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 3669453' -s traitement_sts -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 : 3911723 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 no input labels no input values updating current state to 1 list_input_json: {} Current got : datou_id : 4746, datou_cur_ids : ['3669453'] with mtr_portfolio_ids : ['26290610'] and first list_photo_ids : [] new path : /proc/3911723/ 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 ! 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 14102 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13182 split_time_score_with_photo have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13182 split_time_score_with_photo is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 14090 launch_next_datou_same_portfolio is not consistent : 1 used against 0 in the step definition ! 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 1 of step 13182 doesn't seem to be define in the database( WARNING : type of input 0 of step 14090 doesn't seem to be define in the database( WARNING : type of output 2 of step 14102 doesn't seem to be define in the database( WARNING : type of input 2 of step 13182 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, split_time_score_with_photo, launch_next_datou_same_portfolio over limit max, limiting to limit_max 150 list_input_json : {} origin We have 1 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 53 ; length of list_pids : 53 ; length of list_args : 53 time to download the photos : 15.734790563583374 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 : 3 step1:mask_detect Fri Sep 5 10:41:43 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 Beginning of datou step mask_detect ! save_polygon : False begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 9797 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-09-05 10:41:46.109514: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-09-05 10:41:46.136578: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-09-05 10:41:46.138796: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f1b68000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-09-05 10:41:46.138859: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-09-05 10:41:46.142830: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-09-05 10:41:46.271735: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x230f3fa0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-09-05 10:41:46.271775: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-09-05 10:41:46.272672: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-05 10:41:46.272979: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-05 10:41:46.274999: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-05 10:41:46.277042: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-05 10:41:46.277373: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-05 10:41:46.279530: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-05 10:41:46.280572: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-05 10:41:46.284822: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-05 10:41:46.286291: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-05 10:41:46.286368: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-05 10:41:46.287106: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-05 10:41:46.287122: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-05 10:41:46.287131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-05 10:41:46.288391: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9066 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-09-05 10:41:46.511823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-05 10:41:46.511936: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-05 10:41:46.511953: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-05 10:41:46.511968: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-05 10:41:46.511982: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-05 10:41:46.511996: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-05 10:41:46.512009: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-05 10:41:46.512024: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-05 10:41:46.513190: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-05 10:41:46.514301: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-05 10:41:46.514333: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-05 10:41:46.514349: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-05 10:41:46.514363: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-05 10:41:46.514377: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-05 10:41:46.514391: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-05 10:41:46.514419: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-05 10:41:46.514434: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-05 10:41:46.515569: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-05 10:41:46.515604: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-05 10:41:46.515612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-05 10:41:46.515619: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-05 10:41:46.516817: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9066 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 : thcl3916 thcls : [{'id': 3916, 'mtr_user_id': 31, 'name': 'learn_mask_pancarte_110625_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,badge,pancarte', 'svm_portfolios_learning': '0,0,0', 'photo_hashtag_type': 5014, 'photo_desc_type': 6109, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0'}] thcl {'id': 3916, 'mtr_user_id': 31, 'name': 'learn_mask_pancarte_110625_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,badge,pancarte', 'svm_portfolios_learning': '0,0,0', 'photo_hashtag_type': 5014, 'photo_desc_type': 6109, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0'} Update svm_hashtag_type_desc : 6109 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (6109, 'learn_mask_pancarte_110625_2', 16384, 25088, 'learn_mask_pancarte_110625_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2025, 6, 11, 13, 23, 2), datetime.datetime(2025, 6, 11, 13, 23, 2)) {'thcl': {'id': 3916, 'mtr_user_id': 31, 'name': 'learn_mask_pancarte_110625_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,badge,pancarte', 'svm_portfolios_learning': '0,0,0', 'photo_hashtag_type': 5014, 'photo_desc_type': 6109, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0'}, 'list_hashtags': ['background', 'badge', 'pancarte'], 'list_hashtags_csv': 'background,badge,pancarte', 'svm_portfolios_learning': '0,0,0', 'photo_hashtag_type': 5014, 'svm_hashtag_type_desc': 6109, 'photo_desc_type': 6109, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'badge', 'pancarte'] 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_mask_pancarte_110625_2 NUM_CLASSES 3 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_mask_pancarte_110625_2 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-09-05 10:42:04.685481: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-05 10:42:04.855659: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_mask_pancarte_110625_2 /data/models_weight/learn_mask_pancarte_110625_2/mask_model.h5 size_local : 255878432 size in s3 : 255878432 create time local : 2025-06-11 13:03:21 create time in s3 : 2025-06-11 11:03:27 mask_model.h5 already exist and didn't need to update list_images length : 53 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 2448.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 2448.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 2448.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.88750 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 2448.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.67734 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 2448.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 2448.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 2.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -108.70000 max: 143.97500 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.95000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.82109 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 144.91250 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.76250 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 2.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.59453 max: 150.25234 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.63750 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.83281 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 148.66250 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.55547 max: 149.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.55547 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.51250 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 0 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 246.00000 molded_images shape: (1, 640, 640, 3) min: -121.83672 max: 140.28750 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.58281 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 3120.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 144.66250 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.35000 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 2 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 149.66250 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 1 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 146.22500 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (4160, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 11) min: 0.00000 max: 4160.00000 nb d'objets trouves : 1 Detection mask done ! Trying to reset tf kernel 3912434 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1530 tf kernel not reseted sub process len(results) : 53 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 53 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 : 6819 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl3916 Catched exception ! Connect or reconnect ! thcls : [{'id': 3916, 'mtr_user_id': 31, 'name': 'learn_mask_pancarte_110625_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,badge,pancarte', 'svm_portfolios_learning': '0,0,0', 'photo_hashtag_type': 5014, 'photo_desc_type': 6109, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0'}] thcl {'id': 3916, 'mtr_user_id': 31, 'name': 'learn_mask_pancarte_110625_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,badge,pancarte', 'svm_portfolios_learning': '0,0,0', 'photo_hashtag_type': 5014, 'photo_desc_type': 6109, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0'} Update svm_hashtag_type_desc : 6109 ['background', 'badge', 'pancarte'] time for calcul the mask position with numpy : 0.009229183197021484 nb_pixel_total : 520801 time to create 1 rle with new method : 0.01707291603088379 length of segment : 975 time for calcul the mask position with numpy : 0.00045108795166015625 nb_pixel_total : 32833 time to create 1 rle with old method : 0.037049055099487305 length of segment : 216 time for calcul the mask position with numpy : 0.0004973411560058594 nb_pixel_total : 37559 time to create 1 rle with old method : 0.042059898376464844 length of segment : 227 time for calcul the mask position with numpy : 0.001369476318359375 nb_pixel_total : 108859 time to create 1 rle with old method : 0.11825227737426758 length of segment : 279 time for calcul the mask position with numpy : 0.004091978073120117 nb_pixel_total : 336550 time to create 1 rle with new method : 0.010102510452270508 length of segment : 597 time for calcul the mask position with numpy : 0.00046324729919433594 nb_pixel_total : 30086 time to create 1 rle with old method : 0.04295969009399414 length of segment : 234 time for calcul the mask position with numpy : 0.0013401508331298828 nb_pixel_total : 93875 time to create 1 rle with old method : 0.11041426658630371 length of segment : 311 time for calcul the mask position with numpy : 0.00045680999755859375 nb_pixel_total : 24935 time to create 1 rle with old method : 0.0268704891204834 length of segment : 237 time for calcul the mask position with numpy : 0.0065343379974365234 nb_pixel_total : 392961 time to create 1 rle with new method : 0.019351720809936523 length of segment : 719 time for calcul the mask position with numpy : 0.007662296295166016 nb_pixel_total : 570640 time to create 1 rle with new method : 0.01394510269165039 length of segment : 758 time for calcul the mask position with numpy : 0.0008513927459716797 nb_pixel_total : 63084 time to create 1 rle with old method : 0.07274222373962402 length of segment : 369 time for calcul the mask position with numpy : 0.0004401206970214844 nb_pixel_total : 26195 time to create 1 rle with old method : 0.029574871063232422 length of segment : 305 time for calcul the mask position with numpy : 2.1803526878356934 nb_pixel_total : 3318958 time to create 1 rle with new method : 0.40257787704467773 length of segment : 1831 time for calcul the mask position with numpy : 0.03611350059509277 nb_pixel_total : 1936002 time to create 1 rle with new method : 1.0300462245941162 length of segment : 1482 time for calcul the mask position with numpy : 0.08581995964050293 nb_pixel_total : 2389655 time to create 1 rle with new method : 0.743157148361206 length of segment : 1775 time for calcul the mask position with numpy : 0.001398324966430664 nb_pixel_total : 27584 time to create 1 rle with old method : 0.03098154067993164 length of segment : 401 time for calcul the mask position with numpy : 0.003682851791381836 nb_pixel_total : 185755 time to create 1 rle with new method : 0.009069204330444336 length of segment : 579 time for calcul the mask position with numpy : 0.0003674030303955078 nb_pixel_total : 22106 time to create 1 rle with old method : 0.02519845962524414 length of segment : 205 time for calcul the mask position with numpy : 0.0022232532501220703 nb_pixel_total : 102105 time to create 1 rle with old method : 0.11197614669799805 length of segment : 375 time for calcul the mask position with numpy : 0.0013816356658935547 nb_pixel_total : 85508 time to create 1 rle with old method : 0.09331846237182617 length of segment : 398 time for calcul the mask position with numpy : 0.003467082977294922 nb_pixel_total : 295658 time to create 1 rle with new method : 0.007053375244140625 length of segment : 587 time for calcul the mask position with numpy : 0.01889514923095703 nb_pixel_total : 935087 time to create 1 rle with new method : 0.06105303764343262 length of segment : 1120 time for calcul the mask position with numpy : 0.0015869140625 nb_pixel_total : 104968 time to create 1 rle with old method : 0.11229538917541504 length of segment : 449 time for calcul the mask position with numpy : 0.0023217201232910156 nb_pixel_total : 151362 time to create 1 rle with new method : 0.0036611557006835938 length of segment : 471 time for calcul the mask position with numpy : 0.0013310909271240234 nb_pixel_total : 89486 time to create 1 rle with old method : 0.09560489654541016 length of segment : 274 time for calcul the mask position with numpy : 0.004039764404296875 nb_pixel_total : 164663 time to create 1 rle with new method : 0.005030155181884766 length of segment : 466 time for calcul the mask position with numpy : 0.002969980239868164 nb_pixel_total : 149036 time to create 1 rle with old method : 0.17794179916381836 length of segment : 449 time for calcul the mask position with numpy : 0.0006501674652099609 nb_pixel_total : 18068 time to create 1 rle with old method : 0.020779132843017578 length of segment : 196 time for calcul the mask position with numpy : 0.003620147705078125 nb_pixel_total : 274882 time to create 1 rle with new method : 0.007718324661254883 length of segment : 495 time for calcul the mask position with numpy : 0.0026116371154785156 nb_pixel_total : 153753 time to create 1 rle with new method : 0.0046346187591552734 length of segment : 334 time for calcul the mask position with numpy : 0.0006496906280517578 nb_pixel_total : 44681 time to create 1 rle with old method : 0.051012516021728516 length of segment : 215 time for calcul the mask position with numpy : 0.0007524490356445312 nb_pixel_total : 47454 time to create 1 rle with old method : 0.0563969612121582 length of segment : 264 time for calcul the mask position with numpy : 0.002062082290649414 nb_pixel_total : 122069 time to create 1 rle with old method : 0.14221596717834473 length of segment : 368 time for calcul the mask position with numpy : 0.005604267120361328 nb_pixel_total : 291252 time to create 1 rle with new method : 0.010114669799804688 length of segment : 739 time for calcul the mask position with numpy : 0.0012784004211425781 nb_pixel_total : 63041 time to create 1 rle with old method : 0.07391715049743652 length of segment : 317 time for calcul the mask position with numpy : 0.004639148712158203 nb_pixel_total : 175150 time to create 1 rle with new method : 0.005669593811035156 length of segment : 550 time for calcul the mask position with numpy : 0.0025610923767089844 nb_pixel_total : 161340 time to create 1 rle with new method : 0.005051851272583008 length of segment : 442 time for calcul the mask position with numpy : 0.0014340877532958984 nb_pixel_total : 77963 time to create 1 rle with old method : 0.08743977546691895 length of segment : 480 time for calcul the mask position with numpy : 0.0016982555389404297 nb_pixel_total : 77143 time to create 1 rle with old method : 0.08870506286621094 length of segment : 394 time for calcul the mask position with numpy : 0.0012540817260742188 nb_pixel_total : 91682 time to create 1 rle with old method : 0.10608649253845215 length of segment : 313 time for calcul the mask position with numpy : 0.002151966094970703 nb_pixel_total : 137434 time to create 1 rle with old method : 0.15843677520751953 length of segment : 479 time for calcul the mask position with numpy : 0.0009338855743408203 nb_pixel_total : 47101 time to create 1 rle with old method : 0.05495762825012207 length of segment : 260 time for calcul the mask position with numpy : 0.02251887321472168 nb_pixel_total : 1186432 time to create 1 rle with new method : 0.0398709774017334 length of segment : 671 time for calcul the mask position with numpy : 0.010200262069702148 nb_pixel_total : 526416 time to create 1 rle with new method : 0.012294292449951172 length of segment : 751 time for calcul the mask position with numpy : 0.002203702926635742 nb_pixel_total : 139041 time to create 1 rle with old method : 0.15412282943725586 length of segment : 457 time for calcul the mask position with numpy : 0.0009171962738037109 nb_pixel_total : 59693 time to create 1 rle with old method : 0.06847739219665527 length of segment : 303 time for calcul the mask position with numpy : 0.000682830810546875 nb_pixel_total : 39742 time to create 1 rle with old method : 0.04617953300476074 length of segment : 162 time for calcul the mask position with numpy : 0.0029838085174560547 nb_pixel_total : 262172 time to create 1 rle with new method : 0.0048482418060302734 length of segment : 336 time for calcul the mask position with numpy : 0.0016450881958007812 nb_pixel_total : 97913 time to create 1 rle with old method : 0.10940146446228027 length of segment : 392 time for calcul the mask position with numpy : 0.0031778812408447266 nb_pixel_total : 167715 time to create 1 rle with new method : 0.006296634674072266 length of segment : 572 time for calcul the mask position with numpy : 0.0009322166442871094 nb_pixel_total : 48866 time to create 1 rle with old method : 0.05365443229675293 length of segment : 231 time for calcul the mask position with numpy : 0.0032427310943603516 nb_pixel_total : 154422 time to create 1 rle with new method : 0.005946159362792969 length of segment : 625 time for calcul the mask position with numpy : 0.0009677410125732422 nb_pixel_total : 45647 time to create 1 rle with old method : 0.05133819580078125 length of segment : 200 time for calcul the mask position with numpy : 0.0014367103576660156 nb_pixel_total : 71902 time to create 1 rle with old method : 0.07944583892822266 length of segment : 517 time for calcul the mask position with numpy : 0.005852222442626953 nb_pixel_total : 338590 time to create 1 rle with new method : 0.010326147079467773 length of segment : 705 time for calcul the mask position with numpy : 0.0006017684936523438 nb_pixel_total : 26025 time to create 1 rle with old method : 0.029358863830566406 length of segment : 240 time for calcul the mask position with numpy : 0.00045990943908691406 nb_pixel_total : 32021 time to create 1 rle with old method : 0.03669118881225586 length of segment : 211 time for calcul the mask position with numpy : 0.0009603500366210938 nb_pixel_total : 55906 time to create 1 rle with old method : 0.061983346939086914 length of segment : 216 time for calcul the mask position with numpy : 0.0031175613403320312 nb_pixel_total : 273010 time to create 1 rle with new method : 0.004503011703491211 length of segment : 378 time for calcul the mask position with numpy : 0.18510746955871582 nb_pixel_total : 3136327 time to create 1 rle with new method : 0.8882222175598145 length of segment : 1319 time for calcul the mask position with numpy : 0.09101533889770508 nb_pixel_total : 2099672 time to create 1 rle with new method : 0.22592639923095703 length of segment : 1597 time for calcul the mask position with numpy : 0.01428079605102539 nb_pixel_total : 1189064 time to create 1 rle with new method : 0.28389525413513184 length of segment : 718 time for calcul the mask position with numpy : 0.002848386764526367 nb_pixel_total : 142804 time to create 1 rle with old method : 0.17919683456420898 length of segment : 357 time for calcul the mask position with numpy : 0.003616809844970703 nb_pixel_total : 276037 time to create 1 rle with new method : 0.007868766784667969 length of segment : 562 time for calcul the mask position with numpy : 0.003472566604614258 nb_pixel_total : 231314 time to create 1 rle with new method : 0.0057981014251708984 length of segment : 565 time for calcul the mask position with numpy : 0.0027570724487304688 nb_pixel_total : 203889 time to create 1 rle with new method : 0.004637956619262695 length of segment : 549 time for calcul the mask position with numpy : 0.0017979145050048828 nb_pixel_total : 106637 time to create 1 rle with old method : 0.11780643463134766 length of segment : 362 time for calcul the mask position with numpy : 0.003404378890991211 nb_pixel_total : 252186 time to create 1 rle with new method : 0.005864143371582031 length of segment : 680 time for calcul the mask position with numpy : 0.0031468868255615234 nb_pixel_total : 182751 time to create 1 rle with new method : 0.0054509639739990234 length of segment : 605 time for calcul the mask position with numpy : 0.0021047592163085938 nb_pixel_total : 99818 time to create 1 rle with old method : 0.11248302459716797 length of segment : 524 time for calcul the mask position with numpy : 0.001262664794921875 nb_pixel_total : 86639 time to create 1 rle with old method : 0.09990954399108887 length of segment : 341 time for calcul the mask position with numpy : 0.0010273456573486328 nb_pixel_total : 42260 time to create 1 rle with old method : 0.049477338790893555 length of segment : 217 time for calcul the mask position with numpy : 0.010962724685668945 nb_pixel_total : 566321 time to create 1 rle with new method : 0.016863584518432617 length of segment : 733 time for calcul the mask position with numpy : 0.00130462646484375 nb_pixel_total : 91443 time to create 1 rle with old method : 0.10437965393066406 length of segment : 199 time for calcul the mask position with numpy : 0.001998424530029297 nb_pixel_total : 78143 time to create 1 rle with old method : 0.08563756942749023 length of segment : 502 time for calcul the mask position with numpy : 0.016434907913208008 nb_pixel_total : 1126813 time to create 1 rle with new method : 0.03252983093261719 length of segment : 1304 time for calcul the mask position with numpy : 0.00046181678771972656 nb_pixel_total : 22166 time to create 1 rle with old method : 0.026433229446411133 length of segment : 165 time for calcul the mask position with numpy : 0.0006191730499267578 nb_pixel_total : 17601 time to create 1 rle with old method : 0.020344257354736328 length of segment : 268 time for calcul the mask position with numpy : 0.003752470016479492 nb_pixel_total : 112991 time to create 1 rle with old method : 0.12256669998168945 length of segment : 459 time for calcul the mask position with numpy : 0.001207113265991211 nb_pixel_total : 91701 time to create 1 rle with old method : 0.10227584838867188 length of segment : 268 time for calcul the mask position with numpy : 0.007983684539794922 nb_pixel_total : 475927 time to create 1 rle with new method : 0.010246515274047852 length of segment : 417 time for calcul the mask position with numpy : 0.002490997314453125 nb_pixel_total : 242301 time to create 1 rle with new method : 0.005154848098754883 length of segment : 466 time for calcul the mask position with numpy : 0.010585546493530273 nb_pixel_total : 693861 time to create 1 rle with new method : 0.02964615821838379 length of segment : 1055 time for calcul the mask position with numpy : 0.002298116683959961 nb_pixel_total : 140305 time to create 1 rle with old method : 0.18242406845092773 length of segment : 507 time for calcul the mask position with numpy : 0.00811004638671875 nb_pixel_total : 568347 time to create 1 rle with new method : 0.015398740768432617 length of segment : 1036 time for calcul the mask position with numpy : 0.0006103515625 nb_pixel_total : 23067 time to create 1 rle with old method : 0.02665424346923828 length of segment : 158 time for calcul the mask position with numpy : 0.008505821228027344 nb_pixel_total : 496446 time to create 1 rle with new method : 0.015468358993530273 length of segment : 985 time for calcul the mask position with numpy : 0.0009722709655761719 nb_pixel_total : 55916 time to create 1 rle with old method : 0.06451940536499023 length of segment : 240 time for calcul the mask position with numpy : 0.021129369735717773 nb_pixel_total : 1533021 time to create 1 rle with new method : 0.301149845123291 length of segment : 1491 time for calcul the mask position with numpy : 0.010519027709960938 nb_pixel_total : 737285 time to create 1 rle with new method : 0.058562278747558594 length of segment : 543 time spent for convertir_results : 14.132752895355225 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 101 chid ids of type : 5014 Number RLEs to save : 0 save missing photos in datou_result : time spend for datou_step_exec : 171.18859004974365 time spend to save output : 0.0765390396118164 total time spend for step 1 : 171.26512908935547 step2:split_time_score_with_photo Fri Sep 5 10:44:34 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 Inconsistent list_input size and nb_input information : 0 vs 2 VR 22-3-18 : For now we do not clean correctly the datou structure ----- Debut du copier-coller des param necessaire pour fonction main de STS ----- begin split time score 2022-04-13 10:29:59 0 Catched exception ! Connect or reconnect ! TODO : Insert select and so on Catched exception ! Connect or reconnect ! Begin split_port_in_batch_balle thcls : [{'id': 3379, 'mtr_user_id': 31, 'name': 'learn_classif_flux_maj_generique_effnet_v2_s_02062022', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'aluminium,ela,film_pedb,flux_dev,jrm,pcm,pcnc,pehd_pp,pet_clair,refus,tapis_vide', 'svm_portfolios_learning': '5515864,5515840,5515844,5515850,6244400,6237996,6237998,5515847,5515841,5515868,5515866', 'photo_hashtag_type': 4374, 'photo_desc_type': 5680, 'type_classification': 'tf_classification2', 'hashtag_id_list': '493546845,492741797,2107760237,2107760238,495916461,560181804,1284539308,2107760239,2107755846,538914404,2107748999'}] 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'}] (('40', 5), ('48', 5), ('49', 3), ('01', 1), ('02', 6), ('19', 2), ('20', 5), ('44', 8), ('45', 1), ('15', 4), ('16', 4), ('17', 1), ('59', 1), ('00', 3), ('05', 1), ('06', 3)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 28082025 26290610 Nombre de photos uploadées : 53 / 23040 (0%) 28082025 26290610 Nombre de photos taguées (types de déchets): 0 / 53 (0%) 28082025 26290610 Nombre de photos taguées (volume) : 0 / 53 (0%) elapsed_time : load_data_split_time_score 2.6226043701171875e-06 elapsed_time : order_list_meta_photo_and_scores 7.152557373046875e-06 ????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0020210742950439453 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.20817971229553223 ***** BEGIN SPLIT BY DARK ***** To DO 08/10/21 elapsed_time : SPLIT_BY_DARK 0.005359172821044922 ***** END SPLIT BY DARK ***** ***** BEGIN SPLIT TIME ***** `````````````````````````````````````````````````````list printed: [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9, 10, 11, 12], [13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26], [27, 28, 29, 30, 31, 32, 33, 34, 35], [36, 37, 38, 39, 40, 41, 42, 43, 44], [45, 46, 47, 48], [49, 50, 51, 52]] forced_hashtag: entrant force hashtag to entrant elapsed_time : SPLIT_TIME 0.0064961910247802734 ***** END SPLIT TIME ***** NUMBER BATCH : 8 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['entrant'] ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_entrant:{'day': '28082025', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 33.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20250828_084007.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_entrant:{'day': '28082025', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 54.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20250828_104817.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_entrant:{'day': '28082025', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 54.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20250828_110147.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_entrant:{'day': '28082025', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 49.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20250828_111942.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_entrant:{'day': '28082025', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 53.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20250828_114414.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_entrant:{'day': '28082025', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 88.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20250828_121536.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_entrant:{'day': '28082025', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 47.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20250828_205950.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_entrant:{'day': '28082025', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 29.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20250828_210556.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 8 Catched exception ! Connect or reconnect ! list_same_port_ids : [26292108] find same portfolio which already exist 26292108 , we will use it list_same_port_ids : [26292109] find same portfolio which already exist 26292109 , we will use it list_same_port_ids : [26292110] find same portfolio which already exist 26292110 , we will use it list_same_port_ids : [26292111] find same portfolio which already exist 26292111 , we will use it list_same_port_ids : [26292112] find same portfolio which already exist 26292112 , we will use it list_same_port_ids : [26294096] find same portfolio which already exist 26294096 , we will use it list_same_port_ids : [26307203] find same portfolio which already exist 26307203 , we will use it list_same_port_ids : [26307204] find same portfolio which already exist 26307204 , we will use it batch 1 Loaded 1 chid ids of type : 5014 Qualite : 0.2808311243033833 # 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 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 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 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`=26292108 AND mptpi.`type`=4855 To do batch 1 Loaded 4 chid ids of type : 5014 # 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 ! TODO 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`=26292109 To do batch 1 Loaded 2 chid ids of type : 5014 Qualite : 0.3235829256287251 # 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 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 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 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`=26292110 AND mptpi.`type`=4855 To do batch 1 Loaded 1 chid ids of type : 5014 Qualite : 0.5794230840386216 # 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 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 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 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`=26292111 AND mptpi.`type`=4855 To do batch 1 Loaded 2 chid ids of type : 5014 Qualite : 0.548860349869327 # 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 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 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 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`=26292112 AND mptpi.`type`=4855 To do batch 1 Loaded 6 chid ids of type : 5014 # 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 ! TODO 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`=26294096 To do batch 1 Loaded 1 chid ids of type : 5014 Qualite : 0.2938870389409992 # 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 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 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 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`=26307203 AND mptpi.`type`=4855 To do batch 1 Loaded 1 chid ids of type : 5014 Qualite : 0.39387006977059996 # 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 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 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 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`=26307204 AND mptpi.`type`=4855 To do elapsed_time : count_nb_balles_and_create_portfolio 1.8190953731536865 # DISPLAY ALL COLLECTED DATA : {'28082025': {'nb_upload': 53, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} ------ Fin du Copier-Coller ------ ---------- ONE RESULT --------- ([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9, 10, 11, 12], [13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26], [27, 28, 29, 30, 31, 32, 33, 34, 35], [36, 37, 38, 39, 40, 41, 42, 43, 44], [45, 46, 47, 48], [49, 50, 51, 52]], {'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)]}, {26292108: {'list_of_photos': [1379845557, 1379845574, 1379845594, 1379845977, 1379845997], 'hashtag': 'entrant'}, 26292109: {'list_of_photos': [1379853564, 1379853596, 1379853628, 1379853896, 1379853901, 1379853905, 1379853907, 1379853909], 'hashtag': 'entrant'}, 26292110: {'list_of_photos': [1379853910, 1379854001, 1379854035, 1379854068, 1379854100, 1379854131, 1379854162], 'hashtag': 'entrant'}, 26292111: {'list_of_photos': [1379854307, 1379854319, 1379854338, 1379854358, 1379854373, 1379854377, 1379854444], 'hashtag': 'entrant'}, 26292112: {'list_of_photos': [1379854447, 1379854451, 1379854454, 1379854457, 1379854459, 1379854474, 1379854478, 1379854482, 1379854483], 'hashtag': 'entrant'}, 26294096: {'list_of_photos': [1379877912, 1379877966, 1379878000, 1379878055, 1379878098, 1379878127, 1379878419, 1379878482, 1379878546], 'hashtag': 'entrant'}, 26307203: {'list_of_photos': [1379966986, 1379966990, 1379966995, 1379966997], 'hashtag': 'entrant'}, 26307204: {'list_of_photos': [1379966998, 1379966999, 1379967027, 1379967029], 'hashtag': 'entrant'}}, {2107760258: 53}, {'amount_uploaded_and_tagged': {'28082025': {'nb_upload': 53, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)]}, 'count': {'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)]}}) ---------- END de ONE RESULT ---------- Suppression des photos Telecharges Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score_with_photo we use saveGeneral [1379967029, 1379967027, 1379966999, 1379966998, 1379966997, 1379966995, 1379966990, 1379966986, 1379878546, 1379878482, 1379878419, 1379878127, 1379878098, 1379878055, 1379878000, 1379877966, 1379877912, 1379854483, 1379854482, 1379854478, 1379854474, 1379854459, 1379854457, 1379854454, 1379854451, 1379854447, 1379854444, 1379854377, 1379854373, 1379854358, 1379854338, 1379854319, 1379854307, 1379854162, 1379854131, 1379854100, 1379854068, 1379854035, 1379854001, 1379853910, 1379853909, 1379853907, 1379853905, 1379853901, 1379853896, 1379853628, 1379853596, 1379853564, 1379845997, 1379845977, 1379845594, 1379845574, 1379845557] Looping around the photos to save general results len do output : 5 /[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9, 10, 11, 12], [13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26], [27, 28, 29, 30, 31, 32, 33, 34, 35], [36, 37, 38, 39, 40, 41, 42, 43, 44], [45, 46, 47, 48], [49, 50, 51, 52]] /{'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)]} /{26292108: {'list_of_photos': [1379845557, 1379845574, 1379845594, 1379845977, 1379845997], 'hashtag': 'entrant'}, 26292109: {'list_of_photos': [1379853564, 1379853596, 1379853628, 1379853896, 1379853901, 1379853905, 1379853907, 1379853909], 'hashtag': 'entrant'}, 26292110: {'list_of_photos': [1379853910, 1379854001, 1379854035, 1379854068, 1379854100, 1379854131, 1379854162], 'hashtag': 'entrant'}, 26292111: {'list_of_photos': [1379854307, 1379854319, 1379854338, 1379854358, 1379854373, 1379854377, 1379854444], 'hashtag': 'entrant'}, 26292112: {'list_of_photos': [1379854447, 1379854451, 1379854454, 1379854457, 1379854459, 1379854474, 1379854478, 1379854482, 1379854483], 'hashtag': 'entrant'}, 26294096: {'list_of_photos': [1379877912, 1379877966, 1379878000, 1379878055, 1379878098, 1379878127, 1379878419, 1379878482, 1379878546], 'hashtag': 'entrant'}, 26307203: {'list_of_photos': [1379966986, 1379966990, 1379966995, 1379966997], 'hashtag': 'entrant'}, 26307204: {'list_of_photos': [1379966998, 1379966999, 1379967027, 1379967029], 'hashtag': 'entrant'}} /{2107760258: 53} /{'amount_uploaded_and_tagged': {'28082025': {'nb_upload': 53, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)]}, 'count': {'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)]}} before output type Managing all output in save final without adding information in the mtr_datou_result ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379967029', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379967027', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966999', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966998', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966997', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966995', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966990', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966986', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878546', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878482', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878419', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878127', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878098', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878055', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878000', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379877966', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379877912', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854483', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854482', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854478', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854474', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854459', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854457', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854454', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854451', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854447', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854444', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854377', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854373', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854358', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854338', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854319', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854307', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854162', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854131', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854100', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854068', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854035', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854001', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853910', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853909', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853907', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853905', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853901', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853896', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853628', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853596', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853564', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379845997', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379845977', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379845594', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379845574', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379845557', None, None, None, None, None, '3669453') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 53 time used for this insertion : 0.0198519229888916 save_final save missing photos in datou_result : time spend for datou_step_exec : 5.945362091064453 time spend to save output : 0.020254850387573242 total time spend for step 2 : 5.965616941452026 step3:launch_next_datou_same_portfolio Fri Sep 5 10:44:40 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 VR 22-3-18 : For now we do not clean correctly the datou structure Number of photos for current instance 53 (0 missing) for 53 photos in portfolios Datou 4746 on portfolios [26290610] is finished : launching datou 4148 on portfolios [26290610] new current id 3669462 on portfolio 26290610 for datou 4148 Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : launch_next_datou_same_portfolio we use saveGeneral [1379967029, 1379967027, 1379966999, 1379966998, 1379966997, 1379966995, 1379966990, 1379966986, 1379878546, 1379878482, 1379878419, 1379878127, 1379878098, 1379878055, 1379878000, 1379877966, 1379877912, 1379854483, 1379854482, 1379854478, 1379854474, 1379854459, 1379854457, 1379854454, 1379854451, 1379854447, 1379854444, 1379854377, 1379854373, 1379854358, 1379854338, 1379854319, 1379854307, 1379854162, 1379854131, 1379854100, 1379854068, 1379854035, 1379854001, 1379853910, 1379853909, 1379853907, 1379853905, 1379853901, 1379853896, 1379853628, 1379853596, 1379853564, 1379845997, 1379845977, 1379845594, 1379845574, 1379845557] Looping around the photos to save general results len do output : 0 before output type Managing all output in save final without adding information in the mtr_datou_result ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379967029', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379967027', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966999', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966998', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966997', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966995', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966990', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379966986', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878546', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878482', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878419', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878127', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878098', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878055', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379878000', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379877966', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379877912', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854483', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854482', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854478', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854474', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854459', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854457', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854454', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854451', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854447', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854444', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854377', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854373', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854358', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854338', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854319', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854307', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854162', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854131', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854100', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854068', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854035', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379854001', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853910', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853909', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853907', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853905', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853901', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853896', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853628', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853596', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379853564', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379845997', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379845977', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379845594', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379845574', None, None, None, None, None, '3669453') ('4746', None, None, None, None, None, None, None, '3669453') ('4746', '26290610', '1379845557', None, None, None, None, None, '3669453') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 53 time used for this insertion : 0.020602703094482422 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.013248205184936523 time spend to save output : 0.02098822593688965 total time spend for step 3 : 0.03423643112182617 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 53 set_done_treatment 45.82user 137.11system 3:16.71elapsed 93%CPU (0avgtext+0avgdata 5749364maxresident)k 1752608inputs+309936outputs (59432major+9895299minor)pagefaults 0swaps