python /home/admin/mtr/script_for_cron.py -j python_test3 -m 12 -a ' --short_python3 -v ' -s python_test3 -M 0 -S 0 -U 100,100,120 import MySQLdb succeeded Import error (python version) python version = 3 warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data list_job_run_as_list : ['mask_detection', 'datou', 'CacheModelData_queries', 'CachePhotoData_queries', 'test_fork', 'prepare_maskdata', 'portfolio_queries', 'sla_mensuel'] python version used : 3 liste_fichiers : [('tests/mask_test', True, 'Test mask-detection ', 'mask_detection'), ('tests/datou_test', True, 'Datou All Test', 'datou', 'all'), ('mtr/database_queries/CacheModelData_queries', True, 'Test Cache Model Data', 'CacheModelData_queries'), ('tests/cache_photo_data_test', True, 'Test local_cache_photo ', 'CachePhotoData_queries'), ('mtr/mask_rcnn/prepare_maskdata', True, 'test prepare mask data', 'prepare_maskdata', 'all'), ('mtr/database_queries/portfolio_queries', True, 'test portfolio queries', 'portfolio_queries'), ('prod/memo/memo', True, 'SLA Mensuel', 'sla_mensuel', 'all')] #&_# BEGIN OF TEST : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/mask_test.py Test mask-detection python version used : 3 ############################### TEST memory used ################################ free memory at begining : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10814 run mask_detect Inside batchDatouExec : verbose : False # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.37281227111816406 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 : False number of steps : 1 step1:mask_detect Mon Apr 7 09:35:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 /home/admin/workarea/git/Velours/python/tests/python_tests.py:11: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp 2025-04-07 09:35:33.312170: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-07 09:35:33.351054: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-07 09:35:33.357562: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f17ec000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-07 09:35:33.357639: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-07 09:35:33.376612: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-07 09:35:33.630560: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x42e32560 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-07 09:35:33.630611: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-07 09:35:33.631610: 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 2025-04-07 09:35:33.633397: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:35:33.652628: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:35:33.658617: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:35:33.661791: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:35:33.683540: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:35:33.686264: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:35:33.729213: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:35:33.731012: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:35:33.731095: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:35:33.732573: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-07 09:35:33.732591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-07 09:35:33.732602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-07 09:35:33.734509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl454 thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] 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 mask_coco_origin NUM_CLASSES 81 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 2025-04-07 09:35:34.849797: 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 2025-04-07 09:35:34.849899: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:35:34.849927: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:35:34.849954: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:35:34.849979: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:35:34.850003: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:35:34.850045: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:35:34.850072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:35:34.852320: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:35:34.853947: 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 2025-04-07 09:35:34.854046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:35:34.854066: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:35:34.854084: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:35:34.854102: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:35:34.854119: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:35:34.854137: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:35:34.854155: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:35:34.855834: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:35:34.855892: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-07 09:35:34.855904: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-07 09:35:34.855916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-07 09:35:34.857677: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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. 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 : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-07 09:35:42.621757: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:35:42.988915: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (480, 640, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 Detection mask done ! Trying to reset tf kernel 3752440 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4726 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 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 : 10015 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl454 Catched exception ! Connect or reconnect ! thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.000732421875 nb_pixel_total : 15551 time to create 1 rle with old method : 0.01813530921936035 length of segment : 256 time for calcul the mask position with numpy : 0.003339529037475586 nb_pixel_total : 145327 time to create 1 rle with old method : 0.19038915634155273 length of segment : 371 time for calcul the mask position with numpy : 0.00024390220642089844 nb_pixel_total : 14254 time to create 1 rle with old method : 0.01699090003967285 length of segment : 151 time for calcul the mask position with numpy : 0.00012445449829101562 nb_pixel_total : 5613 time to create 1 rle with old method : 0.007394075393676758 length of segment : 48 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 1823 time to create 1 rle with old method : 0.0023789405822753906 length of segment : 39 time spent for convertir_results : 1.07936692237854 time spend for datou_step_exec : 20.91202735900879 time spend to save output : 4.363059997558594e-05 total time spend for step 1 : 20.912070989608765 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 3327 chid ids of type : 445 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.013634204864501953 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'957285035': [[(957285035, 492601069, 445, 0, 186, 22, 282, 0.9954833, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (127, 30, 27), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 105), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 23), (2, 247, 23), (2, 248, 23), (2, 249, 24), (2, 250, 24), (2, 251, 23), (2, 252, 23), (2, 253, 23), (2, 254, 23), (2, 255, 23), (2, 256, 23), (2, 257, 23), (2, 258, 23), (2, 259, 23), (2, 260, 23), (2, 261, 23), (3, 262, 22), (3, 263, 22), (3, 264, 22), (3, 265, 22), (4, 266, 21), (4, 267, 21), (5, 268, 20), (5, 269, 20), (6, 270, 19), (7, 271, 17), (8, 272, 16), (8, 273, 16), (9, 274, 13), (11, 275, 9), (15, 276, 2)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,219,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.9923861, [(315, 37, 24), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 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(46, 174, 523), (45, 175, 524), (45, 176, 523), (44, 177, 524), (44, 178, 524), (44, 179, 524), (43, 180, 525), (43, 181, 525), (42, 182, 525), (42, 183, 525), (42, 184, 525), (41, 185, 526), (41, 186, 526), (40, 187, 526), (39, 188, 526), (39, 189, 525), (38, 190, 526), (38, 191, 525), (37, 192, 525), (37, 193, 523), (36, 194, 523), (36, 195, 523), (36, 196, 522), (35, 197, 522), (35, 198, 521), (34, 199, 521), (34, 200, 521), (34, 201, 520), (34, 202, 520), (34, 203, 520), (34, 204, 519), (34, 205, 519), (33, 206, 520), (33, 207, 519), (33, 208, 519), (33, 209, 519), (33, 210, 518), (33, 211, 518), (33, 212, 518), (33, 213, 517), (32, 214, 518), (32, 215, 517), (32, 216, 517), (32, 217, 516), (32, 218, 515), (32, 219, 514), (32, 220, 513), (32, 221, 512), (32, 222, 511), (32, 223, 510), (32, 224, 508), (32, 225, 507), (32, 226, 505), (32, 227, 504), (32, 228, 503), (32, 229, 502), (32, 230, 502), (32, 231, 501), (32, 232, 500), (32, 233, 499), (32, 234, 498), (32, 235, 497), (31, 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['598,172,591,172,586,170,578,168,573,164,573,162,568,152,568,149,566,145,566,136,565,132,561,125,560,121,556,116,547,109,543,108,536,104,531,99,527,97,491,62,490,54,495,48,496,45,501,40,514,32,517,29,531,25,539,25,540,24,560,24,561,25,579,25,580,26,593,26,594,25,633,25,634,29,634,56,635,57,635,111,634,112,634,129,632,134,629,138,623,141,619,145,617,149,611,155,608,161,604,166']), (957285035, 492601069, 445, 280, 481, 2, 55, 0.8300017, [(292, 3, 128), (284, 4, 146), (282, 5, 151), (281, 6, 154), (281, 7, 156), (281, 8, 157), (281, 9, 158), (281, 10, 160), (281, 11, 162), (281, 12, 165), (281, 13, 167), (281, 14, 169), (281, 15, 171), (281, 16, 173), (281, 17, 174), (281, 18, 175), (281, 19, 177), (281, 20, 178), (281, 21, 179), (281, 22, 180), (281, 23, 181), (281, 24, 182), (281, 25, 183), (281, 26, 184), (281, 27, 185), (281, 28, 185), (281, 29, 185), (282, 30, 185), (283, 31, 27), (337, 31, 131), (371, 32, 97), (401, 33, 68), (409, 34, 61), (419, 35, 52), (424, 36, 48), (429, 37, 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(474, 33, 36), (475, 34, 33), (475, 35, 32), (476, 36, 30), (476, 37, 29), (477, 38, 26), (478, 39, 23), (479, 40, 20), (480, 41, 17), (488, 42, 5)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,463,10,464,9,466,9,470,12,474,13,476,11,480,10,482,8,499,8,500,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1744011329_3752194_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 error , can't release the memory or there are other process who occupe the free memory ERROR test release memory FAILED ############################### TEST detect object ################################ run mask_detect Inside batchDatouExec : verbose : False # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.1662580966949463 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 : False number of steps : 1 step1:mask_detect Mon Apr 7 09:36:15 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 5525 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-07 09:36:18.603249: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-07 09:36:18.631327: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-07 09:36:18.633058: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f17e4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-07 09:36:18.633106: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-07 09:36:18.636263: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-07 09:36:18.782640: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x434739a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-07 09:36:18.782688: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-07 09:36:18.783553: 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 2025-04-07 09:36:18.783862: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:36:18.786029: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:36:18.788314: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:36:18.788657: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:36:18.790939: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:36:18.791979: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:36:18.796220: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:36:18.797403: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:36:18.797493: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:36:18.798092: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-07 09:36:18.798108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-07 09:36:18.798117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-07 09:36:18.799125: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5051 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. 2025-04-07 09:36:18.883429: 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 2025-04-07 09:36:18.883627: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:36:18.883665: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:36:18.883695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:36:18.883723: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:36:18.883755: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:36:18.883780: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:36:18.883837: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:36:18.884863: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:36:18.886212: 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 2025-04-07 09:36:18.886264: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:36:18.886291: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:36:18.886314: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:36:18.886336: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:36:18.886357: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:36:18.886378: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:36:18.886398: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:36:18.887571: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:36:18.887617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-07 09:36:18.887628: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-07 09:36:18.887640: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-07 09:36:18.888886: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5051 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 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotlocal folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (720, 1280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 1280.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 3756036 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5306 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 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 : 10595 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0006539821624755859 nb_pixel_total : 16902 time to create 1 rle with old method : 0.02438211441040039 length of segment : 107 time for calcul the mask position with numpy : 0.04317784309387207 nb_pixel_total : 480747 time to create 1 rle with new method : 0.0317990779876709 length of segment : 632 time for calcul the mask position with numpy : 0.00048542022705078125 nb_pixel_total : 36582 time to create 1 rle with old method : 0.04504966735839844 length of segment : 132 time for calcul the mask position with numpy : 0.0001838207244873047 nb_pixel_total : 4793 time to create 1 rle with old method : 0.006204366683959961 length of segment : 51 time spent for convertir_results : 0.3470640182495117 time spend for datou_step_exec : 19.34252119064331 time spend to save output : 0.00011110305786132812 total time spend for step 1 : 19.342632293701172 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 412 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.013200044631958008 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917855882': [[(917855882, 492601069, 445, 1092, 1280, 0, 108, 0.9988374, [(1205, 1, 58), (1165, 2, 105), (1159, 3, 113), (1149, 4, 124), (1113, 5, 161), (1100, 6, 174), (1097, 7, 177), (1095, 8, 179), (1095, 9, 179), (1095, 10, 179), (1095, 11, 179), (1095, 12, 179), (1095, 13, 179), (1095, 14, 178), (1095, 15, 178), (1095, 16, 178), (1095, 17, 178), (1095, 18, 177), (1095, 19, 177), (1095, 20, 177), (1095, 21, 177), (1095, 22, 177), (1095, 23, 178), (1095, 24, 178), (1095, 25, 178), (1095, 26, 179), (1095, 27, 179), (1095, 28, 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[(414, 0, 7), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (420, 25, 99), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1744011375_3752194_917855882_da0fa7b7e6b5b551fe26c0ba8713276d.jpg']} ############################### TEST POLYGON ################################ Inside batchDatouExec : verbose : False # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.16715359687805176 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 : False number of steps : 1 step1:mask_detect Mon Apr 7 09:36:36 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10593 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-07 09:36:39.333905: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-07 09:36:39.359186: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-07 09:36:39.361396: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f17e8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-07 09:36:39.361449: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-07 09:36:39.366569: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-07 09:36:39.513934: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x43c4c730 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-07 09:36:39.514015: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-07 09:36:39.515480: 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 2025-04-07 09:36:39.515942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:36:39.518875: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:36:39.521667: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:36:39.522060: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:36:39.524747: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:36:39.526058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:36:39.532348: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:36:39.533810: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:36:39.533896: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:36:39.534642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-07 09:36:39.534657: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-07 09:36:39.534666: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-07 09:36:39.536042: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9815 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. 2025-04-07 09:36:39.614270: 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 2025-04-07 09:36:39.614392: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:36:39.614411: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:36:39.614428: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:36:39.614444: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:36:39.614459: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:36:39.614475: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:36:39.614491: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:36:39.615799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:36:39.617130: 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 2025-04-07 09:36:39.617193: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:36:39.617217: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:36:39.617240: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:36:39.617263: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:36:39.617286: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:36:39.617308: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:36:39.617331: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:36:39.618859: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:36:39.618907: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-07 09:36:39.618920: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-07 09:36:39.618929: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-07 09:36:39.620517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9815 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 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] 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 mask_coco_origin NUM_CLASSES 81 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 : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-07 09:36:48.368807: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:36:48.554750: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 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: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 2448.00000 nb d'objets trouves : 1 Detection mask done ! Trying to reset tf kernel 3757676 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5304 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 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 : 10593 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.34989070892333984 nb_pixel_total : 3693281 time to create 1 rle with new method : 0.3677196502685547 length of segment : 2041 time spent for convertir_results : 1.4052565097808838 time spend for datou_step_exec : 19.77932596206665 time spend to save output : 4.0531158447265625e-05 total time spend for step 1 : 19.779366493225098 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 721 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.011958599090576172 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917877156': [[(917877156, 492601069, 445, 7, 2268, 118, 2241, 0.98498744, [(674, 120, 113), (520, 121, 481), (1050, 121, 381), (502, 122, 948), (486, 123, 981), (470, 124, 1015), (455, 125, 1046), (442, 126, 1092), (429, 127, 1136), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1223), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (363, 135, 1266), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (352, 140, 1303), (351, 141, 1309), (349, 142, 1315), (347, 143, 1320), (345, 144, 1326), (343, 145, 1331), (342, 146, 1335), (340, 147, 1340), (338, 148, 1345), (337, 149, 1349), (335, 150, 1354), (334, 151, 1358), (332, 152, 1363), (331, 153, 1366), (330, 154, 1370), (328, 155, 1374), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (323, 159, 1389), (322, 160, 1393), (321, 161, 1397), (319, 162, 1402), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (310, 168, 1429), (309, 169, 1434), (307, 170, 1439), (305, 171, 1444), (304, 172, 1448), (302, 173, 1453), (300, 174, 1458), (298, 175, 1463), (296, 176, 1469), (294, 177, 1474), (292, 178, 1480), (289, 179, 1487), (286, 180, 1493), (283, 181, 1500), (280, 182, 1508), (278, 183, 1514), (275, 184, 1521), (272, 185, 1529), (269, 186, 1536), (266, 187, 1544), (263, 188, 1552), (260, 189, 1561), (257, 190, 1569), (254, 191, 1579), (251, 192, 1588), (248, 193, 1597), (245, 194, 1606), (242, 195, 1615), (239, 196, 1624), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1682), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 1710), (203, 210, 1712), (201, 211, 1716), (199, 212, 1719), (198, 213, 1721), (196, 214, 1725), (195, 215, 1727), (193, 216, 1730), (192, 217, 1733), (191, 218, 1735), (189, 219, 1738), (188, 220, 1740), (187, 221, 1742), (186, 222, 1744), (185, 223, 1746), (183, 224, 1749), (182, 225, 1751), (181, 226, 1753), (180, 227, 1755), (179, 228, 1757), (178, 229, 1759), (177, 230, 1761), (176, 231, 1762), (176, 232, 1763), (175, 233, 1765), (174, 234, 1767), (173, 235, 1768), (172, 236, 1770), (171, 237, 1772), (170, 238, 1774), (169, 239, 1775), (168, 240, 1777), (167, 241, 1779), (166, 242, 1781), (165, 243, 1783), (164, 244, 1785), (163, 245, 1787), (162, 246, 1789), (161, 247, 1791), (159, 248, 1794), (158, 249, 1796), (157, 250, 1798), (156, 251, 1800), (154, 252, 1803), (153, 253, 1805), (152, 254, 1807), (150, 255, 1810), (149, 256, 1812), (148, 257, 1815), (146, 258, 1818), (145, 259, 1820), (143, 260, 1823), (142, 261, 1826), (140, 262, 1829), (138, 263, 1833), (137, 264, 1835), (135, 265, 1839), (133, 266, 1842), (132, 267, 1845), (130, 268, 1849), (128, 269, 1852), (126, 270, 1856), (125, 271, 1859), (124, 272, 1862), (122, 273, 1865), (121, 274, 1868), (120, 275, 1871), (119, 276, 1873), (117, 277, 1877), (116, 278, 1879), (115, 279, 1881), (114, 280, 1884), (113, 281, 1886), (112, 282, 1888), (111, 283, 1890), (110, 284, 1892), (109, 285, 1895), (108, 286, 1897), (108, 287, 1898), (107, 288, 1900), (106, 289, 1902), (105, 290, 1904), (104, 291, 1906), (103, 292, 1908), (103, 293, 1909), (102, 294, 1910), (101, 295, 1912), (101, 296, 1913), (100, 297, 1915), (99, 298, 1917), (99, 299, 1918), (98, 300, 1919), (97, 301, 1921), (97, 302, 1922), (96, 303, 1924), (95, 304, 1925), (95, 305, 1926), (94, 306, 1928), (94, 307, 1928), (93, 308, 1930), (93, 309, 1930), (93, 310, 1931), (93, 311, 1931), (92, 312, 1933), (92, 313, 1933), (92, 314, 1934), (92, 315, 1934), (91, 316, 1936), (91, 317, 1936), (91, 318, 1937), (91, 319, 1937), (90, 320, 1939), (90, 321, 1939), (90, 322, 1940), (89, 323, 1941), (89, 324, 1942), (89, 325, 1943), (89, 326, 1943), (88, 327, 1945), (88, 328, 1945), (88, 329, 1946), (87, 330, 1948), (87, 331, 1948), (87, 332, 1949), (87, 333, 1949), (86, 334, 1951), (86, 335, 1952), (86, 336, 1952), (85, 337, 1954), (85, 338, 1955), (85, 339, 1956), (85, 340, 1956), (84, 341, 1958), (84, 342, 1959), (84, 343, 1959), (83, 344, 1961), (83, 345, 1962), (83, 346, 1963), (83, 347, 1963), (82, 348, 1965), (82, 349, 1966), (82, 350, 1967), (81, 351, 1969), (81, 352, 1970), (81, 353, 1970), (80, 354, 1972), (80, 355, 1973), (80, 356, 1974), (80, 357, 1975), (79, 358, 1977), (79, 359, 1978), (79, 360, 1979), (78, 361, 1981), (78, 362, 1982), (78, 363, 1983), (77, 364, 1985), (77, 365, 1986), (77, 366, 1987), (77, 367, 1988), (76, 368, 1990), (76, 369, 1991), (76, 370, 1992), (75, 371, 1994), (75, 372, 1995), (75, 373, 1996), (74, 374, 1998), (74, 375, 1999), (74, 376, 2000), (73, 377, 2002), (73, 378, 2003), (73, 379, 2004), (72, 380, 2006), (72, 381, 2006), (72, 382, 2007), (71, 383, 2009), (71, 384, 2009), (71, 385, 2010), (70, 386, 2012), (70, 387, 2012), (70, 388, 2013), (70, 389, 2013), (69, 390, 2015), (69, 391, 2015), (69, 392, 2016), (68, 393, 2018), (68, 394, 2018), (68, 395, 2019), (67, 396, 2020), (67, 397, 2021), (67, 398, 2021), (66, 399, 2023), (66, 400, 2023), (65, 401, 2025), (65, 402, 2025), (65, 403, 2026), (64, 404, 2027), (64, 405, 2028), (64, 406, 2028), (63, 407, 2030), (63, 408, 2030), (63, 409, 2031), (62, 410, 2032), (62, 411, 2033), (61, 412, 2034), (61, 413, 2034), (61, 414, 2035), (60, 415, 2036), (60, 416, 2037), (59, 417, 2038), (59, 418, 2039), (58, 419, 2040), (58, 420, 2041), (58, 421, 2041), (57, 422, 2042), (57, 423, 2043), (56, 424, 2044), (56, 425, 2045), (55, 426, 2046), (55, 427, 2047), (54, 428, 2048), (54, 429, 2048), (53, 430, 2050), (53, 431, 2050), (52, 432, 2052), (52, 433, 2052), (51, 434, 2053), (51, 435, 2054), (50, 436, 2055), (50, 437, 2055), (49, 438, 2057), 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556, 2128), (27, 557, 2129), (27, 558, 2129), (27, 559, 2129), (27, 560, 2130), (26, 561, 2131), (26, 562, 2131), (26, 563, 2131), (26, 564, 2132), (26, 565, 2132), (26, 566, 2132), (26, 567, 2132), (26, 568, 2133), (26, 569, 2133), (26, 570, 2133), (26, 571, 2134), (26, 572, 2134), (26, 573, 2134), (26, 574, 2134), (26, 575, 2135), (26, 576, 2135), (26, 577, 2135), (26, 578, 2135), (25, 579, 2137), (25, 580, 2137), (25, 581, 2137), (25, 582, 2137), (25, 583, 2137), (25, 584, 2138), (25, 585, 2138), (25, 586, 2138), (25, 587, 2138), (25, 588, 2139), (25, 589, 2139), (25, 590, 2139), (25, 591, 2139), (25, 592, 2140), (25, 593, 2140), (25, 594, 2140), (25, 595, 2140), (25, 596, 2140), (25, 597, 2141), (24, 598, 2142), (24, 599, 2142), (24, 600, 2142), (24, 601, 2142), (24, 602, 2143), (24, 603, 2143), (24, 604, 2143), (24, 605, 2143), (24, 606, 2143), (24, 607, 2144), (24, 608, 2144), (24, 609, 2144), (24, 610, 2144), (24, 611, 2144), (24, 612, 2144), (24, 613, 2145), (24, 614, 2145), 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2151), (21, 674, 2151), (21, 675, 2151), (21, 676, 2151), (21, 677, 2151), (21, 678, 2151), (21, 679, 2151), (21, 680, 2152), (21, 681, 2152), (21, 682, 2152), (21, 683, 2152), (21, 684, 2152), (21, 685, 2152), (21, 686, 2152), (21, 687, 2152), (21, 688, 2152), (21, 689, 2152), (21, 690, 2152), (21, 691, 2152), (21, 692, 2152), (21, 693, 2152), (21, 694, 2152), (21, 695, 2152), (21, 696, 2151), (22, 697, 2150), (22, 698, 2150), (22, 699, 2150), (22, 700, 2150), (22, 701, 2150), (22, 702, 2150), (22, 703, 2150), (22, 704, 2150), (22, 705, 2150), (22, 706, 2150), (22, 707, 2150), (22, 708, 2150), (22, 709, 2150), (23, 710, 2149), (23, 711, 2149), (23, 712, 2149), (23, 713, 2149), (23, 714, 2149), (23, 715, 2149), (23, 716, 2149), (23, 717, 2149), (23, 718, 2148), (23, 719, 2148), (23, 720, 2148), (23, 721, 2148), (24, 722, 2147), (24, 723, 2147), (24, 724, 2147), (24, 725, 2147), (24, 726, 2147), (24, 727, 2147), (24, 728, 2147), (24, 729, 2147), (24, 730, 2147), (24, 731, 2147), (24, 732, 2147), (24, 733, 2147), (25, 734, 2146), (25, 735, 2146), (25, 736, 2146), (25, 737, 2146), (25, 738, 2146), (25, 739, 2146), (25, 740, 2145), (25, 741, 2145), (25, 742, 2145), (25, 743, 2145), (25, 744, 2145), (25, 745, 2145), (26, 746, 2144), (26, 747, 2144), (26, 748, 2144), (26, 749, 2144), (26, 750, 2144), (26, 751, 2144), (26, 752, 2144), (26, 753, 2144), (26, 754, 2144), (26, 755, 2144), (26, 756, 2144), (27, 757, 2143), (27, 758, 2143), (27, 759, 2143), (27, 760, 2143), (27, 761, 2142), (27, 762, 2142), (27, 763, 2142), (27, 764, 2142), (27, 765, 2142), (27, 766, 2142), (27, 767, 2142), (27, 768, 2142), (27, 769, 2142), (27, 770, 2142), (27, 771, 2142), (27, 772, 2142), (27, 773, 2142), (27, 774, 2142), (27, 775, 2142), (27, 776, 2142), (27, 777, 2142), (27, 778, 2142), (27, 779, 2142), (27, 780, 2141), (27, 781, 2141), (27, 782, 2141), (27, 783, 2141), (27, 784, 2141), (27, 785, 2141), (27, 786, 2141), (27, 787, 2141), (27, 788, 2141), (27, 789, 2141), (26, 790, 2142), 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['920,2140,759,2089,694,2075,619,2040,371,1987,215,1963,128,1971,108,1945,54,1825,53,1754,39,1677,39,1455,29,1244,27,757,21,696,27,543,39,458,93,308,117,277,210,206,291,179,368,133,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,2021,306,2094,411,2148,535,2168,613,2171,717,2165,833,2128,914,2112,994,2081,1068,2031,1132,1967,1255,1926,1378,1879,1444,1841,1690,1780,1868,1756,1920,1662,2015,1581,2015,1496,2039,1177,2101,1093,2142,1019,2150'])], 'temp/1744011396_3752194_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3690309 proportion of common points : 0.9994621231510483 [('test release memory', 'FAILURE', False), ('test detect objet', 'SUCCESS', True), ('test polygone', 'SUCCESS', True)] res_total : False #&_# TEST FAILED #&_# : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/python_tests.py refs/heads/master_f2b463648cc811bd25985b763ea1aee1dfaee988 SQL :INSERT INTO MTRAdmin.monitor_sys (name, type, server, version_code, result_str, result_bool, lien , test_group ,test_name) VALUES ('python_test3','1','marlene','refs/heads/master_f2b463648cc811bd25985b763ea1aee1dfaee988','{"mask_detection": "fail"}','0','http://marlene.fotonower-preprod.com/job/2025/April/07042025/python_test3//data_2/data_log/job/2025/April/07042025/python_test3/log-python3----short_python3--v--marlene-09:35:02.txt','mask_detection','unknown'); #&_# END OF TEST #&_# : tests/mask_test #&_# #&_# BEGIN OF TEST : tests/datou_test #&_# /home/admin/workarea/git/Velours/python/tests/datou_test.py Datou All Test python version used : 3 ############################### TEST sam ################################ TEST SAM Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4573 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4573 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4573 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4573 # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : sam list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1189321094) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 1189321094 download finish for photo 1189321094 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.2037672996520996 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:sam Mon Apr 7 09:37:45 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011465_3752194_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094} map_photo_id_path_extension : {1189321094: {'path': 'temp/1744011465_3752194_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png', 'extension': 'png'}} map_subphoto_mainphoto : {} Beginning of datou step sam ! pht : 4677 Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0021686553955078125 nb_pixel_total : 3773 time to create 1 rle with old method : 0.004658222198486328 time for calcul the mask position with numpy : 0.001470804214477539 nb_pixel_total : 4206 time to create 1 rle with old method : 0.00537419319152832 time for calcul the mask position with numpy : 0.0018627643585205078 nb_pixel_total : 5615 time to create 1 rle with old method : 0.007042884826660156 time for calcul the mask position with numpy : 0.0019025802612304688 nb_pixel_total : 83789 time to create 1 rle with old method : 0.09987497329711914 time for calcul the mask position with numpy : 0.0015285015106201172 nb_pixel_total : 10832 time to create 1 rle with old method : 0.012954473495483398 time for calcul the mask position with numpy : 0.0015015602111816406 nb_pixel_total : 5968 time to create 1 rle with old method : 0.007396697998046875 time for calcul the mask position with numpy : 0.0016179084777832031 nb_pixel_total : 13906 time to create 1 rle with old method : 0.01796269416809082 time for calcul the mask position with numpy : 0.001438140869140625 nb_pixel_total : 2938 time to create 1 rle with old method : 0.003621816635131836 time for calcul the mask position with numpy : 0.0016222000122070312 nb_pixel_total : 29429 time to create 1 rle with old method : 0.039359092712402344 time for calcul the mask position with numpy : 0.0018169879913330078 nb_pixel_total : 6644 time to create 1 rle with old method : 0.008150100708007812 time for calcul the mask position with numpy : 0.0014390945434570312 nb_pixel_total : 1226 time to create 1 rle with old method : 0.0015840530395507812 time for calcul the mask position with numpy : 0.0014674663543701172 nb_pixel_total : 2452 time to create 1 rle with old method : 0.0030901432037353516 time for calcul the mask position with numpy : 0.0014536380767822266 nb_pixel_total : 3952 time to create 1 rle with old method : 0.006763935089111328 time for calcul the mask position with numpy : 0.0018355846405029297 nb_pixel_total : 2080 time to create 1 rle with old method : 0.003222942352294922 time for calcul the mask position with numpy : 0.001905202865600586 nb_pixel_total : 16338 time to create 1 rle with old method : 0.021299362182617188 time for calcul the mask position with numpy : 0.0014538764953613281 nb_pixel_total : 1679 time to create 1 rle with old method : 0.0021581649780273438 time for calcul the mask position with numpy : 0.0017771720886230469 nb_pixel_total : 38034 time to create 1 rle with old method : 0.045235633850097656 time for calcul the mask position with numpy : 0.0014939308166503906 nb_pixel_total : 7611 time to create 1 rle with old method : 0.009081125259399414 time for calcul the mask position with numpy : 0.0014469623565673828 nb_pixel_total : 4271 time to create 1 rle with old method : 0.005475044250488281 time for calcul the mask position with numpy : 0.0014405250549316406 nb_pixel_total : 2749 time to create 1 rle with old method : 0.0034847259521484375 time for calcul the mask position with numpy : 0.0014858245849609375 nb_pixel_total : 5495 time to create 1 rle with old method : 0.006665945053100586 time for calcul the mask position with numpy : 0.0014557838439941406 nb_pixel_total : 3565 time to create 1 rle with old method : 0.004380941390991211 time for calcul the mask position with numpy : 0.00146484375 nb_pixel_total : 8633 time to create 1 rle with old method : 0.010621070861816406 time for calcul the mask position with numpy : 0.0015780925750732422 nb_pixel_total : 16453 time to create 1 rle with old method : 0.020339488983154297 time for calcul the mask position with numpy : 0.001508474349975586 nb_pixel_total : 3905 time to create 1 rle with old method : 0.004922151565551758 time for calcul the mask position with numpy : 0.0014376640319824219 nb_pixel_total : 2452 time to create 1 rle with old method : 0.0030510425567626953 time for calcul the mask position with numpy : 0.0015397071838378906 nb_pixel_total : 11902 time to create 1 rle with old method : 0.018494844436645508 time for calcul the mask position with numpy : 0.0015714168548583984 nb_pixel_total : 1268 time to create 1 rle with old method : 0.002225637435913086 time for calcul the mask position with numpy : 0.00165557861328125 nb_pixel_total : 14715 time to create 1 rle with old method : 0.025393962860107422 time for calcul the mask position with numpy : 0.0015861988067626953 nb_pixel_total : 2772 time to create 1 rle with old method : 0.004868030548095703 time for calcul the mask position with numpy : 0.0016393661499023438 nb_pixel_total : 9856 time to create 1 rle with old method : 0.017539501190185547 time for calcul the mask position with numpy : 0.0020291805267333984 nb_pixel_total : 27272 time to create 1 rle with old method : 0.05434727668762207 time for calcul the mask position with numpy : 0.002978801727294922 nb_pixel_total : 5405 time to create 1 rle with old method : 0.01261448860168457 time for calcul the mask position with numpy : 0.0029315948486328125 nb_pixel_total : 13075 time to create 1 rle with old method : 0.02015233039855957 time for calcul the mask position with numpy : 0.0016393661499023438 nb_pixel_total : 10637 time to create 1 rle with old method : 0.01303863525390625 time for calcul the mask position with numpy : 0.0015556812286376953 nb_pixel_total : 3327 time to create 1 rle with old method : 0.004168987274169922 time for calcul the mask position with numpy : 0.0015401840209960938 nb_pixel_total : 4141 time to create 1 rle with old method : 0.00524139404296875 time for calcul the mask position with numpy : 0.0014595985412597656 nb_pixel_total : 1843 time to create 1 rle with old method : 0.002329111099243164 time for calcul the mask position with numpy : 0.0014493465423583984 nb_pixel_total : 1025 time to create 1 rle with old method : 0.0013196468353271484 time for calcul the mask position with numpy : 0.0014693737030029297 nb_pixel_total : 344 time to create 1 rle with old method : 0.00048470497131347656 time for calcul the mask position with numpy : 0.0015027523040771484 nb_pixel_total : 4178 time to create 1 rle with old method : 0.008944988250732422 time for calcul the mask position with numpy : 0.0016291141510009766 nb_pixel_total : 1208 time to create 1 rle with old method : 0.0016345977783203125 time for calcul the mask position with numpy : 0.0017688274383544922 nb_pixel_total : 16845 time to create 1 rle with old method : 0.02293848991394043 time for calcul the mask position with numpy : 0.001554250717163086 nb_pixel_total : 874 time to create 1 rle with old method : 0.0012164115905761719 time for calcul the mask position with numpy : 0.00144195556640625 nb_pixel_total : 861 time to create 1 rle with old method : 0.0011799335479736328 time for calcul the mask position with numpy : 0.0014166831970214844 nb_pixel_total : 595 time to create 1 rle with old method : 0.0007309913635253906 time for calcul the mask position with numpy : 0.0013849735260009766 nb_pixel_total : 885 time to create 1 rle with old method : 0.0011610984802246094 time for calcul the mask position with numpy : 0.001466512680053711 nb_pixel_total : 577 time to create 1 rle with old method : 0.0007686614990234375 time for calcul the mask position with numpy : 0.0014483928680419922 nb_pixel_total : 2334 time to create 1 rle with old method : 0.002840280532836914 time for calcul the mask position with numpy : 0.0016350746154785156 nb_pixel_total : 337 time to create 1 rle with old method : 0.0006020069122314453 time for calcul the mask position with numpy : 0.0018129348754882812 nb_pixel_total : 2404 time to create 1 rle with old method : 0.0032720565795898438 time for calcul the mask position with numpy : 0.0014538764953613281 nb_pixel_total : 2379 time to create 1 rle with old method : 0.0030066967010498047 time for calcul the mask position with numpy : 0.0013859272003173828 nb_pixel_total : 692 time to create 1 rle with old method : 0.0009119510650634766 time for calcul the mask position with numpy : 0.0014624595642089844 nb_pixel_total : 1705 time to create 1 rle with old method : 0.002169370651245117 time for calcul the mask position with numpy : 0.0014760494232177734 nb_pixel_total : 2770 time to create 1 rle with old method : 0.003507375717163086 time for calcul the mask position with numpy : 0.0014576911926269531 nb_pixel_total : 1055 time to create 1 rle with old method : 0.0014295578002929688 time for calcul the mask position with numpy : 0.0014383792877197266 nb_pixel_total : 585 time to create 1 rle with old method : 0.0007836818695068359 time for calcul the mask position with numpy : 0.0015134811401367188 nb_pixel_total : 8766 time to create 1 rle with old method : 0.010689496994018555 time for calcul the mask position with numpy : 0.0015106201171875 nb_pixel_total : 1673 time to create 1 rle with old method : 0.002166748046875 time for calcul the mask position with numpy : 0.00147247314453125 nb_pixel_total : 1089 time to create 1 rle with old method : 0.001392364501953125 time for calcul the mask position with numpy : 0.001447439193725586 nb_pixel_total : 3095 time to create 1 rle with old method : 0.003891468048095703 time for calcul the mask position with numpy : 0.0014717578887939453 nb_pixel_total : 713 time to create 1 rle with old method : 0.0009908676147460938 time for calcul the mask position with numpy : 0.0013844966888427734 nb_pixel_total : 1818 time to create 1 rle with old method : 0.0023698806762695312 time for calcul the mask position with numpy : 0.0014836788177490234 nb_pixel_total : 1441 time to create 1 rle with old method : 0.0019221305847167969 time for calcul the mask position with numpy : 0.0014646053314208984 nb_pixel_total : 1744 time to create 1 rle with old method : 0.0021975040435791016 time for calcul the mask position with numpy : 0.0014960765838623047 nb_pixel_total : 8443 time to create 1 rle with old method : 0.010119438171386719 time for calcul the mask position with numpy : 0.0014801025390625 nb_pixel_total : 1513 time to create 1 rle with old method : 0.0018703937530517578 time for calcul the mask position with numpy : 0.001425027847290039 nb_pixel_total : 9068 time to create 1 rle with old method : 0.010946035385131836 time for calcul the mask position with numpy : 0.0015406608581542969 nb_pixel_total : 3169 time to create 1 rle with old method : 0.0042498111724853516 time for calcul the mask position with numpy : 0.0015773773193359375 nb_pixel_total : 268 time to create 1 rle with old method : 0.0003764629364013672 time for calcul the mask position with numpy : 0.0014865398406982422 nb_pixel_total : 1336 time to create 1 rle with old method : 0.001851797103881836 time for calcul the mask position with numpy : 0.0017392635345458984 nb_pixel_total : 9501 time to create 1 rle with old method : 0.012266397476196289 time for calcul the mask position with numpy : 0.0017457008361816406 nb_pixel_total : 18481 time to create 1 rle with old method : 0.025301694869995117 time for calcul the mask position with numpy : 0.0018780231475830078 nb_pixel_total : 248 time to create 1 rle with old method : 0.0005304813385009766 time for calcul the mask position with numpy : 0.0016930103302001953 nb_pixel_total : 615 time to create 1 rle with old method : 0.0010809898376464844 time for calcul the mask position with numpy : 0.001821279525756836 nb_pixel_total : 968 time to create 1 rle with old method : 0.00139617919921875 time for calcul the mask position with numpy : 0.0017178058624267578 nb_pixel_total : 221 time to create 1 rle with old method : 0.0004718303680419922 time for calcul the mask position with numpy : 0.0019025802612304688 nb_pixel_total : 734 time to create 1 rle with old method : 0.001184701919555664 time for calcul the mask position with numpy : 0.0017101764678955078 nb_pixel_total : 1635 time to create 1 rle with old method : 0.0022575855255126953 time for calcul the mask position with numpy : 0.0015447139739990234 nb_pixel_total : 977 time to create 1 rle with old method : 0.0012736320495605469 time for calcul the mask position with numpy : 0.0016145706176757812 nb_pixel_total : 1491 time to create 1 rle with old method : 0.0020563602447509766 time for calcul the mask position with numpy : 0.0017118453979492188 nb_pixel_total : 7491 time to create 1 rle with old method : 0.010125398635864258 time for calcul the mask position with numpy : 0.0017490386962890625 nb_pixel_total : 915 time to create 1 rle with old method : 0.0018305778503417969 time for calcul the mask position with numpy : 0.0016028881072998047 nb_pixel_total : 889 time to create 1 rle with old method : 0.001474142074584961 time for calcul the mask position with numpy : 0.0022079944610595703 nb_pixel_total : 596 time to create 1 rle with old method : 0.00089263916015625 time for calcul the mask position with numpy : 0.001558542251586914 nb_pixel_total : 301 time to create 1 rle with old method : 0.0004990100860595703 time for calcul the mask position with numpy : 0.0017015933990478516 nb_pixel_total : 5038 time to create 1 rle with old method : 0.007012128829956055 time for calcul the mask position with numpy : 0.0016415119171142578 nb_pixel_total : 2208 time to create 1 rle with old method : 0.003042459487915039 time for calcul the mask position with numpy : 0.002270221710205078 nb_pixel_total : 1131 time to create 1 rle with old method : 0.0015902519226074219 time for calcul the mask position with numpy : 0.0015835762023925781 nb_pixel_total : 1319 time to create 1 rle with old method : 0.0019006729125976562 time for calcul the mask position with numpy : 0.0015680789947509766 nb_pixel_total : 1407 time to create 1 rle with old method : 0.0027761459350585938 time for calcul the mask position with numpy : 0.0020051002502441406 nb_pixel_total : 946 time to create 1 rle with old method : 0.002075672149658203 time for calcul the mask position with numpy : 0.002124786376953125 nb_pixel_total : 884 time to create 1 rle with old method : 0.0012934207916259766 time for calcul the mask position with numpy : 0.0016825199127197266 nb_pixel_total : 1605 time to create 1 rle with old method : 0.0024123191833496094 time for calcul the mask position with numpy : 0.0015897750854492188 nb_pixel_total : 2334 time to create 1 rle with old method : 0.003103494644165039 time for calcul the mask position with numpy : 0.0018012523651123047 nb_pixel_total : 829 time to create 1 rle with old method : 0.0012748241424560547 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 96 chid ids of type : 4677 Number RLEs to save : 8767 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3748649713', '201', '535', '9') ... last line : ('3748649808', '815', '44', '5') INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : sam we use saveGeneral [1189321094] map_info['map_portfolio_photo'] : {} final : True mtd_id 4573 list_pids : [1189321094] Looping around the photos to save general results len do output : 1 /1189321094Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4573', None, None, None, None, None, None, None, None) ('4573', None, '1189321094', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4573', None, '1189321094', 'None', None, None, None, None, None)] time used for this insertion : 0.012490987777709961 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.440258979797363 time spend to save output : 0.012829780578613281 total time spend for step 1 : 10.453088760375977 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1744011465_3752194_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 96 ############################### TEST frcnn ################################ test frcnn Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4184 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4184 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4184 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4184 # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : frcnn list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917754606) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917754606 download finish for photo 917754606 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.13939642906188965 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:frcnn Mon Apr 7 09:37:56 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011476_3752194_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606} map_photo_id_path_extension : {917754606: {'path': 'temp/1744011476_3752194_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Faster rcnn ! classes : ['background', 'plaque'] pht : 4370 caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 3375, 'mtr_user_id': 31, 'name': 'detection_plaque_valcor_010622', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,plaque', 'svm_portfolios_learning': '0,0', 'photo_hashtag_type': 4370, 'photo_desc_type': 5676, 'type_classification': 'caffe_faster_rcnn', 'hashtag_id_list': '0,0'} To loadFromThcl() model_param file didn't exist model_name : detection_plaque_valcor_010622 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/detection_plaque_valcor_010622 /data/models_weight/detection_plaque_valcor_010622/caffemodel size_local : 349723073 size in s3 : 349723073 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56 caffemodel already exist and didn't need to update /data/models_weight/detection_plaque_valcor_010622/test.prototxt size_local : 7163 size in s3 : 7163 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel About to compute detect_faster_rcnn : len(args) : 1 Inside frcnn step exec : nb paths : 1 image_path : temp/1744011476_3752194_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg image_size (600, 800, 3) [[[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] ... [[ 14 16 16] [ 13 15 15] [ 11 13 13] ... [198 206 205] [198 206 205] [198 206 205]] [[ 16 18 18] [ 14 16 16] [ 11 13 13] ... [206 214 213] [206 214 213] [206 214 213]] [[ 13 15 15] [ 12 14 14] [ 9 11 11] ... [210 218 217] [210 218 217] [210 218 217]]] Detection took 0.087s for 300 object proposals c : plaque list_crops.shape (72, 5) proba : 0.06384062 (374.12692, 293.91928, 430.81015, 317.80862) proba : 0.05222174 (382.17767, 297.18866, 552.3578, 344.65796) proba : 0.01227123 (345.35678, 272.42987, 468.85764, 320.7243) We are managing local photo_id len de result frcnn : 1 After datou_step_exec type output : time spend for datou_step_exec : 2.519432306289673 time spend to save output : 5.602836608886719e-05 total time spend for step 1 : 2.5194883346557617 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True Inside saveFrcnn : final : True verbose : True threshold to save the result : 0.1 output flattener : [(0, 493029425, 4370, 374, 430, 293, 317, 0.06384062, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05222174, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.01227123, None)] Warning : no hashtag_ids to insert in the database final : True begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4184', None, '917754606', '0', 0, '0', 493061979, '0', None)] time used for this insertion : 0.013822317123413086 [917754606] map_info['map_portfolio_photo'] : {} final : True mtd_id 4184 list_pids : [917754606] Looping around the photos to save general results len do output : 1 /0 before output type Managing all output in save final without adding information in the mtr_datou_result ('4184', None, None, None, None, None, None, None, None) ('4184', None, '917754606', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4184', None, '917754606', None, None, None, None, None, None)] time used for this insertion : 0.012729406356811523 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {0: [[(0, 493029425, 4370, 374, 430, 293, 317, 0.06384062, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05222174, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.01227123, None)], 'temp/1744011476_3752194_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=2 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 2 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=2 # 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 1 thcl is not linked in the step_by_step architecture ! WARNING : step 2 argmax 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 ! no param json to modify List Step Type Loaded in datou : thcl, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (916235064) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 916235064 download finish for photo 916235064 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.19536542892456055 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:thcl Mon Apr 7 09:37:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011479_3752194_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1744011479_3752194_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'355': 1} we are using the classfication for only one thcl 355 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (66, 66, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.007827997207641602 time to convert the images to numpy array : 0.001773834228515625 total time to convert the images to numpy array : 0.009957551956176758 list photo_ids error: [] list photo_ids correct : [916235064] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 355 To do loadFromThcl(), then load ParamDescType : thcl355 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (355) thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'c_elysee_1027_gao__port_506302,mokka_1027_gao__port_506374,captur_1027_gao__port_506399,sorento_1027_gao__port_506192,navara_1027_gao__port_506205,xc90_1027_gao__port_506350,saxo_1027_gao__port_506052,trafic_1027_gao__port_506295,punto_evo_1027_gao__port_506066,5_1027_gao__port_506117,250_1027_gao__port_506065,d_max_1027_gao__port_506125,panamera_1027_gao__port_506387,alhambra_1027_gao__port_506381,x6_1027_gao__port_506349,vitara_1027_gao__port_506328,fiesta_1027_gao__port_506377,qashqai_1027_gao__port_506286,147_1027_gao__port_506124,c5_1027_gao__port_506172,q5_1027_gao__port_506206,giulia_1027_gao__port_506178,karl_1027_gao__port_506371,mehari_1027_gao__port_506076,911_1027_gao__port_506114,508_1027_gao__port_506329,idea_1027_gao__port_506122,megane_1027_gao__port_506220,ghibli_1027_gao__port_506174,touareg_1027_gao__port_506224,i10_1027_gao__port_506232,jumper_1027_gao__port_506234,classe_clk_1027_gao__port_506173,kuga_1027_gao__port_506181,ct_1027_gao__port_506323,leon_1027_gao__port_506326,ds5_1027_gao__port_506376,cordoba_1027_gao__port_506048,classe_cla_1027_gao__port_506400,jumpy_1027_gao__port_506179,avensis_1027_gao__port_506311,juke_1027_gao__port_506325,4008_1027_gao__port_506402,190_series_1027_gao__port_506051,serie_3_1027_gao__port_506294,q7_1027_gao__port_506318,glc_1027_gao__port_506303,grand_vitara_1027_gao__port_506175,s40_1027_gao__port_506099,toledo_1027_gao__port_506061,5008_1027_gao__port_506337,continental_1027_gao__port_506250,coupe_1027_gao__port_506082,iq_1027_gao__port_506166,407_1027_gao__port_506133,touran_1027_gao__port_506308,300c_1027_gao__port_506078,classe_gl_1027_gao__port_506340,vivaro_1027_gao__port_506310,sl_1027_gao__port_506100,elise_1027_gao__port_506121,1007_1027_gao__port_506070,i40_1027_gao__port_506218,bipper_tepee_1027_gao__port_506227,focus_1027_gao__port_506272,primera_1027_gao__port_506147,r4_1027_gao__port_506160,a8_1027_gao__port_506265,boxer_1027_gao__port_506202,s5_1027_gao__port_506222,r21_1027_gao__port_506093,c3_1027_gao__port_506257,santa_fe_1027_gao__port_506208,m4_1027_gao__port_506344,safrane_1027_gao__port_506077,classe_gle_1027_gao__port_506395,0_1027_gao__port_506094,ix35_1027_gao__port_506219,carens_1027_gao__port_506298,classe_a_1027_gao__port_506339,ix20_1027_gao__port_506343,note_1027_gao__port_506365,a5_1027_gao__port_506200,sx4_1027_gao__port_506348,sandero_1027_gao__port_506198,3008_1027_gao__port_506385,q50_1027_gao__port_506239,latitude_1027_gao__port_506236,v40_1027_gao__port_506391,xsara_1027_gao__port_506087,grand_c_max_1027_gao__port_506342,swift_1027_gao__port_506149,serie_1_1027_gao__port_506184,xc70_1027_gao__port_506393,master_1027_gao__port_506203,clio_1027_gao__port_506280,duster_1027_gao__port_506216,traveller_1027_gao__port_506403,tipo_1027_gao__port_506355,rav_4_1027_gao__port_506332,coccinelle_1027_gao__port_506259,spacetourer_1027_gao__port_506401,xe_1027_gao__port_506357,ds3_1027_gao__port_506324,mx_5_1027_gao__port_506098,land_cruiser_1027_gao__port_506315,classe_b_1027_gao__port_506335,806_1027_gao__port_506088,rx_8_1027_gao__port_506046,spark_1027_gao__port_506185,6_1027_gao__port_506171,bravo_1027_gao__port_506080,nx_1027_gao__port_506345,sharan_1027_gao__port_506347,x_type_1027_gao__port_506067,jimny_1027_gao__port_506233,wrangler_1027_gao__port_506225,c_crosser_1027_gao__port_506312,v70_1027_gao__port_506278,classe_e_1027_gao__port_506300,classe_v_1027_gao__port_506258,m3_1027_gao__port_506182,abarth_500_1027_gao__port_506226,serie_6_1027_gao__port_506262,modus_1027_gao__port_506146,3_1027_gao__port_506113,405_1027_gao__port_506108,allroad_1027_gao__port_506297,auris_1027_gao__port_506322,galaxy_1027_gao__port_506143,giulietta_1027_gao__port_506363,106_1027_gao__port_506073,classe_m_1027_gao__port_506154,espace_1027_gao__port_506313,panda_1027_gao__port_506189,rcz_1027_gao__port_506197,4007_1027_gao__port_506162,classe_cl_1027_gao__port_506249,leaf_1027_gao__port_506139,octavia_1027_gao__port_506237,ds4_1027_gao__port_506336,freelander_1027_gao__port_506084,evasion_1027_gao__port_506109,punto_1027_gao__port_506106,2cv_1027_gao__port_506045,x4_1027_gao__port_506392,antara_1027_gao__port_506247,murano_1027_gao__port_506316,alto_1027_gao__port_506201,meriva_1027_gao__port_506353,orlando_1027_gao__port_506305,new_beetle_1027_gao__port_506050,306_1027_gao__port_506145,tiguan_1027_gao__port_506362,s_type_1027_gao__port_506101,c1_1027_gao__port_506128,vectra_1027_gao__port_506044,outlander_1027_gao__port_506317,307_1027_gao__port_506074,a6_s6_1027_gao__port_506134,nemo_combi_1027_gao__port_506196,berlingo_1027_gao__port_506194,partner_1027_gao__port_506285,cayenne_1027_gao__port_506177,quattroporte_1027_gao__port_506240,c_max_1027_gao__port_506282,fabia_1027_gao__port_506396,cx_3_1027_gao__port_506281,x_trail_1027_gao__port_506264,scirocco_1027_gao__port_506276,matiz_1027_gao__port_506144,tigra_1027_gao__port_506069,escort_1027_gao__port_506091,c2_1027_gao__port_506081,mini_1027_gao__port_506168,i30_1027_gao__port_506291,picanto_1027_gao__port_506238,mito_1027_gao__port_506072,impreza_1027_gao__port_506085,kangoo_1027_gao__port_506235,a4_1027_gao__port_506193,cayman_1027_gao__port_506268,sportage_1027_gao__port_506148,up_1027_gao__port_506356,optima_1027_gao__port_506386,defender_1027_gao__port_506229,serie_2_1027_gao__port_506256,edge_1027_gao__port_506187,r19_1027_gao__port_506110,jetta_1027_gao__port_506304,eos_1027_gao__port_506115,accord_1027_gao__port_506214,yaris_1027_gao__port_506334,classe_cls_1027_gao__port_506289,polo_1027_gao__port_506361,serie_4_1027_gao__port_506366,mini_cabriolet_1027_gao__port_506204,prius_1027_gao__port_506190,lodgy_1027_gao__port_506188,serie_7_1027_gao__port_506307,c15_1027_gao__port_506055,kadjar_1027_gao__port_506389,insignia_1027_gao__port_506364,308_1027_gao__port_506279,roomster_1027_gao__port_506241,80_1027_gao__port_506057,309_1027_gao__port_506063,tucson_1027_gao__port_506320,x3_1027_gao__port_506212,xf_1027_gao__port_506263,2008_1027_gao__port_506394,passat_1027_gao__port_506306,compass_1027_gao__port_506260,twingo_1027_gao__port_506309,micra_1027_gao__port_506221,golf_1027_gao__port_506155,soul_1027_gao__port_506176,rapid_1027_gao__port_506398,forester_1027_gao__port_506360,slk_1027_gao__port_506210,forfour_1027_gao__port_506341,serie_5_1027_gao__port_506209,xj_1027_gao__port_506170,pajero_1027_gao__port_506097,agila_1027_gao__port_506119,a6_1027_gao__port_506163,fox_1027_gao__port_506092,boxster_1027_gao__port_506267,altea_1027_gao__port_506246,samurai_1027_gao__port_506047,trax_1027_gao__port_506296,getz_1027_gao__port_506058,cherokee_1027_gao__port_506269,koleos_1027_gao__port_506378,z_series_1027_gao__port_506123,ecosport_1027_gao__port_506271,space_star_1027_gao__port_506277,rs3_sportback_1027_gao__port_506207,civic_1027_gao__port_506141,talisman_1027_gao__port_506390,f_pace_1027_gao__port_506314,classe_c_1027_gao__port_506299,tt_1027_gao__port_506075,pathfinder_1027_gao__port_506183,156_1027_gao__port_506157,cx_5_1027_gao__port_506228,scenic_1027_gao__port_506255,yeti_1027_gao__port_506358,mustang_1027_gao__port_506053,stilo_1027_gao__port_506060,ateca_1027_gao__port_506382,fiorino_1027_gao__port_506217,classe_glk_1027_gao__port_506290,fortwo_1027_gao__port_506230,cruze_1027_gao__port_506186,107_1027_gao__port_506213,aygo_1027_gao__port_506248,rx_1027_gao__port_506354,500_1027_gao__port_506245,bora_1027_gao__port_506104,transit_1027_gao__port_506111,pt_cruiser_1027_gao__port_506054,patrol_1027_gao__port_506068,r8_1027_gao__port_506156,xm_1027_gao__port_506102,s60_1027_gao__port_506191,aveo_1027_gao__port_506158,captiva_1027_gao__port_506159,ax_1027_gao__port_506153,rexton_1027_gao__port_506107,camaro_1027_gao__port_506056,ypsilon_1027_gao__port_506131,delta_1027_gao__port_506165,c4_1027_gao__port_506370,zx_1027_gao__port_506161,verso_1027_gao__port_506242,superb_1027_gao__port_506327,r5_1027_gao__port_506253,caddy_1027_gao__port_506330,x5_1027_gao__port_506243,f_type_1027_gao__port_506231,fusion_1027_gao__port_506096,dokker_1027_gao__port_506331,205_1027_gao__port_506062,macan_1027_gao__port_506195,tourneo_1027_gao__port_506369,108_1027_gao__port_506384,9_3_1027_gao__port_506071,mondeo_1027_gao__port_506116,cr_v_1027_gao__port_506164,c30_1027_gao__port_506090,pulsar_1027_gao__port_506397,ibiza_1027_gao__port_506273,a1_1027_gao__port_506338,matrix_1027_gao__port_506140,carnival_1027_gao__port_506136,xantia_1027_gao__port_506086,terrano_1027_gao__port_506083,q3_1027_gao__port_506275,hr_v_1027_gao__port_506283,expert_1027_gao__port_506142,multivan_1027_gao__port_506383,venga_1027_gao__port_506380,scudo_1027_gao__port_506129,laguna_1027_gao__port_506368,vel_satis_1027_gao__port_506130,b_max_1027_gao__port_506367,ignis_1027_gao__port_506292,159_1027_gao__port_506064,grande_punto_1027_gao__port_506138,logan_1027_gao__port_506167,s_max_1027_gao__port_506223,caravelle_1027_gao__port_506351,adam_1027_gao__port_506079,406_1027_gao__port_506132,q30_1027_gao__port_506293,almera_1027_gao__port_506089,corsa_1027_gao__port_506095,corolla_1027_gao__port_506120,xc60_1027_gao__port_506388,viano_1027_gao__port_506211,pro_cee_d_1027_gao__port_506274,a3_1027_gao__port_506321,v50_1027_gao__port_506150,voyager_1027_gao__port_506169,corvette_1027_gao__port_506049,rio_1027_gao__port_506379,jazz_1027_gao__port_506252,200_1027_gao__port_506112,tts_1027_gao__port_506199,zafira_1027_gao__port_506287,asx_1027_gao__port_506266,607_1027_gao__port_506118,207_1027_gao__port_506103,classe_s_1027_gao__port_506301,c6_1027_gao__port_506105,express_1027_gao__port_506137,classe_gla_1027_gao__port_506352,v60_1027_gao__port_506333,ka_1027_gao__port_506180,range_rover_1027_gao__port_506254,discovery_1027_gao__port_506375,classe_r_1027_gao__port_506270,transporter_1027_gao__port_506319,cee_d_1027_gao__port_506288,zoe_1027_gao__port_506244,i20_1027_gao__port_506284,gtv_1027_gao__port_506059,s4_avant_1027_gao__port_506261,x1_1027_gao__port_506372,autres_1027_gao__port_506127,208_1027_gao__port_506359,c8_1027_gao__po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'506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3390) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) To loadFromThcl() : net_3390 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6275 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3390) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) param : , param.caffemodel : car_360_1027 None mean_file_type : mean_file_path : prototxt_file_path : model : car_360_1027 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : car_360_1027 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/car_360_1027 /data/models_weight/car_360_1027/caffemodel size_local : 542944640 size in s3 : 542944640 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 caffemodel already exist and didn't need to update /data/models_weight/car_360_1027/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy_fc.prototxt size_local : 1132 size in s3 : 1132 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy.prototxt size_local : 5654 size in s3 : 5654 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/mean.npy size_local : 1572944 size in s3 : 1572944 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55 mean.npy already exist and didn't need to update /data/models_weight/car_360_1027/synset_words.txt size_local : 13687 size in s3 : 13687 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/caffe_cuda8_python3/python/:/home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt caffemodel_filename : /data/models_weight/car_360_1027/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6275 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 0.0256192684173584 time used to do the prediction : 0.06524419784545898 save descriptor for thcl : 355 (1, 512, 7, 7) Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] code_as_byte_string:b'0000000000'| time to traite the descriptors : 0.05521035194396973 Testing : ['916235064'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (916235064) Catched exception ! Connect or reconnect ! result : {916235064: {'photo_id': 916235064, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2017/10/14/6293d1bb790dc6902450e7c572b7d10b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': None}} list_photo_exists : [916235064] storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.7961697578430176 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True time used to find the portfolios of the photos select button_legend_list from MTRDatou.classification_theme where id = 355 SAVE THCL, output : {'916235064': [[('916235064', 'c_elysee_1027_gao__port_506302', 0.0018814786, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.0011636035, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.0008158105, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.0011772629, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.002584885, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.0041698986, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.0034811092, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.007366024, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021888178, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.0005797845, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.00459142, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.0031585207, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.0022509291, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.0053196102, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0010999229, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.0054023066, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.003919013, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014787434, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.001977911, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.0012442068, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.0015050303, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.0021694815, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.002708097, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.004704254, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019420206, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.0009585107, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.00076997856, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019468791, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.0013724779, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.0016203104, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.0013925236, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.010042986, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010794201, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.0008447142, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.00125209, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.002584461, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.0012430602, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.002865119, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.0012950209, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010337511, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.0018767869, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.0011343849, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.0015757686, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.0039807935, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.002874148, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023353833, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.0012106788, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.0011447206, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.002233969, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.0017464798, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.0046988884, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021914404, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.0022634668, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.0018174615, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.00090566266, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.0020401254, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.0025335078, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.004488848, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.0034248498, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.0031357943, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.0010257282, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.0015354675, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.0005915256, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.004028648, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.001158627, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.0012159226, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.014967389, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.001132172, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.010543962, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.0011986082, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.0041861627, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.0023634997, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.0016323377, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.0015569017, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.0013960326, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.0021980354, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.0088279685, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.001461386, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.0008824813, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.0024714016, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.0010092782, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.0015961941, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 0.001533138, 332, '355'), ('916235064', 'sx4_1027_gao__port_506348', 0.0014916813, 332, '355'), ('916235064', 'sandero_1027_gao__port_506198', 0.0014585552, 332, '355'), ('916235064', '3008_1027_gao__port_506385', 0.005645686, 332, '355'), ('916235064', 'q50_1027_gao__port_506239', 0.0011165685, 332, '355'), 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('916235064', 'viano_1027_gao__port_506211', 0.0026942543, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.00083202706, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.0037381158, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.00079198513, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.0030522533, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.0037236006, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.0017740403, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.0015304575, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.004087633, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.0011863271, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.0026952173, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.001140711, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012529094, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.0015149334, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.0031656388, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017348797, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.016726706, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.0018256265, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.0021458818, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.0014153523, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.0020553055, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.00229618, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013944588, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.011967214, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.0010548229, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020714751, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.0017870081, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.0057229334, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.0027668108, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.0017144573, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.0048255646, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.0018686032, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.0012579731, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.0012625746, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.0009245161, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.007465598, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.00072905724, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.00103866, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.0006911978, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.0021415532, 332, '355')]]} begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 8.344650268554688e-06 save missing photos in datou_result : time spend for datou_step_exec : 6.651653528213501 time spend to save output : 2.4391777515411377 total time spend for step 1 : 9.090831279754639 step2:argmax Mon Apr 7 09:38:08 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011479_3752194_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064} map_photo_id_path_extension : {916235064: {'path': 'temp/1744011479_3752194_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 355 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.017712189, 332, '355'), 'temp/1744011479_3752194_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('916235064', '2049863950', '332') ... last line : ('916235064', '2049863950', '332') time used for this insertion : 0.017695903778076172 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.016008853912353516 len list_finale : 1, len picture : 1 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('2', None, '916235064', 'c15_1027_gao__port_506055', None, None, '2049863950', '0.017712189', None)] time used for this insertion : 0.014300107955932617 saving photo_ids in datou_result photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 5.245208740234375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.000293731689453125 time spend to save output : 0.048604488372802734 total time spend for step 2 : 0.04889822006225586 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'916235064': [('916235064', 'c15_1027_gao__port_506055', 0.017712189, 332, '355'), 'temp/1744011479_3752194_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']} ############################### TEST tfhub2 ################################ TEST TFHUB2 ######################## test with use_multi_inputs=0 ######################## Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4567 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4567 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4567 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4567 # 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 12835 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12836 argmax 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 ! no param json to modify List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1171252784,1171252764,1171252487) Found this number of photos: 3 ##### Call download_photos : nb_thread : 5 begin to download photo : 1171252487 begin to download photo : 1171252764 begin to download photo : 1171252784 download finish for photo 1171252784 download finish for photo 1171252764 download finish for photo 1171252487 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 ##### After load_data_input time to download the photos : 0.20481371879577637 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:tfhub_classification2 Mon Apr 7 09:38:08 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011488_3752194_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1744011488_3752194_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1744011488_3752194_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487} map_photo_id_path_extension : {1171252784: {'path': 'temp/1744011488_3752194_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1744011488_3752194_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1744011488_3752194_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3609': 1} we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory 2025-04-07 09:38:12.419088: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-07 09:38:12.420633: 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 2025-04-07 09:38:12.420767: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:38:12.420817: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:38:12.429211: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:38:12.429341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:38:12.445248: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:38:12.448994: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:38:12.475815: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:38:12.477455: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:38:12.480735: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-07 09:38:12.519296: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-07 09:38:12.521776: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f154c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-07 09:38:12.521836: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-07 09:38:12.527000: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4b3422a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-07 09:38:12.527030: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-07 09:38:12.528381: 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 2025-04-07 09:38:12.528617: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:38:12.528657: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-07 09:38:12.528870: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-07 09:38:12.528919: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-07 09:38:12.528986: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-07 09:38:12.529041: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-07 09:38:12.529093: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-07 09:38:12.530416: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-07 09:38:12.530522: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-07 09:38:12.530603: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-07 09:38:12.530620: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-07 09:38:12.530641: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-07 09:38:12.532577: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) l 3637 free memory gpu now : 6275 max_wait_temp : 1 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (3609) thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}] thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'} Update svm_hashtag_type_desc : 5832 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (5832) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22)) model_name : tfhub_19_06_2023 model_param file didn't exist model_name : tfhub_19_06_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) local folder : /data/models_weight/tfhub_19_06_2023 /data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png size_local : 57753 size in s3 : 57753 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg size_local : 79724 size in s3 : 79724 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg size_local : 83556 size in s3 : 83556 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg size_local : 74107 size in s3 : 74107 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg size_local : 72705 size in s3 : 72705 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg size_local : 70874 size in s3 : 70874 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 checkpoint already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216488 size in s3 : 216488 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279708 size in s3 : 32279708 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_weights.h5 size_local : 16499144 size in s3 : 16499144 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= module (KerasLayer) (None, 1280) 4049564 _________________________________________________________________ tfhub_19_06_2023dense (Dense (None, 5) 6405 ================================================================= Total params: 4,055,969 Trainable params: 6,405 Non-trainable params: 4,049,564 _________________________________________________________________ Loading Weights... time used to create the model : 11.098881483078003 time used to load_weights : 0.22019696235656738 0it [00:00, ?it/s] 3it [00:00, 459.65it/s]2025-04-07 09:38:26.909271: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1744011488_3752194_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1744011488_3752194_1171252764_29d5179a892cc50aadc9d67245534b59.jpg temp/1744011488_3752194_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 3.9114480018615723 ['temp/image000000000_1744011488_3752194_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'temp/image000000001_1744011488_3752194_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'temp/image000000002_1744011488_3752194_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3609 (3, 1280) Got the blobs of the net to insert : [0, 6, 0, 0, 1, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000001'| Got the blobs of the net to insert : [0, 6, 0, 1, 0, 0, 0, 1, 0, 0] code_as_byte_string:b'0006000100'| Got the blobs of the net to insert : [0, 9, 0, 0, 0, 0, 1, 0, 0, 0] code_as_byte_string:b'0009000000'| time to traite the descriptors : 0.033360958099365234 Testing : ['1171252784', '1171252764', '1171252487'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171252784,1171252764,1171252487) result : {1171252487: {'photo_id': 1171252487, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/5ebdd6b0a6bb39942a3808ed114806de.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_35_005998m0.jpg 0.4259977941513062 for time 6.000020980834961, id_amount 3 this amount prod time diff : 0.006000020980834961'}, 1171252764: {'photo_id': 1171252764, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/29d5179a892cc50aadc9d67245534b59.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_41_005998m0.jpg 0.4319977941513062 for time 6.0, id_amount 3 this amount prod time diff : 0.006'}, 1171252784: {'photo_id': 1171252784, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/22/5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_21_55_47_006033m0.jpg 0.4379978291988373 for time 6.000035047531128, id_amount 4 this amount prod time diff : 0.006000035047531128'}} list_photo_exists : [1171252487, 1171252764, 1171252784] storage_type for insertDescriptorsMulti : 3 To insert : 1171252784 To insert : 1171252764 To insert : 1171252487 time to insert the descriptors : 1.0159177780151367 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171252784, 1171252764, 1171252487] map_info['map_portfolio_photo'] : {} final : False mtd_id 4567 list_pids : [1171252784, 1171252764, 1171252487] Looping around the photos to save general results len do output : 3 /1171252784Didn't retrieve data . /1171252764Didn't retrieve data . /1171252487Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252784', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252764', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252487', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4567', None, '1171252784', 'None', None, None, None, None, None), ('4567', None, '1171252764', 'None', None, None, None, None, None), ('4567', None, '1171252487', 'None', None, None, None, None, None)] time used for this insertion : 0.013387203216552734 save_final save missing photos in datou_result : time spend for datou_step_exec : 22.98430037498474 time spend to save output : 0.013697385787963867 total time spend for step 1 : 22.997997760772705 step2:argmax Mon Apr 7 09:38:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011488_3752194_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1744011488_3752194_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764, 'temp/1744011488_3752194_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487} map_photo_id_path_extension : {1171252784: {'path': 'temp/1744011488_3752194_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1744011488_3752194_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1744011488_3752194_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 3609 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.96774435, 4674, '3609'), 'temp/1744011488_3752194_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.98535687, 4674, '3609'), 'temp/1744011488_3752194_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.92620236, 4674, '3609'), 'temp/1744011488_3752194_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('1171252784', '495916461', '4674') ... last line : ('1171252487', '495916461', '4674') time used for this insertion : 0.014465093612670898 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.016668081283569336 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4567', None, '1171252784', 'jrm', None, None, '495916461', '0.96774435', None), ('4567', None, '1171252764', 'jrm', None, None, '495916461', '0.98535687', None), ('4567', None, '1171252487', 'jrm', None, None, '495916461', '0.92620236', None)] time used for this insertion : 0.011982202529907227 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 3.5762786865234375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00015997886657714844 time spend to save output : 0.0477604866027832 total time spend for step 2 : 0.04792046546936035 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252784': [(1171252784, 'jrm', 0.96774435, 4674, '3609'), 'temp/1744011488_3752194_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.98535687, 4674, '3609'), 'temp/1744011488_3752194_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.92620236, 4674, '3609'), 'temp/1744011488_3752194_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']} --------------------- test with use_multi_inputs=0 is succeded ------------------- ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4621 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=4621 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 4621 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=4621 # 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 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax 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 ! no param json to modify List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1171291875,1171275372,1171275314) Found this number of photos: 3 ##### Call download_photos : nb_thread : 5 begin to download photo : 1171275314 begin to download photo : 1171275372 begin to download photo : 1171291875 download finish for photo 1171291875 download finish for photo 1171275314 download finish for photo 1171275372 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 ##### After load_data_input time to download the photos : 0.2483513355255127 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:tfhub_classification2 Mon Apr 7 09:38:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011511_3752194_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1744011511_3752194_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1744011511_3752194_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171291875: {'path': 'temp/1744011511_3752194_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1744011511_3752194_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1744011511_3752194_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step TFHub with tf2 ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'3655': 1} we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory l 3637 free memory gpu now : 2723 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (3655) thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (5862) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] local folder : /data/models_weight/tfhub_18_7_2023 /data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png size_local : 54360 size in s3 : 54360 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg size_local : 72583 size in s3 : 72583 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg size_local : 81681 size in s3 : 81681 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg size_local : 79510 size in s3 : 79510 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg size_local : 59936 size in s3 : 59936 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg size_local : 78974 size in s3 : 78974 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 checkpoint already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216529 size in s3 : 216529 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279748 size in s3 : 32279748 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_weights.h5 size_local : 16500868 size in s3 : 16500868 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_2 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_1[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_2[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 8.40056562423706 time used to load_weights : 0.14107346534729004 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.1342425346374512 ['temp/1744011511_3752194_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'temp/1744011511_3752194_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'temp/1744011511_3752194_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 (3, 1280) Got the blobs of the net to insert : [0, 1, 0, 0, 11, 0, 2, 2, 0, 0] code_as_byte_string:b'000100000b'| Got the blobs of the net to insert : [0, 0, 0, 0, 8, 0, 0, 0, 3, 0] code_as_byte_string:b'0000000008'| Got the blobs of the net to insert : [0, 0, 0, 0, 14, 0, 1, 4, 0, 0] code_as_byte_string:b'000000000e'| time to traite the descriptors : 0.04352545738220215 Testing : ['1171291875', '1171275314', '1171275372'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (1171291875,1171275314,1171275372) result : {1171275314: {'photo_id': 1171275314, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/6e0a72c8fa00d5e4b018bd689b547133.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_54_22_6187.jpg'}, 1171275372: {'photo_id': 1171275372, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/76d81364ff7df843bff095f45c07ba35.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_56_46_6098.jpg'}, 1171291875: {'photo_id': 1171291875, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2023/2/23/b62cd9e0d976b143f86fe82d072798c0.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22022023_23_59_04_5803.jpg'}} list_photo_exists : [1171275314, 1171275372, 1171291875] storage_type for insertDescriptorsMulti : 3 To insert : 1171291875 To insert : 1171275314 To insert : 1171275372 time to insert the descriptors : 1.464660882949829 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : False verbose : True saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171291875, 1171275314, 1171275372] map_info['map_portfolio_photo'] : {} final : False mtd_id 4621 list_pids : [1171291875, 1171275314, 1171275372] Looping around the photos to save general results len do output : 3 /1171291875Didn't retrieve data . /1171275314Didn't retrieve data . /1171275372Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171291875', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4621', None, '1171291875', 'None', None, None, None, None, None), ('4621', None, '1171275314', 'None', None, None, None, None, None), ('4621', None, '1171275372', 'None', None, None, None, None, None)] time used for this insertion : 0.012798309326171875 save_final save missing photos in datou_result : time spend for datou_step_exec : 20.90977692604065 time spend to save output : 0.013106822967529297 total time spend for step 1 : 20.92288374900818 step2:argmax Mon Apr 7 09:38:52 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011511_3752194_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1744011511_3752194_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1744011511_3752194_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372} map_photo_id_path_extension : {1171291875: {'path': 'temp/1744011511_3752194_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275314: {'path': 'temp/1744011511_3752194_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1744011511_3752194_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 After datou_step_exec type output : map_portfolio_photo : len 0 keys : dict_keys([]) Inside saveOutput : final : True verbose : True photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.970654, 4723, '3655'), 'temp/1744011511_3752194_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.96514684, 4723, '3655'), 'temp/1744011511_3752194_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.96744233, 4723, '3655'), 'temp/1744011511_3752194_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s) first line : ('1171291875', '2107748999', '4723') ... last line : ('1171275372', '2107748999', '4723') time used for this insertion : 0.014119148254394531 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score) time used for this insertion : 0.014993667602539062 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('4621', None, '1171291875', 'tapis_vide', None, None, '2107748999', '0.970654', None), ('4621', None, '1171275314', 'tapis_vide', None, None, '2107748999', '0.96514684', None), ('4621', None, '1171275372', 'tapis_vide', None, None, '2107748999', '0.96744233', None)] time used for this insertion : 0.012629270553588867 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [] time used for this insertion : 3.5762786865234375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.000141143798828125 time spend to save output : 0.04642152786254883 total time spend for step 2 : 0.04656267166137695 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171291875': [(1171291875, 'tapis_vide', 0.970654, 4723, '3655'), 'temp/1744011511_3752194_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96514684, 4723, '3655'), 'temp/1744011511_3752194_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.96744233, 4723, '3655'), 'temp/1744011511_3752194_1171275372_76d81364ff7df843bff095f45c07ba35.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (358) thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 SELECT hashtag_id,hashtag FROM MTRBack.hashtags where hashtag in ('FirstUploadExperveo_vignette__port_505674','CAR_EXTERIEUR_Roue__port_503398','FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486','FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485','CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465','CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198','CAR_EXTERIEUR_Face_avant_axe_droit__port_504451','CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235','FirstUploadExperveo_vin__port_505675','CAR_EXTERIEUR_cote_droite__port_504108','CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565','FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483','CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201','cartegrise_orientation__port_505064','CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217','CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531','CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218','CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214','CAR_EXTERIEUR_Angle_avant_droit__port_504087','FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484','CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563','CAR_EXTERIEUR_Angle_arriere_droit__port_504160','CAR_EXTERIEUR_arriere__port_504184','CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562','INTERIEUR_Compteur_kilometrique__port_503644','CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494','CAR_EXTERIEUR_Angle_arriere_gauche__port_504170','CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226','CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202','CAR_EXTERIEUR_moteur__port_503704','FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487','CAR_INTERIEUR_siege_arriere_class_1__port_506551','CAR_EXTERIEUR_avant__port_504146','CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215','CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225','CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564','FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482','CAR_INTERIEUR_coffre__port_503412','FirstUploadExperveo_rouetranche__port_505677','UploadPhotoImmatBest_class_1__port_505051','CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532','CAR_EXTERIEUR_angle_avant_gauche__port_504098','CAR_EXTERIEUR_face_avant_axe_gauche__port_504236','CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540','CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233','CAR_EXTERIEUR_roue_de_secour__port_503763','CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199','CAR_EXTERIEUR_cote_gauche__port_504017','CAR_INTERIEUR_avant_volant_class_1__port_506503','CAR_INTERIEUR_avant_volant_class_2__port_506504','CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'); 51 dict_keys(['cartegrise_orientation__port_505064', 'car_exterieur_angle_arriere_droit_axe_arriere__port_504217', 'car_exterieur_angle_arriere_droit_axe_droit__port_504215', 'car_exterieur_angle_arriere_droit__port_504160', 'car_exterieur_angle_arriere_gauche_axe_arriere__port_504201', 'car_exterieur_angle_arriere_gauche_axe_gauche__port_504199', 'car_exterieur_angle_arriere_gauche__port_504170', 'car_exterieur_angle_avant_droit_axe_arriere__port_504226', 'car_exterieur_angle_avant_droit_axe_droit__port_504225', 'car_exterieur_angle_avant_droit__port_504087', 'car_exterieur_angle_avant_gauche_axe_avant__port_504235', 'car_exterieur_angle_avant_gauche_axe_gauche__port_504234', 'car_exterieur_angle_avant_gauche__port_504098', 'car_exterieur_arriere__port_504184', 'car_exterieur_avant__port_504146', 'car_exterieur_cote_droite__port_504108', 'car_exterieur_cote_droit_axe_arriere__port_504214', 'car_exterieur_cote_droit_axe_avant__port_504465', 'car_exterieur_cote_gauche_axe_arriere__port_504198', 'car_exterieur_cote_gauche_axe_avant__port_504233', 'car_exterieur_cote_gauche__port_504017', 'car_exterieur_face_arriere_axe_droit__port_504218', 'car_exterieur_face_arriere_axe_gauche__port_504202', 'car_exterieur_face_avant_axe_droit__port_504451', 'car_exterieur_face_avant_axe_gauche__port_504236', 'car_exterieur_moteur__port_503704', 'car_exterieur_roue_de_secour__port_503763', 'car_exterieur_roue__port_503398', 'car_interieur_avant_volant_class_1__port_506503', 'car_interieur_avant_volant_class_2__port_506504', 'car_interieur_avant_volant_class_6_boutonrond__port_506562', 'car_interieur_avant_volant_class_6_class_2__port_506563', 'car_interieur_avant_volant_class_6_ecrangrosplan__port_506564', 'car_interieur_avant_volant_class_6_levierdevitesse__port_506565', 'car_interieur_avant_vue-arriere_class_1__port_506531', 'car_interieur_avant_vue-arriere_class_2__port_506532', 'car_interieur_avant_vue_droite_habitacle_class_1__port_506540', 'car_interieur_avant_vue_gauche_habitacle_class_1__port_506494', 'car_interieur_coffre__port_503412', 'car_interieur_siege_arriere_class_1__port_506551', 'firstuploadexperveo_carrosseriegrosplan_carrosserie__port_506483', 'firstuploadexperveo_carrosseriegrosplan_class_6__port_506487', 'firstuploadexperveo_carrosseriegrosplan_morceauderoue__port_506484', 'firstuploadexperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'firstuploadexperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'firstuploadexperveo_carrosseriegrosplan_vindanslamoquette__port_506486', 'firstuploadexperveo_rouetranche__port_505677', 'firstuploadexperveo_vignette__port_505674', 'firstuploadexperveo_vin__port_505675', 'interieur_compteur_kilometrique__port_503644', 'uploadphotoimmatbest_class_1__port_505051']) select photo_hashtag_type from MTRDatou.classification_theme where id = 358 thcl : 358 photo_hashtag_type : 337 SELECT phi.hashtag_id , phi.photo_id FROM MTRBack.photo_hashtag_ids phi, MTRUser.mtr_portfolio_photos mp where phi.type = 337 and phi.photo_id = mp.mtr_photo_id and mp.mtr_portfolio_id =510365; {510365: [(917973295, 1), (917973297, 1), (917973302, 1), (917973293, 7), (917973296, 11), (917973300, 11), (917973286, 13), (917973289, 13), (917973301, 24), (917973285, 29), (917973290, 29), (917973299, 29), (917973304, 35), (917973287, 36), (917973298, 36), (917973305, 36), (917973292, 37), (917973291, 41), (917973303, 41), (917973294, 42), (917973288, 46)]} ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=230 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=230 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 230 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=230 # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917849322) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917849322 download finish for photo 917849322 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.16888999938964844 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:rotate Mon Apr 7 09:39:15 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_rotate ! We are in a linear step without datou_depend ! rotate photos of 90,180,270 degres batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 917849322) and `type` in (0) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () map_chi : {} photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0 Use all angle ! Rotation of photo 917849322 of 90 degree temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 90 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] 90 [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] shrink_image : False image_rotate : image_path : temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 180 degree temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 180 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] 180 [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] shrink_image : False image_rotate : image_path : temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg image_rotate.mode : RGB Rotation of photo 917849322 of 270 degree temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 270 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] 270 [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] shrink_image : False image_rotate : image_path : temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg image_rotate.mode : RGB About to upload 3 photos upload in portfolio : 551782 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744011556_3752194 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.1839308738708496 map_filename_photo_id : 3 map_filename_photo_id : {'temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 1350256373, 'temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 1350256374, 'temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 1350256375} Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 list_new_chi_with_photo_id : [] After datou_step_exec type output : time spend for datou_step_exec : 1.4212591648101807 time spend to save output : 7.534027099609375e-05 total time spend for step 1 : 1.4213345050811768 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] map_info['map_portfolio_photo'] : {} final : True mtd_id 230 list_pids : [917849322] Looping around the photos to save general results len do output : 3 /1350256373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256375Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('230', None, None, None, None, None, None, None, None) ('230', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('230', None, '1350256373', 'None', None, None, None, None, None), ('230', None, '1350256374', 'None', None, None, None, None, None), ('230', None, '1350256375', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.08019161224365234 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1350256373: ['917849322', 'temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1350256374: ['917849322', 'temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1350256375: ['917849322', 'temp/1744011555_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]} test rotate only is a success ! test rotate conditionnel Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=233 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=233 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 233 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=233 # 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 ! 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 ! no param json to modify List Step Type Loaded in datou : thcl, argmax, rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (917849322) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 917849322 download finish for photo 917849322 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.1223139762878418 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 3 step1:thcl Mon Apr 7 09:39:17 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'500': 1} we are using the classfication for only one thcl 500 In convert_file_to_np l 337 : 1 l343 1 l357 after caffe.io.load_image dimension du image : (3, (2448, 3264, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0002636909484863281 time to convert the images to numpy array : 1.3921928405761719 total time to convert the images to numpy array : 1.3928279876708984 list photo_ids error: [] list photo_ids correct : [917849322] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 500 To do loadFromThcl(), then load ParamDescType : thcl500 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (500) thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}] thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'} Update svm_hashtag_type_desc : 3517 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3517) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) To loadFromThcl() : net_3517 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2944 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (3517) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) param : , param.caffemodel : orientation_carte_grise_all_2 None mean_file_type : mean_file_path : prototxt_file_path : model : orientation_carte_grise_all_2 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : orientation_carte_grise_all_2 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/orientation_carte_grise_all_2 /data/models_weight/orientation_carte_grise_all_2/caffemodel size_local : 537110520 size in s3 : 537110520 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17 caffemodel already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt size_local : 1130 size in s3 : 1130 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt size_local : 5653 size in s3 : 5653 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31 mean.npy already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/synset_words.txt size_local : 159 size in s3 : 159 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/caffe_cuda8_python3/python/:/home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2944 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 4.979146242141724 time used to do the prediction : 0.22847628593444824 save descriptor for thcl : 500 (1, 512, 7, 7) Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] code_as_byte_string:b'0000000000'| time to traite the descriptors : 0.0538175106048584 Testing : ['917849322'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (917849322) result : {917849322: {'photo_id': 917849322, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2022/9/13/2bd260e91e91df8378dde8bb8b8c4548.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_13092022_12_32_19_5566.jpg'}} list_photo_exists : [917849322] storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.860950231552124 After datou_step_exec type output : time spend for datou_step_exec : 12.908347845077515 time spend to save output : 6.127357482910156e-05 total time spend for step 1 : 12.908409118652344 step2:argmax Mon Apr 7 09:39:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.9976489, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005039084, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.00036609598, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014810988, 507, '500')]]} input_args_next_step : {'917849322': ()} output_args : {'917849322': [[('917849322', 'carteGrisesVerticales__port_549774', 0.9976489, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005039084, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.00036609598, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014810988, 507, '500')]]} args : 917849322 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ([('917849322', 'carteGrisesVerticales__port_549774', 0.9976489, 507, '500'), ('917849322', 'cartegrise_90deg__port_550987', 0.0005039084, 507, '500'), ('917849322', 'cartesGrisesEnvers__port_549765', 0.00036609598, 507, '500'), ('917849322', 'portfolio_270deg__port_550988', 0.0014810988, 507, '500')],) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 500 After datou_step_exec type output : time spend for datou_step_exec : 0.00019931793212890625 time spend to save output : 5.054473876953125e-05 total time spend for step 2 : 0.0002498626708984375 step3:rotate Mon Apr 7 09:39:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976489, 507, '500'), 'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ()} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976489, 507, '500'), 'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} args : 917849322 depend.output_id : 1 complete output_args for input 1 : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976489, 507, '500'), 'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} input_args_next_step : {'917849322': ('temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg',)} output_args : {'917849322': [('917849322', 'carteGrisesVerticales__port_549774', 0.9976489, 507, '500'), 'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg']} args : 917849322 depend.output_id : 0 VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', ('917849322', 'carteGrisesVerticales__port_549774', 0.9976489, 507, '500')) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322} map_photo_id_path_extension : {917849322: {'path': 'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_rotate ! We are in a datou with depends ! angle_condi : {'carteGrisesVerticales__port_549774': 0, 'cartegrise_90deg__port_550987': 270, 'portfolio_270deg__port_550988': 90, 'cartesGrisesEnvers__port_549765': 180} rotate photos for hashtag carteGrisesVerticales__port_549774 of 0 degres 1 photos founded : [917849322] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 917849322) and `type` in (0) Loaded 0 chid ids of type : 0 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in () map_chi : {} photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0 Use all angle ! Rotation of photo 917849322 of 0 degree temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 0 remove_crop_border : False version de PIL : 9.5.0 Needs to change image size ! [[ 1. 0.] [-0. 1.]] 0 [[ 1. 0.] [-0. 1.]] shrink_image : False image_rotate : image_path : temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg path_name_rotate : temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg image_rotate.mode : RGB About to upload 1 photos upload in portfolio : 551782 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744011571_3752194 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.6447625160217285 map_filename_photo_id : 1 map_filename_photo_id : {'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg': 1350256392} Len new_chis : 1 Len list_new_chi_with_photo_id : 0 of type : 0 list_new_chi_with_photo_id : [] rotate photos for hashtag cartegrise_90deg__port_550987 of 270 degres 0 photos founded : [] rotate photos for hashtag portfolio_270deg__port_550988 of 90 degres 0 photos founded : [] rotate photos for hashtag cartesGrisesEnvers__port_549765 of 180 degres 0 photos founded : [] After datou_step_exec type output : time spend for datou_step_exec : 0.7614922523498535 time spend to save output : 7.414817810058594e-05 total time spend for step 3 : 0.7615664005279541 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] map_info['map_portfolio_photo'] : {} final : True mtd_id 233 list_pids : [917849322] Looping around the photos to save general results len do output : 1 /1350256392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('233', None, None, None, None, None, None, None, None) ('233', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('233', None, '1350256392', 'None', None, None, None, None, None), ('233', None, '917849322', None, None, None, None, None, None)] time used for this insertion : 0.012298583984375 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1350256392: ['917849322', 'temp/1744011557_3752194_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg', []]} ############################### TEST data_augmentation_ellipse_varroa_tile_rotate ################################ SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=937852786 AND `type`=520 DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3748531348,3748531349,3748531350,3748531351) # 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 ! 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 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : 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 ! DataTypes for each output/input checked ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : DATA AUGMENTATION ELLIPSE VARROA TILE ROTATE Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=243 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=243 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 243 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=243 # 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 ! 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 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : 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 ! DataTypes for each output/input checked ! no param json to modify List Step Type Loaded in datou : crop, tile, rotate list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (937852786) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 937852786 download finish for photo 937852786 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.1644914150238037 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 3 step1:crop Mon Apr 7 09:39:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786} map_photo_id_path_extension : {937852786: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Crop ! param_json : {'hashtag_id_ellipse': 2087736828, 'photo_hashtag_type_from_ellipse': 520, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'crop_detect_varroa', 'photo_hashtag_type': 407, 'feed_id_new_photos_not_used': 549103, 'host': 'www.fotonower.com', 'margin': 8, 'upload_type': 'python'} margin_type : margin margin_value : [8, 8, 8, 8] Loading chi in step crop with photo_hashtag_type : 407 Loading chi in step crop for list_pids : 1 ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786) and `type` in (407) Loaded 4 chid ids of type : 407 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) +WARNING : Unexpected points, we should remove this data for chi_id : 8165075, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165076, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165077, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165078, for now we just ignore these empty polygon points SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (8165075,8165076,8165077,8165078) select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos where photo_id in ( 937852786) WARNING : margin is only used for type bib ! type of cropped photo chosen : we resize croppped photo by 1 on x axis and by 1 on y axis new_file_path_bib_crop : temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg new_file_path_bib_crop : temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg new_file_path_bib_crop : temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg new_file_path_bib_crop : temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg map_result returned by crop_photo_return_map_crop : length : 4 map_result after crop : {8165075: {'crop': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (426, 467, 312, 347), 'sub_photo_infos': (418, 475, 304, 355, 1, 1), 'same_chi': False}, 8165076: {'crop': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (411, 445, 443, 480), 'sub_photo_infos': (403, 453, 435, 480, 1, 1), 'same_chi': False}, 8165077: {'crop': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (103, 138, 358, 396), 'sub_photo_infos': (95, 146, 350, 404, 1, 1), 'same_chi': False}, 8165078: {'crop': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', 'photo_id': 937852786, 'sub_photo_id': -1, 'coordonates': (104, 131, 256, 292), 'sub_photo_infos': (96, 139, 248, 300, 1, 1), 'same_chi': False}} Here we crop with rles About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=crop_detect_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22094757 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744011574_3752194 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (22094757, 1350256397, 0, NOW()),(22094757, 1350256398, 0, NOW()),(22094757, 1350256399, 0, NOW()),(22094757, 1350256400, 0, NOW()) 4 we have uploaded 4 photos in the portfolio 22094757 time of upload the photos Elapsed time : 3.8608150482177734 {'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1350256397, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1350256398, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1350256399, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1350256400} list_errors : [] map_result_insert : {'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1350256397, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1350256398, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1350256399, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1350256400} Now we prepare data that will be used for ellipse search ! chi_id found to be used 8165075 path of cropped varroa found to be used to match on an ellipse temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg sub_photo_id found to be used 1350256397 chi_id found to be used 8165076 path of cropped varroa found to be used to match on an ellipse temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg sub_photo_id found to be used 1350256398 chi_id found to be used 8165077 path of cropped varroa found to be used to match on an ellipse temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg sub_photo_id found to be used 1350256399 chi_id found to be used 8165078 path of cropped varroa found to be used to match on an ellipse temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg sub_photo_id found to be used 1350256400 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165075, '1350256397', 31), (8165076, '1350256398', 31), (8165077, '1350256399', 31), (8165078, '1350256400', 31)] map of cropped photos with some data : {'1350256397': ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', (426, 467, 312, 347)], '1350256398': ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', (411, 445, 443, 480)], '1350256399': ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', (103, 138, 358, 396)], '1350256400': ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', (104, 131, 256, 292)]} About to compute ellipse and record with type : 520 (54, 57) (51, 57) [54, 57] (54, 57) score : 5120 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [14.25, 42.75, 1.78125]), ('yc', [12.75, 38.25, 1.59375]), ('radius', [14.25, 42.75, 1.78125])] {0.5: 18351, 0.55: 16004, 0.6000000000000001: 14127, 0.65: 12575, 0.7000000000000001: 11300, 0.75: 10158, 0.8: 9208, 0.8500000000000001: 8418, 0.9: 7642, 0.9500000000000001: 6925, 1.0: 6364, 1.05: 5843, 1.1: 5353, 1.1500000000000001: 4868, 1.2000000000000002: 4553, 1.25: 4250, 1.3: 3947, 1.35: 3698, 1.4000000000000001: 3438, 1.4500000000000002: 3230, 1.5: 3012, 1.55: 2829, 1.6: 2636, 1.6500000000000001: 2535, 1.7000000000000002: 2460, 1.75: 2388, 1.8: 2372, 1.85: 2354, 1.9000000000000001: 2332, 1.9500000000000002: 2311} [(1.9500000000000002, 2311), (1.9000000000000001, 2332), (1.85, 2354), (1.8, 2372), (1.75, 2388), (1.7000000000000002, 2460), (1.6500000000000001, 2535), (1.6, 2636), (1.55, 2829), (1.5, 3012), (1.4500000000000002, 3230), (1.4000000000000001, 3438), (1.35, 3698), (1.3, 3947), (1.25, 4250), (1.2000000000000002, 4553), (1.1500000000000001, 4868), (1.1, 5353), (1.05, 5843), (1.0, 6364), (0.9500000000000001, 6925), (0.9, 7642), (0.8500000000000001, 8418), (0.8, 9208), (0.75, 10158), (0.7000000000000001, 11300), (0.65, 12575), (0.6000000000000001, 14127), (0.55, 16004), (0.5, 18351)] arg_min reach at : 1.9500000000000002 with value = 2311 | arg_min : 1.9500000000000002 min_score : 2311{-90.0: 3359, -85.0: 3372, -80.0: 3395, -75.0: 3380, -70.0: 3304, -65.0: 3180, -60.0: 2991, -55.0: 2793, -50.0: 2601, -45.0: 2377, -40.0: 2183, -35.0: 2078, -30.0: 1968, -25.0: 1968, -20.0: 2021, -15.0: 2036, -10.0: 2102, -5.0: 2188, 0.0: 2208, 5.0: 2265, 10.0: 2311, 15.0: 2333, 20.0: 2362, 25.0: 2375, 30.0: 2375, 35.0: 2397, 40.0: 2370, 45.0: 2421, 50.0: 2491, 55.0: 2661, 60.0: 2826, 65.0: 2993, 70.0: 3106, 75.0: 3182, 80.0: 3263, 85.0: 3306} [(-30.0, 1968), (-25.0, 1968), (-20.0, 2021), (-15.0, 2036), (-35.0, 2078), (-10.0, 2102), (-40.0, 2183), (-5.0, 2188), (0.0, 2208), (5.0, 2265), (10.0, 2311), (15.0, 2333), (20.0, 2362), (40.0, 2370), (25.0, 2375), (30.0, 2375), (-45.0, 2377), (35.0, 2397), (45.0, 2421), (50.0, 2491), (-50.0, 2601), (55.0, 2661), (-55.0, 2793), (60.0, 2826), (-60.0, 2991), (65.0, 2993), (70.0, 3106), (-65.0, 3180), (75.0, 3182), (80.0, 3263), (-70.0, 3304), (85.0, 3306), (-90.0, 3359), (-85.0, 3372), (-75.0, 3380), (-80.0, 3395)] arg_min reach at : -30.0 with value = 1968 | arg_min : -30.0 min_score : 1968{14.25: 1703, 16.03125: 1654, 17.8125: 1614, 19.59375: 1679, 21.375: 1702, 23.15625: 1712, 24.9375: 1730, 26.71875: 1847, 28.5: 1968, 30.28125: 2177, 32.0625: 2456, 33.84375: 2827, 35.625: 3294, 37.40625: 3723, 39.1875: 4161, 40.96875: 4643} [(17.8125, 1614), (16.03125, 1654), (19.59375, 1679), (21.375, 1702), (14.25, 1703), (23.15625, 1712), (24.9375, 1730), (26.71875, 1847), (28.5, 1968), (30.28125, 2177), (32.0625, 2456), (33.84375, 2827), (35.625, 3294), (37.40625, 3723), (39.1875, 4161), (40.96875, 4643)] arg_min reach at : 17.8125 with value = 1614 | arg_min : 17.8125 min_score : 1614{12.75: 5534, 14.34375: 5352, 15.9375: 5116, 17.53125: 4723, 19.125: 4111, 20.71875: 3506, 22.3125: 2799, 23.90625: 2194, 25.5: 1614, 27.09375: 1339, 28.6875: 1206, 30.28125: 1137, 31.875: 1105, 33.46875: 1339, 35.0625: 1659, 36.65625: 2022} [(31.875, 1105), (30.28125, 1137), (28.6875, 1206), (27.09375, 1339), (33.46875, 1339), (25.5, 1614), (35.0625, 1659), (36.65625, 2022), (23.90625, 2194), (22.3125, 2799), (20.71875, 3506), (19.125, 4111), (17.53125, 4723), (15.9375, 5116), (14.34375, 5352), (12.75, 5534)] arg_min reach at : 31.875 with value = 1105 | arg_min : 31.875 min_score : 1105{14.25: 1942, 16.03125: 1853, 17.8125: 1756, 19.59375: 1651, 21.375: 1533, 23.15625: 1424, 24.9375: 1309, 26.71875: 1199, 28.5: 1105, 30.28125: 1219, 32.0625: 1332, 33.84375: 1547, 35.625: 1881, 37.40625: 2378, 39.1875: 3042, 40.96875: 3824} [(28.5, 1105), (26.71875, 1199), (30.28125, 1219), (24.9375, 1309), (32.0625, 1332), (23.15625, 1424), (21.375, 1533), (33.84375, 1547), (19.59375, 1651), (17.8125, 1756), (16.03125, 1853), (35.625, 1881), (14.25, 1942), (37.40625, 2378), (39.1875, 3042), (40.96875, 3824)] arg_min reach at : 28.5 with value = 1105 | arg_min : 28.5 min_score : 1105{0.5: 17397, 0.55: 15138, 0.6000000000000001: 13352, 0.65: 11859, 0.7000000000000001: 10523, 0.75: 9463, 0.8: 8469, 0.8500000000000001: 7565, 0.9: 6551, 0.9500000000000001: 5592, 1.0: 4856, 1.05: 4291, 1.1: 3654, 1.1500000000000001: 3099, 1.2000000000000002: 2647, 1.25: 2309, 1.3: 1983, 1.35: 1755, 1.4000000000000001: 1594, 1.4500000000000002: 1447, 1.5: 1358, 1.55: 1323, 1.6: 1219, 1.6500000000000001: 1215, 1.7000000000000002: 1201, 1.75: 1131, 1.8: 1134, 1.85: 1142, 1.9000000000000001: 1137, 1.9500000000000002: 1105} [(1.9500000000000002, 1105), (1.75, 1131), (1.8, 1134), (1.9000000000000001, 1137), (1.85, 1142), (1.7000000000000002, 1201), (1.6500000000000001, 1215), (1.6, 1219), (1.55, 1323), (1.5, 1358), (1.4500000000000002, 1447), (1.4000000000000001, 1594), (1.35, 1755), (1.3, 1983), (1.25, 2309), (1.2000000000000002, 2647), (1.1500000000000001, 3099), (1.1, 3654), (1.05, 4291), (1.0, 4856), (0.9500000000000001, 5592), (0.9, 6551), (0.8500000000000001, 7565), (0.8, 8469), (0.75, 9463), (0.7000000000000001, 10523), (0.65, 11859), (0.6000000000000001, 13352), (0.55, 15138), (0.5, 17397)] arg_min reach at : 1.9500000000000002 with value = 1105 arg_min : 1.9500000000000002 min_score : 1105{-90.0: 3514, -85.0: 3396, -80.0: 3238, -75.0: 3017, -70.0: 2764, -65.0: 2549, -60.0: 2339, -55.0: 2105, -50.0: 1912, -45.0: 1701, -40.0: 1467, -35.0: 1266, -30.0: 1105, -25.0: 1110, -20.0: 1114, -15.0: 1111, -10.0: 1110, -5.0: 1121, 0.0: 1120, 5.0: 1118, 10.0: 1109, 15.0: 1109, 20.0: 1099, 25.0: 1088, 30.0: 1108, 35.0: 1355, 40.0: 1649, 45.0: 1925, 50.0: 2257, 55.0: 2542, 60.0: 2845, 65.0: 3137, 70.0: 3370, 75.0: 3500, 80.0: 3578, 85.0: 3540} [(25.0, 1088), (20.0, 1099), (-30.0, 1105), (30.0, 1108), (10.0, 1109), (15.0, 1109), (-25.0, 1110), (-10.0, 1110), (-15.0, 1111), (-20.0, 1114), (5.0, 1118), (0.0, 1120), (-5.0, 1121), (-35.0, 1266), (35.0, 1355), (-40.0, 1467), (40.0, 1649), (-45.0, 1701), (-50.0, 1912), (45.0, 1925), (-55.0, 2105), (50.0, 2257), (-60.0, 2339), (55.0, 2542), (-65.0, 2549), (-70.0, 2764), (60.0, 2845), (-75.0, 3017), (65.0, 3137), (-80.0, 3238), (70.0, 3370), (-85.0, 3396), (75.0, 3500), (-90.0, 3514), (85.0, 3540), (80.0, 3578)] arg_min reach at : 25.0 with value = 1088 arg_min : 25.0 min_score : 1088{14.25: 1174, 16.03125: 1133, 17.8125: 1088, 19.59375: 1051, 21.375: 1026, 23.15625: 997, 24.9375: 979, 26.71875: 1139, 28.5: 1344, 30.28125: 1564, 32.0625: 1949, 33.84375: 2422, 35.625: 2930, 37.40625: 3453, 39.1875: 4006, 40.96875: 4592} [(24.9375, 979), (23.15625, 997), (21.375, 1026), (19.59375, 1051), (17.8125, 1088), (16.03125, 1133), (26.71875, 1139), (14.25, 1174), (28.5, 1344), (30.28125, 1564), (32.0625, 1949), (33.84375, 2422), (35.625, 2930), (37.40625, 3453), (39.1875, 4006), (40.96875, 4592)] arg_min reach at : 24.9375 with value = 979 arg_min : 24.9375 min_score : 979{12.75: 5539, 14.34375: 5239, 15.9375: 4911, 17.53125: 4425, 19.125: 3749, 20.71875: 3181, 22.3125: 2575, 23.90625: 2124, 25.5: 1808, 27.09375: 1478, 28.6875: 1290, 30.28125: 1104, 31.875: 979, 33.46875: 1070, 35.0625: 1368, 36.65625: 1872} [(31.875, 979), (33.46875, 1070), (30.28125, 1104), (28.6875, 1290), (35.0625, 1368), (27.09375, 1478), (25.5, 1808), (36.65625, 1872), (23.90625, 2124), (22.3125, 2575), (20.71875, 3181), (19.125, 3749), (17.53125, 4425), (15.9375, 4911), (14.34375, 5239), (12.75, 5539)] arg_min reach at : 31.875 with value = 979 arg_min : 31.875 min_score : 979{14.25: 1939, 16.03125: 1852, 17.8125: 1755, 19.59375: 1643, 21.375: 1531, 23.15625: 1397, 24.9375: 1264, 26.71875: 1119, 28.5: 979, 30.28125: 1023, 32.0625: 1319, 33.84375: 1771, 35.625: 2432, 37.40625: 3324, 39.1875: 4299, 40.96875: 5405} [(28.5, 979), (30.28125, 1023), (26.71875, 1119), (24.9375, 1264), (32.0625, 1319), (23.15625, 1397), (21.375, 1531), (19.59375, 1643), (17.8125, 1755), (33.84375, 1771), (16.03125, 1852), (14.25, 1939), (35.625, 2432), (37.40625, 3324), (39.1875, 4299), (40.96875, 5405)] arg_min reach at : 28.5 with value = 979 arg_min : 28.5 min_score : 979 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 x0 : 426 y1 : 347 width : 41, height : 35, area : 1435, score : 1.0 x0 : 432 y1 : 355 width : 35, height : 52, area : 1820, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 1 chid ids of type : 520 CHI and polygons saved ! (47, 50) (45, 50) [47, 50] (47, 50) score : 5362 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [12.5, 37.5, 1.5625]), ('yc', [11.25, 33.75, 1.40625]), ('radius', [12.5, 37.5, 1.5625])] {0.5: 15776, 0.55: 13889, 0.6000000000000001: 12446, 0.65: 11147, 0.7000000000000001: 10181, 0.75: 9228, 0.8: 8441, 0.8500000000000001: 7765, 0.9: 7258, 0.9500000000000001: 6684, 1.0: 6195, 1.05: 5920, 1.1: 5487, 1.1500000000000001: 5099, 1.2000000000000002: 4795, 1.25: 4511, 1.3: 4245, 1.35: 4042, 1.4000000000000001: 3828, 1.4500000000000002: 3629, 1.5: 3467, 1.55: 3235, 1.6: 3126, 1.6500000000000001: 2946, 1.7000000000000002: 2826, 1.75: 2700, 1.8: 2549, 1.85: 2472, 1.9000000000000001: 2381, 1.9500000000000002: 2281} [(1.9500000000000002, 2281), (1.9000000000000001, 2381), (1.85, 2472), (1.8, 2549), (1.75, 2700), (1.7000000000000002, 2826), (1.6500000000000001, 2946), (1.6, 3126), (1.55, 3235), (1.5, 3467), (1.4500000000000002, 3629), (1.4000000000000001, 3828), (1.35, 4042), (1.3, 4245), (1.25, 4511), (1.2000000000000002, 4795), (1.1500000000000001, 5099), (1.1, 5487), (1.05, 5920), (1.0, 6195), (0.9500000000000001, 6684), (0.9, 7258), (0.8500000000000001, 7765), (0.8, 8441), (0.75, 9228), (0.7000000000000001, 10181), (0.65, 11147), (0.6000000000000001, 12446), (0.55, 13889), (0.5, 15776)] arg_min reach at : 1.9500000000000002 with value = 2281 | arg_min : 1.9500000000000002 min_score : 2281{-90.0: 3295, -85.0: 3240, -80.0: 3290, -75.0: 3269, -70.0: 3287, -65.0: 3223, -60.0: 3095, -55.0: 3043, -50.0: 2985, -45.0: 2906, -40.0: 2796, -35.0: 2724, -30.0: 2625, -25.0: 2489, -20.0: 2330, -15.0: 2220, -10.0: 2127, -5.0: 2127, 0.0: 2143, 5.0: 2182, 10.0: 2281, 15.0: 2385, 20.0: 2506, 25.0: 2665, 30.0: 2768, 35.0: 2900, 40.0: 2939, 45.0: 2994, 50.0: 3007, 55.0: 3076, 60.0: 3095, 65.0: 3201, 70.0: 3243, 75.0: 3247, 80.0: 3279, 85.0: 3240} [(-10.0, 2127), (-5.0, 2127), (0.0, 2143), (5.0, 2182), (-15.0, 2220), (10.0, 2281), (-20.0, 2330), (15.0, 2385), (-25.0, 2489), (20.0, 2506), (-30.0, 2625), (25.0, 2665), (-35.0, 2724), (30.0, 2768), (-40.0, 2796), (35.0, 2900), (-45.0, 2906), (40.0, 2939), (-50.0, 2985), (45.0, 2994), (50.0, 3007), (-55.0, 3043), (55.0, 3076), (-60.0, 3095), (60.0, 3095), (65.0, 3201), (-65.0, 3223), (-85.0, 3240), (85.0, 3240), (70.0, 3243), (75.0, 3247), (-75.0, 3269), (80.0, 3279), (-70.0, 3287), (-80.0, 3290), (-90.0, 3295)] arg_min reach at : -10.0 with value = 2127 | arg_min : -10.0 min_score : 2127{12.5: 2171, 14.0625: 2288, 15.625: 2314, 17.1875: 2360, 18.75: 2347, 20.3125: 2300, 21.875: 2249, 23.4375: 2188, 25.0: 2127, 26.5625: 2163, 28.125: 2315, 29.6875: 2476, 31.25: 2756, 32.8125: 2992, 34.375: 3319, 35.9375: 3672} [(25.0, 2127), (26.5625, 2163), (12.5, 2171), (23.4375, 2188), (21.875, 2249), (14.0625, 2288), (20.3125, 2300), (15.625, 2314), (28.125, 2315), (18.75, 2347), (17.1875, 2360), (29.6875, 2476), (31.25, 2756), (32.8125, 2992), (34.375, 3319), (35.9375, 3672)] arg_min reach at : 25.0 with value = 2127 | arg_min : 25.0 min_score : 2127{11.25: 6815, 12.65625: 6505, 14.0625: 5928, 15.46875: 5326, 16.875: 4772, 18.28125: 4095, 19.6875: 3413, 21.09375: 2728, 22.5: 2127, 23.90625: 1694, 25.3125: 1345, 26.71875: 1048, 28.125: 823, 29.53125: 728, 30.9375: 714, 32.34375: 1027} [(30.9375, 714), (29.53125, 728), (28.125, 823), (32.34375, 1027), (26.71875, 1048), (25.3125, 1345), (23.90625, 1694), (22.5, 2127), (21.09375, 2728), (19.6875, 3413), (18.28125, 4095), (16.875, 4772), (15.46875, 5326), (14.0625, 5928), (12.65625, 6505), (11.25, 6815)] arg_min reach at : 30.9375 with value = 714 | arg_min : 30.9375 min_score : 714{12.5: 1275, 14.0625: 1207, 15.625: 1132, 17.1875: 1049, 18.75: 966, 20.3125: 866, 21.875: 784, 23.4375: 729, 25.0: 714, 26.5625: 976, 28.125: 1546, 29.6875: 2147, 31.25: 2981, 32.8125: 3767, 34.375: 4761, 35.9375: 5732} [(25.0, 714), (23.4375, 729), (21.875, 784), (20.3125, 866), (18.75, 966), (26.5625, 976), (17.1875, 1049), (15.625, 1132), (14.0625, 1207), (12.5, 1275), (28.125, 1546), (29.6875, 2147), (31.25, 2981), (32.8125, 3767), (34.375, 4761), (35.9375, 5732)] arg_min reach at : 25.0 with value = 714 | arg_min : 25.0 min_score : 714{0.5: 20107, 0.55: 18155, 0.6000000000000001: 16503, 0.65: 15058, 0.7000000000000001: 13673, 0.75: 12474, 0.8: 11100, 0.8500000000000001: 9667, 0.9: 8374, 0.9500000000000001: 7344, 1.0: 6274, 1.05: 5570, 1.1: 4846, 1.1500000000000001: 4170, 1.2000000000000002: 3623, 1.25: 3211, 1.3: 2832, 1.35: 2475, 1.4000000000000001: 2182, 1.4500000000000002: 1949, 1.5: 1655, 1.55: 1513, 1.6: 1333, 1.6500000000000001: 1182, 1.7000000000000002: 1038, 1.75: 921, 1.8: 841, 1.85: 732, 1.9000000000000001: 733, 1.9500000000000002: 714} [(1.9500000000000002, 714), (1.85, 732), (1.9000000000000001, 733), (1.8, 841), (1.75, 921), (1.7000000000000002, 1038), (1.6500000000000001, 1182), (1.6, 1333), (1.55, 1513), (1.5, 1655), (1.4500000000000002, 1949), (1.4000000000000001, 2182), (1.35, 2475), (1.3, 2832), (1.25, 3211), (1.2000000000000002, 3623), (1.1500000000000001, 4170), (1.1, 4846), (1.05, 5570), (1.0, 6274), (0.9500000000000001, 7344), (0.9, 8374), (0.8500000000000001, 9667), (0.8, 11100), (0.75, 12474), (0.7000000000000001, 13673), (0.65, 15058), (0.6000000000000001, 16503), (0.55, 18155), (0.5, 20107)] arg_min reach at : 1.9500000000000002 with value = 714 arg_min : 1.9500000000000002 min_score : 714{-90.0: 2866, -85.0: 3006, -80.0: 3063, -75.0: 3168, -70.0: 3193, -65.0: 3193, -60.0: 3149, -55.0: 3022, -50.0: 2885, -45.0: 2656, -40.0: 2413, -35.0: 2119, -30.0: 1819, -25.0: 1500, -20.0: 1171, -15.0: 865, -10.0: 714, -5.0: 668, 0.0: 689, 5.0: 734, 10.0: 824, 15.0: 898, 20.0: 1116, 25.0: 1368, 30.0: 1610, 35.0: 1866, 40.0: 2105, 45.0: 2304, 50.0: 2467, 55.0: 2604, 60.0: 2731, 65.0: 2753, 70.0: 2753, 75.0: 2761, 80.0: 2722, 85.0: 2786} [(-5.0, 668), (0.0, 689), (-10.0, 714), (5.0, 734), (10.0, 824), (-15.0, 865), (15.0, 898), (20.0, 1116), (-20.0, 1171), (25.0, 1368), (-25.0, 1500), (30.0, 1610), (-30.0, 1819), (35.0, 1866), (40.0, 2105), (-35.0, 2119), (45.0, 2304), (-40.0, 2413), (50.0, 2467), (55.0, 2604), (-45.0, 2656), (80.0, 2722), (60.0, 2731), (65.0, 2753), (70.0, 2753), (75.0, 2761), (85.0, 2786), (-90.0, 2866), (-50.0, 2885), (-85.0, 3006), (-55.0, 3022), (-80.0, 3063), (-60.0, 3149), (-75.0, 3168), (-70.0, 3193), (-65.0, 3193)] arg_min reach at : -5.0 with value = 668 arg_min : -5.0 min_score : 668{12.5: 1164, 14.0625: 1082, 15.625: 1019, 17.1875: 933, 18.75: 866, 20.3125: 765, 21.875: 713, 23.4375: 655, 25.0: 668, 26.5625: 845, 28.125: 1107, 29.6875: 1374, 31.25: 1691, 32.8125: 2036, 34.375: 2375, 35.9375: 2731} [(23.4375, 655), (25.0, 668), (21.875, 713), (20.3125, 765), (26.5625, 845), (18.75, 866), (17.1875, 933), (15.625, 1019), (14.0625, 1082), (28.125, 1107), (12.5, 1164), (29.6875, 1374), (31.25, 1691), (32.8125, 2036), (34.375, 2375), (35.9375, 2731)] arg_min reach at : 23.4375 with value = 655 arg_min : 23.4375 min_score : 655{11.25: 7209, 12.65625: 6826, 14.0625: 6195, 15.46875: 5638, 16.875: 4941, 18.28125: 4177, 19.6875: 3484, 21.09375: 2750, 22.5: 2183, 23.90625: 1709, 25.3125: 1327, 26.71875: 1008, 28.125: 779, 29.53125: 631, 30.9375: 655, 32.34375: 920} [(29.53125, 631), (30.9375, 655), (28.125, 779), (32.34375, 920), (26.71875, 1008), (25.3125, 1327), (23.90625, 1709), (22.5, 2183), (21.09375, 2750), (19.6875, 3484), (18.28125, 4177), (16.875, 4941), (15.46875, 5638), (14.0625, 6195), (12.65625, 6826), (11.25, 7209)] arg_min reach at : 29.53125 with value = 631 arg_min : 29.53125 min_score : 631{12.5: 1282, 14.0625: 1211, 15.625: 1130, 17.1875: 1050, 18.75: 960, 20.3125: 871, 21.875: 772, 23.4375: 672, 25.0: 631, 26.5625: 666, 28.125: 947, 29.6875: 1472, 31.25: 2184, 32.8125: 3032, 34.375: 3932, 35.9375: 5005} [(25.0, 631), (26.5625, 666), (23.4375, 672), (21.875, 772), (20.3125, 871), (28.125, 947), (18.75, 960), (17.1875, 1050), (15.625, 1130), (14.0625, 1211), (12.5, 1282), (29.6875, 1472), (31.25, 2184), (32.8125, 3032), (34.375, 3932), (35.9375, 5005)] arg_min reach at : 25.0 with value = 631 arg_min : 25.0 min_score : 631 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 x0 : 411 y1 : 480 width : 34, height : 37, area : 1258, score : 1.0 x0 : 419 y1 : 483 width : 26, height : 49, area : 1274, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 2 chid ids of type : 520 + CHI and polygons saved ! (55, 54) (54, 51) [55, 54] (55, 54) score : 4603 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [12.75, 38.25, 1.59375]), ('yc', [13.5, 40.5, 1.6875]), ('radius', [13.5, 40.5, 1.6875])] {0.5: 14831, 0.55: 12756, 0.6000000000000001: 10989, 0.65: 9469, 0.7000000000000001: 8158, 0.75: 7251, 0.8: 6254, 0.8500000000000001: 5559, 0.9: 5119, 0.9500000000000001: 4564, 1.0: 4072, 1.05: 3783, 1.1: 3496, 1.1500000000000001: 3342, 1.2000000000000002: 3228, 1.25: 3161, 1.3: 3127, 1.35: 3087, 1.4000000000000001: 3047, 1.4500000000000002: 3050, 1.5: 2990, 1.55: 2992, 1.6: 2992, 1.6500000000000001: 2985, 1.7000000000000002: 2988, 1.75: 2999, 1.8: 2994, 1.85: 2981, 1.9000000000000001: 3002, 1.9500000000000002: 2995} [(1.85, 2981), (1.6500000000000001, 2985), (1.7000000000000002, 2988), (1.5, 2990), (1.55, 2992), (1.6, 2992), (1.8, 2994), (1.9500000000000002, 2995), (1.75, 2999), (1.9000000000000001, 3002), (1.4000000000000001, 3047), (1.4500000000000002, 3050), (1.35, 3087), (1.3, 3127), (1.25, 3161), (1.2000000000000002, 3228), (1.1500000000000001, 3342), (1.1, 3496), (1.05, 3783), (1.0, 4072), (0.9500000000000001, 4564), (0.9, 5119), (0.8500000000000001, 5559), (0.8, 6254), (0.75, 7251), (0.7000000000000001, 8158), (0.65, 9469), (0.6000000000000001, 10989), (0.55, 12756), (0.5, 14831)] arg_min reach at : 1.85 with value = 2981 | arg_min : 1.85 min_score : 2981{-90.0: 1442, -85.0: 1411, -80.0: 1411, -75.0: 1423, -70.0: 1409, -65.0: 1428, -60.0: 1417, -55.0: 1385, -50.0: 1356, -45.0: 1374, -40.0: 1401, -35.0: 1501, -30.0: 1635, -25.0: 1772, -20.0: 1988, -15.0: 2183, -10.0: 2376, -5.0: 2552, 0.0: 2691, 5.0: 2860, 10.0: 2981, 15.0: 3063, 20.0: 3044, 25.0: 2938, 30.0: 2889, 35.0: 2865, 40.0: 2787, 45.0: 2694, 50.0: 2566, 55.0: 2452, 60.0: 2275, 65.0: 2055, 70.0: 1849, 75.0: 1731, 80.0: 1532, 85.0: 1466} [(-50.0, 1356), (-45.0, 1374), (-55.0, 1385), (-40.0, 1401), (-70.0, 1409), (-85.0, 1411), (-80.0, 1411), (-60.0, 1417), (-75.0, 1423), (-65.0, 1428), (-90.0, 1442), (85.0, 1466), (-35.0, 1501), (80.0, 1532), (-30.0, 1635), (75.0, 1731), (-25.0, 1772), (70.0, 1849), (-20.0, 1988), (65.0, 2055), (-15.0, 2183), (60.0, 2275), (-10.0, 2376), (55.0, 2452), (-5.0, 2552), (50.0, 2566), (0.0, 2691), (45.0, 2694), (40.0, 2787), (5.0, 2860), (35.0, 2865), (30.0, 2889), (25.0, 2938), (10.0, 2981), (20.0, 3044), (15.0, 3063)] arg_min reach at : -50.0 with value = 1356 | arg_min : -50.0 min_score : 1356{12.75: 3483, 14.34375: 3154, 15.9375: 2818, 17.53125: 2499, 19.125: 2212, 20.71875: 1992, 22.3125: 1750, 23.90625: 1554, 25.5: 1356, 27.09375: 1213, 28.6875: 1123, 30.28125: 1079, 31.875: 1312, 33.46875: 1586, 35.0625: 1971, 36.65625: 2464} [(30.28125, 1079), (28.6875, 1123), (27.09375, 1213), (31.875, 1312), (25.5, 1356), (23.90625, 1554), (33.46875, 1586), (22.3125, 1750), (35.0625, 1971), (20.71875, 1992), (19.125, 2212), (36.65625, 2464), (17.53125, 2499), (15.9375, 2818), (14.34375, 3154), (12.75, 3483)] arg_min reach at : 30.28125 with value = 1079 | arg_min : 30.28125 min_score : 1079{13.5: 1299, 15.1875: 1190, 16.875: 1129, 18.5625: 1073, 20.25: 1041, 21.9375: 1025, 23.625: 995, 25.3125: 1002, 27.0: 1079, 28.6875: 1280, 30.375: 1506, 32.0625: 1818, 33.75: 2218, 35.4375: 2606, 37.125: 3013, 38.8125: 3545} [(23.625, 995), (25.3125, 1002), (21.9375, 1025), (20.25, 1041), (18.5625, 1073), (27.0, 1079), (16.875, 1129), (15.1875, 1190), (28.6875, 1280), (13.5, 1299), (30.375, 1506), (32.0625, 1818), (33.75, 2218), (35.4375, 2606), (37.125, 3013), (38.8125, 3545)] arg_min reach at : 23.625 with value = 995 | arg_min : 23.625 min_score : 995{13.5: 1907, 15.1875: 1827, 16.875: 1734, 18.5625: 1635, 20.25: 1520, 21.9375: 1400, 23.625: 1268, 25.3125: 1139, 27.0: 995, 28.6875: 1081, 30.375: 1376, 32.0625: 1837, 33.75: 2365, 35.4375: 3034, 37.125: 3644, 38.8125: 4482} [(27.0, 995), (28.6875, 1081), (25.3125, 1139), (23.625, 1268), (30.375, 1376), (21.9375, 1400), (20.25, 1520), (18.5625, 1635), (16.875, 1734), (15.1875, 1827), (32.0625, 1837), (13.5, 1907), (33.75, 2365), (35.4375, 3034), (37.125, 3644), (38.8125, 4482)] arg_min reach at : 27.0 with value = 995 | arg_min : 27.0 min_score : 995{0.5: 16358, 0.55: 14291, 0.6000000000000001: 12544, 0.65: 11117, 0.7000000000000001: 9800, 0.75: 8586, 0.8: 7533, 0.8500000000000001: 6488, 0.9: 5599, 0.9500000000000001: 4779, 1.0: 4075, 1.05: 3520, 1.1: 3021, 1.1500000000000001: 2582, 1.2000000000000002: 2150, 1.25: 1820, 1.3: 1534, 1.35: 1347, 1.4000000000000001: 1219, 1.4500000000000002: 1130, 1.5: 1073, 1.55: 1037, 1.6: 998, 1.6500000000000001: 961, 1.7000000000000002: 981, 1.75: 984, 1.8: 995, 1.85: 995, 1.9000000000000001: 1023, 1.9500000000000002: 1058} [(1.6500000000000001, 961), (1.7000000000000002, 981), (1.75, 984), (1.8, 995), (1.85, 995), (1.6, 998), (1.9000000000000001, 1023), (1.55, 1037), (1.9500000000000002, 1058), (1.5, 1073), (1.4500000000000002, 1130), (1.4000000000000001, 1219), (1.35, 1347), (1.3, 1534), (1.25, 1820), (1.2000000000000002, 2150), (1.1500000000000001, 2582), (1.1, 3021), (1.05, 3520), (1.0, 4075), (0.9500000000000001, 4779), (0.9, 5599), (0.8500000000000001, 6488), (0.8, 7533), (0.75, 8586), (0.7000000000000001, 9800), (0.65, 11117), (0.6000000000000001, 12544), (0.55, 14291), (0.5, 16358)] arg_min reach at : 1.6500000000000001 with value = 961 arg_min : 1.6500000000000001 min_score : 961{-90.0: 906, -85.0: 871, -80.0: 858, -75.0: 866, -70.0: 852, -65.0: 864, -60.0: 855, -55.0: 853, -50.0: 961, -45.0: 1180, -40.0: 1412, -35.0: 1690, -30.0: 1959, -25.0: 2243, -20.0: 2484, -15.0: 2677, -10.0: 2820, -5.0: 2976, 0.0: 3039, 5.0: 3049, 10.0: 3098, 15.0: 3122, 20.0: 3061, 25.0: 2954, 30.0: 2845, 35.0: 2631, 40.0: 2417, 45.0: 2130, 50.0: 1863, 55.0: 1564, 60.0: 1439, 65.0: 1326, 70.0: 1220, 75.0: 1142, 80.0: 1056, 85.0: 986} [(-70.0, 852), (-55.0, 853), (-60.0, 855), (-80.0, 858), (-65.0, 864), (-75.0, 866), (-85.0, 871), (-90.0, 906), (-50.0, 961), (85.0, 986), (80.0, 1056), (75.0, 1142), (-45.0, 1180), (70.0, 1220), (65.0, 1326), (-40.0, 1412), (60.0, 1439), (55.0, 1564), (-35.0, 1690), (50.0, 1863), (-30.0, 1959), (45.0, 2130), (-25.0, 2243), (40.0, 2417), (-20.0, 2484), (35.0, 2631), (-15.0, 2677), (-10.0, 2820), (30.0, 2845), (25.0, 2954), (-5.0, 2976), (0.0, 3039), (5.0, 3049), (20.0, 3061), (10.0, 3098), (15.0, 3122)] arg_min reach at : -70.0 with value = 852 arg_min : -70.0 min_score : 852{12.75: 4966, 14.34375: 4695, 15.9375: 4364, 17.53125: 3945, 19.125: 3465, 20.71875: 2958, 22.3125: 2415, 23.90625: 1841, 25.5: 1295, 27.09375: 936, 28.6875: 847, 30.28125: 852, 31.875: 883, 33.46875: 997, 35.0625: 1322, 36.65625: 1859} [(28.6875, 847), (30.28125, 852), (31.875, 883), (27.09375, 936), (33.46875, 997), (25.5, 1295), (35.0625, 1322), (23.90625, 1841), (36.65625, 1859), (22.3125, 2415), (20.71875, 2958), (19.125, 3465), (17.53125, 3945), (15.9375, 4364), (14.34375, 4695), (12.75, 4966)] arg_min reach at : 28.6875 with value = 847 arg_min : 28.6875 min_score : 847{13.5: 1489, 15.1875: 1374, 16.875: 1244, 18.5625: 1126, 20.25: 996, 21.9375: 893, 23.625: 847, 25.3125: 933, 27.0: 1153, 28.6875: 1446, 30.375: 1827, 32.0625: 2217, 33.75: 2658, 35.4375: 3137, 37.125: 3653, 38.8125: 4142} [(23.625, 847), (21.9375, 893), (25.3125, 933), (20.25, 996), (18.5625, 1126), (27.0, 1153), (16.875, 1244), (15.1875, 1374), (28.6875, 1446), (13.5, 1489), (30.375, 1827), (32.0625, 2217), (33.75, 2658), (35.4375, 3137), (37.125, 3653), (38.8125, 4142)] arg_min reach at : 23.625 with value = 847 arg_min : 23.625 min_score : 847{13.5: 1870, 15.1875: 1783, 16.875: 1676, 18.5625: 1564, 20.25: 1442, 21.9375: 1302, 23.625: 1153, 25.3125: 997, 27.0: 847, 28.6875: 864, 30.375: 1163, 32.0625: 1613, 33.75: 2300, 35.4375: 3191, 37.125: 4251, 38.8125: 5356} [(27.0, 847), (28.6875, 864), (25.3125, 997), (23.625, 1153), (30.375, 1163), (21.9375, 1302), (20.25, 1442), (18.5625, 1564), (32.0625, 1613), (16.875, 1676), (15.1875, 1783), (13.5, 1870), (33.75, 2300), (35.4375, 3191), (37.125, 4251), (38.8125, 5356)] arg_min reach at : 27.0 with value = 847 arg_min : 27.0 min_score : 847 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 x0 : 103 y1 : 396 width : 35, height : 38, area : 1330, score : 1.0 x0 : 93 y1 : 396 width : 51, height : 36, area : 1836, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 3 chid ids of type : 520 ++ CHI and polygons saved ! (57, 52) (52, 43) [57, 52] (57, 52) score : 7970 strategy_opt : 5| strategy_opt : [('excentricity', [0.5, 2.0, 0.05]), ('angle', [-90.0, 90.0, 5.0]), ('xc', [10.75, 32.25, 1.34375]), ('yc', [13.0, 39.0, 1.625]), ('radius', [13.0, 39.0, 1.625])] {0.5: 16167, 0.55: 14430, 0.6000000000000001: 12864, 0.65: 11529, 0.7000000000000001: 10346, 0.75: 9256, 0.8: 8418, 0.8500000000000001: 7609, 0.9: 6862, 0.9500000000000001: 6277, 1.0: 5482, 1.05: 4840, 1.1: 4264, 1.1500000000000001: 3773, 1.2000000000000002: 3275, 1.25: 2948, 1.3: 2727, 1.35: 2514, 1.4000000000000001: 2254, 1.4500000000000002: 2155, 1.5: 1986, 1.55: 1892, 1.6: 1846, 1.6500000000000001: 1782, 1.7000000000000002: 1717, 1.75: 1656, 1.8: 1641, 1.85: 1602, 1.9000000000000001: 1587, 1.9500000000000002: 1576} [(1.9500000000000002, 1576), (1.9000000000000001, 1587), (1.85, 1602), (1.8, 1641), (1.75, 1656), (1.7000000000000002, 1717), (1.6500000000000001, 1782), (1.6, 1846), (1.55, 1892), (1.5, 1986), (1.4500000000000002, 2155), (1.4000000000000001, 2254), (1.35, 2514), (1.3, 2727), (1.25, 2948), (1.2000000000000002, 3275), (1.1500000000000001, 3773), (1.1, 4264), (1.05, 4840), (1.0, 5482), (0.9500000000000001, 6277), (0.9, 6862), (0.8500000000000001, 7609), (0.8, 8418), (0.75, 9256), (0.7000000000000001, 10346), (0.65, 11529), (0.6000000000000001, 12864), (0.55, 14430), (0.5, 16167)] arg_min reach at : 1.9500000000000002 with value = 1576 | arg_min : 1.9500000000000002 min_score : 1576{-90.0: 2291, -85.0: 2369, -80.0: 2390, -75.0: 2401, -70.0: 2341, -65.0: 2390, -60.0: 2418, -55.0: 2445, -50.0: 2599, -45.0: 2701, -40.0: 2755, -35.0: 2787, -30.0: 2753, -25.0: 2678, -20.0: 2611, -15.0: 2478, -10.0: 2313, -5.0: 2098, 0.0: 1870, 5.0: 1702, 10.0: 1576, 15.0: 1422, 20.0: 1258, 25.0: 1061, 30.0: 872, 35.0: 752, 40.0: 632, 45.0: 677, 50.0: 872, 55.0: 1070, 60.0: 1274, 65.0: 1477, 70.0: 1637, 75.0: 1895, 80.0: 2049, 85.0: 2193} [(40.0, 632), (45.0, 677), (35.0, 752), (30.0, 872), (50.0, 872), (25.0, 1061), (55.0, 1070), (20.0, 1258), (60.0, 1274), (15.0, 1422), (65.0, 1477), (10.0, 1576), (70.0, 1637), (5.0, 1702), (0.0, 1870), (75.0, 1895), (80.0, 2049), (-5.0, 2098), (85.0, 2193), (-90.0, 2291), (-10.0, 2313), (-70.0, 2341), (-85.0, 2369), (-80.0, 2390), (-65.0, 2390), (-75.0, 2401), (-60.0, 2418), (-55.0, 2445), (-15.0, 2478), (-50.0, 2599), (-20.0, 2611), (-25.0, 2678), (-45.0, 2701), (-30.0, 2753), (-40.0, 2755), (-35.0, 2787)] arg_min reach at : 40.0 with value = 632 | arg_min : 40.0 min_score : 632{10.75: 982, 12.09375: 841, 13.4375: 751, 14.78125: 672, 16.125: 624, 17.46875: 599, 18.8125: 575, 20.15625: 561, 21.5: 632, 22.84375: 895, 24.1875: 1257, 25.53125: 1708, 26.875: 2192, 28.21875: 2686, 29.5625: 3202, 30.90625: 3701} [(20.15625, 561), (18.8125, 575), (17.46875, 599), (16.125, 624), (21.5, 632), (14.78125, 672), (13.4375, 751), (12.09375, 841), (22.84375, 895), (10.75, 982), (24.1875, 1257), (25.53125, 1708), (26.875, 2192), (28.21875, 2686), (29.5625, 3202), (30.90625, 3701)] arg_min reach at : 20.15625 with value = 561 | arg_min : 20.15625 min_score : 561{13.0: 3254, 14.625: 2871, 16.25: 2436, 17.875: 1977, 19.5: 1474, 21.125: 1071, 22.75: 835, 24.375: 629, 26.0: 561, 27.625: 716, 29.25: 937, 30.875: 1286, 32.5: 1738, 34.125: 2226, 35.75: 2782, 37.375: 3291} [(26.0, 561), (24.375, 629), (27.625, 716), (22.75, 835), (29.25, 937), (21.125, 1071), (30.875, 1286), (19.5, 1474), (32.5, 1738), (17.875, 1977), (34.125, 2226), (16.25, 2436), (35.75, 2782), (14.625, 2871), (13.0, 3254), (37.375, 3291)] arg_min reach at : 26.0 with value = 561 | arg_min : 26.0 min_score : 561{13.0: 1371, 14.625: 1301, 16.25: 1217, 17.875: 1127, 19.5: 1028, 21.125: 926, 22.75: 811, 24.375: 683, 26.0: 561, 27.625: 624, 29.25: 1026, 30.875: 1508, 32.5: 2147, 34.125: 2981, 35.75: 3949, 37.375: 5069} [(26.0, 561), (27.625, 624), (24.375, 683), (22.75, 811), (21.125, 926), (29.25, 1026), (19.5, 1028), (17.875, 1127), (16.25, 1217), (14.625, 1301), (13.0, 1371), (30.875, 1508), (32.5, 2147), (34.125, 2981), (35.75, 3949), (37.375, 5069)] arg_min reach at : 26.0 with value = 561 | arg_min : 26.0 min_score : 561{0.5: 13327, 0.55: 12420, 0.6000000000000001: 11540, 0.65: 10560, 0.7000000000000001: 9652, 0.75: 8782, 0.8: 7834, 0.8500000000000001: 7061, 0.9: 6294, 0.9500000000000001: 5633, 1.0: 4916, 1.05: 4424, 1.1: 3865, 1.1500000000000001: 3323, 1.2000000000000002: 2817, 1.25: 2401, 1.3: 1935, 1.35: 1600, 1.4000000000000001: 1218, 1.4500000000000002: 1019, 1.5: 834, 1.55: 693, 1.6: 614, 1.6500000000000001: 568, 1.7000000000000002: 549, 1.75: 529, 1.8: 520, 1.85: 521, 1.9000000000000001: 534, 1.9500000000000002: 561} [(1.8, 520), (1.85, 521), (1.75, 529), (1.9000000000000001, 534), (1.7000000000000002, 549), (1.9500000000000002, 561), (1.6500000000000001, 568), (1.6, 614), (1.55, 693), (1.5, 834), (1.4500000000000002, 1019), (1.4000000000000001, 1218), (1.35, 1600), (1.3, 1935), (1.25, 2401), (1.2000000000000002, 2817), (1.1500000000000001, 3323), (1.1, 3865), (1.05, 4424), (1.0, 4916), (0.9500000000000001, 5633), (0.9, 6294), (0.8500000000000001, 7061), (0.8, 7834), (0.75, 8782), (0.7000000000000001, 9652), (0.65, 10560), (0.6000000000000001, 11540), (0.55, 12420), (0.5, 13327)] arg_min reach at : 1.8 with value = 520 arg_min : 1.8 min_score : 520{-90.0: 2114, -85.0: 2268, -80.0: 2343, -75.0: 2405, -70.0: 2409, -65.0: 2435, -60.0: 2467, -55.0: 2514, -50.0: 2590, -45.0: 2654, -40.0: 2632, -35.0: 2610, -30.0: 2565, -25.0: 2526, -20.0: 2435, -15.0: 2330, -10.0: 2181, -5.0: 2013, 0.0: 1792, 5.0: 1584, 10.0: 1389, 15.0: 1208, 20.0: 1038, 25.0: 843, 30.0: 673, 35.0: 564, 40.0: 520, 45.0: 597, 50.0: 764, 55.0: 963, 60.0: 1158, 65.0: 1390, 70.0: 1573, 75.0: 1800, 80.0: 1969, 85.0: 2070} [(40.0, 520), (35.0, 564), (45.0, 597), (30.0, 673), (50.0, 764), (25.0, 843), (55.0, 963), (20.0, 1038), (60.0, 1158), (15.0, 1208), (10.0, 1389), (65.0, 1390), (70.0, 1573), (5.0, 1584), (0.0, 1792), (75.0, 1800), (80.0, 1969), (-5.0, 2013), (85.0, 2070), (-90.0, 2114), (-10.0, 2181), (-85.0, 2268), (-15.0, 2330), (-80.0, 2343), (-75.0, 2405), (-70.0, 2409), (-65.0, 2435), (-20.0, 2435), (-60.0, 2467), (-55.0, 2514), (-25.0, 2526), (-30.0, 2565), (-50.0, 2590), (-35.0, 2610), (-40.0, 2632), (-45.0, 2654)] arg_min reach at : 40.0 with value = 520 arg_min : 40.0 min_score : 520{10.75: 1004, 12.09375: 902, 13.4375: 775, 14.78125: 655, 16.125: 580, 17.46875: 515, 18.8125: 494, 20.15625: 520, 21.5: 692, 22.84375: 1028, 24.1875: 1466, 25.53125: 2001, 26.875: 2490, 28.21875: 3027, 29.5625: 3629, 30.90625: 4163} [(18.8125, 494), (17.46875, 515), (20.15625, 520), (16.125, 580), (14.78125, 655), (21.5, 692), (13.4375, 775), (12.09375, 902), (10.75, 1004), (22.84375, 1028), (24.1875, 1466), (25.53125, 2001), (26.875, 2490), (28.21875, 3027), (29.5625, 3629), (30.90625, 4163)] arg_min reach at : 18.8125 with value = 494 arg_min : 18.8125 min_score : 494{13.0: 3121, 14.625: 2763, 16.25: 2397, 17.875: 1962, 19.5: 1409, 21.125: 1056, 22.75: 809, 24.375: 589, 26.0: 494, 27.625: 635, 29.25: 987, 30.875: 1406, 32.5: 1937, 34.125: 2442, 35.75: 2987, 37.375: 3537} [(26.0, 494), (24.375, 589), (27.625, 635), (22.75, 809), (29.25, 987), (21.125, 1056), (30.875, 1406), (19.5, 1409), (32.5, 1937), (17.875, 1962), (16.25, 2397), (34.125, 2442), (14.625, 2763), (35.75, 2987), (13.0, 3121), (37.375, 3537)] arg_min reach at : 26.0 with value = 494 arg_min : 26.0 min_score : 494{13.0: 1347, 14.625: 1266, 16.25: 1182, 17.875: 1086, 19.5: 979, 21.125: 865, 22.75: 737, 24.375: 616, 26.0: 494, 27.625: 559, 29.25: 954, 30.875: 1524, 32.5: 2347, 34.125: 3350, 35.75: 4468, 37.375: 5635} [(26.0, 494), (27.625, 559), (24.375, 616), (22.75, 737), (21.125, 865), (29.25, 954), (19.5, 979), (17.875, 1086), (16.25, 1182), (14.625, 1266), (13.0, 1347), (30.875, 1524), (32.5, 2347), (34.125, 3350), (35.75, 4468), (37.375, 5635)] arg_min reach at : 26.0 with value = 494 arg_min : 26.0 min_score : 494 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 x0 : 104 y1 : 292 width : 27, height : 36, area : 972, score : 1.0 x0 : 102 y1 : 288 width : 39, height : 43, area : 1677, score : 1.0 Now saving polygons points : 1| batch 1 Loaded 4 chid ids of type : 520 +++ CHI and polygons saved ! ['temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_varroa_with_ellipsebest.jpg'] About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22094758 Result OK ! uploaded one batch 0 Elapsed time : 19.40376377105713 After datou_step_exec type output : time spend for datou_step_exec : 24.85249662399292 time spend to save output : 1.9550323486328125e-05 total time spend for step 1 : 24.852516174316406 step2:tile Mon Apr 7 09:39:56 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : ['temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'] We expect there is only one output and this part is used while all output are not tuple or array We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : [('temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg',)] After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1350256397, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1350256398, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1350256399, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1350256400} map_photo_id_path_extension : {937852786: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1350256397: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1350256398: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1350256399: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1350256400: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}} map_subphoto_mainphoto : {1350256397: 937852786, 1350256398: 937852786, 1350256399: 937852786, 1350256400: 937852786} verbose : True param_json : {'photo_tile_type': 17, 'whiten': True, 'remove_crop_border': True, 'minimal_size_crop_border': 900, 'stride': 240, 'crop_hashtag_type_tiled': 521, 'ETA': 86400, 'new_width': 480, 'new_height': 480, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_taggage_varroa', 'crop_hashtag_type': 520, 'host': 'www.fotonower.com', 'arg_aux_upload': {'type_upload': 'python'}} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {520: 521} TO DEPRECATE VR 14-6-18 map_filenames : {937852786: 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786,937852786) and `type` in (520) Loaded 4 chid ids of type : 520 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3748651699,3748651700,3748651701,3748651702) ++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3748651699,3748651700,3748651701,3748651702) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3748651699,3748651700,3748651701,3748651702) https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 22094759 with name tile_taggage_varroa feed_id_new_photos : 22094759 filename : temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg photo_id : 937852786 height_image_input : 480 width_image_input : 480 new_width : 480 new_height : 480 stride : 240 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 4 list_bib_to_crops : 1 [(0, 480, 0, 480, 0)] calcul des nouveaux crops pour le tile x0:0,x1:480,y0:0,y1:480 calcul avec la methode originale calcul avec la methode originale calcul avec la methode originale calcul avec la methode originale chi selectionnes : [, , , ] new_crops_tiles : 1 crop_transformed : 4 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) [(937852786, 2090988864, 17, 0, 480, 0, 480, 1.0)] list_photo_ids_cropped : [937852786] batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 937852786) and `type` in (17) Loaded 1 chid ids of type : 17 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (8165084) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (8165084) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (8165084) treat the image : temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.009444236755371094 on upload les photos avec python init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744011603_3752194 INSERT ignore into MTRUser.mtr_portfolio_photos (`mtr_portfolio_id`, `mtr_photo_id`, `mtr_user_id`, `created_at`) VALUES (22094759, 1350256425, 0, NOW()) 1 we have uploaded 1 photos in the portfolio 22094759 Importing ! upload mediasElapsed time : 0.7567074298858643 , 0insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(8165084, 1350256425, 0)] Saving 4 CHIs. list_chi_tile : [": {'photo_id': 1350256425, 'hashtag_id': 2087736828, 'type': 521, 'x0': 432, 'x1': 467, 'y0': 303, 'y1': 355, 'score': 1.0, 'id': 0, 'points': ['463,352,452,353,439,350,426,342,418,333,417,325,422,319,433,318,446,321,459,328,467,338,469,346', '463,352,452,353,439,350,426,342,418,333,417,325,422,319,433,318,446,321,459,328,467,338,469,346'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1350256425, 'hashtag_id': 2087736828, 'type': 521, 'x0': 419, 'x1': 445, 'y0': 434, 'y1': 480, 'score': 1.0, 'id': 0, 'points': ['451,461,449,467,441,473,429,477,416,477,406,473,402,467,403,461,411,455,423,451,435,452,446,455', '451,461,449,467,441,473,429,477,416,477,406,473,402,467,403,461,411,455,423,451,435,452,446,455'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1350256425, 'hashtag_id': 2087736828, 'type': 521, 'x0': 93, 'x1': 144, 'y0': 360, 'y1': 396, 'score': 1.0, 'id': 0, 'points': ['112,359,120,350,130,348,137,351,141,361,140,374,134,387,126,396,117,399,109,395,105,385,106,372', '112,359,120,350,130,348,137,351,141,361,140,374,134,387,126,396,117,399,109,395,105,385,106,372'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}", ": {'photo_id': 1350256425, 'hashtag_id': 2087736828, 'type': 521, 'x0': 102, 'x1': 141, 'y0': 245, 'y1': 288, 'score': 1.0, 'id': 0, 'points': ['124,293,112,290,102,281,95,271,93,262,96,255,105,254,116,257,127,266,134,276,136,285,132,292', '124,293,112,290,102,281,95,271,93,262,96,255,105,254,116,257,127,266,134,276,136,285,132,292'], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', 'sum_segment': 0}"] insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 4 chid ids of type : 521 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 0 INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! end of tileElapsed time : 1.8776814937591553 map_pid_results : {'1350256425': ['temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} After datou_step_exec type output : time spend for datou_step_exec : 8.898419380187988 time spend to save output : 0.00010609626770019531 total time spend for step 2 : 8.898525476455688 step3:rotate Mon Apr 7 09:40:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 : {'1350256425': ['temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} input_args_next_step : {'1350256425': ()} output_args : {'1350256425': ['temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg']} args : 1350256425 depend.output_id : 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure input_args_next_step, len :1, first value : ('temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg',) After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg': 937852786, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg': 1350256397, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg': 1350256398, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg': 1350256399, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg': 1350256400, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg': 1350256425} map_photo_id_path_extension : {937852786: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg', 'extension': 'jpg'}, 1350256397: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg'}, 1350256398: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg'}, 1350256399: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg'}, 1350256400: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg'}, 1350256425: {'path': 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg'}} map_subphoto_mainphoto : {1350256397: 937852786, 1350256398: 937852786, 1350256399: 937852786, 1350256400: 937852786, 1350256425: 937852786} Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a datou with depends ! rotate photos of 0,15,30,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345 degres batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 1350256425) and `type` in (521) Loaded 4 chid ids of type : 521 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (3748651765,3748651766,3748651764,3748651763) ++WARNING : duplicated polygon, we should remove this data for chi_id : 3748651763. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3748651764. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3748651765. Ignored now ++WARNING : duplicated polygon, we should remove this data for chi_id : 3748651766. Ignored now SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (3748651765,3748651766,3748651764,3748651763) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (3748651765,3748651766,3748651764,3748651763) map_chi : {1350256425: [, , , ]} https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 22094760 photo_id in download_rotate_and_save : 1350256425 list_chi_loc : 4 Use all angle ! Rotation of photo 1350256425 of 0 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 0 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 1. 0.] [-0. 1.]] 0 [[ 1. 0.] [-0. 1.]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004990100860595703 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0022242069244384766 .time for calcul the mask position with numpy : 0.0004382133483886719 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0017049312591552734 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -1, 'hashtag_id': 2087736828, 'type': 529, 'x0': 25, 'x1': 61, 'y0': 268, 'y1': 319, 'score': 1.0, 'id': None, 'points': ['32,279,40,270,50,268,57,271,61,281,60,294,54,307,46,316,37,319,29,315,25,305,26,292'], 'sub_photo_id': 0, 'rles': [(-1, 48, 268, 4), (-1, 43, 269, 11), (-1, 40, 270, 16), (-1, 39, 271, 19), (-1, 38, 272, 20), (-1, 37, 273, 22), (-1, 36, 274, 23), (-1, 36, 275, 24), (-1, 35, 276, 25), (-1, 34, 277, 26), (-1, 33, 278, 28), (-1, 32, 279, 29), (-1, 32, 280, 30), (-1, 31, 281, 31), (-1, 31, 282, 31), (-1, 30, 283, 32), (-1, 30, 284, 32), (-1, 29, 285, 33), (-1, 29, 286, 33), (-1, 28, 287, 34), (-1, 28, 288, 33), (-1, 27, 289, 34), (-1, 27, 290, 34), (-1, 26, 291, 35), (-1, 26, 292, 35), (-1, 26, 293, 35), (-1, 26, 294, 35), (-1, 26, 295, 35), (-1, 26, 296, 34), (-1, 26, 297, 34), (-1, 26, 298, 33), (-1, 25, 299, 34), (-1, 25, 300, 33), (-1, 25, 301, 33), (-1, 25, 302, 32), (-1, 25, 303, 32), (-1, 25, 304, 31), (-1, 25, 305, 31), (-1, 25, 306, 30), (-1, 26, 307, 29), (-1, 26, 308, 28), (-1, 27, 309, 26), (-1, 27, 310, 25), (-1, 27, 311, 24), (-1, 28, 312, 23), (-1, 28, 313, 22), (-1, 29, 314, 20), (-1, 29, 315, 19), (-1, 31, 316, 16), (-1, 33, 317, 12), (-1, 35, 318, 7), (-1, 37, 319, 2)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -1, 'hashtag_id': 2087736828, 'type': 529, 'x0': 13, 'x1': 56, 'y0': 174, 'y1': 213, 'score': 1.0, 'id': None, 'points': ['44,213,32,210,22,201,15,191,13,182,16,175,25,174,36,177,47,186,54,196,56,205,52,212'], 'sub_photo_id': 0, 'rles': [(-1, 21, 174, 6), (-1, 16, 175, 15), (-1, 16, 176, 19), (-1, 15, 177, 22), (-1, 15, 178, 23), (-1, 14, 179, 26), (-1, 14, 180, 27), (-1, 13, 181, 29), (-1, 13, 182, 30), (-1, 13, 183, 31), (-1, 13, 184, 33), (-1, 14, 185, 33), (-1, 14, 186, 34), (-1, 14, 187, 35), (-1, 14, 188, 35), (-1, 15, 189, 35), (-1, 15, 190, 36), (-1, 15, 191, 36), (-1, 16, 192, 36), (-1, 16, 193, 37), (-1, 17, 194, 37), (-1, 18, 195, 36), (-1, 18, 196, 37), (-1, 19, 197, 36), (-1, 20, 198, 35), (-1, 21, 199, 35), (-1, 21, 200, 35), (-1, 22, 201, 34), (-1, 23, 202, 33), (-1, 24, 203, 33), (-1, 25, 204, 32), (-1, 26, 205, 31), (-1, 28, 206, 28), (-1, 29, 207, 27), (-1, 30, 208, 25), (-1, 31, 209, 24), (-1, 32, 210, 22), (-1, 35, 211, 19), (-1, 39, 212, 14), (-1, 43, 213, 6)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 15 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 15 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.96592583 0.25881905] [-0.25881905 0.96592583]] 15 [[ 0.96592583 0.25881905] [-0.25881905 0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004665851593017578 nb_pixel_total : 694 time to create 1 rle with old method : 0.001140594482421875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00046133995056152344 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0018465518951416016 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -2, 'hashtag_id': 2087736828, 'type': 529, 'x0': 24, 'x1': 72, 'y0': 209, 'y1': 242, 'score': 1.0, 'id': None, 'points': ['62,241,50,242,38,236,28,228,24,220,25,212,34,209,45,209,58,215,67,222,72,231,70,238'], 'sub_photo_id': 0, 'rles': [(-1, 33, 209, 3), (-1, 37, 209, 9), (-1, 30, 210, 18), (-1, 49, 210, 1), (-1, 30, 211, 21), (-1, 26, 212, 27), (-1, 26, 213, 29), (-1, 26, 214, 32), (-1, 25, 215, 35), (-1, 25, 216, 36), (-1, 25, 217, 37), (-1, 25, 218, 38), (-1, 24, 219, 40), (-1, 25, 220, 40), (-1, 25, 221, 42), (-1, 25, 222, 43), (-1, 26, 223, 43), (-1, 27, 224, 42), (-1, 27, 225, 43), (-1, 28, 226, 43), (-1, 29, 227, 42), (-1, 29, 228, 42), (-1, 30, 229, 43), (-1, 30, 230, 43), (-1, 32, 231, 41), (-1, 33, 232, 39), (-1, 34, 233, 39), (-1, 36, 234, 36), (-1, 37, 235, 35), (-1, 38, 236, 34), (-1, 40, 237, 32), (-1, 42, 238, 30), (-1, 43, 239, 28), (-1, 46, 240, 22), (-1, 48, 241, 19), (-1, 50, 242, 15)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 30 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 30 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.8660254 0.5 ] [-0.5 0.8660254]] 30 [[ 0.8660254 0.5 ] [-0.5 0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00038814544677734375 nb_pixel_total : 221 time to create 1 rle with old method : 0.00036334991455078125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003635883331298828 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0015170574188232422 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -3, 'hashtag_id': 2087736828, 'type': 529, 'x0': 44, 'x1': 93, 'y0': 238, 'y1': 268, 'score': 1.0, 'id': None, 'points': ['86,264,74,268,61,265,50,260,44,253,43,245,50,240,61,237,75,239,87,245,93,251,93,259'], 'sub_photo_id': 0, 'rles': [(-1, 59, 238, 9), (-1, 55, 239, 17), (-1, 73, 239, 1), (-1, 51, 240, 27), (-1, 49, 241, 30), (-1, 48, 242, 34), (-1, 48, 243, 36), (-1, 46, 244, 40), (-1, 45, 245, 43), (-1, 44, 246, 45), (-1, 44, 247, 46), (-1, 44, 248, 47), (-1, 44, 249, 48), (-1, 44, 250, 49), (-1, 44, 251, 50), (-1, 44, 252, 50), (-1, 44, 253, 50), (-1, 45, 254, 49), (-1, 45, 255, 49), (-1, 47, 256, 47), (-1, 48, 257, 46), (-1, 48, 258, 46), (-1, 50, 259, 44), (-1, 51, 260, 43), (-1, 52, 261, 41), (-1, 54, 262, 37), (-1, 56, 263, 35), (-1, 59, 264, 31), (-1, 60, 265, 28), (-1, 63, 266, 21), (-1, 67, 267, 2), (-1, 70, 267, 11), (-1, 74, 268, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 45 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 45 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.70710678 0.70710678] [-0.70710678 0.70710678]] 45 [[ 0.70710678 0.70710678] [-0.70710678 0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003871917724609375 nb_pixel_total : 143 time to create 1 rle with old method : 0.0002644062042236328 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003666877746582031 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0014870166778564453 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -4, 'hashtag_id': 2087736828, 'type': 529, 'x0': 69, 'x1': 121, 'y0': 258, 'y1': 286, 'score': 1.0, 'id': None, 'points': ['115,279,105,285,91,286,79,284,72,279,69,272,74,265,84,259,98,258,110,260,118,265,120,273'], 'sub_photo_id': 0, 'rles': [(-1, 96, 258, 5), (-1, 88, 259, 17), (-1, 84, 260, 26), (-1, 111, 260, 1), (-1, 82, 261, 31), (-1, 81, 262, 34), (-1, 78, 263, 38), (-1, 77, 264, 42), (-1, 75, 265, 45), (-1, 74, 266, 46), (-1, 73, 267, 47), (-1, 72, 268, 48), (-1, 72, 269, 49), (-1, 71, 270, 50), (-1, 70, 271, 51), (-1, 69, 272, 53), (-1, 70, 273, 52), (-1, 70, 274, 51), (-1, 70, 275, 50), (-1, 71, 276, 48), (-1, 71, 277, 49), (-1, 72, 278, 47), (-1, 71, 279, 47), (-1, 72, 280, 45), (-1, 73, 281, 41), (-1, 76, 282, 37), (-1, 77, 283, 34), (-1, 79, 284, 31), (-1, 82, 285, 25), (-1, 85, 286, 21)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 60 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 60 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.5 0.8660254] [-0.8660254 0.5 ]] 60 [[ 0.5 0.8660254] [-0.8660254 0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00042939186096191406 nb_pixel_total : 414 time to create 1 rle with old method : 0.0007500648498535156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003840923309326172 nb_pixel_total : 1159 time to create 1 rle with old method : 0.001499176025390625 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -5, 'hashtag_id': 2087736828, 'type': 529, 'x0': 102, 'x1': 152, 'y0': 269, 'y1': 300, 'score': 1.0, 'id': None, 'points': ['148,286,140,295,127,299,115,300,106,297,101,291,105,283,113,275,126,270,139,268,147,271,151,278'], 'sub_photo_id': 0, 'rles': [(-1, 132, 269, 1), (-1, 135, 269, 6), (-1, 126, 270, 18), (-1, 124, 271, 24), (-1, 121, 272, 28), (-1, 118, 273, 32), (-1, 116, 274, 34), (-1, 113, 275, 38), (-1, 112, 276, 39), (-1, 111, 277, 41), (-1, 111, 278, 42), (-1, 109, 279, 44), (-1, 108, 280, 44), (-1, 108, 281, 44), (-1, 106, 282, 45), (-1, 105, 283, 47), (-1, 105, 284, 46), (-1, 104, 285, 46), (-1, 104, 286, 46), (-1, 104, 287, 45), (-1, 104, 288, 44), (-1, 103, 289, 44), (-1, 103, 290, 43), (-1, 102, 291, 43), (-1, 102, 292, 42), (-1, 103, 293, 41), (-1, 104, 294, 38), (-1, 104, 295, 37), (-1, 105, 296, 33), (-1, 106, 297, 28), (-1, 107, 298, 27), (-1, 111, 299, 19), (-1, 112, 300, 1), (-1, 114, 300, 9)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 75 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 75 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.25881905 0.96592583] [-0.96592583 0.25881905]] 75 [[ 0.25881905 0.96592583] [-0.96592583 0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00040459632873535156 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0015625953674316406 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003669261932373047 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0014984607696533203 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00035452842712402344 nb_pixel_total : 264 time to create 1 rle with old method : 0.00044798851013183594 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -6, 'hashtag_id': 2087736828, 'type': 529, 'x0': 138, 'x1': 183, 'y0': 271, 'y1': 308, 'score': 1.0, 'id': None, 'points': ['182,285,176,295,164,303,153,307,144,306,138,302,139,293,145,283,156,275,168,270,177,271,183,277'], 'sub_photo_id': 0, 'rles': [(-1, 168, 271, 10), (-1, 165, 272, 14), (-1, 162, 273, 18), (-1, 160, 274, 22), (-1, 157, 275, 25), (-1, 155, 276, 28), (-1, 154, 277, 30), (-1, 153, 278, 31), (-1, 152, 279, 32), (-1, 150, 280, 33), (-1, 148, 281, 36), (-1, 148, 282, 36), (-1, 146, 283, 38), (-1, 145, 284, 38), (-1, 144, 285, 39), (-1, 144, 286, 39), (-1, 143, 287, 39), (-1, 143, 288, 38), (-1, 142, 289, 39), (-1, 141, 290, 40), (-1, 141, 291, 38), (-1, 140, 292, 39), (-1, 140, 293, 39), (-1, 139, 294, 39), (-1, 139, 295, 38), (-1, 139, 296, 38), (-1, 139, 297, 36), (-1, 139, 298, 35), (-1, 139, 299, 33), (-1, 139, 300, 32), (-1, 138, 301, 32), (-1, 138, 302, 30), (-1, 139, 303, 27), (-1, 140, 304, 25), (-1, 142, 305, 19), (-1, 143, 306, 17), (-1, 143, 307, 14), (-1, 150, 308, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 90 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 90 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] 90 [[ 6.123234e-17 1.000000e+00] [-1.000000e+00 6.123234e-17]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00036597251892089844 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0018324851989746094 .time for calcul the mask position with numpy : 0.0003647804260253906 nb_pixel_total : 1157 time to create 1 rle with old method : 0.001476287841796875 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -7, 'hashtag_id': 2087736828, 'type': 529, 'x0': 268, 'x1': 319, 'y0': 258, 'y1': 294, 'score': 1.0, 'id': None, 'points': ['279,287,270,279,268,269,271,262,281,258,294,259,307,265,316,273,319,282,315,290,305,294,292,293'], 'sub_photo_id': 0, 'rles': [(-1, 280, 258, 8), (-1, 278, 259, 18), (-1, 275, 260, 23), (-1, 273, 261, 27), (-1, 271, 262, 31), (-1, 271, 263, 33), (-1, 270, 264, 36), (-1, 270, 265, 38), (-1, 269, 266, 40), (-1, 269, 267, 41), (-1, 268, 268, 43), (-1, 268, 269, 45), (-1, 268, 270, 46), (-1, 268, 271, 47), (-1, 269, 272, 47), (-1, 269, 273, 48), (-1, 269, 274, 48), (-1, 269, 275, 49), (-1, 269, 276, 49), (-1, 270, 277, 48), (-1, 270, 278, 49), (-1, 270, 279, 49), (-1, 271, 280, 48), (-1, 272, 281, 48), (-1, 273, 282, 47), (-1, 274, 283, 45), (-1, 276, 284, 43), (-1, 277, 285, 41), (-1, 278, 286, 40), (-1, 279, 287, 38), (-1, 281, 288, 36), (-1, 283, 289, 33), (-1, 285, 290, 31), (-1, 287, 291, 27), (-1, 289, 292, 23), (-1, 291, 293, 18), (-1, 299, 294, 8)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -7, 'hashtag_id': 2087736828, 'type': 529, 'x0': 174, 'x1': 213, 'y0': 263, 'y1': 306, 'score': 1.0, 'id': None, 'points': ['213,275,210,287,201,297,191,304,182,306,175,303,174,294,177,283,186,272,196,265,205,263,212,267'], 'sub_photo_id': 0, 'rles': [(-1, 203, 263, 3), (-1, 199, 264, 9), (-1, 196, 265, 14), (-1, 194, 266, 18), (-1, 193, 267, 20), (-1, 192, 268, 21), (-1, 190, 269, 23), (-1, 189, 270, 24), (-1, 187, 271, 27), (-1, 186, 272, 28), (-1, 185, 273, 29), (-1, 184, 274, 30), (-1, 184, 275, 30), (-1, 183, 276, 31), (-1, 182, 277, 31), (-1, 181, 278, 32), (-1, 180, 279, 33), (-1, 179, 280, 34), (-1, 179, 281, 33), (-1, 178, 282, 34), (-1, 177, 283, 35), (-1, 177, 284, 35), (-1, 176, 285, 35), (-1, 176, 286, 35), (-1, 176, 287, 35), (-1, 176, 288, 34), (-1, 175, 289, 34), (-1, 175, 290, 33), (-1, 175, 291, 32), (-1, 175, 292, 31), (-1, 174, 293, 32), (-1, 174, 294, 31), (-1, 174, 295, 30), (-1, 174, 296, 29), (-1, 174, 297, 28), (-1, 174, 298, 27), (-1, 175, 299, 24), (-1, 175, 300, 23), (-1, 175, 301, 22), (-1, 175, 302, 20), (-1, 175, 303, 19), (-1, 177, 304, 15), (-1, 179, 305, 10), (-1, 181, 306, 4)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 105 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 105 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.25881905 0.96592583] [-0.96592583 -0.25881905]] 105 [[-0.25881905 0.96592583] [-0.96592583 -0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.000396728515625 nb_pixel_total : 694 time to create 1 rle with old method : 0.0009298324584960938 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003647804260253906 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0014989376068115234 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -8, 'hashtag_id': 2087736828, 'type': 529, 'x0': 209, 'x1': 242, 'y0': 248, 'y1': 296, 'score': 1.0, 'id': None, 'points': ['241,257,242,269,236,281,228,291,220,295,212,294,209,285,209,274,215,261,222,252,231,247,238,249'], 'sub_photo_id': 0, 'rles': [(-1, 229, 248, 3), (-1, 233, 248, 1), (-1, 229, 249, 10), (-1, 226, 250, 14), (-1, 225, 251, 15), (-1, 223, 252, 17), (-1, 222, 253, 19), (-1, 221, 254, 21), (-1, 221, 255, 21), (-1, 220, 256, 23), (-1, 219, 257, 24), (-1, 218, 258, 25), (-1, 217, 259, 26), (-1, 216, 260, 27), (-1, 215, 261, 28), (-1, 215, 262, 28), (-1, 214, 263, 29), (-1, 214, 264, 29), (-1, 214, 265, 29), (-1, 213, 266, 30), (-1, 213, 267, 30), (-1, 212, 268, 31), (-1, 212, 269, 31), (-1, 211, 270, 32), (-1, 210, 271, 32), (-1, 211, 272, 31), (-1, 210, 273, 31), (-1, 210, 274, 31), (-1, 209, 275, 31), (-1, 209, 276, 31), (-1, 209, 277, 31), (-1, 209, 278, 30), (-1, 209, 279, 29), (-1, 209, 280, 29), (-1, 209, 281, 28), (-1, 209, 282, 28), (-1, 209, 283, 27), (-1, 210, 284, 25), (-1, 209, 285, 25), (-1, 209, 286, 25), (-1, 209, 287, 24), (-1, 210, 288, 22), (-1, 210, 289, 21), (-1, 210, 290, 21), (-1, 212, 291, 17), (-1, 212, 292, 15), (-1, 212, 293, 14), (-1, 212, 294, 12), (-1, 215, 295, 8), (-1, 219, 296, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 120 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 120 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.5 0.8660254] [-0.8660254 -0.5 ]] 120 [[-0.5 0.8660254] [-0.8660254 -0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004324913024902344 nb_pixel_total : 221 time to create 1 rle with old method : 0.00040435791015625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035691261291503906 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0014677047729492188 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -9, 'hashtag_id': 2087736828, 'type': 529, 'x0': 238, 'x1': 268, 'y0': 227, 'y1': 276, 'score': 1.0, 'id': None, 'points': ['264,233,268,245,265,258,260,269,253,275,245,276,240,269,237,258,239,244,245,232,251,226,259,226'], 'sub_photo_id': 0, 'rles': [(-1, 251, 227, 10), (-1, 250, 228, 12), (-1, 249, 229, 13), (-1, 248, 230, 16), (-1, 247, 231, 18), (-1, 246, 232, 19), (-1, 245, 233, 21), (-1, 245, 234, 21), (-1, 244, 235, 22), (-1, 244, 236, 22), (-1, 243, 237, 24), (-1, 243, 238, 24), (-1, 242, 239, 25), (-1, 242, 240, 26), (-1, 242, 241, 26), (-1, 241, 242, 27), (-1, 240, 243, 28), (-1, 240, 244, 29), (-1, 240, 245, 29), (-1, 240, 246, 29), (-1, 239, 247, 29), (-1, 240, 248, 28), (-1, 239, 249, 29), (-1, 239, 250, 29), (-1, 239, 251, 28), (-1, 239, 252, 29), (-1, 238, 253, 30), (-1, 238, 254, 29), (-1, 238, 255, 29), (-1, 238, 256, 29), (-1, 238, 257, 29), (-1, 238, 258, 28), (-1, 238, 259, 28), (-1, 238, 260, 28), (-1, 238, 261, 27), (-1, 239, 262, 25), (-1, 239, 263, 25), (-1, 239, 264, 25), (-1, 239, 265, 24), (-1, 240, 266, 23), (-1, 240, 267, 22), (-1, 240, 268, 22), (-1, 240, 269, 21), (-1, 241, 270, 19), (-1, 241, 271, 18), (-1, 242, 272, 17), (-1, 244, 273, 13), (-1, 244, 274, 12), (-1, 245, 275, 11), (-1, 246, 276, 8)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 135 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 135 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.70710678 0.70710678] [-0.70710678 -0.70710678]] 135 [[-0.70710678 0.70710678] [-0.70710678 -0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00045228004455566406 nb_pixel_total : 143 time to create 1 rle with old method : 0.000392913818359375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00041604042053222656 nb_pixel_total : 1160 time to create 1 rle with old method : 0.001787424087524414 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -10, 'hashtag_id': 2087736828, 'type': 529, 'x0': 258, 'x1': 286, 'y0': 199, 'y1': 251, 'score': 1.0, 'id': None, 'points': ['279,204,285,214,286,228,284,240,279,247,272,250,265,245,259,235,258,221,260,209,265,201,273,199'], 'sub_photo_id': 0, 'rles': [(-1, 272, 199, 2), (-1, 269, 200, 6), (-1, 265, 201, 1), (-1, 267, 201, 9), (-1, 277, 201, 1), (-1, 264, 202, 15), (-1, 264, 203, 16), (-1, 264, 204, 17), (-1, 263, 205, 18), (-1, 262, 206, 19), (-1, 262, 207, 20), (-1, 261, 208, 22), (-1, 260, 209, 23), (-1, 261, 210, 23), (-1, 260, 211, 25), (-1, 260, 212, 25), (-1, 260, 213, 25), (-1, 260, 214, 26), (-1, 260, 215, 27), (-1, 259, 216, 28), (-1, 259, 217, 28), (-1, 259, 218, 28), (-1, 259, 219, 28), (-1, 258, 220, 29), (-1, 258, 221, 29), (-1, 258, 222, 29), (-1, 258, 223, 29), (-1, 258, 224, 29), (-1, 259, 225, 28), (-1, 259, 226, 28), (-1, 259, 227, 28), (-1, 259, 228, 28), (-1, 259, 229, 28), (-1, 259, 230, 28), (-1, 259, 231, 28), (-1, 259, 232, 28), (-1, 260, 233, 27), (-1, 260, 234, 27), (-1, 260, 235, 27), (-1, 260, 236, 26), (-1, 261, 237, 25), (-1, 261, 238, 25), (-1, 262, 239, 23), (-1, 263, 240, 22), (-1, 263, 241, 22), (-1, 263, 242, 21), (-1, 264, 243, 20), (-1, 265, 244, 18), (-1, 265, 245, 17), (-1, 266, 246, 16), (-1, 267, 247, 15), (-1, 268, 248, 13), (-1, 270, 249, 8), (-1, 279, 249, 1), (-1, 271, 250, 5), (-1, 272, 251, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 150 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 150 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.8660254 0.5 ] [-0.5 -0.8660254]] 150 [[-0.8660254 0.5 ] [-0.5 -0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00047707557678222656 nb_pixel_total : 414 time to create 1 rle with old method : 0.0006723403930664062 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004048347473144531 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0017540454864501953 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0004088878631591797 nb_pixel_total : 1 time to create 1 rle with old method : 2.5987625122070312e-05 len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -11, 'hashtag_id': 2087736828, 'type': 529, 'x0': 269, 'x1': 300, 'y0': 168, 'y1': 218, 'score': 1.0, 'id': None, 'points': ['286,171,295,179,299,192,300,204,297,213,291,218,283,214,275,206,270,193,268,180,271,172,278,168'], 'sub_photo_id': 0, 'rles': [(-1, 278, 168, 2), (-1, 277, 169, 5), (-1, 283, 169, 1), (-1, 275, 170, 10), (-1, 273, 171, 14), (-1, 272, 172, 16), (-1, 271, 173, 18), (-1, 271, 174, 19), (-1, 271, 175, 20), (-1, 271, 176, 21), (-1, 270, 177, 24), (-1, 270, 178, 24), (-1, 270, 179, 25), (-1, 269, 180, 27), (-1, 269, 181, 27), (-1, 269, 182, 27), (-1, 269, 183, 28), (-1, 269, 184, 28), (-1, 269, 185, 28), (-1, 270, 186, 27), (-1, 270, 187, 29), (-1, 269, 188, 30), (-1, 270, 189, 29), (-1, 270, 190, 29), (-1, 270, 191, 30), (-1, 270, 192, 30), (-1, 270, 193, 30), (-1, 270, 194, 30), (-1, 271, 195, 29), (-1, 271, 196, 29), (-1, 272, 197, 28), (-1, 272, 198, 29), (-1, 272, 199, 29), (-1, 273, 200, 28), (-1, 273, 201, 28), (-1, 273, 202, 28), (-1, 274, 203, 27), (-1, 274, 204, 27), (-1, 275, 205, 26), (-1, 275, 206, 26), (-1, 275, 207, 25), (-1, 276, 208, 25), (-1, 277, 209, 23), (-1, 279, 210, 20), (-1, 279, 211, 20), (-1, 280, 212, 19), (-1, 282, 213, 17), (-1, 282, 214, 16), (-1, 283, 215, 14), (-1, 285, 216, 11), (-1, 289, 217, 5), (-1, 291, 218, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 165 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 165 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.96592583 0.25881905] [-0.25881905 -0.96592583]] 165 [[-0.96592583 0.25881905] [-0.25881905 -0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003979206085205078 nb_pixel_total : 1204 time to create 1 rle with old method : 0.001512765884399414 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003466606140136719 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0015130043029785156 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003609657287597656 nb_pixel_total : 264 time to create 1 rle with old method : 0.00044465065002441406 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -12, 'hashtag_id': 2087736828, 'type': 529, 'x0': 271, 'x1': 308, 'y0': 137, 'y1': 182, 'score': 1.0, 'id': None, 'points': ['285,137,295,143,303,155,307,166,306,175,302,181,293,180,283,174,275,163,270,151,271,142,277,136'], 'sub_photo_id': 0, 'rles': [(-1, 276, 137, 4), (-1, 281, 137, 3), (-1, 276, 138, 11), (-1, 274, 139, 14), (-1, 274, 140, 17), (-1, 273, 141, 18), (-1, 272, 142, 22), (-1, 271, 143, 24), (-1, 271, 144, 26), (-1, 271, 145, 26), (-1, 271, 146, 27), (-1, 271, 147, 28), (-1, 271, 148, 28), (-1, 271, 149, 29), (-1, 271, 150, 30), (-1, 271, 151, 31), (-1, 271, 152, 31), (-1, 272, 153, 31), (-1, 272, 154, 31), (-1, 272, 155, 32), (-1, 273, 156, 32), (-1, 273, 157, 32), (-1, 273, 158, 32), (-1, 274, 159, 31), (-1, 274, 160, 32), (-1, 275, 161, 32), (-1, 275, 162, 32), (-1, 275, 163, 32), (-1, 276, 164, 32), (-1, 276, 165, 32), (-1, 277, 166, 31), (-1, 278, 167, 30), (-1, 279, 168, 29), (-1, 280, 169, 28), (-1, 280, 170, 29), (-1, 281, 171, 27), (-1, 281, 172, 27), (-1, 283, 173, 25), (-1, 283, 174, 25), (-1, 284, 175, 24), (-1, 285, 176, 23), (-1, 287, 177, 21), (-1, 289, 178, 17), (-1, 290, 179, 15), (-1, 292, 180, 13), (-1, 294, 181, 10), (-1, 301, 182, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 180 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 180 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] 180 [[-1.0000000e+00 1.2246468e-16] [-1.2246468e-16 -1.0000000e+00]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004057884216308594 nb_pixel_total : 1389 time to create 1 rle with old method : 0.001728057861328125 .time for calcul the mask position with numpy : 0.00039124488830566406 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0018084049224853516 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -13, 'hashtag_id': 2087736828, 'type': 529, 'x0': 258, 'x1': 294, 'y0': 1, 'y1': 52, 'score': 1.0, 'id': None, 'points': ['287,41,279,50,269,52,262,49,258,39,259,26,265,13,273,4,282,1,290,5,294,15,293,28'], 'sub_photo_id': 0, 'rles': [(-1, 281, 1, 2), (-1, 278, 2, 7), (-1, 275, 3, 12), (-1, 273, 4, 16), (-1, 272, 5, 19), (-1, 271, 6, 20), (-1, 270, 7, 22), (-1, 269, 8, 23), (-1, 269, 9, 24), (-1, 268, 10, 25), (-1, 267, 11, 26), (-1, 266, 12, 28), (-1, 265, 13, 29), (-1, 265, 14, 30), (-1, 264, 15, 31), (-1, 264, 16, 31), (-1, 263, 17, 32), (-1, 263, 18, 32), (-1, 262, 19, 33), (-1, 262, 20, 33), (-1, 261, 21, 34), (-1, 261, 22, 33), (-1, 260, 23, 34), (-1, 260, 24, 34), (-1, 259, 25, 35), (-1, 259, 26, 35), (-1, 259, 27, 35), (-1, 259, 28, 35), (-1, 259, 29, 35), (-1, 259, 30, 34), (-1, 259, 31, 34), (-1, 259, 32, 33), (-1, 258, 33, 34), (-1, 258, 34, 33), (-1, 258, 35, 33), (-1, 258, 36, 32), (-1, 258, 37, 32), (-1, 258, 38, 31), (-1, 258, 39, 31), (-1, 258, 40, 30), (-1, 259, 41, 29), (-1, 259, 42, 28), (-1, 260, 43, 26), (-1, 260, 44, 25), (-1, 260, 45, 24), (-1, 261, 46, 23), (-1, 261, 47, 22), (-1, 262, 48, 20), (-1, 262, 49, 19), (-1, 264, 50, 16), (-1, 266, 51, 11), (-1, 268, 52, 4)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -13, 'hashtag_id': 2087736828, 'type': 529, 'x0': 263, 'x1': 306, 'y0': 107, 'y1': 146, 'score': 1.0, 'id': None, 'points': ['275,107,287,110,297,119,304,129,306,138,303,145,294,146,283,143,272,134,265,124,263,115,267,108'], 'sub_photo_id': 0, 'rles': [(-1, 271, 107, 6), (-1, 267, 108, 14), (-1, 266, 109, 19), (-1, 266, 110, 22), (-1, 265, 111, 24), (-1, 265, 112, 25), (-1, 264, 113, 27), (-1, 264, 114, 28), (-1, 263, 115, 31), (-1, 263, 116, 32), (-1, 263, 117, 33), (-1, 264, 118, 33), (-1, 264, 119, 34), (-1, 264, 120, 35), (-1, 264, 121, 35), (-1, 265, 122, 35), (-1, 265, 123, 36), (-1, 265, 124, 37), (-1, 266, 125, 36), (-1, 266, 126, 37), (-1, 267, 127, 37), (-1, 268, 128, 36), (-1, 269, 129, 36), (-1, 269, 130, 36), (-1, 270, 131, 35), (-1, 271, 132, 35), (-1, 271, 133, 35), (-1, 272, 134, 34), (-1, 273, 135, 33), (-1, 274, 136, 33), (-1, 276, 137, 31), (-1, 277, 138, 30), (-1, 278, 139, 29), (-1, 279, 140, 27), (-1, 280, 141, 26), (-1, 282, 142, 23), (-1, 283, 143, 22), (-1, 285, 144, 19), (-1, 289, 145, 15), (-1, 293, 146, 6)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 195 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 195 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.96592583 -0.25881905] [ 0.25881905 -0.96592583]] 195 [[-0.96592583 -0.25881905] [ 0.25881905 -0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004355907440185547 nb_pixel_total : 727 time to create 1 rle with old method : 0.0011959075927734375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004169940948486328 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0017914772033691406 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -14, 'hashtag_id': 2087736828, 'type': 529, 'x0': 248, 'x1': 296, 'y0': 78, 'y1': 111, 'score': 1.0, 'id': None, 'points': ['257,78,269,77,281,83,291,91,295,99,294,107,285,110,274,110,261,104,252,97,247,88,249,81'], 'sub_photo_id': 0, 'rles': [(-1, 256, 78, 15), (-1, 254, 79, 19), (-1, 253, 80, 22), (-1, 250, 81, 28), (-1, 249, 82, 30), (-1, 249, 83, 32), (-1, 249, 84, 34), (-1, 249, 85, 35), (-1, 249, 86, 36), (-1, 248, 87, 39), (-1, 249, 88, 39), (-1, 248, 89, 41), (-1, 248, 90, 43), (-1, 248, 91, 43), (-1, 250, 92, 42), (-1, 250, 93, 42), (-1, 250, 94, 43), (-1, 251, 95, 43), (-1, 252, 96, 42), (-1, 252, 97, 43), (-1, 253, 98, 43), (-1, 254, 99, 42), (-1, 256, 100, 40), (-1, 257, 101, 40), (-1, 258, 102, 38), (-1, 259, 103, 37), (-1, 260, 104, 36), (-1, 261, 105, 35), (-1, 263, 106, 32), (-1, 266, 107, 29), (-1, 268, 108, 27), (-1, 270, 109, 21), (-1, 271, 110, 1), (-1, 273, 110, 18), (-1, 275, 111, 9), (-1, 285, 111, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 210 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 210 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.8660254 -0.5 ] [ 0.5 -0.8660254]] 210 [[-0.8660254 -0.5 ] [ 0.5 -0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004150867462158203 nb_pixel_total : 250 time to create 1 rle with old method : 0.00044798851013183594 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00041937828063964844 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0018968582153320312 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -15, 'hashtag_id': 2087736828, 'type': 529, 'x0': 227, 'x1': 276, 'y0': 52, 'y1': 82, 'score': 1.0, 'id': None, 'points': ['233,55,245,51,258,54,269,59,275,66,276,74,269,79,258,82,244,80,232,74,226,68,226,60'], 'sub_photo_id': 0, 'rles': [(-1, 244, 52, 3), (-1, 240, 53, 11), (-1, 252, 53, 2), (-1, 237, 54, 21), (-1, 233, 55, 28), (-1, 231, 56, 31), (-1, 230, 57, 35), (-1, 230, 58, 37), (-1, 228, 59, 41), (-1, 227, 60, 43), (-1, 227, 61, 44), (-1, 227, 62, 46), (-1, 227, 63, 46), (-1, 227, 64, 47), (-1, 227, 65, 49), (-1, 227, 66, 49), (-1, 227, 67, 50), (-1, 227, 68, 50), (-1, 227, 69, 50), (-1, 228, 70, 49), (-1, 229, 71, 48), (-1, 230, 72, 47), (-1, 231, 73, 46), (-1, 232, 74, 45), (-1, 233, 75, 43), (-1, 235, 76, 40), (-1, 237, 77, 36), (-1, 239, 78, 34), (-1, 242, 79, 30), (-1, 243, 80, 27), (-1, 247, 81, 1), (-1, 249, 81, 17), (-1, 253, 82, 9)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 225 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 225 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.70710678 -0.70710678] [ 0.70710678 -0.70710678]] 225 [[-0.70710678 -0.70710678] [ 0.70710678 -0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004494190216064453 nb_pixel_total : 169 time to create 1 rle with old method : 0.00035953521728515625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004112720489501953 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0015106201171875 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -16, 'hashtag_id': 2087736828, 'type': 529, 'x0': 199, 'x1': 251, 'y0': 34, 'y1': 62, 'score': 1.0, 'id': None, 'points': ['204,40,214,34,228,33,240,35,247,40,250,47,245,54,235,60,221,61,209,59,201,54,199,46'], 'sub_photo_id': 0, 'rles': [(-1, 215, 34, 21), (-1, 214, 35, 25), (-1, 211, 36, 31), (-1, 210, 37, 34), (-1, 208, 38, 37), (-1, 207, 39, 41), (-1, 204, 40, 45), (-1, 203, 41, 47), (-1, 202, 42, 47), (-1, 201, 43, 49), (-1, 202, 44, 48), (-1, 201, 45, 50), (-1, 200, 46, 51), (-1, 199, 47, 52), (-1, 199, 48, 53), (-1, 200, 49, 51), (-1, 200, 50, 50), (-1, 200, 51, 49), (-1, 201, 52, 48), (-1, 201, 53, 47), (-1, 201, 54, 46), (-1, 201, 55, 45), (-1, 202, 56, 42), (-1, 205, 57, 38), (-1, 206, 58, 34), (-1, 208, 59, 31), (-1, 209, 60, 1), (-1, 211, 60, 26), (-1, 216, 61, 17), (-1, 220, 62, 5)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 240 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 240 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.5 -0.8660254] [ 0.8660254 -0.5 ]] 240 [[-0.5 -0.8660254] [ 0.8660254 -0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003695487976074219 nb_pixel_total : 450 time to create 1 rle with old method : 0.0006802082061767578 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003724098205566406 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0015263557434082031 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003447532653808594 nb_pixel_total : 1 time to create 1 rle with old method : 2.7418136596679688e-05 len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -17, 'hashtag_id': 2087736828, 'type': 529, 'x0': 168, 'x1': 218, 'y0': 20, 'y1': 51, 'score': 1.0, 'id': None, 'points': ['171,33,179,24,192,20,204,19,213,22,218,28,214,36,206,44,193,49,180,51,172,48,168,41'], 'sub_photo_id': 0, 'rles': [(-1, 198, 20, 9), (-1, 208, 20, 1), (-1, 191, 21, 19), (-1, 187, 22, 27), (-1, 187, 23, 28), (-1, 183, 24, 33), (-1, 180, 25, 37), (-1, 179, 26, 38), (-1, 177, 27, 41), (-1, 177, 28, 42), (-1, 176, 29, 43), (-1, 175, 30, 43), (-1, 174, 31, 44), (-1, 173, 32, 44), (-1, 172, 33, 45), (-1, 171, 34, 46), (-1, 171, 35, 46), (-1, 170, 36, 46), (-1, 169, 37, 47), (-1, 170, 38, 45), (-1, 169, 39, 44), (-1, 169, 40, 44), (-1, 168, 41, 44), (-1, 168, 42, 42), (-1, 169, 43, 41), (-1, 170, 44, 39), (-1, 170, 45, 38), (-1, 171, 46, 34), (-1, 171, 47, 32), (-1, 172, 48, 28), (-1, 173, 49, 24), (-1, 177, 50, 18), (-1, 180, 51, 6), (-1, 188, 51, 1)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 255 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 255 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-0.25881905 -0.96592583] [ 0.96592583 -0.25881905]] 255 [[-0.25881905 -0.96592583] [ 0.96592583 -0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00040721893310546875 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0015718936920166016 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003695487976074219 nb_pixel_total : 1158 time to create 1 rle with old method : 0.001489400863647461 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00034499168395996094 nb_pixel_total : 234 time to create 1 rle with old method : 0.0003972053527832031 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -18, 'hashtag_id': 2087736828, 'type': 529, 'x0': 137, 'x1': 182, 'y0': 12, 'y1': 49, 'score': 1.0, 'id': None, 'points': ['137,34,143,24,155,16,166,12,175,13,181,17,180,26,174,36,163,44,151,49,142,48,136,42'], 'sub_photo_id': 0, 'rles': [(-1, 170, 12, 1), (-1, 164, 13, 14), (-1, 161, 14, 17), (-1, 160, 15, 19), (-1, 156, 16, 25), (-1, 155, 17, 27), (-1, 153, 18, 30), (-1, 151, 19, 32), (-1, 150, 20, 32), (-1, 149, 21, 33), (-1, 147, 22, 35), (-1, 146, 23, 36), (-1, 144, 24, 38), (-1, 144, 25, 38), (-1, 143, 26, 39), (-1, 142, 27, 39), (-1, 142, 28, 39), (-1, 142, 29, 38), (-1, 140, 30, 40), (-1, 140, 31, 39), (-1, 140, 32, 38), (-1, 139, 33, 39), (-1, 138, 34, 39), (-1, 138, 35, 39), (-1, 138, 36, 38), (-1, 137, 37, 38), (-1, 137, 38, 36), (-1, 137, 39, 36), (-1, 138, 40, 33), (-1, 137, 41, 32), (-1, 137, 42, 31), (-1, 137, 43, 30), (-1, 137, 44, 29), (-1, 139, 45, 25), (-1, 139, 46, 22), (-1, 141, 47, 18), (-1, 142, 48, 14), (-1, 143, 49, 10)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 270 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 270 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] 270 [[-1.8369702e-16 -1.0000000e+00] [ 1.0000000e+00 -1.8369702e-16]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00036716461181640625 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0017561912536621094 .time for calcul the mask position with numpy : 0.00039577484130859375 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0017783641815185547 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 2 list_crops_rotate : : {'photo_id': -19, 'hashtag_id': 2087736828, 'type': 529, 'x0': 1, 'x1': 52, 'y0': 26, 'y1': 62, 'score': 1.0, 'id': None, 'points': ['41,32,50,40,52,50,49,57,39,61,26,60,13,54,4,46,1,37,5,29,15,25,28,26'], 'sub_photo_id': 0, 'rles': [(-1, 14, 26, 8), (-1, 12, 27, 18), (-1, 9, 28, 23), (-1, 7, 29, 27), (-1, 5, 30, 31), (-1, 5, 31, 33), (-1, 4, 32, 36), (-1, 4, 33, 38), (-1, 3, 34, 40), (-1, 3, 35, 41), (-1, 2, 36, 43), (-1, 2, 37, 45), (-1, 1, 38, 47), (-1, 1, 39, 48), (-1, 2, 40, 48), (-1, 2, 41, 49), (-1, 2, 42, 49), (-1, 3, 43, 48), (-1, 3, 44, 49), (-1, 3, 45, 49), (-1, 4, 46, 48), (-1, 4, 47, 48), (-1, 5, 48, 47), (-1, 6, 49, 47), (-1, 7, 50, 46), (-1, 8, 51, 45), (-1, 10, 52, 43), (-1, 11, 53, 41), (-1, 12, 54, 40), (-1, 13, 55, 38), (-1, 15, 56, 36), (-1, 17, 57, 33), (-1, 19, 58, 31), (-1, 21, 59, 27), (-1, 23, 60, 23), (-1, 25, 61, 18), (-1, 33, 62, 8)], 'hashtag': '', 'sum_segment': 0},: {'photo_id': -19, 'hashtag_id': 2087736828, 'type': 529, 'x0': 107, 'x1': 146, 'y0': 14, 'y1': 57, 'score': 1.0, 'id': None, 'points': ['107,44,110,32,119,22,129,15,138,13,144,16,145,25,143,36,134,47,124,54,115,56,108,52'], 'sub_photo_id': 0, 'rles': [(-1, 136, 14, 4), (-1, 132, 15, 10), (-1, 129, 16, 15), (-1, 127, 17, 19), (-1, 126, 18, 20), (-1, 124, 19, 22), (-1, 123, 20, 23), (-1, 122, 21, 24), (-1, 120, 22, 27), (-1, 119, 23, 28), (-1, 118, 24, 29), (-1, 117, 25, 30), (-1, 116, 26, 31), (-1, 115, 27, 32), (-1, 115, 28, 31), (-1, 114, 29, 32), (-1, 113, 30, 33), (-1, 112, 31, 34), (-1, 111, 32, 34), (-1, 110, 33, 35), (-1, 110, 34, 35), (-1, 110, 35, 35), (-1, 109, 36, 35), (-1, 109, 37, 35), (-1, 109, 38, 34), (-1, 109, 39, 33), (-1, 108, 40, 34), (-1, 108, 41, 33), (-1, 108, 42, 32), (-1, 108, 43, 31), (-1, 107, 44, 31), (-1, 107, 45, 30), (-1, 107, 46, 30), (-1, 107, 47, 29), (-1, 107, 48, 28), (-1, 107, 49, 27), (-1, 108, 50, 24), (-1, 108, 51, 23), (-1, 108, 52, 21), (-1, 108, 53, 20), (-1, 109, 54, 18), (-1, 111, 55, 14), (-1, 113, 56, 9), (-1, 115, 57, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 285 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 285 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.25881905 -0.96592583] [ 0.96592583 0.25881905]] 285 [[ 0.25881905 -0.96592583] [ 0.96592583 0.25881905]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0004487037658691406 nb_pixel_total : 727 time to create 1 rle with old method : 0.001180410385131836 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004093647003173828 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0020072460174560547 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -20, 'hashtag_id': 2087736828, 'type': 529, 'x0': 78, 'x1': 111, 'y0': 24, 'y1': 72, 'score': 1.0, 'id': None, 'points': ['78,62,77,50,83,38,91,28,99,24,107,25,110,34,110,45,104,58,97,67,88,72,81,70'], 'sub_photo_id': 0, 'rles': [(-1, 101, 24, 1), (-1, 98, 25, 8), (-1, 97, 26, 12), (-1, 95, 27, 14), (-1, 94, 28, 15), (-1, 92, 29, 17), (-1, 90, 30, 21), (-1, 90, 31, 21), (-1, 89, 32, 22), (-1, 88, 33, 24), (-1, 87, 34, 25), (-1, 87, 35, 25), (-1, 86, 36, 25), (-1, 85, 37, 27), (-1, 84, 38, 28), (-1, 84, 39, 28), (-1, 83, 40, 29), (-1, 83, 41, 29), (-1, 82, 42, 30), (-1, 81, 43, 31), (-1, 81, 44, 31), (-1, 81, 45, 31), (-1, 80, 46, 31), (-1, 80, 47, 31), (-1, 79, 48, 31), (-1, 79, 49, 32), (-1, 78, 50, 32), (-1, 78, 51, 31), (-1, 78, 52, 31), (-1, 78, 53, 30), (-1, 78, 54, 30), (-1, 78, 55, 29), (-1, 78, 56, 29), (-1, 78, 57, 29), (-1, 78, 58, 28), (-1, 78, 59, 28), (-1, 78, 60, 27), (-1, 78, 61, 26), (-1, 78, 62, 25), (-1, 78, 63, 24), (-1, 78, 64, 23), (-1, 79, 65, 21), (-1, 79, 66, 21), (-1, 80, 67, 19), (-1, 81, 68, 17), (-1, 81, 69, 15), (-1, 81, 70, 14), (-1, 82, 71, 10), (-1, 87, 72, 1), (-1, 89, 72, 3)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 300 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 300 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.5 -0.8660254] [ 0.8660254 0.5 ]] 300 [[ 0.5 -0.8660254] [ 0.8660254 0.5 ]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00046539306640625 nb_pixel_total : 250 time to create 1 rle with old method : 0.0004379749298095703 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00041413307189941406 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0017240047454833984 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -21, 'hashtag_id': 2087736828, 'type': 529, 'x0': 52, 'x1': 82, 'y0': 44, 'y1': 93, 'score': 1.0, 'id': None, 'points': ['55,86,51,74,54,61,59,50,66,44,74,43,79,50,82,61,80,75,74,87,68,93,60,93'], 'sub_photo_id': 0, 'rles': [(-1, 67, 44, 8), (-1, 65, 45, 11), (-1, 65, 46, 12), (-1, 64, 47, 13), (-1, 62, 48, 17), (-1, 62, 49, 18), (-1, 61, 50, 19), (-1, 60, 51, 21), (-1, 59, 52, 22), (-1, 59, 53, 22), (-1, 58, 54, 23), (-1, 58, 55, 24), (-1, 57, 56, 25), (-1, 57, 57, 25), (-1, 57, 58, 25), (-1, 56, 59, 27), (-1, 55, 60, 28), (-1, 55, 61, 28), (-1, 55, 62, 28), (-1, 54, 63, 29), (-1, 54, 64, 29), (-1, 54, 65, 29), (-1, 54, 66, 29), (-1, 53, 67, 30), (-1, 53, 68, 29), (-1, 54, 69, 28), (-1, 53, 70, 29), (-1, 53, 71, 29), (-1, 53, 72, 28), (-1, 53, 73, 29), (-1, 52, 74, 29), (-1, 52, 75, 29), (-1, 52, 76, 29), (-1, 53, 77, 28), (-1, 53, 78, 27), (-1, 53, 79, 26), (-1, 53, 80, 26), (-1, 54, 81, 25), (-1, 54, 82, 24), (-1, 54, 83, 24), (-1, 55, 84, 22), (-1, 55, 85, 22), (-1, 55, 86, 21), (-1, 55, 87, 21), (-1, 56, 88, 19), (-1, 56, 89, 18), (-1, 57, 90, 16), (-1, 59, 91, 13), (-1, 59, 92, 12), (-1, 60, 93, 10)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 315 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 315 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.70710678 -0.70710678] [ 0.70710678 0.70710678]] 315 [[ 0.70710678 -0.70710678] [ 0.70710678 0.70710678]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00035834312438964844 nb_pixel_total : 169 time to create 1 rle with old method : 0.0002987384796142578 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036907196044921875 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0014820098876953125 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -22, 'hashtag_id': 2087736828, 'type': 529, 'x0': 34, 'x1': 62, 'y0': 69, 'y1': 121, 'score': 1.0, 'id': None, 'points': ['40,115,34,105,33,91,35,79,40,72,47,69,54,74,60,84,61,98,59,110,54,118,46,120'], 'sub_photo_id': 0, 'rles': [(-1, 48, 69, 1), (-1, 45, 70, 5), (-1, 41, 71, 1), (-1, 43, 71, 8), (-1, 40, 72, 13), (-1, 39, 73, 15), (-1, 39, 74, 16), (-1, 39, 75, 17), (-1, 38, 76, 18), (-1, 37, 77, 20), (-1, 37, 78, 21), (-1, 36, 79, 22), (-1, 36, 80, 22), (-1, 36, 81, 23), (-1, 35, 82, 25), (-1, 35, 83, 25), (-1, 35, 84, 26), (-1, 34, 85, 27), (-1, 34, 86, 27), (-1, 34, 87, 27), (-1, 34, 88, 28), (-1, 34, 89, 28), (-1, 34, 90, 28), (-1, 34, 91, 28), (-1, 34, 92, 28), (-1, 34, 93, 28), (-1, 34, 94, 28), (-1, 34, 95, 28), (-1, 34, 96, 29), (-1, 34, 97, 29), (-1, 34, 98, 29), (-1, 34, 99, 29), (-1, 34, 100, 29), (-1, 34, 101, 28), (-1, 34, 102, 28), (-1, 34, 103, 28), (-1, 34, 104, 28), (-1, 34, 105, 27), (-1, 35, 106, 26), (-1, 36, 107, 25), (-1, 36, 108, 25), (-1, 36, 109, 25), (-1, 37, 110, 23), (-1, 38, 111, 23), (-1, 38, 112, 22), (-1, 39, 113, 20), (-1, 40, 114, 19), (-1, 40, 115, 18), (-1, 40, 116, 17), (-1, 41, 117, 16), (-1, 42, 118, 15), (-1, 43, 119, 1), (-1, 45, 119, 11), (-1, 46, 120, 6), (-1, 47, 121, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 330 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 330 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.8660254 -0.5 ] [ 0.5 0.8660254]] 330 [[ 0.8660254 -0.5 ] [ 0.5 0.8660254]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.0003783702850341797 nb_pixel_total : 450 time to create 1 rle with old method : 0.0007901191711425781 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003681182861328125 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0014913082122802734 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -23, 'hashtag_id': 2087736828, 'type': 529, 'x0': 20, 'x1': 51, 'y0': 102, 'y1': 152, 'score': 1.0, 'id': None, 'points': ['33,148,24,140,20,127,19,115,22,106,28,101,36,105,44,113,49,126,51,139,48,147,41,151'], 'sub_photo_id': 0, 'rles': [(-1, 28, 102, 2), (-1, 27, 103, 5), (-1, 25, 104, 11), (-1, 24, 105, 14), (-1, 23, 106, 16), (-1, 22, 107, 17), (-1, 22, 108, 19), (-1, 22, 109, 20), (-1, 22, 110, 20), (-1, 21, 111, 23), (-1, 20, 112, 25), (-1, 21, 113, 25), (-1, 20, 114, 26), (-1, 20, 115, 26), (-1, 20, 116, 27), (-1, 20, 117, 27), (-1, 20, 118, 28), (-1, 20, 119, 28), (-1, 20, 120, 28), (-1, 20, 121, 29), (-1, 20, 122, 29), (-1, 21, 123, 28), (-1, 21, 124, 29), (-1, 21, 125, 29), (-1, 21, 126, 30), (-1, 21, 127, 30), (-1, 21, 128, 30), (-1, 21, 129, 30), (-1, 22, 130, 29), (-1, 22, 131, 29), (-1, 22, 132, 30), (-1, 22, 133, 29), (-1, 24, 134, 27), (-1, 24, 135, 28), (-1, 24, 136, 28), (-1, 24, 137, 28), (-1, 25, 138, 27), (-1, 25, 139, 27), (-1, 25, 140, 27), (-1, 26, 141, 25), (-1, 27, 142, 24), (-1, 27, 143, 24), (-1, 29, 144, 21), (-1, 30, 145, 20), (-1, 31, 146, 19), (-1, 32, 147, 18), (-1, 33, 148, 16), (-1, 34, 149, 14), (-1, 36, 150, 10), (-1, 37, 151, 1), (-1, 39, 151, 5), (-1, 41, 152, 2)], 'hashtag': '', 'sum_segment': 0} Rotation of photo 1350256425 of 345 degree temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg [, , , ] 345 remove_crop_border : True version de PIL : 9.5.0 Needs to change image size ! [[ 0.96592583 -0.25881905] [ 0.25881905 0.96592583]] 345 [[ 0.96592583 -0.25881905] [ 0.25881905 0.96592583]] shrink_image : True len(list_crops) : 4 time for calcul the mask position with numpy : 0.00043582916259765625 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0016295909881591797 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00034880638122558594 nb_pixel_total : 1157 time to create 1 rle with old method : 0.027978181838989258 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00036025047302246094 nb_pixel_total : 234 time to create 1 rle with old method : 0.0004010200500488281 On the border Smaller than minimal size ! len(list_crops_rotate) : 1 list_crops_rotate : : {'photo_id': -24, 'hashtag_id': 2087736828, 'type': 529, 'x0': 12, 'x1': 49, 'y0': 138, 'y1': 183, 'score': 1.0, 'id': None, 'points': ['34,182,24,176,16,164,12,153,13,144,17,138,26,139,36,145,44,156,49,168,48,177,42,183'], 'sub_photo_id': 0, 'rles': [(-1, 18, 138, 2), (-1, 17, 139, 10), (-1, 16, 140, 13), (-1, 16, 141, 15), (-1, 15, 142, 17), (-1, 13, 143, 21), (-1, 13, 144, 23), (-1, 13, 145, 24), (-1, 13, 146, 25), (-1, 13, 147, 25), (-1, 13, 148, 27), (-1, 13, 149, 27), (-1, 12, 150, 29), (-1, 13, 151, 28), (-1, 13, 152, 29), (-1, 13, 153, 30), (-1, 13, 154, 31), (-1, 13, 155, 32), (-1, 13, 156, 32), (-1, 14, 157, 32), (-1, 14, 158, 32), (-1, 14, 159, 32), (-1, 15, 160, 32), (-1, 16, 161, 31), (-1, 16, 162, 32), (-1, 16, 163, 32), (-1, 16, 164, 32), (-1, 17, 165, 32), (-1, 18, 166, 31), (-1, 18, 167, 31), (-1, 19, 168, 31), (-1, 19, 169, 31), (-1, 20, 170, 30), (-1, 21, 171, 29), (-1, 22, 172, 28), (-1, 22, 173, 28), (-1, 23, 174, 27), (-1, 24, 175, 26), (-1, 24, 176, 26), (-1, 26, 177, 24), (-1, 27, 178, 22), (-1, 30, 179, 18), (-1, 30, 180, 17), (-1, 33, 181, 14), (-1, 34, 182, 11), (-1, 37, 183, 3), (-1, 41, 183, 3)], 'hashtag': '', 'sum_segment': 0} About to upload 24 photos upload in portfolio : 22094760 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744011608_3752194 we have uploaded 24 photos in the portfolio 22094760 time of upload the photos Elapsed time : 5.756897926330566 map_filename_photo_id : 24 map_filename_photo_id : {'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg': 1350256431, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg': 1350256432, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg': 1350256433, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg': 1350256434, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg': 1350256435, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg': 1350256436, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg': 1350256437, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg': 1350256438, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg': 1350256439, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg': 1350256441, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg': 1350256442, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg': 1350256443, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg': 1350256444, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg': 1350256445, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg': 1350256446, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg': 1350256447, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg': 1350256448, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg': 1350256449, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg': 1350256451, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg': 1350256452, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg': 1350256453, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg': 1350256454, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg': 1350256455, 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg': 1350256456} Len new_chis : 24 Len list_new_chi_with_photo_id : 28 of type : 529 list_new_chi_with_photo_id : [, , , , , , , , , , , , , , , , , , , , , , , , , , , ] batch 1 Loaded 28 chid ids of type : 529 Number RLEs to save : 1197 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 11.315907001495361 time spend to save output : 8.440017700195312e-05 total time spend for step 3 : 11.315991401672363 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [937852786, 937852786, '1350256425'] map_info['map_portfolio_photo'] : {} final : True mtd_id 243 list_pids : [937852786, 937852786, '1350256425'] Looping around the photos to save general results len do output : 24 /1350256431Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256432Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256433Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256434Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256435Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256436Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256437Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256438Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256439Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256441Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256442Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256443Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256444Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256445Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256446Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256447Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256448Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256449Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256451Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256452Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256453Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256454Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256455Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256456Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '1350256425', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 75 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('243', None, '1350256431', 'None', None, None, None, None, None), ('243', None, '1350256432', 'None', None, None, None, None, None), ('243', None, '1350256433', 'None', None, None, None, None, None), ('243', None, '1350256434', 'None', None, None, None, None, None), ('243', None, '1350256435', 'None', None, None, None, None, None), ('243', None, '1350256436', 'None', None, None, None, None, None), ('243', None, '1350256437', 'None', None, None, None, None, None), ('243', None, '1350256438', 'None', None, None, None, None, None), ('243', None, '1350256439', 'None', None, None, None, None, None), ('243', None, '1350256441', 'None', None, None, None, None, None), ('243', None, '1350256442', 'None', None, None, None, None, None), ('243', None, '1350256443', 'None', None, None, None, None, None), ('243', None, '1350256444', 'None', None, None, None, None, None), ('243', None, '1350256445', 'None', None, None, None, None, None), ('243', None, '1350256446', 'None', None, None, None, None, None), ('243', None, '1350256447', 'None', None, None, None, None, None), ('243', None, '1350256448', 'None', None, None, None, None, None), ('243', None, '1350256449', 'None', None, None, None, None, None), ('243', None, '1350256451', 'None', None, None, None, None, None), ('243', None, '1350256452', 'None', None, None, None, None, None), ('243', None, '1350256453', 'None', None, None, None, None, None), ('243', None, '1350256454', 'None', None, None, None, None, None), ('243', None, '1350256455', 'None', None, None, None, None, None), ('243', None, '1350256456', 'None', None, None, None, None, None), ('243', None, '937852786', None, None, None, None, None, None), ('243', None, '1350256425', None, None, None, None, None, None)] time used for this insertion : 0.01987171173095703 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1350256431: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1350256432: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1350256433: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1350256434: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1350256435: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1350256436: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1350256437: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1350256438: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1350256439: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1350256441: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1350256442: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1350256443: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1350256444: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1350256445: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1350256446: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1350256447: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1350256448: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1350256449: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1350256451: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1350256452: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1350256453: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1350256454: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1350256455: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1350256456: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} ret_da : {1350256431: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1350256432: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1350256433: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1350256434: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1350256435: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1350256436: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1350256437: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1350256438: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1350256439: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1350256441: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1350256442: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1350256443: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1350256444: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1350256445: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1350256446: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1350256447: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1350256448: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1350256449: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1350256451: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1350256452: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1350256453: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1350256454: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1350256455: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1350256456: ['937852786', 'temp/1744011571_3752194_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} list chi : [[, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], []] ############################### TEST flip ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=571 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=571 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 571 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=571 # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : flip list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (911785586) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 911785586 download finish for photo 911785586 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.15579485893249512 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:flip Mon Apr 7 09:40:29 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011629_3752194_911785586_d8582feabcd359151ff718b5832248c7-big.jpg': 911785586} map_photo_id_path_extension : {911785586: {'path': 'temp/1744011629_3752194_911785586_d8582feabcd359151ff718b5832248c7-big.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step_flip ! We are in a linear step without datou_depend ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 911785586) and `type` in (741) Loaded 6 chid ids of type : 741 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) +++++WARNING : Unexpected points, we should remove this data for chi_id : 18344210, for now we just ignore these empty polygon points +SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (18344206,18344211,18344210,18344209,18344208,18344207) map_chi_objs : {911785586: [, , , , , ]} photo_id in download_rotate_and_save : 911785586 list_chi_loc : 6 Vertical flip of photo 911785586 version de PIL : 9.5.0 vertically flipped image is saved in temp/1744011629_3752194_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg Horizontal flip of photo 911785586 version de PIL : 9.5.0 horizontally flipped image is saved in temp/1744011629_3752194_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg About to upload 2 photos upload in portfolio : 1090565 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744011630_3752194 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.8047115802764893 map_filename_photo_id : 2 map_filename_photo_id : {'temp/1744011629_3752194_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg': 1350256481, 'temp/1744011629_3752194_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg': 1350256482} Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 list_new_chi_with_photo_id : [, , , , , , , , , , , ] insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 12 chid ids of type : 741 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 0 INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 0.9280469417572021 time spend to save output : 6.651878356933594e-05 total time spend for step 1 : 0.9281134605407715 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : flip we use saveGeneral [911785586] map_info['map_portfolio_photo'] : {} final : True mtd_id 571 list_pids : [911785586] Looping around the photos to save general results len do output : 2 /1350256481 /1350256482 before output type Managing all output in save final without adding information in the mtr_datou_result ('571', None, None, None, None, None, None, None, None) ('571', None, '911785586', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('571', None, '911785586', None, None, None, None, None, None)] time used for this insertion : 0.011781692504882812 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1350256481': ['911785586', 'temp/1744011629_3752194_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1350256482': ['911785586', 'temp/1744011629_3752194_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg', [, , , , , ]]} ############################### TEST crop_rles ################################ # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : TEST CROP RLES Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=686 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=686 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 686 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=686 # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : crop list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (950103132) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 950103132 download finish for photo 950103132 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.12581539154052734 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:crop Mon Apr 7 09:40:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00.jpg': 950103132} map_photo_id_path_extension : {950103132: {'path': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Crop ! param_json : {'photo_hashtag_type': 755, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'feed_id_new_photos': 0, 'host': 'www.fotonower.com', 'crop_type': 'rle', 'margin_relative': 0.1, 'min_score': 0.3, 'upload,type': 'python'} margin_type : margin_relative margin_value : [0.1, 0.1, 0.1, 0.1] Loading chi in step crop with photo_hashtag_type : 755 Loading chi in step crop for list_pids : 1 ! batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 950103132) and `type` in (755) and score>0.3 Loaded 8 chid ids of type : 755 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) ++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947670931,1947670932,1947670933,1947670934,1947670935,1947670936,1947670937,1947670938) select photo_id, sub_photo_id, x0, x1, y0, y1, resize_coeff_x, resize_coeff_y, crop_type, id from MTRPhoto.photo_sub_photos where photo_id in ( 950103132) WARNING : margin is only used for type bib ! type of cropped photo chosen : rle we resize croppped photo by 1 on x axis and by 1 on y axis we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! we have both polygon and rles Here we manage rles ! map_result returned by crop_photo_return_map_crop : length : 8 map_result after crop : {1947670931: {'crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670931_0.jpg', 'coordonates': (183, 199, 15, 41), 'sub_photo_id': -1, 'same_chi': False}, 1947670932: {'crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670932_0.jpg', 'coordonates': (38, 85, 113, 140), 'sub_photo_id': -1, 'same_chi': False}, 1947670933: {'crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670933_0.jpg', 'coordonates': (168, 194, 141, 151), 'sub_photo_id': -1, 'same_chi': False}, 1947670934: {'crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670934_0.jpg', 'coordonates': (47, 101, 16, 110), 'sub_photo_id': -1, 'same_chi': False}, 1947670935: {'crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670935_0.jpg', 'coordonates': (175, 199, 104, 111), 'sub_photo_id': -1, 'same_chi': False}, 1947670936: {'crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670936_0.jpg', 'coordonates': (86, 130, 184, 196), 'sub_photo_id': -1, 'same_chi': False}, 1947670937: {'crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670937_0.jpg', 'coordonates': (79, 195, 0, 61), 'sub_photo_id': -1, 'same_chi': False}, 1947670938: {'crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', 'photo_id': 950103132, 'bib_crop': 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_bib_crop_1947670938_0.jpg', 'coordonates': (131, 155, 181, 195), 'sub_photo_id': -1, 'same_chi': False}} Here we crop with rles About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22094761 in upload media Upload medias : ['temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg'] : url : https://marlene.fotonower.com/api/v1/secured/photo/upload?token=78d09a0790ec6ecbf119343125a81fdc&datou=0 temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg after data_to_send, before sending request after request b'{"photo_ids":["1350256503","1350256500","1350256493","1350256495","1350256489","1350256498","1350256497","1350256491"],"photo_ids_order":["1350256489","1350256491","1350256493","1350256495","1350256497","1350256498","1350256500","1350256503"],"photo_detail":[{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/7/d02a538262e89eb0f5466010fad1c336.jpg","text":"TemporaryFile(/tmp/multipartBody885832507576643141asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744011632776,"filename":"1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/7/d3eac6d22d9ab05d5144184c8d9739ca.jpg","text":"TemporaryFile(/tmp/multipartBody114233258295251698asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744011632776,"filename":"1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/7/ff5921b91725449c58690baeb5ada90b.jpg","text":"TemporaryFile(/tmp/multipartBody4342481452718029826asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744011632776,"filename":"1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/7/88d861ec1f908c3fe12a30bd97a4c197.jpg","text":"TemporaryFile(/tmp/multipartBody3879363153615507355asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744011632776,"filename":"1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/7/507702c3c3d49b0634bc276b5c288a5f.jpg","text":"TemporaryFile(/tmp/multipartBody7124638835946934064asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744011632776,"filename":"1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/7/ac7960112d8774648f4c83f055e43ef3.jpg","text":"TemporaryFile(/tmp/multipartBody6951168754494316475asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744011632776,"filename":"1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/7/6ccf59c701180926a64791b615339ffd.jpg","text":"TemporaryFile(/tmp/multipartBody4043551658773130438asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744011632776,"filename":"1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg","height":0,"width":0},{"mtr_user_id":440,"url":"https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2025/4/7/c5d4e8f29dfdbce77731cb7da0823a62.jpg","text":"TemporaryFile(/tmp/multipartBody4343639912477642680asTemporaryFile)","latitude":0.0,"longitude":0.0,"uploaded_at":1744011632776,"filename":"1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg","height":0,"width":0}],"map_files_photo_id":{"file2":"1350256493","file6":"1350256500","file1":"1350256491","file7":"1350256503","file0":"1350256489","file4":"1350256497","file5":"1350256498","file3":"1350256495"},"map_files_photo_id_array":[{"photo_id":"1350256498","filename":"file5"},{"photo_id":"1350256493","filename":"file2"},{"photo_id":"1350256497","filename":"file4"},{"photo_id":"1350256503","filename":"file7"},{"photo_id":"1350256491","filename":"file1"},{"photo_id":"1350256489","filename":"file0"},{"photo_id":"1350256495","filename":"file3"},{"photo_id":"1350256500","filename":"file6"}],"portfolio_id":22094761,"hashtag_by_photo_ids":[{"1350256503":["hashtag1","hashtag2"]},{"1350256500":["hashtag1","hashtag2"]},{"1350256493":["hashtag1","hashtag2"]},{"1350256495":["hashtag1","hashtag2"]},{"1350256489":["hashtag1","hashtag2"]},{"1350256498":["hashtag1","hashtag2"]},{"1350256497":["hashtag1","hashtag2"]},{"1350256491":["hashtag1","hashtag2"]}],"comms":"Portfolio 22094761 used, photo_id : ArrayBuffer(1350256503, 1350256500, 1350256493, 1350256495, 1350256489, 1350256498, 1350256497, 1350256491)","result":[],"list_datou_current":[]}' Result OK ! uploaded one batch 0 Elapsed time : 19.937685251235962 map_result_insert : {'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg': 1350256493, 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg': 1350256500, 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg': 1350256491, 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg': 1350256503, 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg': 1350256489, 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg': 1350256497, 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg': 1350256498, 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg': 1350256495} Now we prepare data that will be used for ellipse search ! chi_id found to be used 1947670931 path of cropped varroa found to be used to match on an ellipse temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg sub_photo_id found to be used 1350256489 chi_id found to be used 1947670932 path of cropped varroa found to be used to match on an ellipse temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg sub_photo_id found to be used 1350256491 chi_id found to be used 1947670933 path of cropped varroa found to be used to match on an ellipse temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg sub_photo_id found to be used 1350256493 chi_id found to be used 1947670934 path of cropped varroa found to be used to match on an ellipse temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg sub_photo_id found to be used 1350256495 chi_id found to be used 1947670935 path of cropped varroa found to be used to match on an ellipse temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg sub_photo_id found to be used 1350256497 chi_id found to be used 1947670936 path of cropped varroa found to be used to match on an ellipse temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg sub_photo_id found to be used 1350256498 chi_id found to be used 1947670937 path of cropped varroa found to be used to match on an ellipse temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg sub_photo_id found to be used 1350256500 chi_id found to be used 1947670938 path of cropped varroa found to be used to match on an ellipse temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg sub_photo_id found to be used 1350256503 insert ignore into MTRPhoto.crop_sub_photo_ids (crop_hashtag_id, sub_photo_id, mtr_user_id) VALUES (%s,%s,%s) : [(1947670931, '1350256489', 31), (1947670932, '1350256491', 31), (1947670933, '1350256493', 31), (1947670934, '1350256495', 31), (1947670935, '1350256497', 31), (1947670936, '1350256498', 31), (1947670937, '1350256500', 31), (1947670938, '1350256503', 31)] map of cropped photos with some data : {'1350256489': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1350256491': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1350256493': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1350256495': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1350256497': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1350256498': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1350256500': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1350256503': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} After datou_step_exec type output : time spend for datou_step_exec : 20.01344108581543 time spend to save output : 6.4849853515625e-05 total time spend for step 1 : 20.013505935668945 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : True saveOutput not yet implemented for datou_step.type : crop we use saveGeneral [950103132] map_info['map_portfolio_photo'] : {} final : True mtd_id 686 list_pids : [950103132] Looping around the photos to save general results len do output : 8 /1350256489Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350256503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('686', None, None, None, None, None, None, None, None) ('686', None, '950103132', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 25 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [('686', None, '1350256489', 'None', None, None, None, None, None), ('686', None, '1350256491', 'None', None, None, None, None, None), ('686', None, '1350256493', 'None', None, None, None, None, None), ('686', None, '1350256495', 'None', None, None, None, None, None), ('686', None, '1350256497', 'None', None, None, None, None, None), ('686', None, '1350256498', 'None', None, None, None, None, None), ('686', None, '1350256500', 'None', None, None, None, None, None), ('686', None, '1350256503', 'None', None, None, None, None, None), ('686', None, '950103132', None, None, None, None, None, None)] time used for this insertion : 0.013404607772827148 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1350256489': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1350256491': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1350256493': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1350256495': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1350256497': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1350256498': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1350256500': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1350256503': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} ret_da : {'1350256489': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1350256491': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1350256493': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1350256495': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1350256497': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1350256498': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1350256500': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1350256503': ['950103132', 'temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 Found filename_to_hash : temp/1744011631_3752194_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg ############################### TEST angular_coeff ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=852 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=852 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 852 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=852 # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : angular_coeff list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (932296368) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 932296368 download finish for photo 932296368 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.15024232864379883 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:angular_coeff Mon Apr 7 09:40:51 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011651_3752194_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg': 932296368} map_photo_id_path_extension : {932296368: {'path': 'temp/1744011651_3752194_932296368_97c5e7b0f2830e550e2d6eeb248d8006.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} beginning of step detection filter param_json : {'input_type': 846, 'output_type': -1, 'orientation_type': 872, 'ref_crop_type': 846, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'crops_coeffs': {'CAR_EXTERIEUR_angle_avant_droit.*': {'aile-avant': [[15, 0.0], [240, 0.0], [285, 1.0], [345, 1.0]], 'capot': [[45, 1.0], [60, 0.5], [270, 0.0], [315, 1.0], [360, 1.0]]}}} angular_coefficients_to_crops batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 932296368) and `type` in (846) Loaded 19 chid ids of type : 846 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (769189713,769189714,769189715,769189716,769189717,769189718,769189721,769189723,769189724,769189725,769189727,769189729,769189730,769189732,769189733,769189734,769189737,769189738,769189739) select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (932296368) and type=872 treating photo 932296368 select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (932296368) and type=872 After datou_step_exec type output : time spend for datou_step_exec : 0.32457828521728516 time spend to save output : 0.0009770393371582031 total time spend for step 1 : 0.32555532455444336 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {932296368: ([(932296368, 2106233860, 846, 1066, 1277, 93, 340, 0.31964028378983567, 0, []), (932296368, 2106233860, 846, 434, 690, 218, 498, 0.7170410105787726, 0, []), (932296368, 503548896, 846, 902, 1111, 466, 576, 0.31724966, 769189715, []), (932296368, 599722655, 846, 523, 1100, 152, 337, 0.98039776, 0, []), (932296368, 492601069, 846, 143, 1190, 90, 695, 0.9696157, 769189717, []), (932296368, 492601069, 846, 0, 408, 246, 719, 0.9431181, 769189718, []), (932296368, 2096875722, 846, 567, 964, 162, 215, 0.55490255, 769189721, []), (932296368, 2096875709, 846, 437, 939, 24, 198, 0.9983077, 769189723, []), (932296368, 2096875709, 846, 1004, 1263, 28, 144, 0.9485744, 769189724, []), (932296368, 624624117, 846, 595, 1122, 331, 640, 0.99100167, 769189725, []), (932296368, 492624020, 846, 585, 874, 308, 393, 0.78697366, 769189727, []), (932296368, 2096875719, 846, 943, 1100, 428, 547, 0.96733797, 769189729, []), (932296368, 492654799, 846, 253, 467, 35, 441, 0.99621326, 769189730, []), (932296368, 492689227, 846, 1118, 1264, 270, 438, 0.9901647, 769189732, []), (932296368, 492689227, 846, 486, 671, 378, 690, 0.98789483, 769189733, []), (932296368, 492689227, 846, 161, 255, 229, 409, 0.70801014, 769189734, []), (932296368, 492925064, 846, 261, 421, 27, 193, 0.92215157, 769189737, []), (932296368, 492925064, 846, 873, 1045, 46, 156, 0.7535122, 769189738, []), (932296368, 492925064, 846, 1090, 1279, 20, 107, 0.45259848, 769189739, [])],)} test angular coeff is a success ! ############################### TEST detection_filter_by_crop ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=708 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=708 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 708 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=708 # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : detection_filter_by_crop list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (946711423) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 946711423 download finish for photo 946711423 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.16742682456970215 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:detection_filter_by_crop Mon Apr 7 09:40:51 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011651_3752194_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg': 946711423} map_photo_id_path_extension : {946711423: {'path': 'temp/1744011651_3752194_946711423_b4bef6b5c6c4b6ffae23f8718c42183c.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} beginning of step detection filter param_json : {'input_type': 631, 'output_type': -1, 'condition_type': 445, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'min_surface_ratio': 0.7} conditional_crop_copy batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (445) Loaded 3 chid ids of type : 445 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947734477,18345275,18345276) +++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (631) Loaded 35 chid ids of type : 631 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (445) Loaded 3 chid ids of type : 445 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947734477,18345275,18345276) +++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947734477,18345275,18345276) treating photo 946711423 SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 624624117, 'type': 631, 'x0': 226, 'x1': 569, 'y0': 252, 'y1': 425, 'score': 0.99812776, 'id': 1947740368, 'points': ['395,419,341,419,340,418,316,418,315,417,306,417, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 162, 'x1': 245, 'y0': 233, 'y1': 396, 'score': 0.99702626, 'id': 1947740369, 'points': ['215,393,206,393,202,390,200,390,192,383,191,380, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 96, 'x1': 172, 'y0': 39, 'y1': 261, 'score': 0.9928518, 'id': 1947740370, 'points': ['143,252,143,249,141,246,140,246,138,248,138,251,137 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 545, 'x1': 612, 'y0': 186, 'y1': 276, 'score': 0.9876676, 'id': 1947740371, 'points': ['584,267,583,266,578,266,574,262,574,259,573,258,5 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 468, 'x1': 555, 'y0': 292, 'y1': 365, 'score': 0.9830025, 'id': 1947740372, 'points': ['491,350,489,350,488,349,487,350,483,350,480,348, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 599722655, 'type': 631, 'x0': 176, 'x1': 535, 'y0': 138, 'y1': 264, 'score': 0.9818268, 'id': 1947740373, 'points': ['453,253,413,253,412,252,387,252,386,250,386,248,3 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 53, 'x1': 87, 'y0': 127, 'y1': 212, 'score': 0.9786105, 'id': 1947740374, 'points': ['74,201,69,201,67,199,66,199,65,198,62,192,62,190,61 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492844413, 'type': 631, 'x0': 89, 'x1': 163, 'y0': 93, 'y1': 144, 'score': 0.9772748, 'id': 1947740375, 'points': ['159,142,153,141,151,139,148,138,145,135,141,133,139 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 418, 'x1': 522, 'y0': 69, 'y1': 136, 'score': 0.97407305, 'id': 1947740376, 'points': ['510,121,507,121,505,119,501,120,500,119,500,113,4 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875709, 'type': 631, 'x0': 185, 'x1': 431, 'y0': 39, 'y1': 136, 'score': 0.97171515, 'id': 1947740377, 'points': ['331,134,287,134,286,133,284,133,283,134,272,134, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 198, 'x1': 395, 'y0': 118, 'y1': 142, 'score': 0.9699756, 'id': 1947740378, 'points': ['328,137,251,137,250,136,249,137,241,137,240,136, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 499500794, 'type': 631, 'x0': 93, 'x1': 107, 'y0': 127, 'y1': 146, 'score': 0.9574813, 'id': 1947740379, 'points': ['101,143,98,143,95,139,95,131,97,129,100,129,101,13 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 71, 'x1': 125, 'y0': 36, 'y1': 95, 'score': 0.95296955, 'id': 1947740380, 'points': ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 101, 'x1': 167, 'y0': 38, 'y1': 127, 'score': 0.9508439, 'id': 1947740381, 'points': ['154,117,152,115,152,112,150,110,148,106,148,104,14 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 249, 'x1': 400, 'y0': 219, 'y1': 316, 'score': 0.8792459, 'id': 1947740382, 'points': ['395,313,390,313,386,311,384,312,381,312,376,309,3 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 540, 'x1': 625, 'y0': 78, 'y1': 221, 'score': 0.87864035, 'id': 1947740383, 'points': ['567,127,566,127,565,126,564,109,562,106,560,106,5 surface aoutside conditional crop crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 547, 'x1': 640, 'y0': 79, 'y1': 129, 'score': 0.8165246, 'id': 1947740384, 'points': ['630,96,627,96,627,94,628,92,629,92,631,94,631,95', surface aoutside conditional crop crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 360, 'x1': 434, 'y0': 62, 'y1': 116, 'score': 0.74684095, 'id': 1947740385, 'points': ['415,103,413,103,411,101,408,101,405,99,403,99,401 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 302, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.7406652, 'id': 1947740386, 'points': ['442,401,372,401,372,397,370,395,369,392,366,390,3 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 53, 'x1': 85, 'y0': 75, 'y1': 182, 'score': 0.73015845, 'id': 1947740387, 'points': ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,5 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875717, 'type': 631, 'x0': 477, 'x1': 510, 'y0': 220, 'y1': 243, 'score': 0.69028217, 'id': 1947740388, 'points': ['501,241,493,241,489,239,488,237,487,237,480,232 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 61, 'x1': 115, 'y0': 42, 'y1': 188, 'score': 0.6900027, 'id': 1947740389, 'points': ['92,47,91,45,92,44,96,45,94,45', '73,141,73,136,72,1 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 309, 'x1': 326, 'y0': 382, 'y1': 404, 'score': 0.6633776, 'id': 1947740390, 'points': ['309,383,309,382,311,382', '325,385,324,383,319,3 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 427, 'x1': 553, 'y0': 258, 'y1': 315, 'score': 0.6446218, 'id': 1947740391, 'points': ['531,284,526,284,525,283,525,281,523,279,522,280, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 144, 'x1': 267, 'y0': 181, 'y1': 307, 'score': 0.63958377, 'id': 1947740392, 'points': ['212,251,209,251,208,250,203,251,201,250,201,249 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 285, 'x1': 433, 'y0': 343, 'y1': 377, 'score': 0.61493844, 'id': 1947740393, 'points': ['431,376,286,376,285,375,285,368,286,367,286,362 crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 146, 'x1': 287, 'y0': 140, 'y1': 311, 'score': 0.54784286, 'id': 1947740394, 'points': ['234,254,227,254,221,251,219,248,215,253,212,253 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 141, 'y1': 245, 'score': 0.46262404, 'id': 1947740395, 'points': ['603,176,599,173,595,176,593,176,590,174,589,174 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 495920967, 'type': 631, 'x0': 202, 'x1': 524, 'y0': 112, 'y1': 333, 'score': 0.45109355, 'id': 1947740396, 'points': ['483,289,483,286,482,285,482,283,480,279,480,274, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 433, 'x1': 558, 'y0': 248, 'y1': 286, 'score': 0.44133398, 'id': 1947740397, 'points': ['492,272,474,272,473,271,468,271,465,269,460,269 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 535, 'x1': 630, 'y0': 138, 'y1': 231, 'score': 0.42747068, 'id': 1947740398, 'points': ['590,171,589,170,585,170,584,171,581,169,579,169 surface aoutside conditional crop crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 399, 'x1': 569, 'y0': 68, 'y1': 251, 'score': 0.41876298, 'id': 1947740399, 'points': [], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', ' crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 420, 'x1': 552, 'y0': 244, 'y1': 293, 'score': 0.35962066, 'id': 1947740400, 'points': ['474,289,453,289,452,288,439,288,437,286,431,286, crop duplicated : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 301, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.740756, 'id': 3140491551, 'points': ['442,401,371,401,371,397,366,390,365,386,356,386,35 crop not duplicated : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 140, 'y1': 245, 'score': 0.4627206, 'id': 3140491552, 'points': ['595,176,593,176,589,173,586,176,583,176,580,174, surface aoutside conditional crop After datou_step_exec type output : time spend for datou_step_exec : 0.12974977493286133 time spend to save output : 2.7418136596679688e-05 total time spend for step 1 : 0.129777193069458 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {946711423: ([(946711423, 624624117, 631, 226, 569, 252, 425, 0.99812776, 1947740368, ['395,419,341,419,340,418,316,418,315,417,306,417,305,416,293,415,290,413,284,412,283,411,280,411,272,407,264,405,258,400,254,398,250,394,244,391,242,389,242,386,239,380,240,368,239,367,239,347,238,346,238,331,237,330,237,327,238,326,237,314,239,311,239,308,237,304,238,302,243,298,244,296,244,292,246,291,250,291,251,290,259,290,260,289,264,289,265,288,269,288,271,290,273,294,278,299,280,300,285,300,286,301,293,301,294,302,302,304,305,307,309,308,312,310,314,310,317,312,335,312,336,313,343,313,344,314,370,314,371,315,381,315,382,314,389,313,393,311,405,309,406,308,408,308,412,306,414,304,417,304,421,307,426,308,427,309,433,309,434,310,464,309,467,306,471,304,476,304,477,303,489,303,490,302,494,302,495,301,500,301,501,300,515,300,516,299,519,298,522,292,525,290,533,290,534,291,540,291,541,290,543,290,547,288,550,285,550,285,552,289,552,291,553,292,553,313,552,314,552,324,550,328,550,333,549,334,549,336,544,346,543,353,539,361,532,368,531,368,527,372,519,374,509,379,503,384,499,385,498,386,496,386,492,388,490,390,486,392,484,392,479,396,475,397,474,398,472,398,471,399,469,399,462,403,460,403,459,404,457,404,456,405,454,405,450,407,448,407,443,410,425,413,424,414,422,414,416,417,404,417,403,418,396,418']), (946711423, 492689227, 631, 162, 245, 233, 396, 0.99702626, 1947740369, ['215,393,206,393,202,390,200,390,192,383,191,380,187,375,184,369,184,367,180,360,180,358,179,357,177,349,175,347,174,339,172,336,171,330,170,329,169,324,168,323,168,313,167,312,167,304,166,303,166,298,165,297,165,288,164,287,165,286,165,272,166,271,166,268,167,267,167,263,168,262,169,254,173,249,177,247,178,247,181,251,184,251,184,252,187,255,189,255,193,259,193,261,195,263,195,264,201,270,203,278,207,282,208,289,211,293,211,296,213,299,214,304,215,305,216,312,219,316,219,319,220,320,220,325,222,329,222,335,223,336,223,338,225,342,225,349,226,350,226,359,227,360,227,366,228,367,228,371,231,375,231,382,227,385,226,388,225,389,223,388,219,392,216,392']), (946711423, 492654799, 631, 96, 172, 39, 261, 0.9928518, 1947740370, ['143,252,143,249,141,246,140,246,138,248,138,251,137,250,137,248,135,246,134,246,132,248,127,244,124,244,122,241,122,236,121,235,121,232,118,229,117,225,116,224,116,212,113,209,115,207,116,201,111,194,110,184,106,178,107,154,108,152,112,148,113,144,112,143,112,138,110,136,108,136,107,135,103,128,103,124,102,123,102,121,103,120,103,118,106,115,106,106,107,105,110,104,113,101,117,93,117,71,114,65,116,61,116,59,117,58,117,55,118,54,119,49,122,45,122,44,124,42,150,42,151,43,153,43,153,47,152,48,152,50,154,52,155,56,156,57,156,85,155,86,155,95,154,96,154,98,155,99,155,105,156,106,155,107,155,116,157,120,159,121,159,123,156,127,156,134,157,135,157,138,156,139,156,141,154,145,152,147,150,151,149,159,148,160,148,164,149,165,149,174,148,175,148,197,149,198,149,215,150,216,150,241,149,242,149,245,148,247,146,245,144,247', '122,147,121,138,120,141,119,142,119,144,118,145,121,148']), (946711423, 2096875719, 631, 468, 555, 292, 365, 0.9830025, 1947740372, ['491,350,489,350,488,349,487,350,483,350,480,348,480,341,482,339,482,337,485,334,487,334,491,330,494,330,495,328,498,326,501,326,503,324,507,325,509,323,514,321,516,319,518,321,520,321,521,319,522,319,524,321,527,321,530,317,530,315,531,314,535,313,540,309,543,310,544,311,542,313,542,314,544,316,541,318,541,322,536,322,535,323,533,323,532,322,528,322,527,321,524,321,522,323,518,322,516,324,517,327,516,328,512,327,510,329,512,332,513,332,515,330,516,331,516,333,514,332,511,333,511,336,514,337,516,336,516,339,515,339,513,338,511,340,512,341,512,342,510,343,507,343,502,347,500,347,497,349,492,349', '514,325,515,324,513,322,512,322,511,325,512,326', '522,327,521,327,521,326,522,325']), (946711423, 599722655, 631, 176, 535, 138, 264, 0.9818268, 1947740373, ['453,253,413,253,412,252,387,252,386,250,386,248,383,246,379,245,376,243,361,243,361,240,362,239,359,238,358,237,356,237,355,236,352,236,351,235,333,235,332,234,329,234,329,233,331,231,331,229,329,228,328,224,330,222,330,221,324,218,308,219,307,218,302,218,298,216,288,217,287,218,285,218,283,220,283,221,287,224,295,225,295,225,294,226,289,226,288,227,283,227,282,228,273,228,272,229,271,228,259,228,258,227,254,227,253,226,247,225,247,225,251,221,248,218,243,216,247,213,248,213,249,212,248,211,246,211,245,210,241,210,240,209,237,209,236,208,231,207,230,206,228,202,224,201,223,200,221,200,220,199,214,198,213,195,211,193,208,193,203,189,203,184,201,181,201,176,198,171,199,170,199,158,203,154,205,153,205,151,206,149,209,149,210,148,225,148,226,147,283,147,284,148,287,148,288,147,305,147,306,148,312,148,313,147,354,147,355,146,428,146,429,147,433,147,434,148,437,148,438,149,451,149,457,156,459,162,462,165,464,166,471,166,472,165,477,165,480,167,480,171,486,175,488,175,489,176,502,176,503,178,503,180,509,185,509,189,512,193,512,199,513,200,513,203,514,204,514,210,513,211,514,217,512,221,513,222,513,225,510,229,510,235,507,237,504,238,502,243,490,243,489,244,485,244,484,245,480,245,479,246,463,246,462,247,460,247,458,249,457,252,454,252', 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'519,353,518,352,517,353,518,354'])],)} test detection filter by crop is a success ! ############################### TEST detection_filter_by_classif ################################ t SELECT id FROM MTRPhoto.crop_hashtag_ids WHERE photo_id=946711423 AND `type`=816 DELETE FROM MTRPhoto.crop_hashtag_ids WHERE id IN (3748532060,3748532059,3748532058,3748532067,3748532066,3748532065,3748532064,3748532063,3748532072,3748532075,3748532061,3748532062,3748532071,3748532070,3748532076,3748532077,3748532078,3748532080,3748532068,3748532074,3748532073,3748532079,3748532069) Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=672 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=672 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 672 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=672 # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : detection_filter_by_classif list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (946711423) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos ##### After load_data_input time to download the photos : 0.004088640213012695 #### fin chargement data Blocking on flush ? No conitnuing About to test input to load Calling datou_exec Inside datou_exec : verbose : True number of steps : 1 step1:detection_filter_by_classif Mon Apr 7 09:40:52 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 After prepare type args : Here we display some param of map_info ! map_filenames : {} map_photo_id_path_extension : {} map_subphoto_mainphoto : {} beginning of step detection filter with classification results param_json : {'input_type': 631, 'output_type': 816, 'condition_type': 872, 'crops_ok': {'CAR_DOCUMENT.*': {}, 'CAR_INTERIEUR.*': {}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'Retroviseur': 2, 'Roue': 2, 'Capot': 1, 'Pare-brise': 1, 'vitre': 10, 'phare': 2, 'Feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'Plaque-immatriculation': 1, 'Essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1}}, 'separation': {'CAR_EXTERIEUR_avant.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-gauche', 'a-droite-de', 'phare-droit']}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-droite', 'a-gauche-de', 'phare-gauche'], 'porte': ['porte-avant', 'a-droite-de', 'porte-arriere']}}} conditional_crop_by_classif_copy select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (946711423) and type=872 batch 1 select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids where photo_id in ( 946711423) and `type` in (631) Loaded 35 chid ids of type : 631 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++SELECT * FROM MTRPhoto.crop_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) SELECT * FROM MTRPhoto.crop_sum_segments WHERE crop_hashtag_id in (1947740368,1947740369,1947740370,1947740371,1947740372,1947740373,1947740374,1947740375,1947740376,1947740377,1947740378,1947740379,1947740380,1947740381,1947740382,1947740383,1947740384,1947740385,1947740386,1947740387,1947740388,1947740389,1947740390,1947740391,1947740392,1947740393,1947740394,1947740395,1947740396,1947740397,1947740398,1947740399,1947740400,3140491551,3140491552) treating photo 946711423 select distinct hashtag_id from MTRBack.photo_hashtag_ids where photo_id in (946711423) and type=872 list of crops kept {'retroviseur': 2, 'roue': 2, 'capot': 1, 'pare-brise': 1, 'vitre': 10, 'phare': 2, 'feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'plaque-immatriculation': 1, 'essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1} for hahstag car_exterieur_angle_avant_droit_merge__port_551052 crop not duplicated for hashtag aile-arriere : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 144, 'x1': 267, 'y0': 181, 'y1': 307, 'score': 0.63958377, 'id': 1947740392, 'points': ['212,251,209,251,208,250,203,251,201,250,201,249 crop not duplicated for hashtag coffre : : {'photo_id': 946711423, 'hashtag_id': 495920967, 'type': 631, 'x0': 202, 'x1': 524, 'y0': 112, 'y1': 333, 'score': 0.45109355, 'id': 1947740396, 'points': ['483,289,483,286,482,285,482,283,480,279,480,274, crop not duplicated for hashtag aile-arriere : : {'photo_id': 946711423, 'hashtag_id': 2106233861, 'type': 631, 'x0': 535, 'x1': 630, 'y0': 138, 'y1': 231, 'score': 0.42747068, 'id': 1947740398, 'points': ['590,171,589,170,585,170,584,171,581,169,579,169 crop duplicated for hashtag retroviseur : : {'photo_id': 946711423, 'hashtag_id': 492844413, 'type': 631, 'x0': 89, 'x1': 163, 'y0': 93, 'y1': 144, 'score': 0.9772748, 'id': 1947740375, 'points': ['159,142,153,141,151,139,148,138,145,135,141,133,139 crop duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 162, 'x1': 245, 'y0': 233, 'y1': 396, 'score': 0.99702626, 'id': 1947740369, 'points': ['215,393,206,393,202,390,200,390,192,383,191,380, crop duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 545, 'x1': 612, 'y0': 186, 'y1': 276, 'score': 0.9876676, 'id': 1947740371, 'points': ['584,267,583,266,578,266,574,262,574,259,573,258,5 crop not duplicated for hashtag roue : : {'photo_id': 946711423, 'hashtag_id': 492689227, 'type': 631, 'x0': 53, 'x1': 87, 'y0': 127, 'y1': 212, 'score': 0.9786105, 'id': 1947740374, 'points': ['74,201,69,201,67,199,66,199,65,198,62,192,62,190,61 crop duplicated for hashtag capot : : {'photo_id': 946711423, 'hashtag_id': 599722655, 'type': 631, 'x0': 176, 'x1': 535, 'y0': 138, 'y1': 264, 'score': 0.9818268, 'id': 1947740373, 'points': ['453,253,413,253,412,252,387,252,386,250,386,248,3 crop duplicated for hashtag pare-brise : : {'photo_id': 946711423, 'hashtag_id': 2096875709, 'type': 631, 'x0': 185, 'x1': 431, 'y0': 39, 'y1': 136, 'score': 0.97171515, 'id': 1947740377, 'points': ['331,134,287,134,286,133,284,133,283,134,272,134, crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 418, 'x1': 522, 'y0': 69, 'y1': 136, 'score': 0.97407305, 'id': 1947740376, 'points': ['510,121,507,121,505,119,501,120,500,119,500,113,4 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 71, 'x1': 125, 'y0': 36, 'y1': 95, 'score': 0.95296955, 'id': 1947740380, 'points': ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 101, 'x1': 167, 'y0': 38, 'y1': 127, 'score': 0.9508439, 'id': 1947740381, 'points': ['154,117,152,115,152,112,150,110,148,106,148,104,14 crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 547, 'x1': 640, 'y0': 79, 'y1': 129, 'score': 0.8165246, 'id': 1947740384, 'points': ['630,96,627,96,627,94,628,92,629,92,631,94,631,95', crop duplicated for hashtag vitre : : {'photo_id': 946711423, 'hashtag_id': 492925064, 'type': 631, 'x0': 360, 'x1': 434, 'y0': 62, 'y1': 116, 'score': 0.74684095, 'id': 1947740385, 'points': ['415,103,413,103,411,101,408,101,405,99,403,99,401 crop duplicated for hashtag phare : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 249, 'x1': 400, 'y0': 219, 'y1': 316, 'score': 0.8792459, 'id': 1947740382, 'points': ['395,313,390,313,386,311,384,312,381,312,376,309,3 crop duplicated for hashtag phare : : {'photo_id': 946711423, 'hashtag_id': 492624020, 'type': 631, 'x0': 420, 'x1': 552, 'y0': 244, 'y1': 293, 'score': 0.35962066, 'id': 1947740400, 'points': ['474,289,453,289,452,288,439,288,437,286,431,286, crop duplicated for hashtag feu-antibrouillard : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 309, 'x1': 326, 'y0': 382, 'y1': 404, 'score': 0.6633776, 'id': 1947740390, 'points': ['309,383,309,382,311,382', '325,385,324,383,319,3 crop duplicated for hashtag feu-antibrouillard : : {'photo_id': 946711423, 'hashtag_id': 2096875712, 'type': 631, 'x0': 285, 'x1': 433, 'y0': 343, 'y1': 377, 'score': 0.61493844, 'id': 1947740393, 'points': ['431,376,286,376,285,375,285,368,286,367,286,362 crop duplicated for hashtag poignee : : {'photo_id': 946711423, 'hashtag_id': 499500794, 'type': 631, 'x0': 93, 'x1': 107, 'y0': 127, 'y1': 146, 'score': 0.9574813, 'id': 1947740379, 'points': ['101,143,98,143,95,139,95,131,97,129,100,129,101,13 crop duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 96, 'x1': 172, 'y0': 39, 'y1': 261, 'score': 0.9928518, 'id': 1947740370, 'points': ['143,252,143,249,141,246,140,246,138,248,138,251,137 crop duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 540, 'x1': 625, 'y0': 78, 'y1': 221, 'score': 0.87864035, 'id': 1947740383, 'points': ['567,127,566,127,565,126,564,109,562,106,560,106,5 crop not duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 61, 'x1': 115, 'y0': 42, 'y1': 188, 'score': 0.6900027, 'id': 1947740389, 'points': ['92,47,91,45,92,44,96,45,94,45', '73,141,73,136,72,1 crop not duplicated for hashtag porte : : {'photo_id': 946711423, 'hashtag_id': 492654799, 'type': 631, 'x0': 399, 'x1': 569, 'y0': 68, 'y1': 251, 'score': 0.41876298, 'id': 1947740399, 'points': [], 'sub_photo_id': 0, 'rles': [], 'hashtag': '', ' crop duplicated for hashtag calandre : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 301, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.740756, 'id': 3140491551, 'points': ['442,401,371,401,371,397,366,390,365,386,356,386,35 crop not duplicated for hashtag calandre : : {'photo_id': 946711423, 'hashtag_id': 503548896, 'type': 631, 'x0': 302, 'x1': 540, 'y0': 339, 'y1': 403, 'score': 0.7406652, 'id': 1947740386, 'points': ['442,401,372,401,372,397,370,395,369,392,366,390,3 crop duplicated for hashtag logo-marque : : {'photo_id': 946711423, 'hashtag_id': 2096875717, 'type': 631, 'x0': 477, 'x1': 510, 'y0': 220, 'y1': 243, 'score': 0.69028217, 'id': 1947740388, 'points': ['501,241,493,241,489,239,488,237,487,237,480,232 crop duplicated for hashtag plaque-immatriculation : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 468, 'x1': 555, 'y0': 292, 'y1': 365, 'score': 0.9830025, 'id': 1947740372, 'points': ['491,350,489,350,488,349,487,350,483,350,480,348, crop not duplicated for hashtag plaque-immatriculation : : {'photo_id': 946711423, 'hashtag_id': 2096875719, 'type': 631, 'x0': 427, 'x1': 553, 'y0': 258, 'y1': 315, 'score': 0.6446218, 'id': 1947740391, 'points': ['531,284,526,284,525,283,525,281,523,279,522,280, crop duplicated for hashtag essuie-glace : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 198, 'x1': 395, 'y0': 118, 'y1': 142, 'score': 0.9699756, 'id': 1947740378, 'points': ['328,137,251,137,250,136,249,137,241,137,240,136, crop not duplicated for hashtag essuie-glace : : {'photo_id': 946711423, 'hashtag_id': 2096875722, 'type': 631, 'x0': 433, 'x1': 558, 'y0': 248, 'y1': 286, 'score': 0.44133398, 'id': 1947740397, 'points': ['492,272,474,272,473,271,468,271,465,269,460,269 crop duplicated for hashtag pare-choc : : {'photo_id': 946711423, 'hashtag_id': 624624117, 'type': 631, 'x0': 226, 'x1': 569, 'y0': 252, 'y1': 425, 'score': 0.99812776, 'id': 1947740368, 'points': ['395,419,341,419,340,418,316,418,315,417,306,417, crop duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 53, 'x1': 85, 'y0': 75, 'y1': 182, 'score': 0.73015845, 'id': 1947740387, 'points': ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,5 crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 146, 'x1': 287, 'y0': 140, 'y1': 311, 'score': 0.54784286, 'id': 1947740394, 'points': ['234,254,227,254,221,251,219,248,215,253,212,253 crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 140, 'y1': 245, 'score': 0.4627206, 'id': 3140491552, 'points': ['595,176,593,176,589,173,586,176,583,176,580,174, crop not duplicated for hashtag aile-avant : : {'photo_id': 946711423, 'hashtag_id': 2106233860, 'type': 631, 'x0': 496, 'x1': 624, 'y0': 141, 'y1': 245, 'score': 0.46262404, 'id': 1947740395, 'points': ['603,176,599,173,595,176,593,176,590,174,589,174 list of crops kept {'pare-choc': ('pare-chocs-avant',), 'phare': ('phare-droite', 'a-gauche-de', 'phare-gauche'), 'porte': ('porte-avant', 'a-droite-de', 'porte-arriere')} for hahstag car_exterieur_angle_avant_droit_merge__port_551052 batch 1 Loaded 0 chid ids of type : 0 insert ignore into MTRPhoto.crop_hashtag_ids (photo_id, hashtag_id, `type`,x0,x1,y0,y1,score) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) batch 1 Loaded 23 chid ids of type : 816 INSERT IGNORE INTO MTRPhoto.crop_polygon_points (`crop_hashtag_id`, `points`) VALUES (%s, %s) Number RLEs to save : 1600 INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) first line : ('3748652660', '117', '95', '16') ... last line : ('3748652682', '70', '147', '1') INSERT IGNORE INTO MTRPhoto.crop_sum_segments (`crop_hashtag_id`, `sum_segments`) VALUES (%s, %s) TO DO : save crop sub photo not yet done ! After datou_step_exec type output : time spend for datou_step_exec : 0.3013875484466553 time spend to save output : 9.083747863769531e-05 total time spend for step 1 : 0.30147838592529297 caffe_path_current : About to save ! 0 After save, about to update current ! test detection filter by classif is a success ! ############################### TEST blur_detection ################################ t Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1243 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1243 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1243 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1243 # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : blur_detection list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (930729675) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 930729675 download finish for photo 930729675 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.12673497200012207 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:blur_detection Mon Apr 7 09:40:52 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011652_3752194_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg': 930729675} map_photo_id_path_extension : {930729675: {'path': 'temp/1744011652_3752194_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} inside step blur_detection methode: ratio et variance treat image : temp/1744011652_3752194_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 score_blur_detection : {930729675: [(930729675, 12.961859636534896, 492688767)]} After datou_step_exec type output : time spend for datou_step_exec : 0.2259836196899414 time spend to save output : 5.14984130859375e-05 total time spend for step 1 : 0.22603511810302734 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {930729675: [(930729675, 12.961859636534896, 492688767)]} {930729675: [(930729675, 12.961859636534896, 492688767)]} ############################### TEST detect_point_224x224 ################################ test_detect_point_224x224 Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1908 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1908 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1908 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1908 # 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 4589 thcl is not linked in the step_by_step architecture ! WARNING : step 4590 argmax 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 ! no param json to modify List Step Type Loaded in datou : thcl, argmax list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (987515175,987515176,987515177,987515178,987515179,987515180,987515181,987515182,987515183,987515184,987515185,987515186,987515187,987515188,987515189,987515190,987515192,987515193,987515195,987515196,987515198,987515200,987515201,987515202,987515204,987515205,987515207,987515208,987515209,987515211,987515212,987515213,987515215,987515216,987515217,987515219,987515220,987515222,987515223,987515224,987515226,987515227,987515228,987515230,987515231,987515232,987515233,987515234,987515235,987515236,987515237,987515238,987515239,987515240,987515241,987515242,987515243,987515244,987515245,987515246,987515247,987515248,987515249,987515250) Found this number of photos: 64 ##### Call download_photos : nb_thread : 5 begin to download photo : 987515175 begin to download photo : 987515188 begin to download photo : 987515207 begin to download photo : 987515224 begin to download photo : 987515239 download finish for photo 987515188 begin to download photo : 987515189 download finish for photo 987515175 begin to download photo : 987515176 download finish for photo 987515207 begin to download photo : 987515208 download finish for photo 987515239 begin to download photo : 987515240 download finish for photo 987515224 begin to download photo : 987515226 download finish for photo 987515189 begin to download photo : 987515190 download finish for photo 987515208 begin to download photo : 987515209 download finish for photo 987515176 begin to download photo : 987515177 download finish for photo 987515240 begin to download photo : 987515241 download finish for photo 987515226 begin to download photo : 987515227 download finish for photo 987515209 begin to download photo : 987515211 download finish for photo 987515177 begin to download photo : 987515178 download finish for photo 987515227 begin to download photo : 987515228 download finish for photo 987515190 begin to download photo : 987515192 download finish for photo 987515192 begin to download photo : 987515193 download finish for photo 987515211 begin to download photo : 987515212 download finish for photo 987515241 begin to download photo : 987515242 download finish for photo 987515228 begin to download photo : 987515230 download finish for photo 987515193 begin to download photo : 987515195 download finish for photo 987515212 begin to download photo : 987515213 download finish for photo 987515230 begin to download photo : 987515231 download finish for photo 987515242 begin to download photo : 987515243 download finish for photo 987515178 begin to download photo : 987515179 download finish for photo 987515213 begin to download photo : 987515215 download finish for photo 987515195 begin to download photo : 987515196 download finish for photo 987515243 begin to download photo : 987515244 download finish for photo 987515231 begin to download photo : 987515232 download finish for photo 987515215 begin to download photo : 987515216 download finish for photo 987515179 begin to download photo : 987515180 download finish for photo 987515196 begin to download photo : 987515198 download finish for photo 987515180 begin to download photo : 987515181 download finish for photo 987515216 begin to download photo : 987515217 download finish for photo 987515232 begin to download photo : 987515233 download finish for photo 987515198 begin to download photo : 987515200 download finish for photo 987515244 begin to download photo : 987515245 download finish for photo 987515181 begin to download photo : 987515182 download finish for photo 987515233 begin to download photo : 987515234 download finish for photo 987515217 begin to download photo : 987515219 download finish for photo 987515200 begin to download photo : 987515201 download finish for photo 987515245 begin to download photo : 987515246 download finish for photo 987515219 begin to download photo : 987515220 download finish for photo 987515234 begin to download photo : 987515235 download finish for photo 987515246 begin to download photo : 987515247 download finish for photo 987515220 begin to download photo : 987515222 download finish for photo 987515182 begin to download photo : 987515183 download finish for photo 987515201 begin to download photo : 987515202 download finish for photo 987515235 begin to download photo : 987515236 download finish for photo 987515222 begin to download photo : 987515223 download finish for photo 987515247 begin to download photo : 987515248 download finish for photo 987515202 begin to download photo : 987515204 download finish for photo 987515183 begin to download photo : 987515184 download finish for photo 987515248 begin to download photo : 987515249 download finish for photo 987515204 begin to download photo : 987515205 download finish for photo 987515223 download finish for photo 987515236 begin to download photo : 987515237 download finish for photo 987515249 begin to download photo : 987515250 download finish for photo 987515184 begin to download photo : 987515185 download finish for photo 987515205 download finish for photo 987515237 begin to download photo : 987515238 download finish for photo 987515185 begin to download photo : 987515186 download finish for photo 987515250 download finish for photo 987515186 begin to download photo : 987515187 download finish for photo 987515238 download finish for photo 987515187 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 ##### After load_data_input time to download the photos : 1.687943696975708 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 2 step1:thcl Mon Apr 7 09:40:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011652_3752194_987515207_de216ddb041e249524b0fb2b949064a5.jpg': 987515207, 'temp/1744011652_3752194_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg': 987515208, 'temp/1744011652_3752194_987515209_02dfe1ae39f51994652f4a8538844aea.jpg': 987515209, 'temp/1744011652_3752194_987515211_72cc7664d45bd40477351b9b764f1500.jpg': 987515211, 'temp/1744011652_3752194_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515212, 'temp/1744011652_3752194_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515213, 'temp/1744011652_3752194_987515215_902ef348a7eebb9a8b87f42927347936.jpg': 987515215, 'temp/1744011652_3752194_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg': 987515216, 'temp/1744011652_3752194_987515217_78877bb2c5760be28518d17f77d1c609.jpg': 987515217, 'temp/1744011652_3752194_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg': 987515219, 'temp/1744011652_3752194_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg': 987515220, 'temp/1744011652_3752194_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg': 987515222, 'temp/1744011652_3752194_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg': 987515223, 'temp/1744011652_3752194_987515188_4116f9906657a69bb76c2fda982037b9.jpg': 987515188, 'temp/1744011652_3752194_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg': 987515189, 'temp/1744011652_3752194_987515190_d56932bfc6ba2a8c974c691108755017.jpg': 987515190, 'temp/1744011652_3752194_987515192_b661073b218f5f056833d6af1c617153.jpg': 987515192, 'temp/1744011652_3752194_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg': 987515193, 'temp/1744011652_3752194_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515195, 'temp/1744011652_3752194_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg': 987515196, 'temp/1744011652_3752194_987515198_599e80f444c876f407e94b533c89360b.jpg': 987515198, 'temp/1744011652_3752194_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg': 987515200, 'temp/1744011652_3752194_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg': 987515201, 'temp/1744011652_3752194_987515202_3314bd90d1404f31b827d8925abf2d62.jpg': 987515202, 'temp/1744011652_3752194_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg': 987515204, 'temp/1744011652_3752194_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg': 987515205, 'temp/1744011652_3752194_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg': 987515239, 'temp/1744011652_3752194_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg': 987515240, 'temp/1744011652_3752194_987515241_073420d938f5f010ffd5b4353c064e09.jpg': 987515241, 'temp/1744011652_3752194_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg': 987515242, 'temp/1744011652_3752194_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg': 987515243, 'temp/1744011652_3752194_987515244_419530eaef5ef868f75c758b94eea4b4.jpg': 987515244, 'temp/1744011652_3752194_987515245_757d9d208d5bd4375c5f21f68b699148.jpg': 987515245, 'temp/1744011652_3752194_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg': 987515246, 'temp/1744011652_3752194_987515247_e47b65403df916ba909bc9c439b0af73.jpg': 987515247, 'temp/1744011652_3752194_987515248_a70ad88462a22fb62a120721a42b2d42.jpg': 987515248, 'temp/1744011652_3752194_987515249_a70ad88462a22fb62a120721a42b2d42.jpg': 987515249, 'temp/1744011652_3752194_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg': 987515250, 'temp/1744011652_3752194_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg': 987515224, 'temp/1744011652_3752194_987515226_a18048dca1a77ae086b62cf07759f704.jpg': 987515226, 'temp/1744011652_3752194_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg': 987515227, 'temp/1744011652_3752194_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg': 987515228, 'temp/1744011652_3752194_987515230_846ad925884264181565c81d152a2e94.jpg': 987515230, 'temp/1744011652_3752194_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg': 987515231, 'temp/1744011652_3752194_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg': 987515232, 'temp/1744011652_3752194_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg': 987515233, 'temp/1744011652_3752194_987515234_2eca3480aed0f8b876242675ad99b666.jpg': 987515234, 'temp/1744011652_3752194_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg': 987515235, 'temp/1744011652_3752194_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg': 987515236, 'temp/1744011652_3752194_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg': 987515237, 'temp/1744011652_3752194_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg': 987515238, 'temp/1744011652_3752194_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515175, 'temp/1744011652_3752194_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg': 987515176, 'temp/1744011652_3752194_987515177_4a54e9967227806219ddf45d256539d8.jpg': 987515177, 'temp/1744011652_3752194_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg': 987515178, 'temp/1744011652_3752194_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg': 987515179, 'temp/1744011652_3752194_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg': 987515180, 'temp/1744011652_3752194_987515181_1738c2798fb31152809ecb443ac286d6.jpg': 987515181, 'temp/1744011652_3752194_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg': 987515182, 'temp/1744011652_3752194_987515183_6aab9ca0421398b4899892c10c2594c6.jpg': 987515183, 'temp/1744011652_3752194_987515184_19c8c2177209a285df6014d95fe53f2c.jpg': 987515184, 'temp/1744011652_3752194_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg': 987515185, 'temp/1744011652_3752194_987515186_797def426440b544aa80dbd63a19234a.jpg': 987515186, 'temp/1744011652_3752194_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg': 987515187} map_photo_id_path_extension : {987515207: {'path': 'temp/1744011652_3752194_987515207_de216ddb041e249524b0fb2b949064a5.jpg', 'extension': 'jpg'}, 987515208: {'path': 'temp/1744011652_3752194_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'extension': 'jpg'}, 987515209: {'path': 'temp/1744011652_3752194_987515209_02dfe1ae39f51994652f4a8538844aea.jpg', 'extension': 'jpg'}, 987515211: {'path': 'temp/1744011652_3752194_987515211_72cc7664d45bd40477351b9b764f1500.jpg', 'extension': 'jpg'}, 987515212: {'path': 'temp/1744011652_3752194_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515213: {'path': 'temp/1744011652_3752194_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg', 'extension': 'jpg'}, 987515215: {'path': 'temp/1744011652_3752194_987515215_902ef348a7eebb9a8b87f42927347936.jpg', 'extension': 'jpg'}, 987515216: {'path': 'temp/1744011652_3752194_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'extension': 'jpg'}, 987515217: {'path': 'temp/1744011652_3752194_987515217_78877bb2c5760be28518d17f77d1c609.jpg', 'extension': 'jpg'}, 987515219: {'path': 'temp/1744011652_3752194_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'extension': 'jpg'}, 987515220: {'path': 'temp/1744011652_3752194_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg', 'extension': 'jpg'}, 987515222: {'path': 'temp/1744011652_3752194_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg', 'extension': 'jpg'}, 987515223: {'path': 'temp/1744011652_3752194_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg', 'extension': 'jpg'}, 987515188: {'path': 'temp/1744011652_3752194_987515188_4116f9906657a69bb76c2fda982037b9.jpg', 'extension': 'jpg'}, 987515189: {'path': 'temp/1744011652_3752194_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg', 'extension': 'jpg'}, 987515190: {'path': 'temp/1744011652_3752194_987515190_d56932bfc6ba2a8c974c691108755017.jpg', 'extension': 'jpg'}, 987515192: {'path': 'temp/1744011652_3752194_987515192_b661073b218f5f056833d6af1c617153.jpg', 'extension': 'jpg'}, 987515193: {'path': 'temp/1744011652_3752194_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'extension': 'jpg'}, 987515195: {'path': 'temp/1744011652_3752194_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515196: {'path': 'temp/1744011652_3752194_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg', 'extension': 'jpg'}, 987515198: {'path': 'temp/1744011652_3752194_987515198_599e80f444c876f407e94b533c89360b.jpg', 'extension': 'jpg'}, 987515200: {'path': 'temp/1744011652_3752194_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg', 'extension': 'jpg'}, 987515201: {'path': 'temp/1744011652_3752194_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'extension': 'jpg'}, 987515202: {'path': 'temp/1744011652_3752194_987515202_3314bd90d1404f31b827d8925abf2d62.jpg', 'extension': 'jpg'}, 987515204: {'path': 'temp/1744011652_3752194_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg', 'extension': 'jpg'}, 987515205: {'path': 'temp/1744011652_3752194_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'extension': 'jpg'}, 987515239: {'path': 'temp/1744011652_3752194_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg', 'extension': 'jpg'}, 987515240: {'path': 'temp/1744011652_3752194_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'extension': 'jpg'}, 987515241: {'path': 'temp/1744011652_3752194_987515241_073420d938f5f010ffd5b4353c064e09.jpg', 'extension': 'jpg'}, 987515242: {'path': 'temp/1744011652_3752194_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg', 'extension': 'jpg'}, 987515243: {'path': 'temp/1744011652_3752194_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'extension': 'jpg'}, 987515244: {'path': 'temp/1744011652_3752194_987515244_419530eaef5ef868f75c758b94eea4b4.jpg', 'extension': 'jpg'}, 987515245: {'path': 'temp/1744011652_3752194_987515245_757d9d208d5bd4375c5f21f68b699148.jpg', 'extension': 'jpg'}, 987515246: {'path': 'temp/1744011652_3752194_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg', 'extension': 'jpg'}, 987515247: {'path': 'temp/1744011652_3752194_987515247_e47b65403df916ba909bc9c439b0af73.jpg', 'extension': 'jpg'}, 987515248: {'path': 'temp/1744011652_3752194_987515248_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515249: {'path': 'temp/1744011652_3752194_987515249_a70ad88462a22fb62a120721a42b2d42.jpg', 'extension': 'jpg'}, 987515250: {'path': 'temp/1744011652_3752194_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg', 'extension': 'jpg'}, 987515224: {'path': 'temp/1744011652_3752194_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg', 'extension': 'jpg'}, 987515226: {'path': 'temp/1744011652_3752194_987515226_a18048dca1a77ae086b62cf07759f704.jpg', 'extension': 'jpg'}, 987515227: {'path': 'temp/1744011652_3752194_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'extension': 'jpg'}, 987515228: {'path': 'temp/1744011652_3752194_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'extension': 'jpg'}, 987515230: {'path': 'temp/1744011652_3752194_987515230_846ad925884264181565c81d152a2e94.jpg', 'extension': 'jpg'}, 987515231: {'path': 'temp/1744011652_3752194_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg', 'extension': 'jpg'}, 987515232: {'path': 'temp/1744011652_3752194_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg', 'extension': 'jpg'}, 987515233: {'path': 'temp/1744011652_3752194_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg', 'extension': 'jpg'}, 987515234: {'path': 'temp/1744011652_3752194_987515234_2eca3480aed0f8b876242675ad99b666.jpg', 'extension': 'jpg'}, 987515235: {'path': 'temp/1744011652_3752194_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg', 'extension': 'jpg'}, 987515236: {'path': 'temp/1744011652_3752194_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg', 'extension': 'jpg'}, 987515237: {'path': 'temp/1744011652_3752194_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg', 'extension': 'jpg'}, 987515238: {'path': 'temp/1744011652_3752194_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'extension': 'jpg'}, 987515175: {'path': 'temp/1744011652_3752194_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515176: {'path': 'temp/1744011652_3752194_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg', 'extension': 'jpg'}, 987515177: {'path': 'temp/1744011652_3752194_987515177_4a54e9967227806219ddf45d256539d8.jpg', 'extension': 'jpg'}, 987515178: {'path': 'temp/1744011652_3752194_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg', 'extension': 'jpg'}, 987515179: {'path': 'temp/1744011652_3752194_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'extension': 'jpg'}, 987515180: {'path': 'temp/1744011652_3752194_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'extension': 'jpg'}, 987515181: {'path': 'temp/1744011652_3752194_987515181_1738c2798fb31152809ecb443ac286d6.jpg', 'extension': 'jpg'}, 987515182: {'path': 'temp/1744011652_3752194_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg', 'extension': 'jpg'}, 987515183: {'path': 'temp/1744011652_3752194_987515183_6aab9ca0421398b4899892c10c2594c6.jpg', 'extension': 'jpg'}, 987515184: {'path': 'temp/1744011652_3752194_987515184_19c8c2177209a285df6014d95fe53f2c.jpg', 'extension': 'jpg'}, 987515185: {'path': 'temp/1744011652_3752194_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg', 'extension': 'jpg'}, 987515186: {'path': 'temp/1744011652_3752194_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1744011652_3752194_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step Thcl ! multi_thcl or not :False multi_thcl_cond or not :False dic_thcl : {'1528': 1} we are using the classfication for only one thcl 1528 In convert_file_to_np l 337 : 1 l343 1 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.0003521442413330078 time to convert the images to numpy array : 0.0054438114166259766 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! In convert_file_to_np l 337 : 7 l343 7 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.006633758544921875 time to convert the images to numpy array : 0.03681159019470215 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.005950927734375 time to convert the images to numpy array : 0.04196643829345703 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.008481740951538086 time to convert the images to numpy array : 0.039775848388671875 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.00930023193359375 time to convert the images to numpy array : 0.039472341537475586 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.009457826614379883 time to convert the images to numpy array : 0.03985190391540527 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.009744644165039062 time to convert the images to numpy array : 0.04000043869018555 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.006695270538330078 time to convert the images to numpy array : 0.04315328598022461 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.01237630844116211 time to convert the images to numpy array : 0.03788590431213379 l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! l357 after caffe.io.load_image dimension du image : (3, (224, 224, 3)) dimension displayed ! time to import caffe and check if the image exist : 0.018032550811767578 time to convert the images to numpy array : 0.033800363540649414 3 3 total time to convert the images to numpy array : 0.05468606948852539 list photo_ids error: [] list photo_ids correct : [987515187, 987515207, 987515208, 987515209, 987515211, 987515212, 987515213, 987515215, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515247, 987515200, 987515201, 987515202, 987515204, 987515205, 987515239, 987515240, 987515216, 987515217, 987515219, 987515220, 987515222, 987515223, 987515188, 987515248, 987515249, 987515250, 987515224, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515175, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515186] number of photos to traite : 64 try to delete the photos incorrect in DB tagging for thcl : 1528 To do loadFromThcl(), then load ParamDescType : thcl1528 get_desc_type_from_thcl : type of cat SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists, photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at, type_classification FROM MTRDatou.classification_theme WHERE `id` IN (1528) thcls : [{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}] thcl {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} Update svm_hashtag_type_desc : 4421 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (4421) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) To loadFromThcl() : net_4421 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2944 max_wait_temp : 1 max_wait : 0 SELECT * FROM MTRDatou.photo_desc_type_params WHERE id in (4421) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) param : , param.caffemodel : learn_refus_upm_blanches_1924 None mean_file_type : mean_file_path : prototxt_file_path : model : learn_refus_upm_blanches_1924 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/caffe_cuda8_python3/python/:/home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt caffemodel_filename : /data/models_weight/learn_refus_upm_blanches_1924/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2944 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'res5b']) time used to do the prepocess of the images : 0.06419658660888672 time used to do the prediction : 0.26370835304260254 save descriptor for thcl : 1528 (64, 512, 7, 7) Got the blobs of the net to insert : [3, 1, 1, 1, 1, 1, 2, 1, 1, 2] code_as_byte_string:b'0301010101'| Got the blobs of the net to insert : [4, 2, 1, 2, 3, 1, 0, 1, 0, 1] code_as_byte_string:b'0402010203'| Got the blobs of the net to insert : [0, 0, 0, 1, 2, 2, 0, 1, 0, 0] code_as_byte_string:b'0000000102'| Got the blobs of the net to insert : [1, 0, 0, 1, 1, 1, 0, 1, 1, 3] code_as_byte_string:b'0100000101'| Got the blobs of the net to insert : [1, 1, 2, 3, 4, 4, 1, 2, 8, 7] code_as_byte_string:b'0101020304'| Got the blobs of the net to insert : [6, 8, 5, 7, 7, 8, 10, 12, 12, 6] code_as_byte_string:b'0608050707'| Got the blobs of the net to insert : [6, 8, 5, 7, 7, 8, 10, 12, 12, 6] code_as_byte_string:b'0608050707'| Got the blobs of the net to insert : [5, 5, 4, 3, 5, 6, 3, 4, 3, 3] code_as_byte_string:b'0505040305'| Got the blobs of the net to insert : [3, 2, 1, 4, 5, 7, 6, 5, 6, 5] code_as_byte_string:b'0302010405'| Got the blobs of the net to insert : [3, 4, 5, 5, 9, 9, 9, 9, 11, 7] code_as_byte_string:b'0304050509'| Got the blobs of the net to insert : [5, 2, 3, 6, 8, 12, 9, 9, 3, 5] code_as_byte_string:b'0502030608'| Got the blobs of the net to insert : [6, 3, 3, 0, 1, 2, 2, 5, 2, 4] code_as_byte_string:b'0603030001'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 2, 5, 1, 0, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 2, 5, 1, 0, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [1, 1, 0, 0, 1, 2, 0, 0, 0, 1] code_as_byte_string:b'0101000001'| Got the blobs of the net to insert : [6, 6, 3, 3, 8, 8, 6, 6, 2, 0] code_as_byte_string:b'0606030308'| Got the blobs of the net to insert : [5, 5, 2, 4, 6, 5, 9, 9, 4, 2] code_as_byte_string:b'0505020406'| Got the blobs of the net to insert : [2, 1, 3, 5, 7, 5, 3, 4, 1, 3] code_as_byte_string:b'0201030507'| Got the blobs of the net to insert : [0, 0, 0, 1, 1, 3, 2, 1, 0, 2] code_as_byte_string:b'0000000101'| Got the blobs of the net to insert : [0, 0, 0, 0, 0, 0, 1, 3, 1, 0] code_as_byte_string:b'0000000000'| Got the blobs of the net to insert : [0, 0, 0, 1, 0, 0, 0, 0, 1, 3] code_as_byte_string:b'0000000100'| Got the blobs of the net to insert : [0, 0, 0, 1, 2, 0, 1, 4, 0, 2] code_as_byte_string:b'0000000102'| Got the blobs of the net to insert : [0, 1, 0, 1, 3, 4, 2, 1, 3, 5] code_as_byte_string:b'0001000103'| Got the blobs of the net to insert : [3, 3, 3, 8, 7, 5, 5, 4, 5, 3] code_as_byte_string:b'0303030807'| Got the blobs of the net to insert : [2, 5, 4, 6, 9, 9, 10, 9, 11, 5] code_as_byte_string:b'0205040609'| Got the blobs of the net to insert : [5, 2, 3, 7, 5, 7, 6, 2, 4, 3] code_as_byte_string:b'0502030705'| Got the blobs of the net to insert : [5, 7, 3, 2, 1, 2, 2, 2, 0, 0] code_as_byte_string:b'0507030201'| Got the blobs of the net to insert : [1, 1, 0, 0, 0, 0, 0, 3, 5, 1] code_as_byte_string:b'0101000000'| Got the blobs of the net to insert : [0, 3, 0, 1, 1, 6, 7, 5, 5, 3] code_as_byte_string:b'0003000101'| Got the blobs of the net to insert : [5, 3, 3, 1, 1, 1, 2, 2, 2, 4] code_as_byte_string:b'0503030101'| Got the blobs of the net to insert : [2, 1, 2, 2, 2, 0, 0, 1, 0, 3] code_as_byte_string:b'0201020202'| Got the blobs of the net to insert : [0, 0, 2, 3, 3, 1, 0, 0, 0, 0] code_as_byte_string:b'0000020303'| Got the blobs of the net to insert : [1, 0, 0, 1, 0, 0, 0, 3, 4, 5] code_as_byte_string:b'0100000100'| Got the blobs of the net to insert : [0, 1, 1, 4, 2, 1, 3, 7, 9, 9] code_as_byte_string:b'0001010402'| Got the blobs of the net to insert : [7, 10, 10, 2, 4, 7, 8, 5, 5, 2] code_as_byte_string:b'070a0a0204'| Got the blobs of the net to insert : [1, 1, 1, 2, 0, 3, 4, 3, 4, 5] code_as_byte_string:b'0101010200'| Got the blobs of the net to insert : [4, 1, 3, 4, 7, 3, 4, 2, 1, 0] code_as_byte_string:b'0401030407'| Got the blobs of the net to insert : [4, 1, 3, 4, 7, 3, 4, 2, 1, 0] code_as_byte_string:b'0401030407'| Got the blobs of the net to insert : [3, 3, 1, 4, 7, 7, 9, 6, 2, 1] code_as_byte_string:b'0303010407'| Got the blobs of the net to insert : [7, 7, 3, 8, 9, 6, 7, 10, 11, 6] code_as_byte_string:b'0707030809'| Got the blobs of the net to insert : [3, 3, 3, 1, 6, 7, 10, 5, 3, 5] code_as_byte_string:b'0303030106'| Got the blobs of the net to insert : [3, 2, 2, 1, 1, 2, 2, 3, 2, 5] code_as_byte_string:b'0302020101'| Got the blobs of the net to insert : [1, 0, 3, 1, 0, 0, 0, 0, 0, 5] code_as_byte_string:b'0100030100'| Got the blobs of the net to insert : [0, 0, 2, 2, 0, 0, 0, 0, 0, 3] code_as_byte_string:b'0000020200'| Got the blobs of the net to insert : [1, 1, 2, 1, 0, 0, 0, 3, 6, 7] code_as_byte_string:b'0101020100'| Got the blobs of the net to insert : [1, 3, 3, 3, 0, 1, 5, 6, 7, 4] code_as_byte_string:b'0103030300'| Got the blobs of the net to insert : [5, 7, 3, 2, 5, 9, 10, 3, 5, 0] code_as_byte_string:b'0507030205'| Got the blobs of the net to insert : [6, 8, 3, 6, 6, 6, 5, 10, 10, 3] code_as_byte_string:b'0608030606'| Got the blobs of the net to insert : [1, 2, 1, 5, 7, 10, 8, 2, 2, 1] code_as_byte_string:b'0102010507'| Got the blobs of the net to insert : [0, 0, 0, 1, 5, 4, 6, 3, 1, 3] code_as_byte_string:b'0000000105'| Got the blobs of the net to insert : [0, 2, 1, 0, 0, 0, 0, 0, 4, 2] code_as_byte_string:b'0002010000'| Got the blobs of the net to insert : [0, 2, 0, 1, 1, 0, 0, 0, 1, 3] code_as_byte_string:b'0002000101'| Got the blobs of the net to insert : [13, 9, 9, 8, 11, 13, 6, 14, 9, 18] code_as_byte_string:b'0d0909080b'| Got the blobs of the net to insert : [13, 9, 9, 8, 11, 13, 6, 14, 9, 18] code_as_byte_string:b'0d0909080b'| Got the blobs of the net to insert : [2, 2, 6, 7, 8, 6, 4, 3, 1, 8] code_as_byte_string:b'0202060708'| Got the blobs of the net to insert : [1, 2, 2, 1, 0, 1, 1, 2, 1, 1] code_as_byte_string:b'0102020100'| Got the blobs of the net to insert : [2, 2, 1, 3, 2, 3, 2, 0, 0, 1] code_as_byte_string:b'0202010302'| Got the blobs of the net to insert : [1, 0, 1, 2, 1, 0, 3, 3, 3, 8] code_as_byte_string:b'0100010201'| Got the blobs of the net to insert : [5, 5, 6, 4, 3, 6, 9, 7, 7, 7] code_as_byte_string:b'0505060403'| Got the blobs of the net to insert : [4, 6, 6, 4, 7, 8, 8, 8, 12, 8] code_as_byte_string:b'0406060407'| Got the blobs of the net to insert : [11, 8, 5, 9, 12, 14, 13, 14, 12, 6] code_as_byte_string:b'0b0805090c'| Got the blobs of the net to insert : [8, 7, 6, 4, 2, 1, 2, 4, 4, 4] code_as_byte_string:b'0807060402'| Got the blobs of the net to insert : [2, 3, 5, 5, 2, 2, 3, 0, 1, 4] code_as_byte_string:b'0203050502'| Got the blobs of the net to insert : [0, 0, 0, 1, 1, 1, 3, 3, 0, 0] code_as_byte_string:b'0000000101'| time to traite the descriptors : 3.3480212688446045 Testing : ['987515187', '987515207', '987515208', '987515209', '987515211', '987515212', '987515213', '987515215', '987515189', '987515190', '987515192', '987515193', '987515195', '987515196', '987515198', '987515241', '987515242', '987515243', '987515244', '987515245', '987515246', '987515247', '987515200', '987515201', '987515202', '987515204', '987515205', '987515239', '987515240', '987515216', '987515217', '987515219', '987515220', '987515222', '987515223', '987515188', '987515248', '987515249', '987515250', '987515224', '987515226', '987515227', '987515228', '987515230', '987515231', '987515232', '987515233', '987515234', '987515235', '987515236', '987515237', '987515238', '987515175', '987515176', '987515177', '987515178', '987515179', '987515180', '987515181', '987515182', '987515183', '987515184', '987515185', '987515186'] In select_photos_meta_from_ids: SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text FROM MTRBack.photos WHERE photo_id IN (987515187,987515207,987515208,987515209,987515211,987515212,987515213,987515215,987515189,987515190,987515192,987515193,987515195,987515196,987515198,987515241,987515242,987515243,987515244,987515245,987515246,987515247,987515200,987515201,987515202,987515204,987515205,987515239,987515240,987515216,987515217,987515219,987515220,987515222,987515223,987515188,987515248,987515249,987515250,987515224,987515226,987515227,987515228,987515230,987515231,987515232,987515233,987515234,987515235,987515236,987515237,987515238,987515175,987515176,987515177,987515178,987515179,987515180,987515181,987515182,987515183,987515184,987515185,987515186) result : {987515175: {'photo_id': 987515175, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b398cba2f448622cd9657f5eb3f9796.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_14_16_02_694514_0001.jpg'}, 987515176: {'photo_id': 987515176, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b398cba2f448622cd9657f5eb3f9796.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_144.jpg'}, 987515177: {'photo_id': 987515177, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4a54e9967227806219ddf45d256539d8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_176.jpg'}, 987515178: {'photo_id': 987515178, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/298b3d2bfe0fda6787b59a78e2e68867.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_208.jpg'}, 987515179: {'photo_id': 987515179, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/f7d4d1757a470f4c96dc3541eac88b9e.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_240.jpg'}, 987515180: {'photo_id': 987515180, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/776a5d7d8486ee2961bbe3a0d90f95b5.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_272.jpg'}, 987515181: {'photo_id': 987515181, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1738c2798fb31152809ecb443ac286d6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_304.jpg'}, 987515182: {'photo_id': 987515182, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/fe7f29bf6d13e08c3e985f91b5232178.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_112_y_336.jpg'}, 987515183: {'photo_id': 987515183, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/6aab9ca0421398b4899892c10c2594c6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_112.jpg'}, 987515184: {'photo_id': 987515184, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/19c8c2177209a285df6014d95fe53f2c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_144.jpg'}, 987515185: {'photo_id': 987515185, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e172d54457cabee9d7f02ee1300f3ae9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_176.jpg'}, 987515186: {'photo_id': 987515186, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/797def426440b544aa80dbd63a19234a.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_208.jpg'}, 987515187: {'photo_id': 987515187, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9f62f98efd3caca0b9c17d27f5c70440.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_240.jpg'}, 987515188: {'photo_id': 987515188, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4116f9906657a69bb76c2fda982037b9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_272.jpg'}, 987515189: {'photo_id': 987515189, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8e8590a26f72249d4c2116dffd0cf668.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_304.jpg'}, 987515190: {'photo_id': 987515190, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/d56932bfc6ba2a8c974c691108755017.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_144_y_336.jpg'}, 987515192: {'photo_id': 987515192, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b661073b218f5f056833d6af1c617153.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_112.jpg'}, 987515193: {'photo_id': 987515193, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1a97fceb4dcbf5821d783b2e00b52fe6.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_144.jpg'}, 987515195: {'photo_id': 987515195, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/30ccb89dfe410c445878a7f2819ddc36.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_17_37_58_622227.jpg'}, 987515196: {'photo_id': 987515196, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/30ccb89dfe410c445878a7f2819ddc36.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_208.jpg'}, 987515198: {'photo_id': 987515198, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/599e80f444c876f407e94b533c89360b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_240.jpg'}, 987515200: {'photo_id': 987515200, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/978964436b5d5fb0eeda17e3bfafe889.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_272.jpg'}, 987515201: {'photo_id': 987515201, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b224d2acdc7fa2bbb134c09db6bca7ce.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_304.jpg'}, 987515202: {'photo_id': 987515202, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/3314bd90d1404f31b827d8925abf2d62.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_176_y_336.jpg'}, 987515204: {'photo_id': 987515204, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9779c4f9d44360a9c80499e3b01e8a09.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_112.jpg'}, 987515205: {'photo_id': 987515205, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/fd4b136d0b3a9a1a347942d7191f6fea.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_144.jpg'}, 987515207: {'photo_id': 987515207, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/de216ddb041e249524b0fb2b949064a5.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_176.jpg'}, 987515208: {'photo_id': 987515208, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a2b90cb74908aa64bbc4aae58f0c5ae8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_208.jpg'}, 987515209: {'photo_id': 987515209, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/02dfe1ae39f51994652f4a8538844aea.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_208_y_240.jpg'}, 987515211: {'photo_id': 987515211, 'url': 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'photo_origin_x_240_y_112.jpg'}, 987515216: {'photo_id': 987515216, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4f7dc21f1d2cd3fcabadc4a6755921e1.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_144.jpg'}, 987515217: {'photo_id': 987515217, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/78877bb2c5760be28518d17f77d1c609.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_176.jpg'}, 987515219: {'photo_id': 987515219, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/c2d417a5ba6ccf7c84527636f8d5eef9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_208.jpg'}, 987515220: {'photo_id': 987515220, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e729f316c4c3b32049adfbaaa336d95c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_240.jpg'}, 987515222: {'photo_id': 987515222, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/067a027bc7402f969b6277d0dcb47eaa.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_272.jpg'}, 987515223: {'photo_id': 987515223, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/ebb57f09941cd11d7ee45a9368a883c1.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_304.jpg'}, 987515224: {'photo_id': 987515224, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e8747b400e713ecbd08d5b75db4d7568.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_240_y_336.jpg'}, 987515226: {'photo_id': 987515226, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a18048dca1a77ae086b62cf07759f704.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_112.jpg'}, 987515227: {'photo_id': 987515227, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e9c45a0e576ec9e44c1379c3fc5fec7c.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_144.jpg'}, 987515228: {'photo_id': 987515228, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/9f1759f20c9e603bccb9f9879d2f0d54.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_176.jpg'}, 987515230: {'photo_id': 987515230, 'url': 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'photo_origin_x_272_y_304.jpg'}, 987515234: {'photo_id': 987515234, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/2eca3480aed0f8b876242675ad99b666.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_272_y_336.jpg'}, 987515235: {'photo_id': 987515235, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/87075955a2f76b3948b47ffe1825ecd9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_112.jpg'}, 987515236: {'photo_id': 987515236, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/8b44a98b1aceadad73ed000d65836a9a.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_144.jpg'}, 987515237: {'photo_id': 987515237, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/1183dfa371a457f11ce2b622c7cf9467.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_176.jpg'}, 987515238: {'photo_id': 987515238, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e6292cb81e05894cfeb4b99f21a1d3f8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_208.jpg'}, 987515239: {'photo_id': 987515239, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b3fa6f29636080b5138c8d8c33fea309.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_240.jpg'}, 987515240: {'photo_id': 987515240, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_272.jpg'}, 987515241: {'photo_id': 987515241, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/073420d938f5f010ffd5b4353c064e09.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_304.jpg'}, 987515242: {'photo_id': 987515242, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/327abb5215d6fd1f0aad51f53ed8c324.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_304_y_336.jpg'}, 987515243: {'photo_id': 987515243, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/4375283f3bc5cdaa431c2fc6f17f53a4.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_112.jpg'}, 987515244: {'photo_id': 987515244, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/419530eaef5ef868f75c758b94eea4b4.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_144.jpg'}, 987515245: {'photo_id': 987515245, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/757d9d208d5bd4375c5f21f68b699148.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_176.jpg'}, 987515246: {'photo_id': 987515246, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/671a708f67f2efa19004b8257fc7b9c8.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_208.jpg'}, 987515247: {'photo_id': 987515247, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/e47b65403df916ba909bc9c439b0af73.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_240.jpg'}, 987515248: {'photo_id': 987515248, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a70ad88462a22fb62a120721a42b2d42.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'image_22062023_14_16_02_694514_0002.jpg'}, 987515249: {'photo_id': 987515249, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/a70ad88462a22fb62a120721a42b2d42.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_304.jpg'}, 987515250: {'photo_id': 987515250, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2019/11/22/b2827c9639df69656f23abcc7f2f82d9.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': 'photo_origin_x_336_y_336.jpg'}} list_photo_exists : [987515175, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515186, 987515187, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515207, 987515208, 987515209, 987515211, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515222, 987515223, 987515224, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515247, 987515248, 987515249, 987515250] storage_type for insertDescriptorsMulti : 1 To insert : 987515187 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515212 To insert : 987515213 To insert : 987515215 To insert : 987515189 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515198 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515246 To insert : 987515247 To insert : 987515200 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515239 To insert : 987515240 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515220 To insert : 987515222 To insert : 987515223 To insert : 987515188 To insert : 987515248 To insert : 987515249 To insert : 987515250 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515228 To insert : 987515230 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515238 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515179 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515186 time to insert the descriptors : 19.62740921974182 After datou_step_exec type output : time spend for datou_step_exec : 27.297566175460815 time spend to save output : 0.0001010894775390625 total time spend for step 1 : 27.297667264938354 step2:argmax Mon Apr 7 09:41:21 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011652_3752194_987515207_de216ddb041e249524b0fb2b949064a5.jpg': 987515207, 'temp/1744011652_3752194_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg': 987515208, 'temp/1744011652_3752194_987515209_02dfe1ae39f51994652f4a8538844aea.jpg': 987515209, 'temp/1744011652_3752194_987515211_72cc7664d45bd40477351b9b764f1500.jpg': 987515211, 'temp/1744011652_3752194_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515212, 'temp/1744011652_3752194_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg': 987515213, 'temp/1744011652_3752194_987515215_902ef348a7eebb9a8b87f42927347936.jpg': 987515215, 'temp/1744011652_3752194_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg': 987515216, 'temp/1744011652_3752194_987515217_78877bb2c5760be28518d17f77d1c609.jpg': 987515217, 'temp/1744011652_3752194_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg': 987515219, 'temp/1744011652_3752194_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg': 987515220, 'temp/1744011652_3752194_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg': 987515222, 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'extension': 'jpg'}, 987515186: {'path': 'temp/1744011652_3752194_987515186_797def426440b544aa80dbd63a19234a.jpg', 'extension': 'jpg'}, 987515187: {'path': 'temp/1744011652_3752194_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 After datou_step_exec type output : time spend for datou_step_exec : 0.0009298324584960938 time spend to save output : 7.295608520507812e-05 total time spend for step 2 : 0.0010027885437011719 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515187': [('987515187', 'Carton', 0.9810837, 1927, '1528'), 'temp/1744011652_3752194_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.8739033, 1927, '1528'), 'temp/1744011652_3752194_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 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'1528'), 'temp/1744011652_3752194_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999113, 1927, '1528'), 'temp/1744011652_3752194_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993962, 1927, '1528'), 'temp/1744011652_3752194_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846769, 1927, '1528'), 'temp/1744011652_3752194_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.98466957, 1927, '1528'), 'temp/1744011652_3752194_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.96612495, 1927, '1528'), 'temp/1744011652_3752194_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515241': [('987515241', 'Carton', 0.9821484, 1927, '1528'), 'temp/1744011652_3752194_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.93585473, 1927, '1528'), 'temp/1744011652_3752194_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.874155, 1927, '1528'), 'temp/1744011652_3752194_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.8178073, 1927, '1528'), 'temp/1744011652_3752194_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.8657718, 1927, '1528'), 'temp/1744011652_3752194_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992318, 1927, '1528'), 'temp/1744011652_3752194_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.99966836, 1927, '1528'), 'temp/1744011652_3752194_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515200': [('987515200', 'Carton', 0.9859929, 1927, '1528'), 'temp/1744011652_3752194_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.99546343, 1927, '1528'), 'temp/1744011652_3752194_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911219, 1927, '1528'), 'temp/1744011652_3752194_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9950812, 1927, '1528'), 'temp/1744011652_3752194_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.99086297, 1927, '1528'), 'temp/1744011652_3752194_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515239': [('987515239', 'Carton', 0.9997832, 1927, '1528'), 'temp/1744011652_3752194_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.9995221, 1927, '1528'), 'temp/1744011652_3752194_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774728, 1927, '1528'), 'temp/1744011652_3752194_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.5292385, 1927, '1528'), 'temp/1744011652_3752194_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.999366, 1927, '1528'), 'temp/1744011652_3752194_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963781, 1927, '1528'), 'temp/1744011652_3752194_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.99747473, 1927, '1528'), 'temp/1744011652_3752194_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.9920893, 1927, '1528'), 'temp/1744011652_3752194_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515188': [('987515188', 'Carton', 0.99564856, 1927, '1528'), 'temp/1744011652_3752194_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515248': [('987515248', 'Carton', 0.981292, 1927, '1528'), 'temp/1744011652_3752194_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9813131, 1927, '1528'), 'temp/1744011652_3752194_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9807889, 1927, '1528'), 'temp/1744011652_3752194_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515224': [('987515224', 'Carton', 0.90850794, 1927, '1528'), 'temp/1744011652_3752194_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.98693764, 1927, '1528'), 'temp/1744011652_3752194_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.90037256, 1927, '1528'), 'temp/1744011652_3752194_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.5223867, 1927, '1528'), 'temp/1744011652_3752194_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.99940467, 1927, '1528'), 'temp/1744011652_3752194_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.9994217, 1927, '1528'), 'temp/1744011652_3752194_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.99924374, 1927, '1528'), 'temp/1744011652_3752194_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.9835105, 1927, '1528'), 'temp/1744011652_3752194_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.94474703, 1927, '1528'), 'temp/1744011652_3752194_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.8918829, 1927, '1528'), 'temp/1744011652_3752194_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5367283, 1927, '1528'), 'temp/1744011652_3752194_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7701552, 1927, '1528'), 'temp/1744011652_3752194_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.9995734, 1927, '1528'), 'temp/1744011652_3752194_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998134, 1927, '1528'), 'temp/1744011652_3752194_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.99981374, 1927, '1528'), 'temp/1744011652_3752194_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.9771593, 1927, '1528'), 'temp/1744011652_3752194_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8579792, 1927, '1528'), 'temp/1744011652_3752194_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9270351, 1927, '1528'), 'temp/1744011652_3752194_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.98999166, 1927, '1528'), 'temp/1744011652_3752194_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977799, 1927, '1528'), 'temp/1744011652_3752194_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.99241674, 1927, '1528'), 'temp/1744011652_3752194_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1744011652_3752194_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997322, 1927, '1528'), 'temp/1744011652_3752194_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7975906, 1927, '1528'), 'temp/1744011652_3752194_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9847073, 1927, '1528'), 'temp/1744011652_3752194_987515186_797def426440b544aa80dbd63a19234a.jpg']} Inside batchDatouExec : verbose : True ##### chargement datou SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=1879 SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output, mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt WHERE mtdt.`id`=mtd.`type` AND mtd.mtd_id=1879 SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt WHERE mtd.`type`=msid.`mtr_datou_step_type` AND mtd.mtd_id= 1879 AND msid.data_type=mdsdt.id SELECT mts_id_output, id_output, mts_id_input, id_input FROM MTRDatou.mtr_datou_step_by_step WHERE mtd_id=1879 # 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 ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! no param json to modify List Step Type Loaded in datou : detect_points list_input_json : [] ##### fin chargement datou ##### chargement data ##### Call load_data_input : nb_thread : 5 origin SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (987515173) Found this number of photos: 1 ##### Call download_photos : nb_thread : 5 begin to download photo : 987515173 download finish for photo 987515173 we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB ##### After download_photos length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 ##### After load_data_input time to download the photos : 0.11433243751525879 #### fin chargement data Blocking on flush ? No conitnuing 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 : True number of steps : 1 step1:detect_points Mon Apr 7 09:41:21 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 After prepare type args : Here we display some param of map_info ! map_filenames : {'temp/1744011681_3752194_987515173_91fa471b1a04f95b356afdbaf021f623.jpg': 987515173} map_photo_id_path_extension : {987515173: {'path': 'temp/1744011681_3752194_987515173_91fa471b1a04f95b356afdbaf021f623.jpg', 'extension': 'jpg'}} map_subphoto_mainphoto : {} Beginning of datou step predict points ! Inside try reload ! classes : ['Autre_Environement', 'Carton', 'Kraft', 'Lointain_Papier_Magazine', 'Metal', 'Papier_Magazine', 'Plastique', 'Sol_Environement', 'Teint_Dans_La_Masse', 'autre_refus'] pht : 1927 model_name : learn_refus_upm_blanches_1924 {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} gpu_mode in detect_points : 1 To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 1 Inside predict_points step exec : nb paths : 1 treate image : temp/1744011681_3752194_987515173_91fa471b1a04f95b356afdbaf021f623.jpg size of numpy array img : 2408584 scale method : caffe/skimage size of numpy array img_scale : 2408584 (448, 448, 3) nb_h 8 nb_w 8 size of sub images : (224, 224, 3) size of caffe_input : 38535320 (64, 3, 224, 224) WARNING: Logging before InitGoogleLogging() is written to STDERR F0407 09:41:23.224098 3752194 syncedmem.cpp:78] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Command terminated by signal 6 80.85user 51.07system 5:57.74elapsed 36%CPU (0avgtext+0avgdata 6320312maxresident)k 6914352inputs+39408outputs (12874major+5702787minor)pagefaults 0swaps