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
<module 'tests.mask_test' from '/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 : 401

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 : 0.11640572547912598
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
Fri Apr  4 06:35:27 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
havn't enough memory gpu , need / 3000
l 3632 free memory gpu now : 401
wait 20 seconds
l 3637 free memory gpu now : 401  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
2025-04-04 06:35:50.692050: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-04-04 06:35:50.723217: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz
2025-04-04 06:35:50.726428: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0788000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-04-04 06:35:50.726542: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-04-04 06:35:50.731790: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-04-04 06:35:51.012786: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x12c564a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-04-04 06:35:51.012850: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-04-04 06:35:51.014110: 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
2025-04-04 06:35:51.017371: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:35:51.043522: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:35:51.064523: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-04-04 06:35:51.069024: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-04-04 06:35:51.096991: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-04-04 06:35:51.100735: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-04-04 06:35:51.155864: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:35:51.156954: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-04-04 06:35:51.157285: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:35:51.158105: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-04-04 06:35:51.158126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-04-04 06:35:51.158138: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-04-04 06:35:51.162710: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 24 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
2025-04-04 06:35:51.858130: 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
2025-04-04 06:35:51.858216: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:35:51.858243: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:35:51.858269: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-04-04 06:35:51.858294: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-04-04 06:35:51.858319: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-04-04 06:35:51.858357: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-04-04 06:35:51.858384: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:35:51.859466: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-04-04 06:35:51.864993: 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
2025-04-04 06:35:51.865036: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:35:51.865056: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:35:51.865074: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-04-04 06:35:51.865091: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-04-04 06:35:51.865109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-04-04 06:35:51.865127: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-04-04 06:35:51.865145: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:35:51.865923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-04-04 06:35:51.865961: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-04-04 06:35:51.865972: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-04-04 06:35:51.865981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-04-04 06:35:51.866815: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 24 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
2025-04-04 06:36:03.350588: W tensorflow/core/common_runtime/bfc_allocator.cc:434] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.25MiB (rounded to 2359296)
Current allocation summary follows.
2025-04-04 06:36:03.350649: I tensorflow/core/common_runtime/bfc_allocator.cc:934] BFCAllocator dump for GPU_0_bfc
2025-04-04 06:36:03.350669: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (256): 	Total Chunks: 60, Chunks in use: 60. 15.0KiB allocated for chunks. 15.0KiB in use in bin. 8.8KiB client-requested in use in bin.
2025-04-04 06:36:03.350685: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (512): 	Total Chunks: 40, Chunks in use: 40. 20.0KiB allocated for chunks. 20.0KiB in use in bin. 20.0KiB client-requested in use in bin.
2025-04-04 06:36:03.350701: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (1024): 	Total Chunks: 46, Chunks in use: 46. 46.2KiB allocated for chunks. 46.2KiB in use in bin. 46.0KiB client-requested in use in bin.
2025-04-04 06:36:03.350716: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (2048): 	Total Chunks: 25, Chunks in use: 25. 50.0KiB allocated for chunks. 50.0KiB in use in bin. 50.0KiB client-requested in use in bin.
2025-04-04 06:36:03.350731: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (4096): 	Total Chunks: 15, Chunks in use: 15. 60.0KiB allocated for chunks. 60.0KiB in use in bin. 60.0KiB client-requested in use in bin.
2025-04-04 06:36:03.350745: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (8192): 	Total Chunks: 1, Chunks in use: 0. 12.2KiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2025-04-04 06:36:03.350775: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (16384): 	Total Chunks: 1, Chunks in use: 1. 16.0KiB allocated for chunks. 16.0KiB in use in bin. 16.0KiB client-requested in use in bin.
2025-04-04 06:36:03.350791: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (32768): 	Total Chunks: 1, Chunks in use: 1. 36.8KiB allocated for chunks. 36.8KiB in use in bin. 36.8KiB client-requested in use in bin.
2025-04-04 06:36:03.350806: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (65536): 	Total Chunks: 7, Chunks in use: 6. 480.0KiB allocated for chunks. 384.0KiB in use in bin. 384.0KiB client-requested in use in bin.
2025-04-04 06:36:03.350822: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (131072): 	Total Chunks: 4, Chunks in use: 4. 560.0KiB allocated for chunks. 560.0KiB in use in bin. 560.0KiB client-requested in use in bin.
2025-04-04 06:36:03.350836: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (262144): 	Total Chunks: 9, Chunks in use: 7. 2.41MiB allocated for chunks. 1.91MiB in use in bin. 1.75MiB client-requested in use in bin.
2025-04-04 06:36:03.350851: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (524288): 	Total Chunks: 8, Chunks in use: 6. 4.95MiB allocated for chunks. 3.44MiB in use in bin. 3.25MiB client-requested in use in bin.
2025-04-04 06:36:03.350865: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (1048576): 	Total Chunks: 6, Chunks in use: 4. 6.25MiB allocated for chunks. 4.00MiB in use in bin. 4.00MiB client-requested in use in bin.
2025-04-04 06:36:03.350880: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (2097152): 	Total Chunks: 4, Chunks in use: 4. 10.00MiB allocated for chunks. 10.00MiB in use in bin. 8.75MiB client-requested in use in bin.
2025-04-04 06:36:03.350893: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (4194304): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2025-04-04 06:36:03.350921: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (8388608): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2025-04-04 06:36:03.350934: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (16777216): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2025-04-04 06:36:03.350948: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (33554432): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2025-04-04 06:36:03.350961: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (67108864): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2025-04-04 06:36:03.350974: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2025-04-04 06:36:03.350987: I tensorflow/core/common_runtime/bfc_allocator.cc:941] Bin (268435456): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2025-04-04 06:36:03.351001: I tensorflow/core/common_runtime/bfc_allocator.cc:957] Bin for 2.25MiB was 2.00MiB, Chunk State: 
2025-04-04 06:36:03.351013: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 2097152
2025-04-04 06:36:03.351030: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa600000 of size 147456 next 55
2025-04-04 06:36:03.351042: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa624000 of size 65536 next 78
2025-04-04 06:36:03.351054: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa634000 of size 4096 next 191
2025-04-04 06:36:03.351066: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa635000 of size 4096 next 192
2025-04-04 06:36:03.351086: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa636000 of size 4096 next 193
2025-04-04 06:36:03.351099: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa637000 of size 256 next 194
2025-04-04 06:36:03.351110: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa637100 of size 256 next 195
2025-04-04 06:36:03.351122: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa637200 of size 4096 next 197
2025-04-04 06:36:03.351133: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa638200 of size 4096 next 198
2025-04-04 06:36:03.351145: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa639200 of size 4096 next 199
2025-04-04 06:36:03.351157: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63a200 of size 4096 next 200
2025-04-04 06:36:03.351168: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63b200 of size 4096 next 201
2025-04-04 06:36:03.351181: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63c200 of size 1024 next 202
2025-04-04 06:36:03.351192: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63c600 of size 1024 next 204
2025-04-04 06:36:03.351204: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63ca00 of size 1024 next 205
2025-04-04 06:36:03.351215: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63ce00 of size 1024 next 206
2025-04-04 06:36:03.351227: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63d200 of size 1024 next 207
2025-04-04 06:36:03.351239: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63d600 of size 1024 next 208
2025-04-04 06:36:03.351250: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63da00 of size 1024 next 210
2025-04-04 06:36:03.351262: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63de00 of size 1024 next 211
2025-04-04 06:36:03.351273: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63e200 of size 1024 next 212
2025-04-04 06:36:03.351285: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63e600 of size 1024 next 213
2025-04-04 06:36:03.351296: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63ea00 of size 4096 next 214
2025-04-04 06:36:03.351308: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa63fa00 of size 4096 next 216
2025-04-04 06:36:03.351320: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa640a00 of size 4096 next 217
2025-04-04 06:36:03.351331: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa641a00 of size 4096 next 218
2025-04-04 06:36:03.351343: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa642a00 of size 4096 next 219
2025-04-04 06:36:03.351355: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa643a00 of size 1024 next 220
2025-04-04 06:36:03.351366: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa643e00 of size 1024 next 222
2025-04-04 06:36:03.351378: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa644200 of size 1024 next 223
2025-04-04 06:36:03.351389: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa644600 of size 1024 next 224
2025-04-04 06:36:03.351401: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa644a00 of size 1024 next 225
2025-04-04 06:36:03.351413: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa644e00 of size 256 next 226
2025-04-04 06:36:03.351424: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free  at 7f05aa644f00 of size 12544 next 72
2025-04-04 06:36:03.351436: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa648000 of size 147456 next 71
2025-04-04 06:36:03.351448: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa66c000 of size 131072 next 87
2025-04-04 06:36:03.351467: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free  at 7f05aa68c000 of size 262144 next 103
2025-04-04 06:36:03.351480: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa6cc000 of size 262144 next 102
2025-04-04 06:36:03.351492: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free  at 7f05aa70c000 of size 999424 next 18446744073709551615
2025-04-04 06:36:03.351503: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 4194304
2025-04-04 06:36:03.351515: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa800000 of size 589824 next 96
2025-04-04 06:36:03.351527: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa890000 of size 262144 next 117
2025-04-04 06:36:03.351538: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free  at 7f05aa8d0000 of size 262144 next 112
2025-04-04 06:36:03.351550: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa910000 of size 524288 next 111
2025-04-04 06:36:03.351562: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa990000 of size 262144 next 135
2025-04-04 06:36:03.351574: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aa9d0000 of size 327680 next 124
2025-04-04 06:36:03.351586: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aaa20000 of size 589824 next 123
2025-04-04 06:36:03.351597: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aaab0000 of size 262144 next 152
2025-04-04 06:36:03.351609: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aaaf0000 of size 327680 next 142
2025-04-04 06:36:03.351621: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aab40000 of size 786432 next 18446744073709551615
2025-04-04 06:36:03.351632: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 8388608
2025-04-04 06:36:03.351644: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free  at 7f05aac00000 of size 589824 next 160
2025-04-04 06:36:03.351656: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aac90000 of size 589824 next 158
2025-04-04 06:36:03.351667: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aad20000 of size 524288 next 171
2025-04-04 06:36:03.351679: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free  at 7f05aada0000 of size 1048576 next 221
2025-04-04 06:36:03.351691: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aaea0000 of size 1048576 next 190
2025-04-04 06:36:03.351702: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aafa0000 of size 1048576 next 189
2025-04-04 06:36:03.351714: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05ab0a0000 of size 1048576 next 203
2025-04-04 06:36:03.351726: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05ab1a0000 of size 2490368 next 18446744073709551615
2025-04-04 06:36:03.351737: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 10354688
2025-04-04 06:36:03.351749: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05ab400000 of size 2359296 next 181
2025-04-04 06:36:03.351761: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05ab640000 of size 2097152 next 196
2025-04-04 06:36:03.351773: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05ab840000 of size 1048576 next 215
2025-04-04 06:36:03.351784: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free  at 7f05ab940000 of size 1310720 next 209
2025-04-04 06:36:03.351796: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f05aba80000 of size 3538944 next 18446744073709551615
2025-04-04 06:36:03.351808: I tensorflow/core/common_runtime/bfc_allocator.cc:970] Next region of size 1048576
2025-04-04 06:36:03.351820: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae00000 of size 1280 next 1
2025-04-04 06:36:03.351832: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae00500 of size 256 next 5
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2025-04-04 06:36:03.352404: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae06200 of size 256 next 66
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2025-04-04 06:36:03.352438: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae06500 of size 256 next 69
2025-04-04 06:36:03.352450: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae06600 of size 256 next 70
2025-04-04 06:36:03.352462: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae06700 of size 256 next 73
2025-04-04 06:36:03.352473: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae06800 of size 256 next 74
2025-04-04 06:36:03.352485: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae06900 of size 256 next 75
2025-04-04 06:36:03.352496: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae06a00 of size 256 next 76
2025-04-04 06:36:03.352508: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae06b00 of size 256 next 77
2025-04-04 06:36:03.352519: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae06c00 of size 1024 next 79
2025-04-04 06:36:03.352531: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae07000 of size 1024 next 80
2025-04-04 06:36:03.352543: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae07400 of size 1024 next 81
2025-04-04 06:36:03.352554: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae07800 of size 1024 next 82
2025-04-04 06:36:03.352566: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae07c00 of size 1024 next 83
2025-04-04 06:36:03.352577: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae08000 of size 256 next 85
2025-04-04 06:36:03.352589: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae08100 of size 256 next 86
2025-04-04 06:36:03.352601: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae08200 of size 512 next 84
2025-04-04 06:36:03.352620: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae08400 of size 512 next 88
2025-04-04 06:36:03.352632: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae08600 of size 512 next 89
2025-04-04 06:36:03.352643: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae08800 of size 512 next 90
2025-04-04 06:36:03.352655: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae08a00 of size 512 next 91
2025-04-04 06:36:03.352666: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae08c00 of size 256 next 93
2025-04-04 06:36:03.352678: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae08d00 of size 256 next 94
2025-04-04 06:36:03.352690: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae08e00 of size 512 next 92
2025-04-04 06:36:03.352701: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae09000 of size 512 next 97
2025-04-04 06:36:03.352713: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae09200 of size 512 next 98
2025-04-04 06:36:03.352724: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae09400 of size 512 next 99
2025-04-04 06:36:03.352736: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae09600 of size 512 next 2
2025-04-04 06:36:03.352747: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae09800 of size 256 next 3
2025-04-04 06:36:03.352759: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae09900 of size 256 next 4
2025-04-04 06:36:03.352771: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae09a00 of size 16384 next 18
2025-04-04 06:36:03.352783: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae0da00 of size 256 next 100
2025-04-04 06:36:03.352794: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae0db00 of size 256 next 101
2025-04-04 06:36:03.352806: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae0dc00 of size 2048 next 104
2025-04-04 06:36:03.352818: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae0e400 of size 2048 next 105
2025-04-04 06:36:03.352829: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae0ec00 of size 2048 next 106
2025-04-04 06:36:03.352841: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae0f400 of size 2048 next 107
2025-04-04 06:36:03.352853: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae0fc00 of size 2048 next 108
2025-04-04 06:36:03.352864: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae10400 of size 256 next 109
2025-04-04 06:36:03.352876: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae10500 of size 256 next 110
2025-04-04 06:36:03.352888: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae10600 of size 2048 next 113
2025-04-04 06:36:03.352899: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae10e00 of size 2048 next 114
2025-04-04 06:36:03.352911: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae11600 of size 2048 next 115
2025-04-04 06:36:03.352922: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae11e00 of size 2048 next 116
2025-04-04 06:36:03.352934: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae12600 of size 2048 next 6
2025-04-04 06:36:03.352946: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae12e00 of size 37632 next 7
2025-04-04 06:36:03.352957: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1c100 of size 512 next 118
2025-04-04 06:36:03.352969: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1c300 of size 512 next 119
2025-04-04 06:36:03.352981: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1c500 of size 512 next 120
2025-04-04 06:36:03.352992: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1c700 of size 512 next 121
2025-04-04 06:36:03.353011: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1c900 of size 512 next 122
2025-04-04 06:36:03.353024: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1cb00 of size 512 next 125
2025-04-04 06:36:03.353035: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1cd00 of size 512 next 126
2025-04-04 06:36:03.353047: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1cf00 of size 512 next 127
2025-04-04 06:36:03.353058: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1d100 of size 512 next 128
2025-04-04 06:36:03.353070: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1d300 of size 512 next 129
2025-04-04 06:36:03.353081: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1d500 of size 2048 next 130
2025-04-04 06:36:03.353093: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1dd00 of size 2048 next 131
2025-04-04 06:36:03.353105: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1e500 of size 2048 next 132
2025-04-04 06:36:03.353116: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1ed00 of size 2048 next 133
2025-04-04 06:36:03.353128: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1f500 of size 2048 next 134
2025-04-04 06:36:03.353139: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1fd00 of size 512 next 136
2025-04-04 06:36:03.353151: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae1ff00 of size 512 next 137
2025-04-04 06:36:03.353163: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae20100 of size 512 next 138
2025-04-04 06:36:03.353174: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae20300 of size 512 next 139
2025-04-04 06:36:03.353186: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae20500 of size 512 next 140
2025-04-04 06:36:03.353197: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae20700 of size 512 next 141
2025-04-04 06:36:03.353209: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae20900 of size 512 next 143
2025-04-04 06:36:03.353220: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae20b00 of size 512 next 144
2025-04-04 06:36:03.353232: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae20d00 of size 512 next 145
2025-04-04 06:36:03.353244: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae20f00 of size 512 next 146
2025-04-04 06:36:03.353255: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae21100 of size 2048 next 147
2025-04-04 06:36:03.353267: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae21900 of size 2048 next 148
2025-04-04 06:36:03.353278: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae22100 of size 2048 next 149
2025-04-04 06:36:03.353290: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae22900 of size 2048 next 150
2025-04-04 06:36:03.353301: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae23100 of size 2048 next 151
2025-04-04 06:36:03.353313: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae23900 of size 512 next 153
2025-04-04 06:36:03.353325: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae23b00 of size 512 next 154
2025-04-04 06:36:03.353336: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae23d00 of size 512 next 155
2025-04-04 06:36:03.353348: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae23f00 of size 512 next 156
2025-04-04 06:36:03.353359: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae24100 of size 512 next 157
2025-04-04 06:36:03.353371: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae24300 of size 512 next 161
2025-04-04 06:36:03.353391: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae24500 of size 512 next 162
2025-04-04 06:36:03.353406: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae24700 of size 512 next 163
2025-04-04 06:36:03.353418: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae24900 of size 512 next 164
2025-04-04 06:36:03.353430: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae24b00 of size 512 next 165
2025-04-04 06:36:03.353443: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae24d00 of size 2048 next 166
2025-04-04 06:36:03.353455: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae25500 of size 2048 next 167
2025-04-04 06:36:03.353467: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae25d00 of size 2048 next 168
2025-04-04 06:36:03.353479: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae26500 of size 2048 next 169
2025-04-04 06:36:03.353490: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae26d00 of size 2048 next 170
2025-04-04 06:36:03.353502: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae27500 of size 1024 next 172
2025-04-04 06:36:03.353513: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae27900 of size 1024 next 173
2025-04-04 06:36:03.353525: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae27d00 of size 1024 next 174
2025-04-04 06:36:03.353537: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae28100 of size 1024 next 175
2025-04-04 06:36:03.353548: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae28500 of size 1024 next 176
2025-04-04 06:36:03.353560: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae28900 of size 256 next 178
2025-04-04 06:36:03.353572: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae28a00 of size 256 next 179
2025-04-04 06:36:03.353583: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae28b00 of size 1024 next 177
2025-04-04 06:36:03.353595: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae28f00 of size 1024 next 182
2025-04-04 06:36:03.353607: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae29300 of size 1024 next 183
2025-04-04 06:36:03.353618: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae29700 of size 1024 next 184
2025-04-04 06:36:03.353630: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae29b00 of size 1024 next 185
2025-04-04 06:36:03.353641: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae29f00 of size 256 next 187
2025-04-04 06:36:03.353653: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae2a000 of size 256 next 188
2025-04-04 06:36:03.353665: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae2a100 of size 4096 next 186
2025-04-04 06:36:03.353676: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae2b100 of size 4096 next 42
2025-04-04 06:36:03.353688: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae2c100 of size 65536 next 36
2025-04-04 06:36:03.353700: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae3c100 of size 65536 next 35
2025-04-04 06:36:03.353711: I tensorflow/core/common_runtime/bfc_allocator.cc:990] Free  at 7f06eae4c100 of size 98304 next 27
2025-04-04 06:36:03.353723: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae64100 of size 147456 next 26
2025-04-04 06:36:03.353735: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae88100 of size 65536 next 47
2025-04-04 06:36:03.353746: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eae98100 of size 65536 next 59
2025-04-04 06:36:03.353758: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eaea8100 of size 65536 next 65
2025-04-04 06:36:03.353770: I tensorflow/core/common_runtime/bfc_allocator.cc:990] InUse at 7f06eaeb8100 of size 294656 next 18446744073709551615
2025-04-04 06:36:03.353790: I tensorflow/core/common_runtime/bfc_allocator.cc:995]      Summary of in-use Chunks by size: 
2025-04-04 06:36:03.353805: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 60 Chunks of size 256 totalling 15.0KiB
2025-04-04 06:36:03.353819: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 40 Chunks of size 512 totalling 20.0KiB
2025-04-04 06:36:03.353832: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 45 Chunks of size 1024 totalling 45.0KiB
2025-04-04 06:36:03.353845: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 1280 totalling 1.2KiB
2025-04-04 06:36:03.353859: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 25 Chunks of size 2048 totalling 50.0KiB
2025-04-04 06:36:03.353875: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 15 Chunks of size 4096 totalling 60.0KiB
2025-04-04 06:36:03.353888: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 16384 totalling 16.0KiB
2025-04-04 06:36:03.353901: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 37632 totalling 36.8KiB
2025-04-04 06:36:03.353916: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 6 Chunks of size 65536 totalling 384.0KiB
2025-04-04 06:36:03.353929: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 131072 totalling 128.0KiB
2025-04-04 06:36:03.353942: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 3 Chunks of size 147456 totalling 432.0KiB
2025-04-04 06:36:03.353955: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 4 Chunks of size 262144 totalling 1.00MiB
2025-04-04 06:36:03.353968: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 294656 totalling 287.8KiB
2025-04-04 06:36:03.353981: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 2 Chunks of size 327680 totalling 640.0KiB
2025-04-04 06:36:03.353993: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 2 Chunks of size 524288 totalling 1.00MiB
2025-04-04 06:36:03.354006: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 3 Chunks of size 589824 totalling 1.69MiB
2025-04-04 06:36:03.354019: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 786432 totalling 768.0KiB
2025-04-04 06:36:03.354032: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 4 Chunks of size 1048576 totalling 4.00MiB
2025-04-04 06:36:03.354044: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 2097152 totalling 2.00MiB
2025-04-04 06:36:03.354057: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 2359296 totalling 2.25MiB
2025-04-04 06:36:03.354069: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 2490368 totalling 2.38MiB
2025-04-04 06:36:03.354082: I tensorflow/core/common_runtime/bfc_allocator.cc:998] 1 Chunks of size 3538944 totalling 3.38MiB
2025-04-04 06:36:03.354095: I tensorflow/core/common_runtime/bfc_allocator.cc:1002] Sum Total of in-use chunks: 20.50MiB
2025-04-04 06:36:03.354107: I tensorflow/core/common_runtime/bfc_allocator.cc:1004] total_region_allocated_bytes_: 26083328 memory_limit_: 26083328 available bytes: 0 curr_region_allocation_bytes_: 33554432
2025-04-04 06:36:03.354123: I tensorflow/core/common_runtime/bfc_allocator.cc:1010] Stats: 
Limit:                    26083328
InUse:                    21499648
MaxInUse:                 21730816
NumAllocs:                     699
MaxAllocSize:              4325376

2025-04-04 06:36:03.354142: W tensorflow/core/common_runtime/bfc_allocator.cc:439] *****___****************x_*****___********************************************____**********xxx*****
2025-04-04 06:36:03.354582: W tensorflow/core/framework/op_kernel.cc:1753] OP_REQUIRES failed at cwise_ops_common.h:134 : Resource exhausted: OOM when allocating tensor with shape[3,3,256,256] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
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


 Exception in mask_detect :  OOM when allocating tensor with shape[3,3,256,256] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Mul]
we want to redo the detection
Using TensorFlow backend.
max_time_sub_proc : 3600
erreur pendant la detection
Useless call to update_current_state
in case -12
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 !
ERROR : mask output needs to be a dictionnary now !
No output to save, continue without doing anything !

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : -12
free memory after detection : 
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 88

############################### TEST detect object  ################################

run mask_detect

Inside batchDatouExec : verbose : False
Catched exception !
Connect or reconnect !
# 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 : 0.17382335662841797
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
Fri Apr  4 06:36:04 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
havn't enough memory gpu , need / 3000
l 3632 free memory gpu now : 401
wait 20 seconds
l 3637 free memory gpu now : 401  max_wait_temp :  1 max_wait :  0
 gpu_flag :  0
2025-04-04 06:36:27.721445: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-04-04 06:36:27.747231: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz
2025-04-04 06:36:27.749159: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0788000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-04-04 06:36:27.749188: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-04-04 06:36:27.752965: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-04-04 06:36:28.016661: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x12c597d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-04-04 06:36:28.016739: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-04-04 06:36:28.017793: 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
2025-04-04 06:36:28.018216: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:36:28.021156: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:36:28.023721: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-04-04 06:36:28.024973: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-04-04 06:36:28.028077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-04-04 06:36:28.029718: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-04-04 06:36:28.038778: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:36:28.042372: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-04-04 06:36:28.042536: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:36:28.043351: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-04-04 06:36:28.043379: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-04-04 06:36:28.043390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-04-04 06:36:28.044809: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3728 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
2025-04-04 06:36:28.621235: 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
2025-04-04 06:36:28.621334: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:36:28.621367: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:36:28.621397: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-04-04 06:36:28.621427: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-04-04 06:36:28.621455: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-04-04 06:36:28.621483: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-04-04 06:36:28.621512: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:36:28.623027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-04-04 06:36:28.624341: 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
2025-04-04 06:36:28.624389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:36:28.624405: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:36:28.624418: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-04-04 06:36:28.624432: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-04-04 06:36:28.624445: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-04-04 06:36:28.624459: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-04-04 06:36:28.624472: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:36:28.625299: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-04-04 06:36:28.625326: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-04-04 06:36:28.625334: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-04-04 06:36:28.625341: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-04-04 06:36:28.626216: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3728 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 : 
[]
2025-04-04 06:36:35.931414: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:36:36.255580: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:36:38.161133: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.14G (3374186496 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2025-04-04 06:36:38.964808: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:36:38.964887: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
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: (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
2998235
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 89
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 : 4180
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 : 0.00047707557678222656
nb_pixel_total : 16902
time to create 1 rle with old method : 0.020499229431152344
length of segment :  107
time for calcul the mask position with numpy : 0.04471302032470703
nb_pixel_total : 480748
time to create 1 rle with new method : 0.0249941349029541
length of segment :  632
time for calcul the mask position with numpy : 0.0004582405090332031
nb_pixel_total : 36587
time to create 1 rle with old method : 0.042507171630859375
length of segment :  132
time for calcul the mask position with numpy : 0.00010085105895996094
nb_pixel_total : 4794
time to create 1 rle with old method : 0.005888223648071289
length of segment :  51

time spent for convertir_results : 0.9644603729248047

time spend for datou_step_exec : 38.821720123291016
time spend to save output : 3.528594970703125e-05
total time spend for step 1 : 38.82175540924072

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 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 : 0.013938188552856445

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, 0.99883705, [(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, 180), (1095, 29, 181), (1095, 30, 182), (1095, 31, 183), (1095, 32, 183), (1095, 33, 184), (1095, 34, 184), (1096, 35, 183), (1096, 36, 183), (1096, 37, 184), (1097, 38, 183), (1097, 39, 183), (1097, 40, 183), (1098, 41, 182), (1098, 42, 182), (1098, 43, 182), (1099, 44, 181), (1099, 45, 181), (1099, 46, 181), (1100, 47, 180), (1100, 48, 180), (1101, 49, 179), (1101, 50, 179), (1102, 51, 178), (1102, 52, 178), (1103, 53, 177), (1103, 54, 177), (1104, 55, 176), (1104, 56, 176), (1104, 57, 176), (1104, 58, 176), (1105, 59, 175), (1105, 60, 175), (1105, 61, 175), (1105, 62, 175), (1105, 63, 175), (1106, 64, 174), (1106, 65, 174), (1106, 66, 174), (1106, 67, 174), (1106, 68, 174), (1106, 69, 174), (1106, 70, 174), (1106, 71, 174), (1106, 72, 174), (1106, 73, 174), (1107, 74, 173), (1107, 75, 173), (1107, 76, 173), (1107, 77, 173), (1107, 78, 173), (1107, 79, 173), (1108, 80, 172), (1108, 81, 172), (1109, 82, 171), (1110, 83, 170), (1110, 84, 170), (1111, 85, 169), (1112, 86, 168), (1113, 87, 166), (1114, 88, 165), (1115, 89, 164), (1117, 90, 162), (1120, 91, 159), (1138, 92, 141), (1146, 93, 133), (1154, 94, 125), (1167, 95, 112), (1177, 96, 102), (1183, 97, 95), (1185, 98, 93), (1187, 99, 90), (1188, 100, 55), (1264, 100, 12), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1275,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.9977476, [(711, 22, 22), (925, 22, 47), (608, 23, 146), (894, 23, 103), (598, 24, 234), (850, 24, 158), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (489, 47, 589), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (476, 56, 608), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (449, 76, 641), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 92, 658), (431, 93, 659), (430, 94, 660), (429, 95, 661), (428, 96, 662), (427, 97, 663), (425, 98, 665), (423, 99, 667), (421, 100, 669), (419, 101, 671), (417, 102, 673), (413, 103, 677), (410, 104, 680), (405, 105, 685), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (368, 113, 722), (365, 114, 725), (362, 115, 728), (358, 116, 732), (356, 117, 734), (353, 118, 737), (351, 119, 739), (348, 120, 742), (346, 121, 744), (344, 122, 746), (341, 123, 749), (338, 124, 752), (335, 125, 755), (331, 126, 759), (327, 127, 763), (323, 128, 767), (319, 129, 770), (314, 130, 775), (308, 131, 781), (303, 132, 786), (294, 133, 795), (286, 134, 803), (279, 135, 810), (273, 136, 816), (267, 137, 822), (262, 138, 827), (258, 139, 831), (255, 140, 834), (252, 141, 837), (250, 142, 839), (247, 143, 842), (245, 144, 844), (242, 145, 847), (240, 146, 849), (237, 147, 852), (233, 148, 856), (230, 149, 859), (226, 150, 863), (220, 151, 869), (213, 152, 876), (206, 153, 883), (200, 154, 889), (193, 155, 896), (187, 156, 902), (183, 157, 906), (181, 158, 908), (178, 159, 911), (176, 160, 913), (174, 161, 915), (172, 162, 917), (170, 163, 919), (168, 164, 921), (167, 165, 922), (165, 166, 924), (164, 167, 925), (162, 168, 927), (161, 169, 928), (159, 170, 930), (157, 171, 932), (155, 172, 934), (153, 173, 935), (151, 174, 937), (148, 175, 940), (146, 176, 942), (144, 177, 944), (142, 178, 946), (140, 179, 948), (139, 180, 949), (137, 181, 951), (136, 182, 952), (134, 183, 954), (133, 184, 955), (132, 185, 956), (131, 186, 957), (130, 187, 958), (129, 188, 959), (128, 189, 960), (127, 190, 960), (126, 191, 961), (126, 192, 961), (125, 193, 962), (124, 194, 963), (123, 195, 964), (122, 196, 965), (122, 197, 965), (121, 198, 966), (120, 199, 967), (119, 200, 968), (118, 201, 969), (117, 202, 970), (116, 203, 971), (114, 204, 973), (113, 205, 973), (112, 206, 974), (111, 207, 975), (109, 208, 977), (108, 209, 978), (107, 210, 979), (106, 211, 980), (105, 212, 981), (104, 213, 982), (103, 214, 983), (102, 215, 984), (101, 216, 985), (101, 217, 984), (100, 218, 985), (100, 219, 985), (99, 220, 986), (98, 221, 987), (98, 222, 987), (97, 223, 988), (97, 224, 987), (96, 225, 988), (96, 226, 988), (95, 227, 989), (95, 228, 989), (94, 229, 990), (94, 230, 990), (94, 231, 990), (93, 232, 990), (93, 233, 990), (92, 234, 991), (92, 235, 991), (92, 236, 991), (91, 237, 992), (91, 238, 991), (91, 239, 991), (91, 240, 990), (91, 241, 990), (90, 242, 991), (90, 243, 990), (90, 244, 990), (90, 245, 989), (90, 246, 989), (89, 247, 990), (89, 248, 989), (89, 249, 989), (89, 250, 988), (89, 251, 988), (88, 252, 988), (88, 253, 988), (88, 254, 987), (88, 255, 986), (88, 256, 986), (87, 257, 986), (87, 258, 985), (87, 259, 985), (87, 260, 984), (87, 261, 983), (86, 262, 983), (86, 263, 982), (86, 264, 982), (86, 265, 981), (85, 266, 981), (85, 267, 980), (85, 268, 980), (84, 269, 980), (84, 270, 979), (84, 271, 979), (84, 272, 978), (83, 273, 979), (83, 274, 978), (83, 275, 977), (82, 276, 978), (82, 277, 977), (82, 278, 977), (81, 279, 977), (81, 280, 977), (81, 281, 977), (80, 282, 977), (80, 283, 977), (80, 284, 976), (79, 285, 977), (79, 286, 976), (79, 287, 976), (78, 288, 976), (78, 289, 976), (78, 290, 975), (77, 291, 976), (77, 292, 975), (77, 293, 975), (76, 294, 975), (76, 295, 975), (76, 296, 974), (75, 297, 975), (75, 298, 974), (74, 299, 975), (74, 300, 974), (74, 301, 974), (73, 302, 974), (73, 303, 974), (72, 304, 974), (72, 305, 974), (71, 306, 974), (71, 307, 973), (71, 308, 972), (70, 309, 972), (70, 310, 971), (70, 311, 970), (70, 312, 968), (69, 313, 968), (69, 314, 966), (69, 315, 964), (69, 316, 962), (68, 317, 961), (68, 318, 959), (68, 319, 958), (68, 320, 956), (67, 321, 955), (67, 322, 954), (67, 323, 952), (67, 324, 951), (66, 325, 951), (66, 326, 950), (66, 327, 948), (66, 328, 947), (65, 329, 947), (65, 330, 946), (65, 331, 946), (65, 332, 945), (65, 333, 944), (65, 334, 942), (65, 335, 941), (65, 336, 940), (65, 337, 939), (65, 338, 938), (64, 339, 937), (64, 340, 936), (64, 341, 934), (64, 342, 932), (64, 343, 930), (64, 344, 928), (64, 345, 927), (64, 346, 925), (64, 347, 923), (64, 348, 922), (64, 349, 920), (64, 350, 919), (63, 351, 919), (63, 352, 918), (63, 353, 917), (63, 354, 916), (63, 355, 915), (63, 356, 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############################### 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 : 0.16271257400512695
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
Fri Apr  4 06:36:55 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 : 3961  max_wait_temp :  1 max_wait :  0
 gpu_flag :  0
2025-04-04 06:36:57.957860: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-04-04 06:36:57.987269: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz
2025-04-04 06:36:57.989271: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f078c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-04-04 06:36:57.989332: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-04-04 06:36:57.993141: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-04-04 06:36:58.179698: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1394d570 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-04-04 06:36:58.179746: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-04-04 06:36:58.180672: 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
2025-04-04 06:36:58.181077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:36:58.184047: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:36:58.186454: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-04-04 06:36:58.186830: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-04-04 06:36:58.189505: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-04-04 06:36:58.190787: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-04-04 06:36:58.196128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:36:58.197093: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-04-04 06:36:58.197167: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:36:58.197670: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-04-04 06:36:58.197686: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-04-04 06:36:58.197695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-04-04 06:36:58.198504: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1484 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.

2025-04-04 06:36:58.404054: 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
2025-04-04 06:36:58.404209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:36:58.404238: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:36:58.404259: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-04-04 06:36:58.404279: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-04-04 06:36:58.404298: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-04-04 06:36:58.404317: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-04-04 06:36:58.404337: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:36:58.405083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-04-04 06:36:58.405951: 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
2025-04-04 06:36:58.405986: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-04-04 06:36:58.406006: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:36:58.406022: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-04-04 06:36:58.406039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-04-04 06:36:58.406055: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-04-04 06:36:58.406071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-04-04 06:36:58.406087: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:36:58.406812: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-04-04 06:36:58.406847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-04-04 06:36:58.406856: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-04-04 06:36:58.406864: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-04-04 06:36:58.407646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1484 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 : 
[]
2025-04-04 06:37:08.221404: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-04-04 06:37:08.407739: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-04-04 06:37:09.784574: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:37:09.784641: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:37:09.791141: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:37:09.791164: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:37:09.840958: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:37:09.841014: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:37:09.882939: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:37:09.882971: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:37:09.934225: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:37:09.934260: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2025-04-04 06:37:09.965763: W tensorflow/core/common_runtime/bfc_allocator.cc:311] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature.
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
2999825
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 1792
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 : 3959
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 : 0.2594759464263916
nb_pixel_total : 3697371
time to create 1 rle with new method : 0.33108019828796387
length of segment :  2051

time spent for convertir_results : 1.6518561840057373

time spend for datou_step_exec : 20.376511335372925
time spend to save output : 3.9577484130859375e-05
total time spend for step 1 : 20.376550912857056

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 : 0.01969599723815918

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, 11, 2268, 107, 2256, 0.98633724, [(638, 113, 267), (1204, 113, 135), (530, 114, 463), (1068, 114, 359), (521, 115, 915), (512, 116, 932), (503, 117, 949), (495, 118, 965), (487, 119, 980), (479, 120, 996), (472, 121, 1010), (465, 122, 1023), (458, 123, 1037), (451, 124, 1050), (445, 125, 1067), (439, 126, 1086), (433, 127, 1104), (427, 128, 1121), (421, 129, 1139), (416, 130, 1155), (410, 131, 1171), (405, 132, 1181), (400, 133, 1190), (396, 134, 1198), (391, 135, 1207), (386, 136, 1216), (382, 137, 1223), (378, 138, 1231), (374, 139, 1238), (372, 140, 1244), (370, 141, 1249), (368, 142, 1254), (367, 143, 1258), (365, 144, 1263), (364, 145, 1267), (362, 146, 1272), (361, 147, 1276), (359, 148, 1281), (358, 149, 1285), (356, 150, 1290), (354, 151, 1295), (353, 152, 1300), (351, 153, 1305), (349, 154, 1311), (347, 155, 1316), (345, 156, 1322), (343, 157, 1328), (341, 158, 1334), (339, 159, 1340), (337, 160, 1346), (335, 161, 1352), (333, 162, 1358), (331, 163, 1365), (329, 164, 1371), (326, 165, 1379), (324, 166, 1386), (321, 167, 1394), (319, 168, 1402), (316, 169, 1410), (313, 170, 1419), (311, 171, 1427), (308, 172, 1436), (305, 173, 1445), (302, 174, 1455), (299, 175, 1465), (295, 176, 1476), (292, 177, 1486), (288, 178, 1498), (285, 179, 1508), (281, 180, 1520), (278, 181, 1531), (275, 182, 1542), (271, 183, 1554), (268, 184, 1563), (265, 185, 1571), (262, 186, 1579), (259, 187, 1587), (256, 188, 1595), (252, 189, 1604), (249, 190, 1611), (247, 191, 1618), (244, 192, 1625), (241, 193, 1632), (238, 194, 1639), (235, 195, 1646), (232, 196, 1653), (230, 197, 1659), (227, 198, 1665), (224, 199, 1672), (222, 200, 1677), (219, 201, 1684), (217, 202, 1689), (214, 203, 1693), (212, 204, 1697), (210, 205, 1700), (209, 206, 1703), (207, 207, 1706), (206, 208, 1708), (204, 209, 1712), (203, 210, 1714), (201, 211, 1717), (200, 212, 1719), (199, 213, 1721), (197, 214, 1724), (196, 215, 1726), (195, 216, 1728), (194, 217, 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nb pixel non reg : 3692295
nb pixel common : 3684531
proportion of common points : 0.997897242771772
[('test release memory', 'SUCCESS', True), ('test detect objet', 'SUCCESS', True), ('test polygone', 'SUCCESS', True)]
 res_total : True


#&_# TEST SUCCEEDED #&_# : tests/mask_test #&_# 


/home/admin/workarea/git/Velours/python/tests/python_tests.py refs/heads/master_47a3e4a81bc81b59dfbf2b2b102e93cf6aa9db97
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_47a3e4a81bc81b59dfbf2b2b102e93cf6aa9db97','{"mask_detection": "success"}','1','http://marlene.fotonower-preprod.com/job/2025/April/04042025/python_test3//data_2/data_log/job/2025/April/04042025/python_test3/log-python3----short_python3--v--marlene-06:35:00.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
<module 'tests.datou_test' from '/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 : 0.25670766830444336
#### 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
Fri Apr  4 06:37:50 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 : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1743741470_2995536_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094}
map_photo_id_path_extension : {1189321094: {'path': 'temp/1743741470_2995536_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 : 0.0020132064819335938
nb_pixel_total : 16193
time to create 1 rle with old method : 0.01771831512451172
time for calcul the mask position with numpy : 0.0013747215270996094
nb_pixel_total : 7576
time to create 1 rle with old method : 0.008926868438720703
time for calcul the mask position with numpy : 0.0017948150634765625
nb_pixel_total : 83613
time to create 1 rle with old method : 0.09610509872436523
time for calcul the mask position with numpy : 0.0014462471008300781
nb_pixel_total : 5615
time to create 1 rle with old method : 0.006660938262939453
time for calcul the mask position with numpy : 0.0014340877532958984
nb_pixel_total : 3914
time to create 1 rle with old method : 0.004526376724243164
time for calcul the mask position with numpy : 0.0014181137084960938
nb_pixel_total : 5782
time to create 1 rle with old method : 0.006809711456298828
time for calcul the mask position with numpy : 0.0014846324920654297
nb_pixel_total : 15069
time to create 1 rle with old method : 0.017605304718017578
time for calcul the mask position with numpy : 0.0013535022735595703
nb_pixel_total : 5336
time to create 1 rle with old method : 0.006234884262084961
time for calcul the mask position with numpy : 0.0014982223510742188
nb_pixel_total : 29487
time to create 1 rle with old method : 0.0338597297668457
time for calcul the mask position with numpy : 0.0014944076538085938
nb_pixel_total : 9584
time to create 1 rle with old method : 0.011217832565307617
time for calcul the mask position with numpy : 0.0014004707336425781
nb_pixel_total : 13914
time to create 1 rle with old method : 0.016133785247802734
time for calcul the mask position with numpy : 0.0013670921325683594
nb_pixel_total : 3771
time to create 1 rle with old method : 0.004183292388916016
time for calcul the mask position with numpy : 0.0013852119445800781
nb_pixel_total : 10792
time to create 1 rle with old method : 0.012224674224853516
time for calcul the mask position with numpy : 0.0013113021850585938
nb_pixel_total : 2937
time to create 1 rle with old method : 0.003526926040649414
time for calcul the mask position with numpy : 0.001405477523803711
nb_pixel_total : 2445
time to create 1 rle with old method : 0.002883434295654297
time for calcul the mask position with numpy : 0.0013246536254882812
nb_pixel_total : 4270
time to create 1 rle with old method : 0.004743814468383789
time for calcul the mask position with numpy : 0.0013163089752197266
nb_pixel_total : 1226
time to create 1 rle with old method : 0.0017838478088378906
time for calcul the mask position with numpy : 0.0014336109161376953
nb_pixel_total : 2840
time to create 1 rle with old method : 0.003561735153198242
time for calcul the mask position with numpy : 0.0013048648834228516
nb_pixel_total : 2370
time to create 1 rle with old method : 0.0028955936431884766
time for calcul the mask position with numpy : 0.001332998275756836
nb_pixel_total : 6646
time to create 1 rle with old method : 0.007982492446899414
time for calcul the mask position with numpy : 0.001428365707397461
nb_pixel_total : 592
time to create 1 rle with old method : 0.0007941722869873047
time for calcul the mask position with numpy : 0.0014891624450683594
nb_pixel_total : 38982
time to create 1 rle with old method : 0.04528927803039551
time for calcul the mask position with numpy : 0.0014281272888183594
nb_pixel_total : 693
time to create 1 rle with old method : 0.0008816719055175781
time for calcul the mask position with numpy : 0.0013911724090576172
nb_pixel_total : 2077
time to create 1 rle with old method : 0.002493143081665039
time for calcul the mask position with numpy : 0.0014216899871826172
nb_pixel_total : 3508
time to create 1 rle with old method : 0.004288434982299805
time for calcul the mask position with numpy : 0.001436471939086914
nb_pixel_total : 4272
time to create 1 rle with old method : 0.005466938018798828
time for calcul the mask position with numpy : 0.0014243125915527344
nb_pixel_total : 5483
time to create 1 rle with old method : 0.0066187381744384766
time for calcul the mask position with numpy : 0.0015320777893066406
nb_pixel_total : 16478
time to create 1 rle with old method : 0.0193479061126709
time for calcul the mask position with numpy : 0.0014889240264892578
nb_pixel_total : 13016
time to create 1 rle with old method : 0.015955209732055664
time for calcul the mask position with numpy : 0.0013625621795654297
nb_pixel_total : 8561
time to create 1 rle with old method : 0.010185480117797852
time for calcul the mask position with numpy : 0.0014278888702392578
nb_pixel_total : 3900
time to create 1 rle with old method : 0.004904747009277344
time for calcul the mask position with numpy : 0.0015094280242919922
nb_pixel_total : 11894
time to create 1 rle with old method : 0.014183759689331055
time for calcul the mask position with numpy : 0.0014109611511230469
nb_pixel_total : 970
time to create 1 rle with old method : 0.0012576580047607422
time for calcul the mask position with numpy : 0.0014531612396240234
nb_pixel_total : 9870
time to create 1 rle with old method : 0.01192331314086914
time for calcul the mask position with numpy : 0.0014421939849853516
nb_pixel_total : 2747
time to create 1 rle with old method : 0.0032858848571777344
time for calcul the mask position with numpy : 0.0014121532440185547
nb_pixel_total : 3329
time to create 1 rle with old method : 0.0038738250732421875
time for calcul the mask position with numpy : 0.0013914108276367188
nb_pixel_total : 18474
time to create 1 rle with old method : 0.020977258682250977
time for calcul the mask position with numpy : 0.0013480186462402344
nb_pixel_total : 1025
time to create 1 rle with old method : 0.001199960708618164
time for calcul the mask position with numpy : 0.0013611316680908203
nb_pixel_total : 4173
time to create 1 rle with old method : 0.00822758674621582
time for calcul the mask position with numpy : 0.0014619827270507812
nb_pixel_total : 4102
time to create 1 rle with old method : 0.004908323287963867
time for calcul the mask position with numpy : 0.0014181137084960938
nb_pixel_total : 1657
time to create 1 rle with old method : 0.002048969268798828
time for calcul the mask position with numpy : 0.0014026165008544922
nb_pixel_total : 341
time to create 1 rle with old method : 0.0004570484161376953
time for calcul the mask position with numpy : 0.0013759136199951172
nb_pixel_total : 1243
time to create 1 rle with old method : 0.0014977455139160156
time for calcul the mask position with numpy : 0.0014829635620117188
nb_pixel_total : 10582
time to create 1 rle with old method : 0.012485742568969727
time for calcul the mask position with numpy : 0.0013015270233154297
nb_pixel_total : 1839
time to create 1 rle with old method : 0.0021970272064208984
time for calcul the mask position with numpy : 0.0013713836669921875
nb_pixel_total : 2404
time to create 1 rle with old method : 0.0031075477600097656
time for calcul the mask position with numpy : 0.0014104843139648438
nb_pixel_total : 1195
time to create 1 rle with old method : 0.0014832019805908203
time for calcul the mask position with numpy : 0.0014295578002929688
nb_pixel_total : 2393
time to create 1 rle with old method : 0.0030181407928466797
time for calcul the mask position with numpy : 0.0013682842254638672
nb_pixel_total : 874
time to create 1 rle with old method : 0.0011425018310546875
time for calcul the mask position with numpy : 0.0013966560363769531
nb_pixel_total : 861
time to create 1 rle with old method : 0.0011706352233886719
time for calcul the mask position with numpy : 0.0014255046844482422
nb_pixel_total : 886
time to create 1 rle with old method : 0.001161813735961914
time for calcul the mask position with numpy : 0.0013308525085449219
nb_pixel_total : 1086
time to create 1 rle with old method : 0.0013697147369384766
time for calcul the mask position with numpy : 0.0014460086822509766
nb_pixel_total : 2339
time to create 1 rle with old method : 0.002850770950317383
time for calcul the mask position with numpy : 0.0014317035675048828
nb_pixel_total : 2764
time to create 1 rle with old method : 0.0033032894134521484
time for calcul the mask position with numpy : 0.0014011859893798828
nb_pixel_total : 1673
time to create 1 rle with old method : 0.0020720958709716797
time for calcul the mask position with numpy : 0.00135040283203125
nb_pixel_total : 1065
time to create 1 rle with old method : 0.001285552978515625
time for calcul the mask position with numpy : 0.0013337135314941406
nb_pixel_total : 337
time to create 1 rle with old method : 0.00046896934509277344
time for calcul the mask position with numpy : 0.0013756752014160156
nb_pixel_total : 574
time to create 1 rle with old method : 0.0007891654968261719
time for calcul the mask position with numpy : 0.0014331340789794922
nb_pixel_total : 1677
time to create 1 rle with old method : 0.001962423324584961
time for calcul the mask position with numpy : 0.0015659332275390625
nb_pixel_total : 27548
time to create 1 rle with old method : 0.03197073936462402
time for calcul the mask position with numpy : 0.001405954360961914
nb_pixel_total : 585
time to create 1 rle with old method : 0.0007245540618896484
time for calcul the mask position with numpy : 0.0014536380767822266
nb_pixel_total : 8593
time to create 1 rle with old method : 0.010354280471801758
time for calcul the mask position with numpy : 0.0014557838439941406
nb_pixel_total : 3089
time to create 1 rle with old method : 0.003866434097290039
time for calcul the mask position with numpy : 0.0014600753784179688
nb_pixel_total : 1193
time to create 1 rle with old method : 0.0015506744384765625
time for calcul the mask position with numpy : 0.0014336109161376953
nb_pixel_total : 982
time to create 1 rle with old method : 0.0012564659118652344
time for calcul the mask position with numpy : 0.0014028549194335938
nb_pixel_total : 9119
time to create 1 rle with old method : 0.01083993911743164
time for calcul the mask position with numpy : 0.0016205310821533203
nb_pixel_total : 39175
time to create 1 rle with old method : 0.045496225357055664
time for calcul the mask position with numpy : 0.0014886856079101562
nb_pixel_total : 16682
time to create 1 rle with old method : 0.020404815673828125
time for calcul the mask position with numpy : 0.0014495849609375
nb_pixel_total : 1741
time to create 1 rle with old method : 0.0021560192108154297
time for calcul the mask position with numpy : 0.0013837814331054688
nb_pixel_total : 8441
time to create 1 rle with old method : 0.009778261184692383
time for calcul the mask position with numpy : 0.0014078617095947266
nb_pixel_total : 712
time to create 1 rle with old method : 0.0009288787841796875
time for calcul the mask position with numpy : 0.0013828277587890625
nb_pixel_total : 1519
time to create 1 rle with old method : 0.0018157958984375
time for calcul the mask position with numpy : 0.001386880874633789
nb_pixel_total : 265
time to create 1 rle with old method : 0.00035071372985839844
time for calcul the mask position with numpy : 0.0014121532440185547
nb_pixel_total : 1334
time to create 1 rle with old method : 0.0016777515411376953
time for calcul the mask position with numpy : 0.0013761520385742188
nb_pixel_total : 834
time to create 1 rle with old method : 0.0010595321655273438
time for calcul the mask position with numpy : 0.0014193058013916016
nb_pixel_total : 3172
time to create 1 rle with old method : 0.003906965255737305
time for calcul the mask position with numpy : 0.001383066177368164
nb_pixel_total : 613
time to create 1 rle with old method : 0.0008220672607421875
time for calcul the mask position with numpy : 0.0014176368713378906
nb_pixel_total : 248
time to create 1 rle with old method : 0.00036406517028808594
time for calcul the mask position with numpy : 0.001390695571899414
nb_pixel_total : 4056
time to create 1 rle with old method : 0.004976034164428711
time for calcul the mask position with numpy : 0.0014164447784423828
nb_pixel_total : 1504
time to create 1 rle with old method : 0.0018351078033447266
time for calcul the mask position with numpy : 0.001416921615600586
nb_pixel_total : 976
time to create 1 rle with old method : 0.0012698173522949219
time for calcul the mask position with numpy : 0.0012955665588378906
nb_pixel_total : 221
time to create 1 rle with old method : 0.00030040740966796875
time for calcul the mask position with numpy : 0.0014126300811767578
nb_pixel_total : 734
time to create 1 rle with old method : 0.001003265380859375
time for calcul the mask position with numpy : 0.0012927055358886719
nb_pixel_total : 943
time to create 1 rle with old method : 0.0013461112976074219
time for calcul the mask position with numpy : 0.0014584064483642578
nb_pixel_total : 7498
time to create 1 rle with old method : 0.008603096008300781
time for calcul the mask position with numpy : 0.0012977123260498047
nb_pixel_total : 1438
time to create 1 rle with old method : 0.0018012523651123047
time for calcul the mask position with numpy : 0.0013225078582763672
nb_pixel_total : 300
time to create 1 rle with old method : 0.0004322528839111328
time for calcul the mask position with numpy : 0.0013697147369384766
nb_pixel_total : 3919
time to create 1 rle with old method : 0.004630565643310547
time for calcul the mask position with numpy : 0.001344442367553711
nb_pixel_total : 595
time to create 1 rle with old method : 0.0008111000061035156
time for calcul the mask position with numpy : 0.0014374256134033203
nb_pixel_total : 1463
time to create 1 rle with old method : 0.0018572807312011719
time for calcul the mask position with numpy : 0.0013301372528076172
nb_pixel_total : 1123
time to create 1 rle with old method : 0.0014450550079345703
time for calcul the mask position with numpy : 0.0014297962188720703
nb_pixel_total : 2650
time to create 1 rle with old method : 0.003326892852783203
time for calcul the mask position with numpy : 0.0013287067413330078
nb_pixel_total : 2195
time to create 1 rle with old method : 0.0028090476989746094
time for calcul the mask position with numpy : 0.0013098716735839844
nb_pixel_total : 884
time to create 1 rle with old method : 0.0011696815490722656
time for calcul the mask position with numpy : 0.0014162063598632812
nb_pixel_total : 1609
time to create 1 rle with old method : 0.002043008804321289
time for calcul the mask position with numpy : 0.0013878345489501953
nb_pixel_total : 884
time to create 1 rle with old method : 0.0010991096496582031
time for calcul the mask position with numpy : 0.001432180404663086
nb_pixel_total : 1321
time to create 1 rle with old method : 0.0016057491302490234
time for calcul the mask position with numpy : 0.0013453960418701172
nb_pixel_total : 949
time to create 1 rle with old method : 0.0012784004211425781
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 98 chid ids of type : 4677
Number RLEs to save : 8964
INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) 
 first line : ('3747411148', '928', '287', '9')
...
 last line : ('3747411245', '322', '285', '6')
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 : <class 'dict'>
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 : <mtr.datou.datou_lib_object.DatouData object at 0x7f08a77dc160>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7f08a77dc1c0>
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 : 0.015771150588989258
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 25.992315530776978
time spend to save output : 0.016095876693725586
total time spend for step 1 : 26.008411407470703

caffe_path_current : 
About to save ! 2
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {'1189321094': [[<mtr.simple_image_editor.image_utils.CHI object at 0x7f080434ed60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080434ed90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080434ed30>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080434eca0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080434ec10>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080434ec40>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080434ec70>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043479d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043477c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347910>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347760>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347790>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347670>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347730>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347610>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043476a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043474f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347640>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347580>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347430>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347400>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043473d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043472e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043472b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347220>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043471f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043471c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347190>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347100>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043470d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043470a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f0804347070>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433ffa0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433ff70>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433ff40>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433ff10>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fe80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fe50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fdc0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fd90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fd00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fcd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fc40>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fc10>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fb80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fb50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fb20>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433faf0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fa60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fa30>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433fa00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f9d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f940>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f910>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f880>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f850>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f7c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f790>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f760>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f730>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f6a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f670>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f640>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f610>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f580>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f4f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f430>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f400>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f3d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f280>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f250>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f1c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f190>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f160>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f130>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f0a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f070>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f080433f040>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043b8fd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043b8f40>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043b8f10>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043b8ee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043b8eb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043b8e20>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043b8df0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043b8dc0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043b8d90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7f08043b8d00>], 'temp/1743741470_2995536_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']}
nb_objects detect : 98

############################### 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 : 0.17267847061157227
#### 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
Fri Apr  4 06:38:17 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 : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1743741496_2995536_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606}
map_photo_id_path_extension : {917754606: {'path': 'temp/1743741496_2995536_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 : 
[]
WARNING: Logging before InitGoogleLogging() is written to STDERR
F0404 06:38:19.175082 2995536 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0)  out of memory
*** Check failure stack trace: ***
Command terminated by signal 6
31.83user 24.60system 2:55.12elapsed 32%CPU (0avgtext+0avgdata 3609128maxresident)k
4205192inputs+4680outputs (12663major+2887718minor)pagefaults 0swaps