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 3623797' -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 : 3496361 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 : ['3623797'] with mtr_portfolio_ids : ['26290610'] and first list_photo_ids : [] new path : /proc/3496361/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 36 ; length of list_pids : 36 ; length of list_args : 36 time to download the photos : 7.79465913772583 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 Thu Aug 28 12:30:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 10603 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-08-28 12:30:06.536136: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-08-28 12:30:06.568518: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-08-28 12:30:06.570732: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f7c74000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-08-28 12:30:06.570794: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-08-28 12:30:06.575518: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-08-28 12:30:06.757649: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x37c01120 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-08-28 12:30:06.757718: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-08-28 12:30:06.759242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-08-28 12:30:06.759694: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-08-28 12:30:06.763703: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-08-28 12:30:06.774561: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-08-28 12:30:06.775927: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-08-28 12:30:06.782881: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-08-28 12:30:06.786306: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-08-28 12:30:06.800217: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-08-28 12:30:06.802205: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-08-28 12:30:06.802665: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-08-28 12:30:06.803682: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-08-28 12:30:06.803701: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-08-28 12:30:06.803712: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-08-28 12:30:06.805758: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9821 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-08-28 12:30:07.093325: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-08-28 12:30:07.093419: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-08-28 12:30:07.093441: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-08-28 12:30:07.093460: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-08-28 12:30:07.093480: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-08-28 12:30:07.093502: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-08-28 12:30:07.093520: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-08-28 12:30:07.093539: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-08-28 12:30:07.095158: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-08-28 12:30:07.096949: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-08-28 12:30:07.097009: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-08-28 12:30:07.097041: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-08-28 12:30:07.097071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-08-28 12:30:07.097099: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-08-28 12:30:07.097128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-08-28 12:30:07.097151: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-08-28 12:30:07.097201: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-08-28 12:30:07.098988: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-08-28 12:30:07.099045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-08-28 12:30:07.099062: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-08-28 12:30:07.099077: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-08-28 12:30:07.100881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9821 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 : [] 2025-08-28 12:30:24.005704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-08-28 12:30:24.212567: 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 : 36 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 3496767 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1866 tf kernel not reseted sub process len(results) : 36 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 36 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 : 6823 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 : 0.0030965805053710938 nb_pixel_total : 122043 time to create 1 rle with old method : 0.1451709270477295 length of segment : 368 time for calcul the mask position with numpy : 0.006060123443603516 nb_pixel_total : 291246 time to create 1 rle with new method : 0.018809795379638672 length of segment : 739 time for calcul the mask position with numpy : 0.0013761520385742188 nb_pixel_total : 63040 time to create 1 rle with old method : 0.07832837104797363 length of segment : 317 time for calcul the mask position with numpy : 0.003871917724609375 nb_pixel_total : 175142 time to create 1 rle with new method : 0.008423089981079102 length of segment : 550 time for calcul the mask position with numpy : 0.0028502941131591797 nb_pixel_total : 161364 time to create 1 rle with new method : 0.00839996337890625 length of segment : 442 time for calcul the mask position with numpy : 0.0013866424560546875 nb_pixel_total : 77965 time to create 1 rle with old method : 0.09038090705871582 length of segment : 480 time for calcul the mask position with numpy : 0.0019736289978027344 nb_pixel_total : 77148 time to create 1 rle with old method : 0.09334611892700195 length of segment : 394 time for calcul the mask position with numpy : 0.0014119148254394531 nb_pixel_total : 91681 time to create 1 rle with old method : 0.10956048965454102 length of segment : 313 time for calcul the mask position with numpy : 0.002530813217163086 nb_pixel_total : 137433 time to create 1 rle with old method : 0.1615757942199707 length of segment : 479 time for calcul the mask position with numpy : 0.0010449886322021484 nb_pixel_total : 47101 time to create 1 rle with old method : 0.054900407791137695 length of segment : 260 time for calcul the mask position with numpy : 0.02580547332763672 nb_pixel_total : 1186399 time to create 1 rle with new method : 1.0360875129699707 length of segment : 671 time for calcul the mask position with numpy : 0.015266180038452148 nb_pixel_total : 526416 time to create 1 rle with new method : 0.026465654373168945 length of segment : 751 time for calcul the mask position with numpy : 0.0025854110717773438 nb_pixel_total : 139041 time to create 1 rle with old method : 0.15264606475830078 length of segment : 457 time for calcul the mask position with numpy : 0.001176595687866211 nb_pixel_total : 59691 time to create 1 rle with old method : 0.06913304328918457 length of segment : 303 time for calcul the mask position with numpy : 0.0007965564727783203 nb_pixel_total : 39743 time to create 1 rle with old method : 0.04571890830993652 length of segment : 162 time for calcul the mask position with numpy : 0.004591941833496094 nb_pixel_total : 262150 time to create 1 rle with new method : 0.007700443267822266 length of segment : 336 time for calcul the mask position with numpy : 0.002198934555053711 nb_pixel_total : 97911 time to create 1 rle with old method : 0.11204314231872559 length of segment : 392 time for calcul the mask position with numpy : 0.00468897819519043 nb_pixel_total : 181208 time to create 1 rle with new method : 0.012411117553710938 length of segment : 597 time for calcul the mask position with numpy : 0.0013356208801269531 nb_pixel_total : 48866 time to create 1 rle with old method : 0.05701017379760742 length of segment : 231 time for calcul the mask position with numpy : 0.004106760025024414 nb_pixel_total : 154433 time to create 1 rle with new method : 0.009575843811035156 length of segment : 625 time for calcul the mask position with numpy : 0.0009353160858154297 nb_pixel_total : 45647 time to create 1 rle with old method : 0.0533444881439209 length of segment : 200 time for calcul the mask position with numpy : 0.0016627311706542969 nb_pixel_total : 71902 time to create 1 rle with old method : 0.14890503883361816 length of segment : 517 time for calcul the mask position with numpy : 0.007863759994506836 nb_pixel_total : 338607 time to create 1 rle with new method : 0.01738572120666504 length of segment : 705 time for calcul the mask position with numpy : 0.0007257461547851562 nb_pixel_total : 26023 time to create 1 rle with old method : 0.0315852165222168 length of segment : 240 time for calcul the mask position with numpy : 0.0007596015930175781 nb_pixel_total : 32023 time to create 1 rle with old method : 0.03878378868103027 length of segment : 211 time for calcul the mask position with numpy : 0.0011141300201416016 nb_pixel_total : 55907 time to create 1 rle with old method : 0.06968235969543457 length of segment : 216 time for calcul the mask position with numpy : 0.004917144775390625 nb_pixel_total : 273006 time to create 1 rle with new method : 0.007222414016723633 length of segment : 378 time for calcul the mask position with numpy : 1.1884355545043945 nb_pixel_total : 3136531 time to create 1 rle with new method : 0.8082304000854492 length of segment : 1318 time for calcul the mask position with numpy : 0.040924787521362305 nb_pixel_total : 2099804 time to create 1 rle with new method : 0.5247097015380859 length of segment : 1597 time for calcul the mask position with numpy : 0.01572728157043457 nb_pixel_total : 1189051 time to create 1 rle with new method : 0.02451777458190918 length of segment : 718 time for calcul the mask position with numpy : 0.001650094985961914 nb_pixel_total : 142804 time to create 1 rle with old method : 0.1586322784423828 length of segment : 357 time for calcul the mask position with numpy : 0.002930879592895508 nb_pixel_total : 276035 time to create 1 rle with new method : 0.006020069122314453 length of segment : 562 time for calcul the mask position with numpy : 0.0026290416717529297 nb_pixel_total : 231312 time to create 1 rle with new method : 0.005923748016357422 length of segment : 565 time for calcul the mask position with numpy : 0.002317190170288086 nb_pixel_total : 203895 time to create 1 rle with new method : 0.004688262939453125 length of segment : 549 time for calcul the mask position with numpy : 0.0015416145324707031 nb_pixel_total : 106636 time to create 1 rle with old method : 0.11705541610717773 length of segment : 362 time for calcul the mask position with numpy : 0.003100156784057617 nb_pixel_total : 252198 time to create 1 rle with new method : 0.006531238555908203 length of segment : 680 time for calcul the mask position with numpy : 0.002931356430053711 nb_pixel_total : 182756 time to create 1 rle with new method : 0.006228208541870117 length of segment : 605 time for calcul the mask position with numpy : 0.0020475387573242188 nb_pixel_total : 99819 time to create 1 rle with old method : 0.11185169219970703 length of segment : 524 time for calcul the mask position with numpy : 0.0011982917785644531 nb_pixel_total : 86640 time to create 1 rle with old method : 0.0970907211303711 length of segment : 341 time for calcul the mask position with numpy : 0.0006453990936279297 nb_pixel_total : 42260 time to create 1 rle with old method : 0.04756975173950195 length of segment : 217 time for calcul the mask position with numpy : 0.008919239044189453 nb_pixel_total : 566334 time to create 1 rle with new method : 0.016314268112182617 length of segment : 733 time for calcul the mask position with numpy : 0.0011663436889648438 nb_pixel_total : 91443 time to create 1 rle with old method : 0.11530518531799316 length of segment : 199 time for calcul the mask position with numpy : 0.0016973018646240234 nb_pixel_total : 78144 time to create 1 rle with old method : 0.09040951728820801 length of segment : 502 time for calcul the mask position with numpy : 0.016284704208374023 nb_pixel_total : 1124509 time to create 1 rle with new method : 0.028439760208129883 length of segment : 1305 time for calcul the mask position with numpy : 0.00030159950256347656 nb_pixel_total : 22166 time to create 1 rle with old method : 0.02596426010131836 length of segment : 165 time for calcul the mask position with numpy : 0.0005292892456054688 nb_pixel_total : 17601 time to create 1 rle with old method : 0.020310640335083008 length of segment : 268 time for calcul the mask position with numpy : 0.001575469970703125 nb_pixel_total : 112986 time to create 1 rle with old method : 0.13501310348510742 length of segment : 459 time for calcul the mask position with numpy : 0.0013689994812011719 nb_pixel_total : 91701 time to create 1 rle with old method : 0.11322164535522461 length of segment : 268 time for calcul the mask position with numpy : 0.010379791259765625 nb_pixel_total : 475895 time to create 1 rle with new method : 0.010948896408081055 length of segment : 417 time for calcul the mask position with numpy : 0.003039121627807617 nb_pixel_total : 242275 time to create 1 rle with new method : 0.0054666996002197266 length of segment : 466 time for calcul the mask position with numpy : 0.01196908950805664 nb_pixel_total : 693925 time to create 1 rle with new method : 0.022125244140625 length of segment : 1055 time for calcul the mask position with numpy : 0.0026051998138427734 nb_pixel_total : 140257 time to create 1 rle with old method : 0.17378997802734375 length of segment : 506 time for calcul the mask position with numpy : 0.009827852249145508 nb_pixel_total : 568366 time to create 1 rle with new method : 0.013834714889526367 length of segment : 1036 time for calcul the mask position with numpy : 0.0005407333374023438 nb_pixel_total : 23273 time to create 1 rle with old method : 0.028426647186279297 length of segment : 163 time for calcul the mask position with numpy : 0.008796215057373047 nb_pixel_total : 496444 time to create 1 rle with new method : 0.018233299255371094 length of segment : 985 time for calcul the mask position with numpy : 0.0010237693786621094 nb_pixel_total : 55917 time to create 1 rle with old method : 0.06598353385925293 length of segment : 240 time for calcul the mask position with numpy : 0.027295351028442383 nb_pixel_total : 1616973 time to create 1 rle with new method : 0.12651681900024414 length of segment : 1497 time for calcul the mask position with numpy : 0.01078343391418457 nb_pixel_total : 737249 time to create 1 rle with new method : 0.05992388725280762 length of segment : 543 time spent for convertir_results : 7.944710969924927 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 58 chid ids of type : 5014 Number RLEs to save : 0 save missing photos in datou_result : time spend for datou_step_exec : 105.1826868057251 time spend to save output : 0.07707738876342773 total time spend for step 1 : 105.25976419448853 step2:split_time_score_with_photo Thu Aug 28 12:31:48 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)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 28082025 26290610 Nombre de photos uploadées : 36 / 23040 (0%) 28082025 26290610 Nombre de photos taguées (types de déchets): 0 / 36 (0%) 28082025 26290610 Nombre de photos taguées (volume) : 0 / 36 (0%) elapsed_time : load_data_split_time_score 7.152557373046875e-06 elapsed_time : order_list_meta_photo_and_scores 2.09808349609375e-05 ???????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.003153562545776367 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.2045297622680664 ***** BEGIN SPLIT BY DARK ***** To DO 08/10/21 elapsed_time : SPLIT_BY_DARK 0.009267568588256836 ***** 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]] forced_hashtag: entrant force hashtag to entrant elapsed_time : SPLIT_TIME 0.00593876838684082 ***** END SPLIT TIME ***** NUMBER BATCH : 5 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 We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 5 Catched exception ! Connect or reconnect ! list_same_port_ids : [] list_same_port_ids : [] list_same_port_ids : [] list_same_port_ids : [] list_same_port_ids : [] batch 1 Loaded 1 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) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already 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 # 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 # 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 # 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 elapsed_time : count_nb_balles_and_create_portfolio 5.78893256187439 # DISPLAY ALL COLLECTED DATA : {'28082025': {'nb_upload': 36, '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]], {'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]}, {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'}}, {2107760258: 36}, {'amount_uploaded_and_tagged': {'28082025': {'nb_upload': 36, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]}, 'count': {'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]}}) ---------- 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 [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]] /{'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]} /{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'}} /{2107760258: 36} /{'amount_uploaded_and_tagged': {'28082025': {'nb_upload': 36, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]}, 'count': {'Rungis_entrant': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]}} 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, '3623797') ('4746', '26290610', '1379854483', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854482', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854478', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854474', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854459', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854457', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854454', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854451', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854447', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854444', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854377', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854373', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854358', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854338', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854319', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854307', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854162', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854131', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854100', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854068', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854035', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854001', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853910', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853909', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853907', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853905', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853901', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853896', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853628', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853596', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853564', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379845997', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379845977', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379845594', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379845574', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379845557', None, None, None, None, None, '3623797') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.01988840103149414 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.49877667427063 time spend to save output : 0.02041459083557129 total time spend for step 2 : 9.519191265106201 step3:launch_next_datou_same_portfolio Thu Aug 28 12:31:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed VR 22-3-18 : For now we do not clean correctly the datou structure Number of photos for current instance 36 (0 missing) for 36 photos in portfolios Datou 4746 on portfolios [26290610] is finished : launching datou 4148 on portfolios [26290610] new current id 3623699 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 [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, '3623797') ('4746', '26290610', '1379854483', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854482', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854478', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854474', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854459', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854457', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854454', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854451', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854447', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854444', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854377', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854373', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854358', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854338', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854319', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854307', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854162', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854131', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854100', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854068', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854035', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379854001', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853910', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853909', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853907', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853905', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853901', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853896', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853628', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853596', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379853564', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379845997', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379845977', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379845594', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379845574', None, None, None, None, None, '3623797') ('4746', None, None, None, None, None, None, None, '3623797') ('4746', '26290610', '1379845557', None, None, None, None, None, '3623797') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.02032327651977539 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.015773296356201172 time spend to save output : 0.020890235900878906 total time spend for step 3 : 0.03666353225708008 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 36 set_done_treatment 36.74user 81.72system 2:05.63elapsed 94%CPU (0avgtext+0avgdata 5628884maxresident)k 1426128inputs+228232outputs (7958major+7802238minor)pagefaults 0swaps