1330501377, 1330501376, 1330501258, 1330501257, 1330501208, 1330501207, 1330501140, 1330501137, 1330500943, 1330500930, 1330500898, 1330500895, 1330500893, 1330500891] Looping around the photos to save general results len do output : 20 /1330514538.Didn't retrieve data . /1330514533.Didn't retrieve data . /1330501519.Didn't retrieve data . /1330501508.Didn't retrieve data . /1330501454.Didn't retrieve data . /1330501453.Didn't retrieve data . /1330501377.Didn't retrieve data . /1330501376.Didn't retrieve data . /1330501258.Didn't retrieve data . /1330501257.Didn't retrieve data . /1330501208.Didn't retrieve data . /1330501207.Didn't retrieve data . /1330501140.Didn't retrieve data . /1330501137.Didn't retrieve data . /1330500943.Didn't retrieve data . /1330500930.Didn't retrieve data . /1330500898.Didn't retrieve data . /1330500895.Didn't retrieve data . /1330500893.Didn't retrieve data . /1330500891.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 ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514538', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514533', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501519', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501508', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501454', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501453', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501377', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501376', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501258', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501257', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501208', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501207', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501140', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501137', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500943', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500930', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500898', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500895', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500893', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500891', None, None, None, None, None, '2502549') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 60 time used for this insertion : 0.013100624084472656 save_final save missing photos in datou_result : time spend for datou_step_exec : 147.56590056419373 time spend to save output : 0.013996362686157227 total time spend for step 5 : 147.57989692687988 step6:crop_condition Tue Feb 4 14:25:43 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 some photos are not treated, begin crop_condition Loading chi in step crop with photo_hashtag_type : 3726 Loading chi in step crop for list_pids : 20 ! batch 1 Loaded 344 chid ids of type : 3726 begin to crop the class : teint_dans_la_masse param for this class : {'min_score': 0.7} filtre for class : teint_dans_la_masse hashtag_id of this class : 2107752385 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 17 About to insert : list_path_to_insert length 17 new photo from crops ! About to upload 17 photos upload in portfolio : 4869462 init cache_photo without model_param we have 17 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675551_4167303 we have uploaded 17 photos in the portfolio 4869462 time of upload the photos Elapsed time : 5.124894857406616 we have finished the crop for the class : teint_dans_la_masse begin to crop the class : autre_refus param for this class : {'min_score': 0.5} filtre for class : autre_refus hashtag_id of this class : 2107752406 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 20 About to insert : list_path_to_insert length 20 new photo from crops ! About to upload 20 photos upload in portfolio : 4869462 init cache_photo without model_param we have 20 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675574_4167303 we have uploaded 20 photos in the portfolio 4869462 time of upload the photos Elapsed time : 5.568870544433594 we have finished the crop for the class : autre_refus begin to crop the class : carton_gris param for this class : {'min_score': 0.5} filtre for class : carton_gris hashtag_id of this class : 2107753020 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 7 About to insert : list_path_to_insert length 7 new photo from crops ! About to upload 7 photos upload in portfolio : 4869462 init cache_photo without model_param we have 7 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675584_4167303 we have uploaded 7 photos in the portfolio 4869462 time of upload the photos Elapsed time : 2.1635851860046387 we have finished the crop for the class : carton_gris begin to crop the class : cartonnette param for this class : {'min_score': 0.5} filtre for class : cartonnette hashtag_id of this class : 702398920 Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 4869462 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675589_4167303 we have uploaded 3 photos in the portfolio 4869462 time of upload the photos Elapsed time : 0.9577264785766602 we have finished the crop for the class : cartonnette begin to crop the class : carton_brun param for this class : {'min_score': 0.7} filtre for class : carton_brun hashtag_id of this class : 2107753024 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 18 About to insert : list_path_to_insert length 18 new photo from crops ! About to upload 18 photos upload in portfolio : 4869462 init cache_photo without model_param we have 18 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675596_4167303 we have uploaded 18 photos in the portfolio 4869462 time of upload the photos Elapsed time : 4.291621446609497 we have finished the crop for the class : carton_brun begin to crop the class : plastique param for this class : {'min_score': 0.5} filtre for class : plastique hashtag_id of this class : 492725882 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 6 About to insert : list_path_to_insert length 6 new photo from crops ! About to upload 6 photos upload in portfolio : 4869462 init cache_photo without model_param we have 6 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675605_4167303 we have uploaded 6 photos in the portfolio 4869462 time of upload the photos Elapsed time : 1.4522850513458252 we have finished the crop for the class : plastique begin to crop the class : kraft param for this class : {'min_score': 0.5} filtre for class : kraft hashtag_id of this class : 493202403 Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 4869462 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738675609_4167303 we have uploaded 2 photos in the portfolio 4869462 time of upload the photos Elapsed time : 0.9785923957824707 we have finished the crop for the class : kraft begin to crop the class : metal param for this class : {'min_score': 0.5} filtre for class : metal hashtag_id of this class : 492628673 delete rles for these photos Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1330514538, 1330514533, 1330501519, 1330501508, 1330501454, 1330501453, 1330501377, 1330501376, 1330501258, 1330501257, 1330501208, 1330501207, 1330501140, 1330501137, 1330500943, 1330500930, 1330500898, 1330500895, 1330500893, 1330500891] Looping around the photos to save general results len do output : 73 /1334564099Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564100Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564101Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564102Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564103Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564106Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564107Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564108Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564109Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564110Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564111Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564112Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564113Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564114Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564115Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564126Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564127Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564128Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564129Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564130Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564131Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564132Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564133Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564134Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564135Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564137Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564138Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564139Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564140Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564142Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564144Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564145Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564146Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564147Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564151Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564152Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564153Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564154Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564155Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564156Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564160Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564161Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564172Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564173Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564174Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564175Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564176Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564177Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564178Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564179Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564180Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564181Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564182Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564183Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564184Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564185Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564186Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564187Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564189Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564201Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564202Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564206Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564234Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334564235Didn'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 ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514538', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514533', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501519', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501508', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501454', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501453', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501377', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501376', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501258', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501257', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501208', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501207', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501140', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501137', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500943', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500930', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500898', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500895', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500893', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500891', None, None, None, None, None, '2502549') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 239 time used for this insertion : 0.9156296253204346 save_final save missing photos in datou_result : time spend for datou_step_exec : 72.64795923233032 time spend to save output : 0.917548418045044 total time spend for step 6 : 73.56550765037537 step7:ventilate_hashtags_in_portfolio Tue Feb 4 14:26:56 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 ! To do loadFromThcl(), then load ParamDescType : thcl2725 thcls : [{'id': 2725, 'mtr_user_id': 31, 'name': 'learn_qualipapia_rle_210302_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,environnement,kraft,metal,papier,plastique', 'svm_portfolios_learning': '3460440,3460441,3460446,3460434,3460439,3467416,3460442,3460443,3486028,3460445', 'photo_hashtag_type': 3410, 'photo_desc_type': 5186, 'type_classification': 'caffe', 'hashtag_id_list': '2107753024,2107753020,2107752385,2107752406,702398920,493012381,493202403,492628673,492668766,492725882'}] thcl {'id': 2725, 'mtr_user_id': 31, 'name': 'learn_qualipapia_rle_210302_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,environnement,kraft,metal,papier,plastique', 'svm_portfolios_learning': '3460440,3460441,3460446,3460434,3460439,3467416,3460442,3460443,3486028,3460445', 'photo_hashtag_type': 3410, 'photo_desc_type': 5186, 'type_classification': 'caffe', 'hashtag_id_list': '2107753024,2107753020,2107752385,2107752406,702398920,493012381,493202403,492628673,492668766,492725882'} Update svm_hashtag_type_desc : 5186 Iterating over portfolio : 19816777 get user id for portfolio 19816777 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`=19816777 AND mptpi.`type`=3726 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('Carton_brun','environnement','papier','metal','Carton_gris','mal_croppe','autre_refus','Teint_Dans_La_Masse','flou','plastique','kraft','cartonnette')) 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") 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`=19816777 AND mptpi.`type`=3726 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('Carton_brun','environnement','papier','metal','Carton_gris','mal_croppe','autre_refus','Teint_Dans_La_Masse','flou','plastique','kraft','cartonnette')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/19817252,19817253,19817254,19817255,19817256,19817257,19817258,19817259,19817260,19817261,19817262,19817263?tags=Carton_brun,Teint_Dans_La_Masse,Carton_gris,kraft,autre_refus,papier,flou,environnement,plastique,cartonnette,metal,mal_croppe Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1330514538, 1330514533, 1330501519, 1330501508, 1330501454, 1330501453, 1330501377, 1330501376, 1330501258, 1330501257, 1330501208, 1330501207, 1330501140, 1330501137, 1330500943, 1330500930, 1330500898, 1330500895, 1330500893, 1330500891] Looping around the photos to save general results len do output : 1 /19816777. 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 ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514538', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514533', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501519', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501508', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501454', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501453', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501377', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501376', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501258', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501257', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501208', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501207', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501140', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501137', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500943', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500930', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500898', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500895', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500893', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500891', None, None, None, None, None, '2502549') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.015569210052490234 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.4392621517181396 time spend to save output : 0.015874624252319336 total time spend for step 7 : 1.455136775970459 step8:final Tue Feb 4 14:26:58 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 : {1330514538: ('0.09927487305279824',), 1330514533: ('0.09927487305279824',), 1330501519: ('0.09927487305279824',), 1330501508: ('0.09927487305279824',), 1330501454: ('0.09927487305279824',), 1330501453: ('0.09927487305279824',), 1330501377: ('0.09927487305279824',), 1330501376: ('0.09927487305279824',), 1330501258: ('0.09927487305279824',), 1330501257: ('0.09927487305279824',), 1330501208: ('0.09927487305279824',), 1330501207: ('0.09927487305279824',), 1330501140: ('0.09927487305279824',), 1330501137: ('0.09927487305279824',), 1330500943: ('0.09927487305279824',), 1330500930: ('0.09927487305279824',), 1330500898: ('0.09927487305279824',), 1330500895: ('0.09927487305279824',), 1330500893: ('0.09927487305279824',), 1330500891: ('0.09927487305279824',)} new output for save of step final : {1330514538: ('0.09927487305279824',), 1330514533: ('0.09927487305279824',), 1330501519: ('0.09927487305279824',), 1330501508: ('0.09927487305279824',), 1330501454: ('0.09927487305279824',), 1330501453: ('0.09927487305279824',), 1330501377: ('0.09927487305279824',), 1330501376: ('0.09927487305279824',), 1330501258: ('0.09927487305279824',), 1330501257: ('0.09927487305279824',), 1330501208: ('0.09927487305279824',), 1330501207: ('0.09927487305279824',), 1330501140: ('0.09927487305279824',), 1330501137: ('0.09927487305279824',), 1330500943: ('0.09927487305279824',), 1330500930: ('0.09927487305279824',), 1330500898: ('0.09927487305279824',), 1330500895: ('0.09927487305279824',), 1330500893: ('0.09927487305279824',), 1330500891: ('0.09927487305279824',)} [1330514538, 1330514533, 1330501519, 1330501508, 1330501454, 1330501453, 1330501377, 1330501376, 1330501258, 1330501257, 1330501208, 1330501207, 1330501140, 1330501137, 1330500943, 1330500930, 1330500898, 1330500895, 1330500893, 1330500891] Looping around the photos to save general results len do output : 20 /1330514538.Didn't retrieve data . /1330514533.Didn't retrieve data . /1330501519.Didn't retrieve data . /1330501508.Didn't retrieve data . /1330501454.Didn't retrieve data . /1330501453.Didn't retrieve data . /1330501377.Didn't retrieve data . /1330501376.Didn't retrieve data . /1330501258.Didn't retrieve data . /1330501257.Didn't retrieve data . /1330501208.Didn't retrieve data . /1330501207.Didn't retrieve data . /1330501140.Didn't retrieve data . /1330501137.Didn't retrieve data . /1330500943.Didn't retrieve data . /1330500930.Didn't retrieve data . /1330500898.Didn't retrieve data . /1330500895.Didn't retrieve data . /1330500893.Didn't retrieve data . /1330500891.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 ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514538', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514533', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501519', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501508', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501454', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501453', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501377', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501376', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501258', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501257', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501208', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501207', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501140', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501137', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500943', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500930', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500898', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500895', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500893', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500891', None, None, None, None, None, '2502549') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 60 time used for this insertion : 0.016459226608276367 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.18725037574768066 time spend to save output : 0.017384767532348633 total time spend for step 8 : 0.2046351432800293 step9:velours_tree Tue Feb 4 14:26:58 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 : 0.03649425506591797 time spend to save output : 4.410743713378906e-05 total time spend for step 9 : 0.03653836250305176 step10:send_mail_cod Tue Feb 4 14:26:58 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 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_P19816777_04-02-2025_14_26_58.pdf 19817255 change filename to text .change filename to text .change filename to text .change filename to text .imagette198172551738675618 19817256 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette198172561738675619 19817258 imagette198172581738675620 19817260 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette198172601738675620 19817261 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette198172611738675622 19817262 imagette198172621738675626 19817263 imagette198172631738675626 19817252 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette198172521738675626 19817253 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette198172531738675627 19817254 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 .imagette198172541738675628 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=19816777 and hashtag_type = 3726 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/19817252,19817253,19817254,19817255,19817256,19817257,19817258,19817259,19817260,19817261,19817262,19817263?tags=Carton_brun,Teint_Dans_La_Masse,Carton_gris,kraft,autre_refus,papier,flou,environnement,plastique,cartonnette,metal,mal_croppe your option no_mail is active, we will not send the real mail to your client args[1330514538] : ('0.09927487305279824',) no score found for photo 1330514538 We are sending mail with results at report@fotonower.com args[1330514533] : ('0.09927487305279824',) no score found for photo 1330514533 We are sending mail with results at report@fotonower.com args[1330501519] : ('0.09927487305279824',) no score found for photo 1330501519 We are sending mail with results at report@fotonower.com args[1330501508] : ('0.09927487305279824',) no score found for photo 1330501508 We are sending mail with results at report@fotonower.com args[1330501454] : ('0.09927487305279824',) no score found for photo 1330501454 We are sending mail with results at report@fotonower.com args[1330501453] : ('0.09927487305279824',) no score found for photo 1330501453 We are sending mail with results at report@fotonower.com args[1330501377] : ('0.09927487305279824',) no score found for photo 1330501377 We are sending mail with results at report@fotonower.com args[1330501376] : ('0.09927487305279824',) no score found for photo 1330501376 We are sending mail with results at report@fotonower.com args[1330501258] : ('0.09927487305279824',) no score found for photo 1330501258 We are sending mail with results at report@fotonower.com args[1330501257] : ('0.09927487305279824',) no score found for photo 1330501257 We are sending mail with results at report@fotonower.com args[1330501208] : ('0.09927487305279824',) no score found for photo 1330501208 We are sending mail with results at report@fotonower.com args[1330501207] : ('0.09927487305279824',) no score found for photo 1330501207 We are sending mail with results at report@fotonower.com args[1330501140] : ('0.09927487305279824',) no score found for photo 1330501140 We are sending mail with results at report@fotonower.com args[1330501137] : ('0.09927487305279824',) no score found for photo 1330501137 We are sending mail with results at report@fotonower.com args[1330500943] : ('0.09927487305279824',) no score found for photo 1330500943 We are sending mail with results at report@fotonower.com args[1330500930] : ('0.09927487305279824',) no score found for photo 1330500930 We are sending mail with results at report@fotonower.com args[1330500898] : ('0.09927487305279824',) no score found for photo 1330500898 We are sending mail with results at report@fotonower.com args[1330500895] : ('0.09927487305279824',) no score found for photo 1330500895 We are sending mail with results at report@fotonower.com args[1330500893] : ('0.09927487305279824',) no score found for photo 1330500893 We are sending mail with results at report@fotonower.com args[1330500891] : ('0.09927487305279824',) no score found for photo 1330500891 We are sending mail with results at report@fotonower.com refus_total : 0.09927487305279824 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=19816777 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1330514590,1330500943,1330501508,1330501519,1330514765,1330514538,1330514543,1330514547,1330514576,1330514578,1330500713,1330514606,1330514615,1330514625,1330514636,1330514643,1330514761,1330514690,1330514697,1330514703) Found this number of photos: 20 begin to download photo : 1330514590 begin to download photo : 1330514538 begin to download photo : 1330500713 begin to download photo : 1330514643 download finish for photo 1330514643 download finish for photo 1330500713 begin to download photo : 1330514761 begin to download photo : 1330514606 download finish for photo 1330514538 begin to download photo : 1330514543 download finish for photo 1330514590 begin to download photo : 1330500943 download finish for photo 1330514761 begin to download photo : 1330514690 download finish for photo 1330514606 begin to download photo : 1330514615 download finish for photo 1330514543 begin to download photo : 1330514547 download finish for photo 1330500943 begin to download photo : 1330501508 download finish for photo 1330514690 begin to download photo : 1330514697 download finish for photo 1330514615 begin to download photo : 1330514625 download finish for photo 1330501508 begin to download photo : 1330501519 download finish for photo 1330514547 begin to download photo : 1330514576 download finish for photo 1330514697 begin to download photo : 1330514703 download finish for photo 1330514625 begin to download photo : 1330514636 download finish for photo 1330501519 begin to download photo : 1330514765 download finish for photo 1330514576 begin to download photo : 1330514578 download finish for photo 1330514765 download finish for photo 1330514636 download finish for photo 1330514703 download finish for photo 1330514578 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816777_04-02-2025_14_26_58.pdf results_Auto_P19816777_04-02-2025_14_26_58.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816777_04-02-2025_14_26_58.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3459','19816777','results_Auto_P19816777_04-02-2025_14_26_58.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816777_04-02-2025_14_26_58.pdf','pdf','','1.7','0.09927487305279824') Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1330514538, 1330514533, 1330501519, 1330501508, 1330501454, 1330501453, 1330501377, 1330501376, 1330501258, 1330501257, 1330501208, 1330501207, 1330501140, 1330501137, 1330500943, 1330500930, 1330500898, 1330500895, 1330500893, 1330500891] 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 ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514538', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514533', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501519', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501508', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501454', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501453', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501377', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501376', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501258', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501257', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501208', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501207', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501140', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501137', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500943', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500930', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500898', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500895', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500893', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500891', None, None, None, None, None, '2502549') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.014787912368774414 save_final save missing photos in datou_result : time spend for datou_step_exec : 15.872394800186157 time spend to save output : 0.015187263488769531 total time spend for step 10 : 15.887582063674927 step11:split_time_score Tue Feb 4 14:27:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array 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 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('13', 43),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 21012025 19816777 Nombre de photos uploadées : 43 / 23040 (0%) 21012025 19816777 Nombre de photos taguées (types de déchets): 0 / 43 (0%) 21012025 19816777 Nombre de photos taguées (volume) : 0 / 43 (0%) elapsed_time : load_data_split_time_score 1.6689300537109375e-06 elapsed_time : order_list_meta_photo_and_scores 5.4836273193359375e-06 ??????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0020699501037597656 elapsed_time : insert_dashboard_record_day_entry 0.024008750915527344 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 : 0.2230084638414699 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20179390_03-02-2025_23_32_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179390 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 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 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20179390 AND mptpi.`type`=4200 To do find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20179392_04-02-2025_05_17_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179392 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179394 order by id desc limit 1 Qualite : 0.05387208105761745 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816755_23-01-2025_07_31_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816755 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816755 AND mptpi.`type`=4207 To do Qualite : 0.0760774019933227 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816756_24-01-2025_22_35_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816756 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816756 AND mptpi.`type`=4207 To do Qualite : 0.06329979943409107 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816757_23-01-2025_06_04_06.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816757 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816757 AND mptpi.`type`=4207 To do Qualite : 0.06172958751434479 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816758_23-01-2025_05_17_15.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816758 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816758 AND mptpi.`type`=4207 To do Qualite : 0.03727838864711241 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816759_23-01-2025_04_19_06.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816759 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816759 AND mptpi.`type`=4207 To do Qualite : 0.05087485454670065 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816760_23-01-2025_04_22_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816760 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816760 AND mptpi.`type`=4207 To do Qualite : 0.0789340382514203 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816761_23-01-2025_06_35_43.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816761 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 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 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816761 AND mptpi.`type`=4203 To do Qualite : 0.22282188265931363 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816762_22-01-2025_14_02_25.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816762 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`=19816762 AND mptpi.`type`=3594 To do find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816763_02-02-2025_01_47_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816763 order by id desc limit 1 Qualite : 0.09843363108892862 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816764_23-01-2025_06_24_26.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816764 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 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 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816764 AND mptpi.`type`=4200 To do find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816765_27-01-2025_00_53_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816765 order by id desc limit 1 Qualite : 0.06255063392022828 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816766_23-01-2025_06_41_40.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816766 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 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 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816766 AND mptpi.`type`=4200 To do find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816767_23-01-2025_06_40_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816767 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816768_24-01-2025_17_20_36.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816768 order by id desc limit 1 Qualite : 0.05556085476725516 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816769_25-01-2025_05_07_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816769 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 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 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816769 AND mptpi.`type`=4200 To do find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816770_25-01-2025_00_47_25.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816770 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816771_24-01-2025_17_12_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816771 order by id desc limit 1 Qualite : 0.20721409641531513 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816772_22-01-2025_12_38_26.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816772 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`=19816772 AND mptpi.`type`=3594 To do find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816773_03-02-2025_20_00_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816773 order by id desc limit 1 Qualite : 0.3234784143682751 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816774_23-01-2025_06_53_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816774 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 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 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816774 AND mptpi.`type`=4200 To do Qualite : 0.060778303124716805 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816775_23-01-2025_06_51_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816775 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816775 AND mptpi.`type`=4207 To do Qualite : 0.0724126681632548 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816776_23-01-2025_06_14_13.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816776 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 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 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816776 AND mptpi.`type`=4207 To do Qualite : 0.08682079789744311 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816777_04-02-2025_14_26_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816777 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816777 AND mptpi.`type`=3726 To do Qualite : 0.0 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P19816778_25-01-2025_03_51_59.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 19816778 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 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 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=19816778 AND mptpi.`type`=4209 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179396 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179397 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179399 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179400 order by id desc limit 1 Qualite : 0.221696952475692 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20179401_03-02-2025_21_17_27.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179401 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`=20179401 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179403 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179404 order by id desc limit 1 Qualite : 0.13038829167398272 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20179405_04-02-2025_04_44_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179405 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! 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 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20179405 AND mptpi.`type`=3327 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179407 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179408 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179410 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179411 order by id desc limit 1 Qualite : 0.07917954679979532 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20179412_03-02-2025_16_36_06.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179412 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20179412 AND mptpi.`type`=3726 To do Qualite : 0.2637435983266763 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20179414_03-02-2025_19_55_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179414 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`=20179414 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20179415 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'21012025': {'nb_upload': 43, '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 [1330514538, 1330514533, 1330501519, 1330501508, 1330501454, 1330501453, 1330501377, 1330501376, 1330501258, 1330501257, 1330501208, 1330501207, 1330501140, 1330501137, 1330500943, 1330500930, 1330500898, 1330500895, 1330500893, 1330500891] Looping around the photos to save general results len do output : 1 /19816777Didn'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 ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514538', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330514533', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501519', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501508', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501454', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501453', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501377', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501376', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501258', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501257', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501208', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501207', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501140', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330501137', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500943', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500930', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500898', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500895', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500893', None, None, None, None, None, '2502549') ('3459', None, None, None, None, None, None, None, '2502549') ('3459', None, '1330500891', None, None, None, None, None, '2502549') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.015377283096313477 save_final save missing photos in datou_result : time spend for datou_step_exec : 13.994512796401978 time spend to save output : 0.015685319900512695 total time spend for step 11 : 14.01019811630249 caffe_path_current : About to save ! 2 After save, about to update current ! update_current_state 354.49user 93.52system 8:03.71elapsed 92%CPU (0avgtext+0avgdata 6495616maxresident)k 189032inputs+278024outputs (1181major+16788488minor)pagefaults 0swaps