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 : 207

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 : 0.2624053955078125
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
Sun Mar 16 18:35:28 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 : 207
wait 20 seconds
l 3637 free memory gpu now : 207  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
2025-03-16 18:35:52.606250: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-03-16 18:35:52.639146: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz
2025-03-16 18:35:52.640871: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd990000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-03-16 18:35:52.640936: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-03-16 18:35:52.644825: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-03-16 18:35:52.895272: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xb0e33f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-03-16 18:35:52.895321: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-03-16 18:35:52.896469: 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
2025-03-16 18:35:52.897384: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 18:35:52.926259: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 18:35:52.945274: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 18:35:52.948255: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 18:35:52.981402: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 18:35:52.986769: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 18:35:53.042575: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 18:35:53.043677: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 18:35:53.044180: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 18:35:53.045036: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-03-16 18:35:53.045057: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-03-16 18:35:53.045070: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-03-16 18:35:53.046361: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 53 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
2025-03-16 18:35:53.828589: 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
2025-03-16 18:35:53.828684: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 18:35:53.828712: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 18:35:53.828738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 18:35:53.828762: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 18:35:53.828786: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 18:35:53.828823: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 18:35:53.828848: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 18:35:53.829850: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 18:35:53.831096: 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
2025-03-16 18:35:53.831127: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 18:35:53.831142: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 18:35:53.831157: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 18:35:53.831171: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 18:35:53.831185: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 18:35:53.831198: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 18:35:53.831212: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 18:35:53.831849: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 18:35:53.831881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-03-16 18:35:53.831890: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-03-16 18:35:53.831897: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-03-16 18:35:53.832592: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 53 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
2025-03-16 18:35:53.963397: F ./tensorflow/core/kernels/random_op_gpu.h:232] Non-OK-status: GpuLaunchKernel(FillPhiloxRandomKernelLaunch<Distribution>, num_blocks, block_size, 0, d.stream(), gen, data, size, dist) status: Internal: out of memory
max_time_sub_proc : 3600

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 : 5525

############################### 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 : 0.1702868938446045
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
Sun Mar 16 19:35:51 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Beginning of datou step mask_detect !
save_polygon : True

 begin detect 

begin to check gpu status 
inside check gpu memory
l 3637 free memory gpu now : 5525  max_wait_temp :  1 max_wait :  0
 gpu_flag :  0
2025-03-16 19:35:55.840504: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-03-16 19:35:55.867288: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz
2025-03-16 19:35:55.869276: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd990000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-03-16 19:35:55.869336: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-03-16 19:35:55.873263: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-03-16 19:35:56.157546: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xa2aa810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-03-16 19:35:56.157601: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-03-16 19:35:56.159635: 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
2025-03-16 19:35:56.160386: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:35:56.165820: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 19:35:56.169788: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 19:35:56.171204: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 19:35:56.177471: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 19:35:56.181872: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 19:35:56.192032: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 19:35:56.193976: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 19:35:56.194113: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:35:56.195434: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-03-16 19:35:56.195473: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-03-16 19:35:56.195489: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-03-16 19:35:56.197385: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5051 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

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
2025-03-16 19:35:56.857826: 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
2025-03-16 19:35:56.857937: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:35:56.857959: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 19:35:56.857978: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 19:35:56.857997: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 19:35:56.858015: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 19:35:56.858033: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 19:35:56.858052: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 19:35:56.859303: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 19:35:56.860660: 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
2025-03-16 19:35:56.860693: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:35:56.860707: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 19:35:56.860720: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 19:35:56.860733: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 19:35:56.860746: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 19:35:56.860758: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 19:35:56.860771: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 19:35:56.861645: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 19:35:56.861686: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-03-16 19:35:56.861694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-03-16 19:35:56.861701: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-03-16 19:35:56.862629: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5051 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
Using TensorFlow backend.
WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version.
Instructions for updating:
box_ind is deprecated, use box_indices instead
WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
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 : 
[]
2025-03-16 19:36:04.662833: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 19:36:04.998491: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
local folder  : /data/models_weight/mask_coco_origin


/data/models_weight/mask_coco_origin/mask_model.h5
size_local : 257557808 size in s3 : 257557808
create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17
mask_model.h5 already exist and didn't need to update
 list_images length : 1
NEW PHOTO
Processing 1 images
image                    shape: (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
610785
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 236
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 : 5525
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 : 0.0004394054412841797
nb_pixel_total : 16902
time to create 1 rle with old method : 0.025820255279541016
length of segment :  107
time for calcul the mask position with numpy : 0.18858075141906738
nb_pixel_total : 480759
time to create 1 rle with new method : 0.02623438835144043
length of segment :  632
time for calcul the mask position with numpy : 0.0004630088806152344
nb_pixel_total : 36581
time to create 1 rle with old method : 0.04309201240539551
length of segment :  132
time for calcul the mask position with numpy : 0.00010848045349121094
nb_pixel_total : 4794
time to create 1 rle with old method : 0.005899906158447266
length of segment :  51

time spent for convertir_results : 1.1428182125091553

time spend for datou_step_exec : 20.800694704055786
time spend to save output : 3.8623809814453125e-05
total time spend for step 1 : 20.8007333278656

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 403 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 : 0.013996124267578125

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, 0.9988373, [(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, 0.9977423, [(710, 22, 23), (925, 22, 47), (608, 23, 146), (893, 23, 104), (598, 24, 235), (849, 24, 159), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (545, 33, 502), (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), (477, 56, 607), (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), (450, 76, 640), (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), (361, 115, 729), (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), (266, 137, 823), (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), (166, 165, 923), (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), (99, 219, 986), (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), (68, 316, 963), (68, 317, 961), (68, 318, 959), (68, 319, 958), (67, 320, 957), (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, 926), (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, 914), 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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 : 0.15594029426574707
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
Sun Mar 16 19:36:13 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Beginning of datou step mask_detect !
save_polygon : True

 begin detect 

begin to check gpu status 
inside check gpu memory
l 3637 free memory gpu now : 5525  max_wait_temp :  1 max_wait :  0
 gpu_flag :  0
2025-03-16 19:36:15.748187: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-03-16 19:36:15.775069: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz
2025-03-16 19:36:15.776601: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd98c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-03-16 19:36:15.776657: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-03-16 19:36:15.779364: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-03-16 19:36:16.028807: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xb6c31c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-03-16 19:36:16.028844: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-03-16 19:36:16.029659: 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
2025-03-16 19:36:16.029977: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:36:16.031939: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 19:36:16.037121: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 19:36:16.037450: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 19:36:16.039611: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 19:36:16.040590: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 19:36:16.050847: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 19:36:16.052143: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 19:36:16.052238: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:36:16.052836: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-03-16 19:36:16.052852: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-03-16 19:36:16.052861: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-03-16 19:36:16.053854: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5051 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

2025-03-16 19:36:16.129158: 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
2025-03-16 19:36:16.129292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:36:16.129313: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 19:36:16.129332: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 19:36:16.129349: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 19:36:16.129366: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 19:36:16.129383: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 19:36:16.129401: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 19:36:16.130295: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 19:36:16.131297: 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
2025-03-16 19:36:16.131330: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:36:16.131345: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 19:36:16.131359: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 19:36:16.131373: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 19:36:16.131387: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 19:36:16.131401: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 19:36:16.131416: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 19:36:16.132291: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 19:36:16.132323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-03-16 19:36:16.132344: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-03-16 19:36:16.132351: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-03-16 19:36:16.133274: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5051 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
Using TensorFlow backend.
WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version.
Instructions for updating:
box_ind is deprecated, use box_indices instead
WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
Inside mask_sub_process
Inside mask_detect 
About to load cache.load_thcl_param 
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21))
{'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,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 : 
[]
2025-03-16 19:36:24.490947: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 19:36:24.650844: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
local folder  : /data/models_weight/mask_coco_origin


/data/models_weight/mask_coco_origin/mask_model.h5
size_local : 257557808 size in s3 : 257557808
create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17
mask_model.h5 already exist and didn't need to update
 list_images length : 1
NEW PHOTO
Processing 1 images
image                    shape: (2448, 2448, 3)       min:    0.00000  max:  255.00000
molded_images            shape: (1, 640, 640, 3)      min: -123.70000  max:  151.10000
image_metas              shape: (1, 89)               min:    0.00000  max: 2448.00000
nb d'objets trouves : 1
Detection mask done !
Trying to reset tf kernel
612115
begin to check gpu status 
inside check gpu memory
l 3610 free memory gpu now : 236
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 : 5525
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 : 0.4692878723144531
nb_pixel_total : 3700109
time to create 1 rle with new method : 0.39855217933654785
length of segment :  2051

time spent for convertir_results : 1.9091072082519531

time spend for datou_step_exec : 19.8457350730896
time spend to save output : 2.384185791015625e-05
total time spend for step 1 : 19.84575891494751

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 : 0.013180017471313477

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {'917877156': [[(917877156, 492601069, 445, 7, 2268, 110, 2249, 0.98576725, [(669, 114, 139), (1239, 114, 50), (529, 115, 444), (1115, 115, 309), (517, 116, 918), (507, 117, 940), (496, 118, 961), (487, 119, 980), (477, 120, 1000), (468, 121, 1019), (459, 122, 1037), (451, 123, 1057), (443, 124, 1082), (436, 125, 1106), (429, 126, 1130), (422, 127, 1152), (415, 128, 1169), (408, 129, 1181), (402, 130, 1192), (396, 131, 1203), (390, 132, 1213), (384, 133, 1224), (379, 134, 1233), (374, 135, 1242), (370, 136, 1250), (368, 137, 1256), (366, 138, 1261), (364, 139, 1267), (362, 140, 1272), (360, 141, 1278), (359, 142, 1282), (357, 143, 1287), (356, 144, 1291), (354, 145, 1297), (352, 146, 1301), (351, 147, 1305), (350, 148, 1309), (348, 149, 1314), (347, 150, 1318), (345, 151, 1322), (344, 152, 1326), (342, 153, 1331), (341, 154, 1335), (339, 155, 1340), (338, 156, 1344), (336, 157, 1349), (334, 158, 1355), (333, 159, 1359), (331, 160, 1365), (329, 161, 1370), (327, 162, 1376), (325, 163, 1382), (323, 164, 1388), (321, 165, 1394), (319, 166, 1400), (317, 167, 1406), (315, 168, 1413), (313, 169, 1420), (310, 170, 1428), (308, 171, 1435), (306, 172, 1442), (303, 173, 1450), (300, 174, 1458), (298, 175, 1466), (295, 176, 1474), (292, 177, 1483), (289, 178, 1492), (286, 179, 1501), (282, 180, 1512), (279, 181, 1521), (276, 182, 1531), (272, 183, 1542), (269, 184, 1552), (266, 185, 1562), (263, 186, 1570), (260, 187, 1579), (257, 188, 1587), (254, 189, 1596), (251, 190, 1604), (248, 191, 1612), (245, 192, 1620), (242, 193, 1628), (239, 194, 1635), (236, 195, 1643), (234, 196, 1650), (231, 197, 1657), (228, 198, 1664), (225, 199, 1671), (223, 200, 1677), (220, 201, 1684), (217, 202, 1689), (215, 203, 1693), (212, 204, 1697), (210, 205, 1701), (208, 206, 1704), (206, 207, 1708), (205, 208, 1710), (203, 209, 1713), (202, 210, 1716), (200, 211, 1719), (199, 212, 1721), (197, 213, 1724), (196, 214, 1726), (195, 215, 1729), (193, 216, 1732), (192, 217, 1734), (191, 218, 1736), (190, 219, 1738), (189, 220, 1740), (188, 221, 1742), (187, 222, 1744), (186, 223, 1745), (185, 224, 1747), (184, 225, 1749), (183, 226, 1751), (182, 227, 1753), (181, 228, 1754), (180, 229, 1756), (179, 230, 1758), (178, 231, 1760), (177, 232, 1762), (176, 233, 1763), (175, 234, 1765), (173, 235, 1768), (172, 236, 1770), (171, 237, 1772), (170, 238, 1774), (169, 239, 1776), (168, 240, 1778), (166, 241, 1781), (165, 242, 1783), (164, 243, 1785), (163, 244, 1787), (161, 245, 1790), (160, 246, 1792), (159, 247, 1794), (157, 248, 1798), (156, 249, 1800), (155, 250, 1802), (153, 251, 1805), (152, 252, 1807), (150, 253, 1811), (149, 254, 1813), (147, 255, 1816), (145, 256, 1820), (144, 257, 1822), (142, 258, 1826), (140, 259, 1829), (139, 260, 1832), (137, 261, 1836), (135, 262, 1839), (133, 263, 1843), (131, 264, 1847), (129, 265, 1850), (128, 266, 1853), (126, 267, 1857), (125, 268, 1860), (123, 269, 1864), (122, 270, 1866), (121, 271, 1869), (120, 272, 1871), (119, 273, 1874), (117, 274, 1877), (116, 275, 1879), (115, 276, 1882), (114, 277, 1884), (113, 278, 1886), (112, 279, 1888), (111, 280, 1891), (110, 281, 1893), (109, 282, 1895), (109, 283, 1896), (108, 284, 1898), (107, 285, 1900), (106, 286, 1902), (105, 287, 1904), (104, 288, 1906), (104, 289, 1906), (103, 290, 1908), (102, 291, 1910), (101, 292, 1912), (101, 293, 1913), (100, 294, 1915), (99, 295, 1916), (99, 296, 1917), (98, 297, 1919), (98, 298, 1920), (97, 299, 1921), (96, 300, 1923), (96, 301, 1923), (96, 302, 1924), (95, 303, 1925), (95, 304, 1926), (95, 305, 1926), (94, 306, 1928), (94, 307, 1928), (94, 308, 1929), (94, 309, 1929), (93, 310, 1931), (93, 311, 1931), (93, 312, 1932), (92, 313, 1933), (92, 314, 1934), (92, 315, 1934), (92, 316, 1935), (91, 317, 1936), (91, 318, 1937), (91, 319, 1938), (90, 320, 1939), (90, 321, 1940), (90, 322, 1940), (89, 323, 1942), (89, 324, 1942), (89, 325, 1943), (89, 326, 1944), (88, 327, 1945), (88, 328, 1946), (88, 329, 1946), (87, 330, 1948), (87, 331, 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nb pixel non reg : 3692295
nb pixel common : 3689036
proportion of common points : 0.9991173511325612
[('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_6419193983607b8ec28e462733ffc29d3101ac80
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_6419193983607b8ec28e462733ffc29d3101ac80','{"mask_detection": "success"}','1','http://marlene.fotonower-preprod.com/job/2025/March/16032025/python_test3//data_2/data_log/job/2025/March/16032025/python_test3/log-python3----short_python3--v--marlene-18:35:02.txt','mask_detection','unknown');

#&_# END OF TEST #&_# : tests/mask_test #&_# 





#&_# BEGIN OF TEST : tests/datou_test #&_# 


/home/admin/workarea/git/Velours/python/tests/datou_test.py
<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 : 0.21254992485046387
#### 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
Sun Mar 16 19:36:54 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1742150214_446243_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png': 1189321094}
map_photo_id_path_extension : {1189321094: {'path': 'temp/1742150214_446243_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 : 0.0019271373748779297
nb_pixel_total : 5625
time to create 1 rle with old method : 0.0067822933197021484
time for calcul the mask position with numpy : 0.0014083385467529297
nb_pixel_total : 11896
time to create 1 rle with old method : 0.015115976333618164
time for calcul the mask position with numpy : 0.0013320446014404297
nb_pixel_total : 8599
time to create 1 rle with old method : 0.00930166244506836
time for calcul the mask position with numpy : 0.0013725757598876953
nb_pixel_total : 16291
time to create 1 rle with old method : 0.018796682357788086
time for calcul the mask position with numpy : 0.0015625953674316406
nb_pixel_total : 13969
time to create 1 rle with old method : 0.016285419464111328
time for calcul the mask position with numpy : 0.0015537738800048828
nb_pixel_total : 16451
time to create 1 rle with old method : 0.0187380313873291
time for calcul the mask position with numpy : 0.0019040107727050781
nb_pixel_total : 83773
time to create 1 rle with old method : 0.09239816665649414
time for calcul the mask position with numpy : 0.0014667510986328125
nb_pixel_total : 10764
time to create 1 rle with old method : 0.012304306030273438
time for calcul the mask position with numpy : 0.001421213150024414
nb_pixel_total : 3780
time to create 1 rle with old method : 0.004248619079589844
time for calcul the mask position with numpy : 0.0014431476593017578
nb_pixel_total : 2379
time to create 1 rle with old method : 0.002814054489135742
time for calcul the mask position with numpy : 0.0014450550079345703
nb_pixel_total : 29423
time to create 1 rle with old method : 0.031743526458740234
time for calcul the mask position with numpy : 0.0013561248779296875
nb_pixel_total : 2951
time to create 1 rle with old method : 0.0034716129302978516
time for calcul the mask position with numpy : 0.0014116764068603516
nb_pixel_total : 5274
time to create 1 rle with old method : 0.0060405731201171875
time for calcul the mask position with numpy : 0.0015347003936767578
nb_pixel_total : 27398
time to create 1 rle with old method : 0.030176401138305664
time for calcul the mask position with numpy : 0.001390218734741211
nb_pixel_total : 3927
time to create 1 rle with old method : 0.0046138763427734375
time for calcul the mask position with numpy : 0.0014884471893310547
nb_pixel_total : 13100
time to create 1 rle with old method : 0.014636516571044922
time for calcul the mask position with numpy : 0.00131988525390625
nb_pixel_total : 2454
time to create 1 rle with old method : 0.0028734207153320312
time for calcul the mask position with numpy : 0.0014147758483886719
nb_pixel_total : 9886
time to create 1 rle with old method : 0.011517524719238281
time for calcul the mask position with numpy : 0.0013110637664794922
nb_pixel_total : 1213
time to create 1 rle with old method : 0.0015380382537841797
time for calcul the mask position with numpy : 0.0013232231140136719
nb_pixel_total : 4279
time to create 1 rle with old method : 0.004876852035522461
time for calcul the mask position with numpy : 0.0014870166778564453
nb_pixel_total : 6625
time to create 1 rle with old method : 0.0073664188385009766
time for calcul the mask position with numpy : 0.0013408660888671875
nb_pixel_total : 7648
time to create 1 rle with old method : 0.008518457412719727
time for calcul the mask position with numpy : 0.001312255859375
nb_pixel_total : 2082
time to create 1 rle with old method : 0.0024254322052001953
time for calcul the mask position with numpy : 0.0013384819030761719
nb_pixel_total : 5524
time to create 1 rle with old method : 0.006256103515625
time for calcul the mask position with numpy : 0.001321554183959961
nb_pixel_total : 4184
time to create 1 rle with old method : 0.0048122406005859375
time for calcul the mask position with numpy : 0.0015864372253417969
nb_pixel_total : 39010
time to create 1 rle with old method : 0.04242706298828125
time for calcul the mask position with numpy : 0.0014178752899169922
nb_pixel_total : 2787
time to create 1 rle with old method : 0.0032196044921875
time for calcul the mask position with numpy : 0.0013017654418945312
nb_pixel_total : 866
time to create 1 rle with old method : 0.001087188720703125
time for calcul the mask position with numpy : 0.0013344287872314453
nb_pixel_total : 4298
time to create 1 rle with old method : 0.004961967468261719
time for calcul the mask position with numpy : 0.0013134479522705078
nb_pixel_total : 3328
time to create 1 rle with old method : 0.0037071704864501953
time for calcul the mask position with numpy : 0.0013320446014404297
nb_pixel_total : 1529
time to create 1 rle with old method : 0.0017490386962890625
time for calcul the mask position with numpy : 0.0012943744659423828
nb_pixel_total : 1651
time to create 1 rle with old method : 0.0019099712371826172
time for calcul the mask position with numpy : 0.0013301372528076172
nb_pixel_total : 3537
time to create 1 rle with old method : 0.003924846649169922
time for calcul the mask position with numpy : 0.0012886524200439453
nb_pixel_total : 1318
time to create 1 rle with old method : 0.001689910888671875
time for calcul the mask position with numpy : 0.0012979507446289062
nb_pixel_total : 2448
time to create 1 rle with old method : 0.002753019332885742
time for calcul the mask position with numpy : 0.001299142837524414
nb_pixel_total : 2802
time to create 1 rle with old method : 0.0031654834747314453
time for calcul the mask position with numpy : 0.0013003349304199219
nb_pixel_total : 2776
time to create 1 rle with old method : 0.003056764602661133
time for calcul the mask position with numpy : 0.0012879371643066406
nb_pixel_total : 1269
time to create 1 rle with old method : 0.0015101432800292969
time for calcul the mask position with numpy : 0.001386880874633789
nb_pixel_total : 14727
time to create 1 rle with old method : 0.016120195388793945
time for calcul the mask position with numpy : 0.0013904571533203125
nb_pixel_total : 1037
time to create 1 rle with old method : 0.0012428760528564453
time for calcul the mask position with numpy : 0.0013744831085205078
nb_pixel_total : 12894
time to create 1 rle with old method : 0.014708518981933594
time for calcul the mask position with numpy : 0.0013954639434814453
nb_pixel_total : 10634
time to create 1 rle with old method : 0.011857748031616211
time for calcul the mask position with numpy : 0.0014345645904541016
nb_pixel_total : 344
time to create 1 rle with old method : 0.0004258155822753906
time for calcul the mask position with numpy : 0.0013017654418945312
nb_pixel_total : 1025
time to create 1 rle with old method : 0.0011973381042480469
time for calcul the mask position with numpy : 0.0014107227325439453
nb_pixel_total : 3852
time to create 1 rle with old method : 0.004552364349365234
time for calcul the mask position with numpy : 0.0013136863708496094
nb_pixel_total : 2408
time to create 1 rle with old method : 0.002912282943725586
time for calcul the mask position with numpy : 0.0013031959533691406
nb_pixel_total : 1073
time to create 1 rle with old method : 0.0012700557708740234
time for calcul the mask position with numpy : 0.0013153553009033203
nb_pixel_total : 4176
time to create 1 rle with old method : 0.004784345626831055
time for calcul the mask position with numpy : 0.0013949871063232422
nb_pixel_total : 7492
time to create 1 rle with old method : 0.008114814758300781
time for calcul the mask position with numpy : 0.0012891292572021484
nb_pixel_total : 858
time to create 1 rle with old method : 0.001085519790649414
time for calcul the mask position with numpy : 0.0013027191162109375
nb_pixel_total : 594
time to create 1 rle with old method : 0.00070953369140625
time for calcul the mask position with numpy : 0.0012853145599365234
nb_pixel_total : 886
time to create 1 rle with old method : 0.0010256767272949219
time for calcul the mask position with numpy : 0.0013110637664794922
nb_pixel_total : 2337
time to create 1 rle with old method : 0.002705812454223633
time for calcul the mask position with numpy : 0.0013358592987060547
nb_pixel_total : 2028
time to create 1 rle with old method : 0.0022797584533691406
time for calcul the mask position with numpy : 0.0013093948364257812
nb_pixel_total : 574
time to create 1 rle with old method : 0.0007321834564208984
time for calcul the mask position with numpy : 0.001348257064819336
nb_pixel_total : 1727
time to create 1 rle with old method : 0.0019974708557128906
time for calcul the mask position with numpy : 0.0012934207916259766
nb_pixel_total : 687
time to create 1 rle with old method : 0.0008661746978759766
time for calcul the mask position with numpy : 0.0013997554779052734
nb_pixel_total : 18505
time to create 1 rle with old method : 0.020227909088134766
time for calcul the mask position with numpy : 0.0012917518615722656
nb_pixel_total : 338
time to create 1 rle with old method : 0.0004336833953857422
time for calcul the mask position with numpy : 0.0013089179992675781
nb_pixel_total : 3181
time to create 1 rle with old method : 0.0035893917083740234
time for calcul the mask position with numpy : 0.0014498233795166016
nb_pixel_total : 8410
time to create 1 rle with old method : 0.00927591323852539
time for calcul the mask position with numpy : 0.0013310909271240234
nb_pixel_total : 3095
time to create 1 rle with old method : 0.0036466121673583984
time for calcul the mask position with numpy : 0.0013988018035888672
nb_pixel_total : 1671
time to create 1 rle with old method : 0.0020012855529785156
time for calcul the mask position with numpy : 0.001337289810180664
nb_pixel_total : 583
time to create 1 rle with old method : 0.0007748603820800781
time for calcul the mask position with numpy : 0.0013289451599121094
nb_pixel_total : 1177
time to create 1 rle with old method : 0.001367330551147461
time for calcul the mask position with numpy : 0.001359701156616211
nb_pixel_total : 1053
time to create 1 rle with old method : 0.0013513565063476562
time for calcul the mask position with numpy : 0.001439809799194336
nb_pixel_total : 4328
time to create 1 rle with old method : 0.005736351013183594
time for calcul the mask position with numpy : 0.0014863014221191406
nb_pixel_total : 8600
time to create 1 rle with old method : 0.00977778434753418
time for calcul the mask position with numpy : 0.0014216899871826172
nb_pixel_total : 1319
time to create 1 rle with old method : 0.0015985965728759766
time for calcul the mask position with numpy : 0.001512765884399414
nb_pixel_total : 12954
time to create 1 rle with old method : 0.01468348503112793
time for calcul the mask position with numpy : 0.0015659332275390625
nb_pixel_total : 16696
time to create 1 rle with old method : 0.028431177139282227
time for calcul the mask position with numpy : 0.0018613338470458984
nb_pixel_total : 39036
time to create 1 rle with old method : 0.05876636505126953
time for calcul the mask position with numpy : 0.001440286636352539
nb_pixel_total : 9101
time to create 1 rle with old method : 0.010397195816040039
time for calcul the mask position with numpy : 0.0014350414276123047
nb_pixel_total : 1731
time to create 1 rle with old method : 0.0020835399627685547
time for calcul the mask position with numpy : 0.0013539791107177734
nb_pixel_total : 9536
time to create 1 rle with old method : 0.010885477066040039
time for calcul the mask position with numpy : 0.0013549327850341797
nb_pixel_total : 1521
time to create 1 rle with old method : 0.0017852783203125
time for calcul the mask position with numpy : 0.0013113021850585938
nb_pixel_total : 936
time to create 1 rle with old method : 0.0013248920440673828
time for calcul the mask position with numpy : 0.0013399124145507812
nb_pixel_total : 969
time to create 1 rle with old method : 0.0011076927185058594
time for calcul the mask position with numpy : 0.0013647079467773438
nb_pixel_total : 267
time to create 1 rle with old method : 0.0003542900085449219
time for calcul the mask position with numpy : 0.0013134479522705078
nb_pixel_total : 713
time to create 1 rle with old method : 0.0008823871612548828
time for calcul the mask position with numpy : 0.004746437072753906
nb_pixel_total : 5014
time to create 1 rle with old method : 0.0058252811431884766
time for calcul the mask position with numpy : 0.0013229846954345703
nb_pixel_total : 888
time to create 1 rle with old method : 0.0010845661163330078
time for calcul the mask position with numpy : 0.0013043880462646484
nb_pixel_total : 616
time to create 1 rle with old method : 0.0007383823394775391
time for calcul the mask position with numpy : 0.0012989044189453125
nb_pixel_total : 248
time to create 1 rle with old method : 0.00037598609924316406
time for calcul the mask position with numpy : 0.0013859272003173828
nb_pixel_total : 221
time to create 1 rle with old method : 0.00032806396484375
time for calcul the mask position with numpy : 0.0014028549194335938
nb_pixel_total : 735
time to create 1 rle with old method : 0.0010423660278320312
time for calcul the mask position with numpy : 0.0013058185577392578
nb_pixel_total : 1441
time to create 1 rle with old method : 0.0018191337585449219
time for calcul the mask position with numpy : 0.0014379024505615234
nb_pixel_total : 1126
time to create 1 rle with old method : 0.001279592514038086
time for calcul the mask position with numpy : 0.0013053417205810547
nb_pixel_total : 2212
time to create 1 rle with old method : 0.002693653106689453
time for calcul the mask position with numpy : 0.0013043880462646484
nb_pixel_total : 1092
time to create 1 rle with old method : 0.0013396739959716797
time for calcul the mask position with numpy : 0.0013017654418945312
nb_pixel_total : 596
time to create 1 rle with old method : 0.0007297992706298828
time for calcul the mask position with numpy : 0.0013890266418457031
nb_pixel_total : 296
time to create 1 rle with old method : 0.0004596710205078125
time for calcul the mask position with numpy : 0.0013124942779541016
nb_pixel_total : 1608
time to create 1 rle with old method : 0.0019066333770751953
time for calcul the mask position with numpy : 0.0013692378997802734
nb_pixel_total : 948
time to create 1 rle with old method : 0.0011417865753173828
time for calcul the mask position with numpy : 0.0013098716735839844
nb_pixel_total : 951
time to create 1 rle with old method : 0.0011408329010009766
time for calcul the mask position with numpy : 0.0012969970703125
nb_pixel_total : 335
time to create 1 rle with old method : 0.000415802001953125
time for calcul the mask position with numpy : 0.0013158321380615234
nb_pixel_total : 876
time to create 1 rle with old method : 0.0010383129119873047
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 97 chid ids of type : 4677
Number RLEs to save : 9198
INSERT IGNORE INTO MTRPhoto.crop_segments (`crop_hashtag_id`, `x0`, `y0`, `length`) VALUES (%s, %s, %s , %s) 
 first line : ('3714639465', '463', '201', '5')
...
 last line : ('3714639561', '773', '75', '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 0x7fda59dbf820>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7fda59dbf880>
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 : 0.2562825679779053
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 9.75528335571289
time spend to save output : 0.25653076171875
total time spend for step 1 : 10.01181411743164

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 0x7fd98799ad90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98799ae20>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98799ad60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98799ad30>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98799ac40>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98799ac70>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98799ad00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd9879939a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd9879935e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993850>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993940>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993790>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993820>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993700>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993550>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993760>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993640>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993730>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993520>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993670>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993610>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993490>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd9879933d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd9879933a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993310>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd9879932e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993250>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993220>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd9879931f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd9879931c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993130>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987993100>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd9879930d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd9879930a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798afd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798afa0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798af10>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798aee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798ae50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798ae20>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798adf0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798adc0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798ad30>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798ad00>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798acd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798aca0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798ac10>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798abe0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798abb0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798ab80>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798aaf0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798aac0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798aa90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798aa60>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a9d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a9a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a910>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a8e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a850>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a820>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a7f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a7c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a730>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a700>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a6d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a6a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a610>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a5e0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a5b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a580>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a4f0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a4c0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a490>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a460>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a3d0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a3a0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a370>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a340>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a2b0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a280>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a250>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a220>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a190>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a160>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a130>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a100>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a070>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd98798a040>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987a03fd0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987a03fa0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987a03f10>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987a03ee0>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987a03e50>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987a03e20>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987a03d90>, <mtr.simple_image_editor.image_utils.CHI object at 0x7fd987a03d60>], 'temp/1742150214_446243_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']}
nb_objects detect : 97

############################### 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 : 0.49875712394714355
#### 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
Sun Mar 16 19:37:05 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1742150225_446243_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg': 917754606}
map_photo_id_path_extension : {917754606: {'path': 'temp/1742150225_446243_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step Faster rcnn !
classes : ['background', 'plaque']
pht : 4370
caffemodel_name (should be vgg16_immat_307 but not used because net loaded outside in the fonction) : {'id': 3375, 'mtr_user_id': 31, 'name': 'detection_plaque_valcor_010622', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,plaque', 'svm_portfolios_learning': '0,0', 'photo_hashtag_type': 4370, 'photo_desc_type': 5676, 'type_classification': 'caffe_faster_rcnn', 'hashtag_id_list': '0,0'}
To loadFromThcl()
model_param file didn't exist 
model_name : detection_plaque_valcor_010622
model_type : caffe_faster_rcnn
list file need :
['caffemodel', 'test.prototxt']
file exist in s3 :
['caffemodel', 'test.prototxt']
file manque in s3 : 
[]
local folder  : /data/models_weight/detection_plaque_valcor_010622


/data/models_weight/detection_plaque_valcor_010622/caffemodel
size_local : 349723073 size in s3 : 349723073
create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56
caffemodel already exist and didn't need to update


/data/models_weight/detection_plaque_valcor_010622/test.prototxt
size_local : 7163 size in s3 : 7163
create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55
test.prototxt already exist and didn't need to update
 prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt
 caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel


Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel
About to compute detect_faster_rcnn : len(args) : 1
Inside frcnn step exec : nb paths : 1
image_path : temp/1742150225_446243_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg
image_size (600, 800, 3)
[[[  4   6   6]
  [  5   7   7]
  [  6   8   8]
  ...
  [207 215 214]
  [206 214 213]
  [206 214 213]]

 [[  4   6   6]
  [  5   7   7]
  [  6   8   8]
  ...
  [207 215 214]
  [206 214 213]
  [206 214 213]]

 [[  4   6   6]
  [  5   7   7]
  [  6   8   8]
  ...
  [207 215 214]
  [206 214 213]
  [206 214 213]]

 ...

 [[ 14  16  16]
  [ 13  15  15]
  [ 11  13  13]
  ...
  [198 206 205]
  [198 206 205]
  [198 206 205]]

 [[ 16  18  18]
  [ 14  16  16]
  [ 11  13  13]
  ...
  [206 214 213]
  [206 214 213]
  [206 214 213]]

 [[ 13  15  15]
  [ 12  14  14]
  [  9  11  11]
  ...
  [210 218 217]
  [210 218 217]
  [210 218 217]]]
Detection took 0.109s for 300 object proposals
 c : plaque
 list_crops.shape (72, 5)
 proba :  0.06384013
(374.1269, 293.91934, 430.81024, 317.80875)
 proba :  0.05228839
(382.18152, 297.1892, 552.3604, 344.65796)
 proba :  0.012271115
(345.3568, 272.42972, 468.8576, 320.7242)
We are managing local photo_id
len de result frcnn : 1

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 2.4707417488098145
time spend to save output : 5.7697296142578125e-05
total time spend for step 1 : 2.470799446105957

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
Inside saveFrcnn : final : True verbose : True
threshold to save the result : 0.1
output flattener :  [(0, 493029425, 4370, 374, 430, 293, 317, 0.06384013, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05228839, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271115, None)]
Warning : no hashtag_ids to insert in the database
final : True


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4184', None, '917754606', '0', 0, '0', 493061979, '0', None)]
time used for this insertion : 0.40924668312072754
[917754606]
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 4184 list_pids : [917754606]
Looping around the photos to save general results
len do output : 1
/0
before output type
Managing all output in save final without adding information in the mtr_datou_result 
('4184', None, None, None, None, None, None, None, None)
('4184', None, '917754606', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4184', None, '917754606', None, None, None, None, None, None)]
time used for this insertion : 0.012920618057250977
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {0: [[(0, 493029425, 4370, 374, 430, 293, 317, 0.06384013, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05228839, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271115, None)], 'temp/1742150225_446243_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']}

############################### TEST thcl  ################################
TEST THCL
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=2

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=2
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 2
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=2
# VR 17-11-17 : to create in DB !
Here we check the datou graph and we reorder steps !
 Tree builded and cycle checked, now we need to re-order the steps ! 
We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue
We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id)
 DONE and to test : checkNoCycle !
Here we check the consistency of inputs/outputs number between the given ones and the db !
eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering !
WARNING : step 1 thcl is not linked in the step_by_step architecture !
WARNING : step 2 argmax is not linked in the step_by_step architecture !
Number of inputs / outputs for each step checked !
Here we check the consistency of outputs/inputs types during steps connections
eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : thcl, argmax
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos ph
        WHERE photo_id IN (916235064) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 916235064
download finish for photo 916235064
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.3569457530975342
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
we should then remove the video here, and this would fix the bug of datou_current ! 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  2
step1:thcl
Sun Mar 16 19:37:08 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1742150228_446243_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064}
map_photo_id_path_extension : {916235064: {'path': 'temp/1742150228_446243_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step Thcl !
multi_thcl or not :False
multi_thcl_cond or not :False
dic_thcl :  {'355': 1}
we are using the classfication for only one thcl
355


In convert_file_to_np l 337 : 1
 l343 1
l357 after caffe.io.load_image
dimension du image : (3, (66, 66, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.009493350982666016
time to convert the images to numpy array :  0.0009500980377197266
total time to convert the images to numpy array :  0.010703086853027344
list photo_ids error:
[]
list photo_ids correct :
[916235064]
number of photos to traite : 1
try to delete the photos incorrect in DB

tagging for thcl : 355
To do loadFromThcl(), then load ParamDescType : thcl355
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (355) 
 thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'c_elysee_1027_gao__port_506302,mokka_1027_gao__port_506374,captur_1027_gao__port_506399,sorento_1027_gao__port_506192,navara_1027_gao__port_506205,xc90_1027_gao__port_506350,saxo_1027_gao__port_506052,trafic_1027_gao__port_506295,punto_evo_1027_gao__port_506066,5_1027_gao__port_506117,250_1027_gao__port_506065,d_max_1027_gao__port_506125,panamera_1027_gao__port_506387,alhambra_1027_gao__port_506381,x6_1027_gao__port_506349,vitara_1027_gao__port_506328,fiesta_1027_gao__port_506377,qashqai_1027_gao__port_506286,147_1027_gao__port_506124,c5_1027_gao__port_506172,q5_1027_gao__port_506206,giulia_1027_gao__port_506178,karl_1027_gao__port_506371,mehari_1027_gao__port_506076,911_1027_gao__port_506114,508_1027_gao__port_506329,idea_1027_gao__port_506122,megane_1027_gao__port_506220,ghibli_1027_gao__port_506174,touareg_1027_gao__port_506224,i10_1027_gao__port_506232,jumper_1027_gao__port_506234,classe_clk_1027_gao__port_506173,kuga_1027_gao__port_506181,ct_1027_gao__port_506323,leon_1027_gao__port_506326,ds5_1027_gao__port_506376,cordoba_1027_gao__port_506048,classe_cla_1027_gao__port_506400,jumpy_1027_gao__port_506179,avensis_1027_gao__port_506311,juke_1027_gao__port_506325,4008_1027_gao__port_506402,190_series_1027_gao__port_506051,serie_3_1027_gao__port_506294,q7_1027_gao__port_506318,glc_1027_gao__port_506303,grand_vitara_1027_gao__port_506175,s40_1027_gao__port_506099,toledo_1027_gao__port_506061,5008_1027_gao__port_506337,continental_1027_gao__port_506250,coupe_1027_gao__port_506082,iq_1027_gao__port_506166,407_1027_gao__port_506133,touran_1027_gao__port_506308,300c_1027_gao__port_506078,classe_gl_1027_gao__port_506340,vivaro_1027_gao__port_506310,sl_1027_gao__port_506100,elise_1027_gao__port_506121,1007_1027_gao__port_506070,i40_1027_gao__port_506218,bipper_tepee_1027_gao__port_506227,focus_1027_gao__port_506272,primera_1027_gao__port_506147,r4_1027_gao__port_506160,a8_1027_gao__port_506265,boxer_1027_gao__port_506202,s5_1027_gao__port_506222,r21_1027_gao__port_506093,c3_1027_gao__port_506257,santa_fe_1027_gao__port_506208,m4_1027_gao__port_506344,safrane_1027_gao__port_506077,classe_gle_1027_gao__port_506395,0_1027_gao__port_506094,ix35_1027_gao__port_506219,carens_1027_gao__port_506298,classe_a_1027_gao__port_506339,ix20_1027_gao__port_506343,note_1027_gao__port_506365,a5_1027_gao__port_506200,sx4_1027_gao__port_506348,sandero_1027_gao__port_506198,3008_1027_gao__port_506385,q50_1027_gao__port_506239,latitude_1027_gao__port_506236,v40_1027_gao__port_506391,xsara_1027_gao__port_506087,grand_c_max_1027_gao__port_506342,swift_1027_gao__port_506149,serie_1_1027_gao__port_506184,xc70_1027_gao__port_506393,master_1027_gao__port_506203,clio_1027_gao__port_506280,duster_1027_gao__port_506216,traveller_1027_gao__port_506403,tipo_1027_gao__port_506355,rav_4_1027_gao__port_506332,coccinelle_1027_gao__port_506259,spacetourer_1027_gao__port_506401,xe_1027_gao__port_506357,ds3_1027_gao__port_506324,mx_5_1027_gao__port_506098,land_cruiser_1027_gao__port_506315,classe_b_1027_gao__port_506335,806_1027_gao__port_506088,rx_8_1027_gao__port_506046,spark_1027_gao__port_506185,6_1027_gao__port_506171,bravo_1027_gao__port_506080,nx_1027_gao__port_506345,sharan_1027_gao__port_506347,x_type_1027_gao__port_506067,jimny_1027_gao__port_506233,wrangler_1027_gao__port_506225,c_crosser_1027_gao__port_506312,v70_1027_gao__port_506278,classe_e_1027_gao__port_506300,classe_v_1027_gao__port_506258,m3_1027_gao__port_506182,abarth_500_1027_gao__port_506226,serie_6_1027_gao__port_506262,modus_1027_gao__port_506146,3_1027_gao__port_506113,405_1027_gao__port_506108,allroad_1027_gao__port_506297,auris_1027_gao__port_506322,galaxy_1027_gao__port_506143,giulietta_1027_gao__port_506363,106_1027_gao__port_506073,classe_m_1027_gao__port_506154,espace_1027_gao__port_506313,panda_1027_gao__port_506189,rcz_1027_gao__port_506197,4007_1027_gao__port_506162,classe_cl_1027_gao__port_506249,leaf_1027_gao__port_506139,octavia_1027_gao__port_506237,ds4_1027_gao__port_506336,freelander_1027_gao__port_506084,evasion_1027_gao__port_506109,punto_1027_gao__port_506106,2cv_1027_gao__port_506045,x4_1027_gao__port_506392,antara_1027_gao__port_506247,murano_1027_gao__port_506316,alto_1027_gao__port_506201,meriva_1027_gao__port_506353,orlando_1027_gao__port_506305,new_beetle_1027_gao__port_506050,306_1027_gao__port_506145,tiguan_1027_gao__port_506362,s_type_1027_gao__port_506101,c1_1027_gao__port_506128,vectra_1027_gao__port_506044,outlander_1027_gao__port_506317,307_1027_gao__port_506074,a6_s6_1027_gao__port_506134,nemo_combi_1027_gao__port_506196,berlingo_1027_gao__port_506194,partner_1027_gao__port_506285,cayenne_1027_gao__port_506177,quattroporte_1027_gao__port_506240,c_max_1027_gao__port_506282,fabia_1027_gao__port_506396,cx_3_1027_gao__port_506281,x_trail_1027_gao__port_506264,scirocco_1027_gao__port_506276,matiz_1027_gao__port_506144,tigra_1027_gao__port_506069,escort_1027_gao__port_506091,c2_1027_gao__port_506081,mini_1027_gao__port_506168,i30_1027_gao__port_506291,picanto_1027_gao__port_506238,mito_1027_gao__port_506072,impreza_1027_gao__port_506085,kangoo_1027_gao__port_506235,a4_1027_gao__port_506193,cayman_1027_gao__port_506268,sportage_1027_gao__port_506148,up_1027_gao__port_506356,optima_1027_gao__port_506386,defender_1027_gao__port_506229,serie_2_1027_gao__port_506256,edge_1027_gao__port_506187,r19_1027_gao__port_506110,jetta_1027_gao__port_506304,eos_1027_gao__port_506115,accord_1027_gao__port_506214,yaris_1027_gao__port_506334,classe_cls_1027_gao__port_506289,polo_1027_gao__port_506361,serie_4_1027_gao__port_506366,mini_cabriolet_1027_gao__port_506204,prius_1027_gao__port_506190,lodgy_1027_gao__port_506188,serie_7_1027_gao__port_506307,c15_1027_gao__port_506055,kadjar_1027_gao__port_506389,insignia_1027_gao__port_506364,308_1027_gao__port_506279,roomster_1027_gao__port_506241,80_1027_gao__port_506057,309_1027_gao__port_506063,tucson_1027_gao__port_506320,x3_1027_gao__port_506212,xf_1027_gao__port_506263,2008_1027_gao__port_506394,passat_1027_gao__port_506306,compass_1027_gao__port_506260,twingo_1027_gao__port_506309,micra_1027_gao__port_506221,golf_1027_gao__port_506155,soul_1027_gao__port_506176,rapid_1027_gao__port_506398,forester_1027_gao__port_506360,slk_1027_gao__port_506210,forfour_1027_gao__port_506341,serie_5_1027_gao__port_506209,xj_1027_gao__port_506170,pajero_1027_gao__port_506097,agila_1027_gao__port_506119,a6_1027_gao__port_506163,fox_1027_gao__port_506092,boxster_1027_gao__port_506267,altea_1027_gao__port_506246,samurai_1027_gao__port_506047,trax_1027_gao__port_506296,getz_1027_gao__port_506058,cherokee_1027_gao__port_506269,koleos_1027_gao__port_506378,z_series_1027_gao__port_506123,ecosport_1027_gao__port_506271,space_star_1027_gao__port_506277,rs3_sportback_1027_gao__port_506207,civic_1027_gao__port_506141,talisman_1027_gao__port_506390,f_pace_1027_gao__port_506314,classe_c_1027_gao__port_506299,tt_1027_gao__port_506075,pathfinder_1027_gao__port_506183,156_1027_gao__port_506157,cx_5_1027_gao__port_506228,scenic_1027_gao__port_506255,yeti_1027_gao__port_506358,mustang_1027_gao__port_506053,stilo_1027_gao__port_506060,ateca_1027_gao__port_506382,fiorino_1027_gao__port_506217,classe_glk_1027_gao__port_506290,fortwo_1027_gao__port_506230,cruze_1027_gao__port_506186,107_1027_gao__port_506213,aygo_1027_gao__port_506248,rx_1027_gao__port_506354,500_1027_gao__port_506245,bora_1027_gao__port_506104,transit_1027_gao__port_506111,pt_cruiser_1027_gao__port_506054,patrol_1027_gao__port_506068,r8_1027_gao__port_506156,xm_1027_gao__port_506102,s60_1027_gao__port_506191,aveo_1027_gao__port_506158,captiva_1027_gao__port_506159,ax_1027_gao__port_506153,rexton_1027_gao__port_506107,camaro_1027_gao__port_506056,ypsilon_1027_gao__port_506131,delta_1027_gao__port_506165,c4_1027_gao__port_506370,zx_1027_gao__port_506161,verso_1027_gao__port_506242,superb_1027_gao__port_506327,r5_1027_gao__port_506253,caddy_1027_gao__port_506330,x5_1027_gao__port_506243,f_type_1027_gao__port_506231,fusion_1027_gao__port_506096,dokker_1027_gao__port_506331,205_1027_gao__port_506062,macan_1027_gao__port_506195,tourneo_1027_gao__port_506369,108_1027_gao__port_506384,9_3_1027_gao__port_506071,mondeo_1027_gao__port_506116,cr_v_1027_gao__port_506164,c30_1027_gao__port_506090,pulsar_1027_gao__port_506397,ibiza_1027_gao__port_506273,a1_1027_gao__port_506338,matrix_1027_gao__port_506140,carnival_1027_gao__port_506136,xantia_1027_gao__port_506086,terrano_1027_gao__port_506083,q3_1027_gao__port_506275,hr_v_1027_gao__port_506283,expert_1027_gao__port_506142,multivan_1027_gao__port_506383,venga_1027_gao__port_506380,scudo_1027_gao__port_506129,laguna_1027_gao__port_506368,vel_satis_1027_gao__port_506130,b_max_1027_gao__port_506367,ignis_1027_gao__port_506292,159_1027_gao__port_506064,grande_punto_1027_gao__port_506138,logan_1027_gao__port_506167,s_max_1027_gao__port_506223,caravelle_1027_gao__port_506351,adam_1027_gao__port_506079,406_1027_gao__port_506132,q30_1027_gao__port_506293,almera_1027_gao__port_506089,corsa_1027_gao__port_506095,corolla_1027_gao__port_506120,xc60_1027_gao__port_506388,viano_1027_gao__port_506211,pro_cee_d_1027_gao__port_506274,a3_1027_gao__port_506321,v50_1027_gao__port_506150,voyager_1027_gao__port_506169,corvette_1027_gao__port_506049,rio_1027_gao__port_506379,jazz_1027_gao__port_506252,200_1027_gao__port_506112,tts_1027_gao__port_506199,zafira_1027_gao__port_506287,asx_1027_gao__port_506266,607_1027_gao__port_506118,207_1027_gao__port_506103,classe_s_1027_gao__port_506301,c6_1027_gao__port_506105,express_1027_gao__port_506137,classe_gla_1027_gao__port_506352,v60_1027_gao__port_506333,ka_1027_gao__port_506180,range_rover_1027_gao__port_506254,discovery_1027_gao__port_506375,classe_r_1027_gao__port_506270,transporter_1027_gao__port_506319,cee_d_1027_gao__port_506288,zoe_1027_gao__port_506244,i20_1027_gao__port_506284,gtv_1027_gao__port_506059,s4_avant_1027_gao__port_506261,x1_1027_gao__port_506372,autres_1027_gao__port_506127,208_1027_gao__port_506359,c8_1027_gao__port_506135,astra_1027_gao__port_506215,2_1027_gao__port_506151,doblo_1027_gao__port_506251,807_1027_gao__port_506152,206_1027_gao__port_506126,a7_1027_gao__port_506373,renegade_1027_gao__port_506346', 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'svm_portfolios_learning': '506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}
 Update svm_hashtag_type_desc : 3390
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (3390)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27))
To loadFromThcl() : net_3390
begin to check gpu status 
inside check gpu memory
havn't enough memory gpu , need / 2500
l 3632 free memory gpu now : 1428
wait 20 seconds
l 3637 free memory gpu now : 1428  max_wait_temp :  1 max_wait :  0
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (3390)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27))
 param : <mtr.database_queries.descriptor_queries.ParamDescriptorType object at 0x7fd9865af6a0>, param.caffemodel : car_360_1027
None
 mean_file_type : 
 mean_file_path : 
 prototxt_file_path : 
 model : car_360_1027
 Inside get_net  
 Inside get_net before cache_data_model 
model_param file didn't exist 
 Inside get_net before  CDM.load_model_par_type  
model_name : car_360_1027
model_type : caffe
list file need :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file exist in s3 :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file manque in s3 : 
[]
local folder  : /data/models_weight/car_360_1027


/data/models_weight/car_360_1027/caffemodel
size_local : 542944640 size in s3 : 542944640
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43
caffemodel already exist and didn't need to update


/data/models_weight/car_360_1027/deploy_conv_normal.prototxt
size_local : 4626 size in s3 : 4626
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42
deploy_conv_normal.prototxt already exist and didn't need to update


/data/models_weight/car_360_1027/deploy_fc.prototxt
size_local : 1132 size in s3 : 1132
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43
deploy_fc.prototxt already exist and didn't need to update


/data/models_weight/car_360_1027/deploy.prototxt
size_local : 5654 size in s3 : 5654
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42
deploy.prototxt already exist and didn't need to update


/data/models_weight/car_360_1027/mean.npy
size_local : 1572944 size in s3 : 1572944
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55
mean.npy already exist and didn't need to update


/data/models_weight/car_360_1027/synset_words.txt
size_local : 13687 size in s3 : 13687
create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43
synset_words.txt already exist and didn't need to update
 Inside get_net after CDM.load_model_par_type  
  After if not only_with_local_cache:
/home/admin/workarea/install/caffe_cuda8_python3/python/:/home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/
Here before set mode gpu
Doing nothing but we could set mode gpu
after set mode gpu
 prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt
 caffemodel_filename : /data/models_weight/car_360_1027/caffemodel
now we set caffe to gpu mode


 before predict
begin to check gpu status 
inside check gpu memory
havn't enough memory gpu , need / 2500
l 3632 free memory gpu now : 1428
wait 20 seconds
l 3637 free memory gpu now : 1428  max_wait_temp :  1 max_wait :  0
dict_keys(['prob', 'pool5'])
time used to do the prepocess of the images : 0.013667106628417969
time used to do the prediction : 0.1588115692138672


save descriptor for thcl : 355
(1, 512, 7, 7)
Got the blobs of the net
 to insert :  
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
code_as_byte_string:b'0000000000'|
time to traite the descriptors :  0.05398750305175781
Testing : ['916235064']
 In select_photos_meta_from_ids: 
        SELECT photo_id, url, FROM_UNIXTIME(uploaded_at), latitude, longitude, text
        FROM MTRBack.photos
        WHERE photo_id IN (916235064) 
Catched exception !
Connect or reconnect !
result : {916235064: {'photo_id': 916235064, 'url': 'https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/2017/10/14/6293d1bb790dc6902450e7c572b7d10b.jpg', 'latitude': 0.0, 'longitude': 0.0, 'text': None}}
list_photo_exists : [916235064]
storage_type for insertDescriptorsMulti : 1
To insert : 916235064
time to insert the descriptors : 0.8248546123504639






After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 0 keys : dict_keys([])
Inside saveOutput : final : False verbose : True
time used to find the portfolios of the photos
select button_legend_list from MTRDatou.classification_theme where id = 355

 SAVE THCL, output : {'916235064': [[('916235064', 'c_elysee_1027_gao__port_506302', 0.0018815483, 332, '355'), ('916235064', 'mokka_1027_gao__port_506374', 0.001163604, 332, '355'), ('916235064', 'captur_1027_gao__port_506399', 0.0008158389, 332, '355'), ('916235064', 'sorento_1027_gao__port_506192', 0.001177412, 332, '355'), ('916235064', 'navara_1027_gao__port_506205', 0.0025848544, 332, '355'), ('916235064', 'xc90_1027_gao__port_506350', 0.0041699945, 332, '355'), ('916235064', 'saxo_1027_gao__port_506052', 0.003481308, 332, '355'), ('916235064', 'trafic_1027_gao__port_506295', 0.007366657, 332, '355'), ('916235064', 'punto_evo_1027_gao__port_506066', 0.0021888285, 332, '355'), ('916235064', '5_1027_gao__port_506117', 0.00057978625, 332, '355'), ('916235064', '250_1027_gao__port_506065', 0.004591738, 332, '355'), ('916235064', 'd_max_1027_gao__port_506125', 0.0031584764, 332, '355'), ('916235064', 'panamera_1027_gao__port_506387', 0.00225087, 332, '355'), ('916235064', 'alhambra_1027_gao__port_506381', 0.005320407, 332, '355'), ('916235064', 'x6_1027_gao__port_506349', 0.0011000332, 332, '355'), ('916235064', 'vitara_1027_gao__port_506328', 0.005402056, 332, '355'), ('916235064', 'fiesta_1027_gao__port_506377', 0.0039191414, 332, '355'), ('916235064', 'qashqai_1027_gao__port_506286', 0.0014789115, 332, '355'), ('916235064', '147_1027_gao__port_506124', 0.0019777853, 332, '355'), ('916235064', 'c5_1027_gao__port_506172', 0.0012441913, 332, '355'), ('916235064', 'q5_1027_gao__port_506206', 0.0015050942, 332, '355'), ('916235064', 'giulia_1027_gao__port_506178', 0.0021694207, 332, '355'), ('916235064', 'karl_1027_gao__port_506371', 0.002708186, 332, '355'), ('916235064', 'mehari_1027_gao__port_506076', 0.004702921, 332, '355'), ('916235064', '911_1027_gao__port_506114', 0.0019418036, 332, '355'), ('916235064', '508_1027_gao__port_506329', 0.0009586301, 332, '355'), ('916235064', 'idea_1027_gao__port_506122', 0.0007699915, 332, '355'), ('916235064', 'megane_1027_gao__port_506220', 0.0019471323, 332, '355'), ('916235064', 'ghibli_1027_gao__port_506174', 0.0013725854, 332, '355'), ('916235064', 'touareg_1027_gao__port_506224', 0.001620381, 332, '355'), ('916235064', 'i10_1027_gao__port_506232', 0.001392535, 332, '355'), ('916235064', 'jumper_1027_gao__port_506234', 0.010043922, 332, '355'), ('916235064', 'classe_clk_1027_gao__port_506173', 0.0010794732, 332, '355'), ('916235064', 'kuga_1027_gao__port_506181', 0.00084472896, 332, '355'), ('916235064', 'ct_1027_gao__port_506323', 0.0012521337, 332, '355'), ('916235064', 'leon_1027_gao__port_506326', 0.0025845314, 332, '355'), ('916235064', 'ds5_1027_gao__port_506376', 0.0012431227, 332, '355'), ('916235064', 'cordoba_1027_gao__port_506048', 0.0028650027, 332, '355'), ('916235064', 'classe_cla_1027_gao__port_506400', 0.0012951111, 332, '355'), ('916235064', 'jumpy_1027_gao__port_506179', 0.010338031, 332, '355'), ('916235064', 'avensis_1027_gao__port_506311', 0.001876917, 332, '355'), ('916235064', 'juke_1027_gao__port_506325', 0.0011343885, 332, '355'), ('916235064', '4008_1027_gao__port_506402', 0.0015759068, 332, '355'), ('916235064', '190_series_1027_gao__port_506051', 0.003980474, 332, '355'), ('916235064', 'serie_3_1027_gao__port_506294', 0.0028743658, 332, '355'), ('916235064', 'q7_1027_gao__port_506318', 0.0023355528, 332, '355'), ('916235064', 'glc_1027_gao__port_506303', 0.0012108157, 332, '355'), ('916235064', 'grand_vitara_1027_gao__port_506175', 0.0011447468, 332, '355'), ('916235064', 's40_1027_gao__port_506099', 0.002234116, 332, '355'), ('916235064', 'toledo_1027_gao__port_506061', 0.0017466153, 332, '355'), ('916235064', '5008_1027_gao__port_506337', 0.004699659, 332, '355'), ('916235064', 'continental_1027_gao__port_506250', 0.0021917624, 332, '355'), ('916235064', 'coupe_1027_gao__port_506082', 0.0022631157, 332, '355'), ('916235064', 'iq_1027_gao__port_506166', 0.0018174584, 332, '355'), ('916235064', '407_1027_gao__port_506133', 0.0009057366, 332, '355'), ('916235064', 'touran_1027_gao__port_506308', 0.0020403687, 332, '355'), ('916235064', '300c_1027_gao__port_506078', 0.0025334551, 332, '355'), ('916235064', 'classe_gl_1027_gao__port_506340', 0.0044893967, 332, '355'), ('916235064', 'vivaro_1027_gao__port_506310', 0.0034251015, 332, '355'), ('916235064', 'sl_1027_gao__port_506100', 0.0031354812, 332, '355'), ('916235064', 'elise_1027_gao__port_506121', 0.0010255001, 332, '355'), ('916235064', '1007_1027_gao__port_506070', 0.001535584, 332, '355'), ('916235064', 'i40_1027_gao__port_506218', 0.0005915497, 332, '355'), ('916235064', 'bipper_tepee_1027_gao__port_506227', 0.0040291613, 332, '355'), ('916235064', 'focus_1027_gao__port_506272', 0.0011587279, 332, '355'), ('916235064', 'primera_1027_gao__port_506147', 0.00121587, 332, '355'), ('916235064', 'r4_1027_gao__port_506160', 0.014964086, 332, '355'), ('916235064', 'a8_1027_gao__port_506265', 0.0011321352, 332, '355'), ('916235064', 'boxer_1027_gao__port_506202', 0.010544675, 332, '355'), ('916235064', 's5_1027_gao__port_506222', 0.001198462, 332, '355'), ('916235064', 'r21_1027_gao__port_506093', 0.0041856864, 332, '355'), ('916235064', 'c3_1027_gao__port_506257', 0.0023637032, 332, '355'), ('916235064', 'santa_fe_1027_gao__port_506208', 0.001632383, 332, '355'), ('916235064', 'm4_1027_gao__port_506344', 0.0015569557, 332, '355'), ('916235064', 'safrane_1027_gao__port_506077', 0.001395915, 332, '355'), ('916235064', 'classe_gle_1027_gao__port_506395', 0.0021980773, 332, '355'), ('916235064', '0_1027_gao__port_506094', 0.00882733, 332, '355'), ('916235064', 'ix35_1027_gao__port_506219', 0.0014615335, 332, '355'), ('916235064', 'carens_1027_gao__port_506298', 0.0008825935, 332, '355'), ('916235064', 'classe_a_1027_gao__port_506339', 0.0024717082, 332, '355'), ('916235064', 'ix20_1027_gao__port_506343', 0.0010093267, 332, '355'), ('916235064', 'note_1027_gao__port_506365', 0.0015963495, 332, '355'), ('916235064', 'a5_1027_gao__port_506200', 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'q30_1027_gao__port_506293', 0.0009717226, 332, '355'), ('916235064', 'almera_1027_gao__port_506089', 0.0010240373, 332, '355'), ('916235064', 'corsa_1027_gao__port_506095', 0.0025206148, 332, '355'), ('916235064', 'corolla_1027_gao__port_506120', 0.002682143, 332, '355'), ('916235064', 'xc60_1027_gao__port_506388', 0.0018987743, 332, '355'), ('916235064', 'viano_1027_gao__port_506211', 0.002694328, 332, '355'), ('916235064', 'pro_cee_d_1027_gao__port_506274', 0.0008320495, 332, '355'), ('916235064', 'a3_1027_gao__port_506321', 0.0037384813, 332, '355'), ('916235064', 'v50_1027_gao__port_506150', 0.0007920092, 332, '355'), ('916235064', 'voyager_1027_gao__port_506169', 0.003052671, 332, '355'), ('916235064', 'corvette_1027_gao__port_506049', 0.0037229431, 332, '355'), ('916235064', 'rio_1027_gao__port_506379', 0.0017740934, 332, '355'), ('916235064', 'jazz_1027_gao__port_506252', 0.0015307243, 332, '355'), ('916235064', '200_1027_gao__port_506112', 0.0040875985, 332, '355'), ('916235064', 'tts_1027_gao__port_506199', 0.0011862431, 332, '355'), ('916235064', 'zafira_1027_gao__port_506287', 0.0026956003, 332, '355'), ('916235064', 'asx_1027_gao__port_506266', 0.0011407505, 332, '355'), ('916235064', '607_1027_gao__port_506118', 0.0012531118, 332, '355'), ('916235064', '207_1027_gao__port_506103', 0.0015149772, 332, '355'), ('916235064', 'classe_s_1027_gao__port_506301', 0.0031656493, 332, '355'), ('916235064', 'c6_1027_gao__port_506105', 0.0017348656, 332, '355'), ('916235064', 'express_1027_gao__port_506137', 0.016724762, 332, '355'), ('916235064', 'classe_gla_1027_gao__port_506352', 0.001825757, 332, '355'), ('916235064', 'v60_1027_gao__port_506333', 0.0021461071, 332, '355'), ('916235064', 'ka_1027_gao__port_506180', 0.0014152351, 332, '355'), ('916235064', 'range_rover_1027_gao__port_506254', 0.002055239, 332, '355'), ('916235064', 'discovery_1027_gao__port_506375', 0.0022964566, 332, '355'), ('916235064', 'classe_r_1027_gao__port_506270', 0.0013944281, 332, '355'), ('916235064', 'transporter_1027_gao__port_506319', 0.011967104, 332, '355'), ('916235064', 'cee_d_1027_gao__port_506288', 0.0010548886, 332, '355'), ('916235064', 'zoe_1027_gao__port_506244', 0.0020715825, 332, '355'), ('916235064', 'i20_1027_gao__port_506284', 0.0017869307, 332, '355'), ('916235064', 'gtv_1027_gao__port_506059', 0.0057222988, 332, '355'), ('916235064', 's4_avant_1027_gao__port_506261', 0.0027663333, 332, '355'), ('916235064', 'x1_1027_gao__port_506372', 0.0017145933, 332, '355'), ('916235064', 'autres_1027_gao__port_506127', 0.0048249466, 332, '355'), ('916235064', '208_1027_gao__port_506359', 0.001869061, 332, '355'), ('916235064', 'c8_1027_gao__port_506135', 0.0012580788, 332, '355'), ('916235064', 'astra_1027_gao__port_506215', 0.0012626554, 332, '355'), ('916235064', '2_1027_gao__port_506151', 0.00092456315, 332, '355'), ('916235064', 'doblo_1027_gao__port_506251', 0.007465632, 332, '355'), ('916235064', '807_1027_gao__port_506152', 0.00072904775, 332, '355'), ('916235064', '206_1027_gao__port_506126', 0.0010386885, 332, '355'), ('916235064', 'a7_1027_gao__port_506373', 0.0006911918, 332, '355'), ('916235064', 'renegade_1027_gao__port_506346', 0.002141824, 332, '355')]]}


begin to insert list_values into class_photo_scores : 
length of list_valuse in save_photo_hashtag_id_thcl_score : 0
insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score)
time used for this insertion : 1.8358230590820312e-05

save missing photos in datou_result :

time spend for datou_step_exec : 46.18425416946411
time spend to save output : 1.7864046096801758
total time spend for step 1 : 47.97065877914429

step2:argmax
Sun Mar 16 19:37:56 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'dict'>

Here we display some param of map_info ! 
map_filenames : {'temp/1742150228_446243_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg': 916235064}
map_photo_id_path_extension : {916235064: {'path': 'temp/1742150228_446243_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step Argmax !
calculate argmax for thcl : 355

After datou_step_exec type output : <class 'dict'>
map_portfolio_photo : len 0 keys : dict_keys([])
Inside saveOutput : final : True verbose : True
 photo_id : 916235064 output[photo_id] :  [('916235064', 'c15_1027_gao__port_506055', 0.017710287, 332, '355'), 'temp/1742150228_446243_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']


begin to insert list_values into photo_hahstag_ids : 
length of list_valuse in save_photo_hashtag_id_type : 1
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
insert ignore into MTRBack.photo_hashtag_ids (photo_id, hashtag_id, type) values (%s,%s,%s)
 first line : ('916235064', '2049863950', '332')
...
 last line : ('916235064', '2049863950', '332')
time used for this insertion : 0.012841463088989258


begin to insert list_values into class_photo_scores : 
length of list_valuse in save_photo_hashtag_id_thcl_score : 1
insert into MTRPhoto.class_photo_score (thcl, photo_id, hashtag_id, score) values (%s,%s,%s,%s) on duplicate key update score = values(score)
time used for this insertion : 0.015041351318359375
len list_finale : 1, len picture : 1


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 1
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('2', None, '916235064', 'c15_1027_gao__port_506055', None, None, '2049863950', '0.017710287', None)]
time used for this insertion : 0.01399540901184082
saving photo_ids in datou_result
photo id not in port


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 0
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : []
time used for this insertion : 4.5299530029296875e-06

save missing photos in datou_result :

time spend for datou_step_exec : 0.003847837448120117
time spend to save output : 0.042337894439697266
total time spend for step 2 : 0.04618573188781738

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 2
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 2
 output : {'916235064': [('916235064', 'c15_1027_gao__port_506055', 0.017710287, 332, '355'), 'temp/1742150228_446243_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']}

############################### TEST tfhub2  ################################
TEST TFHUB2
######################## test with use_multi_inputs=0 ########################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4567

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=4567
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 4567
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=4567
# VR 17-11-17 : to create in DB !
Here we check the datou graph and we reorder steps !
 Tree builded and cycle checked, now we need to re-order the steps ! 
We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue
We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id)
 DONE and to test : checkNoCycle !
Here we check the consistency of inputs/outputs number between the given ones and the db !
eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering !
WARNING : step 12835 tfhub_classification2 is not linked in the step_by_step architecture !
WARNING : step 12836 argmax is not linked in the step_by_step architecture !
Number of inputs / outputs for each step checked !
Here we check the consistency of outputs/inputs types during steps connections
eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : tfhub_classification2, argmax
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos ph
        WHERE photo_id IN (1171252784,1171252764,1171252487) 
 Found this number of photos: 3
##### Call download_photos : nb_thread : 5
begin to download photo : 1171252487
begin to download photo : 1171252764
begin to download photo : 1171252784
download finish for photo 1171252784
download finish for photo 1171252487
download finish for photo 1171252764
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3
##### After load_data_input 
time to download the photos : 0.2086637020111084
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
we should then remove the video here, and this would fix the bug of datou_current ! 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  2
step1:tfhub_classification2
Sun Mar 16 19:37:57 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/1742150276_446243_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg': 1171252784, 'temp/1742150276_446243_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg': 1171252487, 'temp/1742150276_446243_1171252764_29d5179a892cc50aadc9d67245534b59.jpg': 1171252764}
map_photo_id_path_extension : {1171252784: {'path': 'temp/1742150276_446243_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg', 'extension': 'jpg'}, 1171252487: {'path': 'temp/1742150276_446243_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg', 'extension': 'jpg'}, 1171252764: {'path': 'temp/1742150276_446243_1171252764_29d5179a892cc50aadc9d67245534b59.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step TFHub with tf2 !
multi_thcl or not :False
multi_thcl_cond or not :False
dic_thcl :  {'3609': 1}
we are using the classfication for only one thcl
3609


begin to check gpu status 
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 1428
wait 20 seconds
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 1428
wait 20 seconds
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 1428
wait 20 seconds
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 1428
wait 20 seconds
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 1428
wait 20 seconds
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 1428
wait 20 seconds
2025-03-16 19:40:05.475801: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2025-03-16 19:40:05.476369: 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
2025-03-16 19:40:05.476481: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:40:05.476532: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 19:40:05.478100: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 19:40:05.478162: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 19:40:05.480139: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 19:40:05.481396: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 19:40:05.485098: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 19:40:05.486006: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 19:40:05.486389: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2025-03-16 19:40:05.515121: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz
2025-03-16 19:40:05.516729: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd6f4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2025-03-16 19:40:05.516760: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2025-03-16 19:40:05.519765: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x17a5fa00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-03-16 19:40:05.519811: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2025-03-16 19:40:05.520617: 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
2025-03-16 19:40:05.520745: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:40:05.520780: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2025-03-16 19:40:05.520885: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2025-03-16 19:40:05.520931: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2025-03-16 19:40:05.520989: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2025-03-16 19:40:05.521052: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2025-03-16 19:40:05.521115: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2025-03-16 19:40:05.522281: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2025-03-16 19:40:05.522364: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2025-03-16 19:40:05.522415: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2025-03-16 19:40:05.522430: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2025-03-16 19:40:05.522447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2025-03-16 19:40:05.523923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5)
l 3637 free memory gpu now : 1428  max_wait_temp :  6 max_wait :  5
1 Physical GPUs, 1 Logical GPUs

tagging for thcl : 3609
To do loadFromThcl(), then load ParamDescType : thcl3609
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (3609) 
 thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}]
 thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}
 Update svm_hashtag_type_desc : 5832
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (5832)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22))
model_name : tfhub_19_06_2023
model_param file didn't exist 
model_name : tfhub_19_06_2023
model_type : tf_classification2
list file need :
['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5']
file exist in s3 :
['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5']
file manque in s3 : 
[]
2025-03-16 19:40:12.427377: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.02G (3246391296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2025-03-16 19:40:12.428005: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.72G (2921752064 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2025-03-16 19:40:12.428565: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.45G (2629576704 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2025-03-16 19:40:12.429138: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.20G (2366618880 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2025-03-16 19:40:12.429695: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.98G (2129957120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2025-03-16 19:40:12.430210: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.79G (1916961536 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2025-03-16 19:40:12.430723: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.61G (1725265408 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2025-03-16 19:40:12.431306: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.45G (1552738816 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
local folder  : /data/models_weight/tfhub_19_06_2023


/data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png
size_local : 57753 size in s3 : 57753
create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15
Confusion_Matrix.png already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg
size_local : 79724 size in s3 : 79724
create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20
Precision_Recall_jrm.jpg already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg
size_local : 83556 size in s3 : 83556
create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15
Precision_Recall_pcm.jpg already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg
size_local : 74107 size in s3 : 74107
create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20
Precision_Recall_pcnc.jpg already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg
size_local : 72705 size in s3 : 72705
create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20
Precision_Recall_pehd.jpg already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg
size_local : 70874 size in s3 : 70874
create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15
Precision_Recall_tapis_vide.jpg already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/Result_Summary.txt
size_local : 642 size in s3 : 642
create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22
Result_Summary.txt already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/checkpoint
size_local : 99 size in s3 : 99
create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22
checkpoint already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002
size_local : 216488 size in s3 : 216488
create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22
model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002
size_local : 32279708 size in s3 : 32279708
create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21
model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index
size_local : 43546 size in s3 : 43546
create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22
model_checkpoint.ckpt.index already exist and didn't need to update


/data/models_weight/tfhub_19_06_2023/model_weights.h5
size_local : 16499144 size in s3 : 16499144
create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15
model_weights.h5 already exist and didn't need to update
ERROR in datou_step_exec, will save and exit !
assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse
  File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec
    output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id)
  File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec
    return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose)
  File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3138, in datou_step_tfhub2
    this_model = model_evaluator(model_name, model_type=model_type, fc_size=fc_size,use_multi_inputs=use_multi_inputs)
  File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 156, in __init__
    self.model, _, _ = create_tfhub_model(module_handle=self.tfhub_module,
  File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 77, in create_tfhub_model
    hub.KerasLayer(module_handle, trainable=do_fine_tuning, name="module"),
  File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 152, in __init__
    self._func = load_module(handle, tags, self._load_options)
  File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 421, in load_module
    return module_v2.load(handle, tags=tags, options=set_load_options)
  File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_v2.py", line 106, in load
    obj = tf.compat.v1.saved_model.load_v2(module_path, tags=tags)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 578, in load
    return load_internal(export_dir, tags)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 602, in load_internal
    loader = loader_cls(object_graph_proto,
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 123, in __init__
    self._load_all()
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 134, in _load_all
    self._load_nodes()
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 264, in _load_nodes
    node, setter = self._recreate(proto, node_id)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 370, in _recreate
    return factory[kind]()
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 363, in <lambda>
    "variable": lambda: self._recreate_variable(proto.variable),
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 426, in _recreate_variable
    return variables.Variable(
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 261, in __call__
    return cls._variable_v2_call(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 243, in _variable_v2_call
    return previous_getter(
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 66, in getter
    return captured_getter(captured_previous, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 418, in uninitialized_variable_creator
    return resource_variable_ops.UninitializedVariable(**kwargs)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 263, in __call__
    return super(VariableMetaclass, cls).__call__(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 1795, in __init__
    handle = _variable_handle_from_shape_and_dtype(
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 174, in _variable_handle_from_shape_and_dtype
    gen_logging_ops._assert(  # pylint: disable=protected-access
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py", line 55, in _assert
    _ops.raise_from_not_ok_status(e, name)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 6653, in raise_from_not_ok_status
    six.raise_from(core._status_to_exception(e.code, message), None)
  File "<string>", line 3, in raise_from

[1171252784, 1171252487, 1171252764]
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 4567 list_pids : [1171252784, 1171252487, 1171252764]


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 3
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4567', None, '1171252784', "[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 156 in test_tfhub2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 819 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2346 in datou_exec>, 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None), ('4567', None, '1171252487', "[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 156 in test_tfhub2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 819 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2346 in datou_exec>, 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None), ('4567', None, '1171252764', "[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 156 in test_tfhub2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 819 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2346 in datou_exec>, 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None)]
time used for this insertion : 0.01748514175415039
 save_final 
ERROR in last step tfhub_classification2, assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse

time spend for datou_step_exec : 135.8176577091217
time spend to save output : 0.023081302642822266
total time spend for step 0 : 135.84073901176453
need to delete datou_research and reload, so keep current state 1
caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 2
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 2
 output : None
probably due to empty image bug
ERROR
expected : {'1171252784': [(1171252784, 'jrm', 0.9677492, 4674, '3609'), 'temp/1687511175_1882837_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.9853587, 4674, '3609'), 'temp/1687511175_1882837_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262757, 4674, '3609'), 'temp/1687511175_1882837_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']}
got : None
######################## test with use_multi_inputs=1 ########################
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=4621

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=4621
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 4621
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=4621
# VR 17-11-17 : to create in DB !
Here we check the datou graph and we reorder steps !
 Tree builded and cycle checked, now we need to re-order the steps ! 
We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue
We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id)
 DONE and to test : checkNoCycle !
Here we check the consistency of inputs/outputs number between the given ones and the db !
eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering !
WARNING : step 12927 tfhub_classification2 is not linked in the step_by_step architecture !
WARNING : step 12928 argmax is not linked in the step_by_step architecture !
Number of inputs / outputs for each step checked !
Here we check the consistency of outputs/inputs types during steps connections
eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : tfhub_classification2, argmax
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos ph
        WHERE photo_id IN (1171291875,1171275372,1171275314) 
 Found this number of photos: 3
##### Call download_photos : nb_thread : 5
begin to download photo : 1171275314
begin to download photo : 1171275372
begin to download photo : 1171291875
download finish for photo 1171275314
download finish for photo 1171291875
download finish for photo 1171275372
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3
##### After load_data_input 
time to download the photos : 0.21540451049804688
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
we should then remove the video here, and this would fix the bug of datou_current ! 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  2
step1:tfhub_classification2
Sun Mar 16 19:40:13 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/1742150413_446243_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg': 1171275314, 'temp/1742150413_446243_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg': 1171291875, 'temp/1742150413_446243_1171275372_76d81364ff7df843bff095f45c07ba35.jpg': 1171275372}
map_photo_id_path_extension : {1171275314: {'path': 'temp/1742150413_446243_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg', 'extension': 'jpg'}, 1171291875: {'path': 'temp/1742150413_446243_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg', 'extension': 'jpg'}, 1171275372: {'path': 'temp/1742150413_446243_1171275372_76d81364ff7df843bff095f45c07ba35.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step TFHub with tf2 !
multi_thcl or not :False
multi_thcl_cond or not :False
dic_thcl :  {'3655': 1}
we are using the classfication for only one thcl
3655


begin to check gpu status 
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 12
wait 20 seconds
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 12
wait 20 seconds
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 12
wait 20 seconds
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 12
wait 20 seconds
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 12
wait 20 seconds
inside check gpu memory
havn't enough memory gpu , need / 3096
l 3632 free memory gpu now : 12
wait 20 seconds
l 3637 free memory gpu now : 12  max_wait_temp :  6 max_wait :  5
1 Physical GPUs, 1 Logical GPUs

tagging for thcl : 3655
To do loadFromThcl(), then load ParamDescType : thcl3655
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (3655) 
 thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}]
 thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}
 Update svm_hashtag_type_desc : 5862
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (5862)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29))
model_name : tfhub_18_7_2023
model_param file didn't exist 
model_name : tfhub_18_7_2023
model_type : tf_classification2
list file need :
['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5']
file exist in s3 :
['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5']
file manque in s3 : 
[]
local folder  : /data/models_weight/tfhub_18_7_2023


/data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png
size_local : 54360 size in s3 : 54360
create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28
Confusion_Matrix.png already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg
size_local : 72583 size in s3 : 72583
create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23
Precision_Recall_jrm.jpg already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg
size_local : 81681 size in s3 : 81681
create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17
Precision_Recall_pcm.jpg already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg
size_local : 79510 size in s3 : 79510
create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23
Precision_Recall_pcnc.jpg already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg
size_local : 59936 size in s3 : 59936
create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23
Precision_Recall_pehd.jpg already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg
size_local : 78974 size in s3 : 78974
create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17
Precision_Recall_tapis_vide.jpg already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/Result_Summary.txt
size_local : 642 size in s3 : 642
create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23
Result_Summary.txt already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/checkpoint
size_local : 99 size in s3 : 99
create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23
checkpoint already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002
size_local : 216529 size in s3 : 216529
create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17
model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002
size_local : 32279748 size in s3 : 32279748
create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19
model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index
size_local : 43546 size in s3 : 43546
create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19
model_checkpoint.ckpt.index already exist and didn't need to update


/data/models_weight/tfhub_18_7_2023/model_weights.h5
size_local : 16500868 size in s3 : 16500868
create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18
model_weights.h5 already exist and didn't need to update
ERROR in datou_step_exec, will save and exit !
assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse
  File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec
    output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id)
  File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec
    return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose)
  File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3138, in datou_step_tfhub2
    this_model = model_evaluator(model_name, model_type=model_type, fc_size=fc_size,use_multi_inputs=use_multi_inputs)
  File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 156, in __init__
    self.model, _, _ = create_tfhub_model(module_handle=self.tfhub_module,
  File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 62, in create_tfhub_model
    fe_layer = hub.KerasLayer(module_handle, trainable=do_fine_tuning, name="module",
  File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 152, in __init__
    self._func = load_module(handle, tags, self._load_options)
  File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 421, in load_module
    return module_v2.load(handle, tags=tags, options=set_load_options)
  File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_v2.py", line 106, in load
    obj = tf.compat.v1.saved_model.load_v2(module_path, tags=tags)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 578, in load
    return load_internal(export_dir, tags)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 602, in load_internal
    loader = loader_cls(object_graph_proto,
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 123, in __init__
    self._load_all()
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 134, in _load_all
    self._load_nodes()
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 264, in _load_nodes
    node, setter = self._recreate(proto, node_id)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 370, in _recreate
    return factory[kind]()
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 363, in <lambda>
    "variable": lambda: self._recreate_variable(proto.variable),
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 426, in _recreate_variable
    return variables.Variable(
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 261, in __call__
    return cls._variable_v2_call(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 243, in _variable_v2_call
    return previous_getter(
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 66, in getter
    return captured_getter(captured_previous, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 418, in uninitialized_variable_creator
    return resource_variable_ops.UninitializedVariable(**kwargs)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 263, in __call__
    return super(VariableMetaclass, cls).__call__(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 1795, in __init__
    handle = _variable_handle_from_shape_and_dtype(
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 174, in _variable_handle_from_shape_and_dtype
    gen_logging_ops._assert(  # pylint: disable=protected-access
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py", line 55, in _assert
    _ops.raise_from_not_ok_status(e, name)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 6653, in raise_from_not_ok_status
    six.raise_from(core._status_to_exception(e.code, message), None)
  File "<string>", line 3, in raise_from

[1171275314, 1171291875, 1171275372]
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 4621 list_pids : [1171275314, 1171291875, 1171275372]


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 3
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('4621', None, '1171275314', "[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 202 in test_tfhub2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 819 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2346 in datou_exec>, 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None), ('4621', None, '1171291875', "[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 202 in test_tfhub2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 819 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2346 in datou_exec>, 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None), ('4621', None, '1171275372', "[<FrameSummary file /home/admin/workarea/git/Velours/python/tests/python_tests.py, line 245 in <module>>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 3680 in test>, <FrameSummary file /home/admin/workarea/git/Velours/python/tests/datou_test.py, line 202 in test_tfhub2>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 819 in batchDatouExec>, <FrameSummary file /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py, line 2346 in datou_exec>, 'assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse']", '-1', '-1.0', '501120777', '1.0', None)]
time used for this insertion : 0.02022528648376465
 save_final 
ERROR in last step tfhub_classification2, assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse

time spend for datou_step_exec : 134.28580784797668
time spend to save output : 0.020699739456176758
total time spend for step 0 : 134.30650758743286
need to delete datou_research and reload, so keep current state 1
caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 2
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 2
 output : None
probably due to empty image bug
ERROR
expected : {'1171291875': [(1171291875, 'tapis_vide', 0.97062814, 4723, '3655'), 'temp/1691745841_1143057_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.9674145, 4723, '3655'), 'temp/1691745841_1143057_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96509415, 4723, '3655'), 'temp/1691745841_1143057_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']}
got : None
ERROR tfhub2 FAILED

############################### TEST ordonner  ################################
To do loadFromThcl(), then load ParamDescType : thcl358
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (358) 
 thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}]
 thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}
 Update svm_hashtag_type_desc : 3392
['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234']
51
SELECT hashtag_id,hashtag FROM MTRBack.hashtags where hashtag in ('FirstUploadExperveo_vignette__port_505674','CAR_EXTERIEUR_Roue__port_503398','FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486','FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485','CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465','CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198','CAR_EXTERIEUR_Face_avant_axe_droit__port_504451','CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235','FirstUploadExperveo_vin__port_505675','CAR_EXTERIEUR_cote_droite__port_504108','CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565','FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483','CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201','cartegrise_orientation__port_505064','CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217','CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531','CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218','CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214','CAR_EXTERIEUR_Angle_avant_droit__port_504087','FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484','CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563','CAR_EXTERIEUR_Angle_arriere_droit__port_504160','CAR_EXTERIEUR_arriere__port_504184','CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562','INTERIEUR_Compteur_kilometrique__port_503644','CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494','CAR_EXTERIEUR_Angle_arriere_gauche__port_504170','CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226','CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202','CAR_EXTERIEUR_moteur__port_503704','FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487','CAR_INTERIEUR_siege_arriere_class_1__port_506551','CAR_EXTERIEUR_avant__port_504146','CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215','CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225','CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564','FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482','CAR_INTERIEUR_coffre__port_503412','FirstUploadExperveo_rouetranche__port_505677','UploadPhotoImmatBest_class_1__port_505051','CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532','CAR_EXTERIEUR_angle_avant_gauche__port_504098','CAR_EXTERIEUR_face_avant_axe_gauche__port_504236','CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540','CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233','CAR_EXTERIEUR_roue_de_secour__port_503763','CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199','CAR_EXTERIEUR_cote_gauche__port_504017','CAR_INTERIEUR_avant_volant_class_1__port_506503','CAR_INTERIEUR_avant_volant_class_2__port_506504','CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'); 
51
dict_keys(['cartegrise_orientation__port_505064', 'car_exterieur_angle_arriere_droit_axe_arriere__port_504217', 'car_exterieur_angle_arriere_droit_axe_droit__port_504215', 'car_exterieur_angle_arriere_droit__port_504160', 'car_exterieur_angle_arriere_gauche_axe_arriere__port_504201', 'car_exterieur_angle_arriere_gauche_axe_gauche__port_504199', 'car_exterieur_angle_arriere_gauche__port_504170', 'car_exterieur_angle_avant_droit_axe_arriere__port_504226', 'car_exterieur_angle_avant_droit_axe_droit__port_504225', 'car_exterieur_angle_avant_droit__port_504087', 'car_exterieur_angle_avant_gauche_axe_avant__port_504235', 'car_exterieur_angle_avant_gauche_axe_gauche__port_504234', 'car_exterieur_angle_avant_gauche__port_504098', 'car_exterieur_arriere__port_504184', 'car_exterieur_avant__port_504146', 'car_exterieur_cote_droite__port_504108', 'car_exterieur_cote_droit_axe_arriere__port_504214', 'car_exterieur_cote_droit_axe_avant__port_504465', 'car_exterieur_cote_gauche_axe_arriere__port_504198', 'car_exterieur_cote_gauche_axe_avant__port_504233', 'car_exterieur_cote_gauche__port_504017', 'car_exterieur_face_arriere_axe_droit__port_504218', 'car_exterieur_face_arriere_axe_gauche__port_504202', 'car_exterieur_face_avant_axe_droit__port_504451', 'car_exterieur_face_avant_axe_gauche__port_504236', 'car_exterieur_moteur__port_503704', 'car_exterieur_roue_de_secour__port_503763', 'car_exterieur_roue__port_503398', 'car_interieur_avant_volant_class_1__port_506503', 'car_interieur_avant_volant_class_2__port_506504', 'car_interieur_avant_volant_class_6_boutonrond__port_506562', 'car_interieur_avant_volant_class_6_class_2__port_506563', 'car_interieur_avant_volant_class_6_ecrangrosplan__port_506564', 'car_interieur_avant_volant_class_6_levierdevitesse__port_506565', 'car_interieur_avant_vue-arriere_class_1__port_506531', 'car_interieur_avant_vue-arriere_class_2__port_506532', 'car_interieur_avant_vue_droite_habitacle_class_1__port_506540', 'car_interieur_avant_vue_gauche_habitacle_class_1__port_506494', 'car_interieur_coffre__port_503412', 'car_interieur_siege_arriere_class_1__port_506551', 'firstuploadexperveo_carrosseriegrosplan_carrosserie__port_506483', 'firstuploadexperveo_carrosseriegrosplan_class_6__port_506487', 'firstuploadexperveo_carrosseriegrosplan_morceauderoue__port_506484', 'firstuploadexperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'firstuploadexperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'firstuploadexperveo_carrosseriegrosplan_vindanslamoquette__port_506486', 'firstuploadexperveo_rouetranche__port_505677', 'firstuploadexperveo_vignette__port_505674', 'firstuploadexperveo_vin__port_505675', 'interieur_compteur_kilometrique__port_503644', 'uploadphotoimmatbest_class_1__port_505051'])
select photo_hashtag_type from MTRDatou.classification_theme where id = 358
thcl :  358
photo_hashtag_type :  337

                    SELECT phi.hashtag_id , phi.photo_id FROM MTRBack.photo_hashtag_ids phi,
                    MTRUser.mtr_portfolio_photos mp
                    where phi.type = 337 and phi.photo_id = mp.mtr_photo_id and mp.mtr_portfolio_id =510365;
{510365: [(917973295, 1), (917973297, 1), (917973302, 1), (917973293, 7), (917973296, 11), (917973300, 11), (917973286, 13), (917973289, 13), (917973301, 24), (917973285, 29), (917973290, 29), (917973299, 29), (917973304, 35), (917973287, 36), (917973298, 36), (917973305, 36), (917973292, 37), (917973291, 41), (917973303, 41), (917973294, 42), (917973288, 46)]}

############################### TEST rotate  ################################
test rotate only
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=230

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=230
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 230
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=230
# VR 17-11-17 : to create in DB !
Here we check the datou graph and we reorder steps !
 Tree builded and cycle checked, now we need to re-order the steps ! 
We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue
We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id)
 DONE and to test : checkNoCycle !
We are managing only one step so we do not consider checkConsistencyNbInputNbOutput !
We are managing only one step so we do not consider checkConsistencyTypeOutputInput !
no param json to modify
List Step Type Loaded in datou : rotate
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos ph
        WHERE photo_id IN (917849322) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 917849322
download finish for photo 917849322
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.14465022087097168
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
we should then remove the video here, and this would fix the bug of datou_current ! 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  1
step1:rotate
Sun Mar 16 19:42:34 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322}
map_photo_id_path_extension : {917849322: {'path': 'temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou_step_rotate !
We are in a linear step without datou_depend !
rotate photos of 90,180,270 degres
batch 1
select photo_id, hashtag_id, `type`, x0, x1, y0, y1, score, id from MTRPhoto.crop_hashtag_ids  where photo_id in ( 917849322) and `type` in (0) 
Loaded 0 chid ids of type : 0
 SELECT crop_hashtag_id, points FROM MTRPhoto.crop_polygon_points WHERE crop_hashtag_id in ()
 map_chi : {}
 photo_id in download_rotate_and_save : 917849322 list_chi_loc : 0
Use all angle !
Rotation of photo 917849322 of 90 degree
temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 90
 remove_crop_border : False
version de PIL : 9.5.0
Needs to change image size !
[[ 6.123234e-17  1.000000e+00]
 [-1.000000e+00  6.123234e-17]]
90
[[ 6.123234e-17  1.000000e+00]
 [-1.000000e+00  6.123234e-17]]
 shrink_image : False
 image_rotate :  <PIL.Image.Image image mode=RGB size=2448x3264 at 0x7FD5A1C8F730>
 image_path : temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg
 path_name_rotate : temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg
 image_rotate.mode : RGB
Rotation of photo 917849322 of 180 degree
temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 180
 remove_crop_border : False
version de PIL : 9.5.0
Needs to change image size !
[[-1.0000000e+00  1.2246468e-16]
 [-1.2246468e-16 -1.0000000e+00]]
180
[[-1.0000000e+00  1.2246468e-16]
 [-1.2246468e-16 -1.0000000e+00]]
 shrink_image : False
 image_rotate :  <PIL.Image.Image image mode=RGB size=3264x2448 at 0x7FD5A1C8F730>
 image_path : temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg
 path_name_rotate : temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg
 image_rotate.mode : RGB
Rotation of photo 917849322 of 270 degree
temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg [] 270
 remove_crop_border : False
version de PIL : 9.5.0
Needs to change image size !
[[-1.8369702e-16 -1.0000000e+00]
 [ 1.0000000e+00 -1.8369702e-16]]
270
[[-1.8369702e-16 -1.0000000e+00]
 [ 1.0000000e+00 -1.8369702e-16]]
 shrink_image : False
 image_rotate :  <PIL.Image.Image image mode=RGB size=2448x3264 at 0x7FD5A1C8F730>
 image_path : temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg
 path_name_rotate : temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg
 image_rotate.mode : RGB

About to upload 3 photos
upload in portfolio : 551782
init cache_photo without model_param  
we have 3 photo to upload
uploaded to storage server : ovh
folder_temporaire : temp/1742150555_446243
we have uploaded 3 photos in the portfolio 551782

time of upload the photos Elapsed time : 1.073667287826538

 map_filename_photo_id : 3
 map_filename_photo_id : {'temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg': 1344617590, 'temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg': 1344617591, 'temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg': 1344617592}
Len new_chis : 3
Len list_new_chi_with_photo_id : 0 of type : 0
 list_new_chi_with_photo_id : []

After datou_step_exec type output : <class 'dict'>

time spend for datou_step_exec : 1.3253099918365479
time spend to save output : 6.866455078125e-05
total time spend for step 1 : 1.325378656387329

caffe_path_current : 
<module 'caffe' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py'>
<module 'caffe.classifier' from '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py'>
About to save ! 1
Inside saveOutput : final : True verbose : True
 saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral 
[917849322]
 map_info['map_portfolio_photo'] : {}
 final :  True mtd_id 230 list_pids : [917849322]
Looping around the photos to save general results
len do output : 3
/1344617590Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1344617591Didn't retrieve data
.Didn't retrieve data
.Didn't retrieve data
.
/1344617592Didn't retrieve data
.Didn'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 0x7fd6186aeb50>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7fd6186aed00>
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7fd618e9e310>
Managing all output in save final without adding information in the mtr_datou_result 
('230', None, None, None, None, None, None, None, None)
('230', None, '917849322', None, None, None, None, None, None)


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 10
insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id
 list_values : [('230', None, '1344617590', 'None', None, None, None, None, None), ('230', None, '1344617591', 'None', None, None, None, None, None), ('230', None, '1344617592', 'None', None, None, None, None, None), ('230', None, '917849322', None, None, None, None, None, None)]
time used for this insertion : 0.01273965835571289
 save_final 

save missing photos in datou_result :
After save, about to update current !
 datou_cur_ids : []
 len(datou.list_steps) : 1
 output : {1344617590: ['917849322', 'temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1344617591: ['917849322', 'temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1344617592: ['917849322', 'temp/1742150554_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]}
test rotate only is a success !
test rotate conditionnel
Inside batchDatouExec : verbose : True
##### chargement datou
SELECT name, created_at,limit_max FROM MTRDatou.mtr_datou WHERE id=233

SELECT mtd.id, mtdt.`type`, mtd.`param`, mtd.param_json, mtdt.nb_input, mtdt.nb_output,
mtdt.prod, mtdt.is_local, mtdt.is_datou_depend, mtdt.is_photo_id_local
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_step_types mtdt
WHERE mtdt.`id`=mtd.`type`
AND mtd.mtd_id=233
 
SELECT mtd.id, mtd.mtd_id, mdsdt.id, mdsdt.name, mdsdt.description, msid.output_or_input, msid.data_order_id, mdsdt.type
FROM MTRDatou.mtr_datou_step mtd, MTRDatou.mtr_datou_steptype_io_datatypes msid, MTRDatou.mtr_datou_step_data_types mdsdt
WHERE mtd.`type`=msid.`mtr_datou_step_type`
AND mtd.mtd_id= 233
AND msid.data_type=mdsdt.id
                    

SELECT mts_id_output, id_output, mts_id_input, id_input 
FROM MTRDatou.mtr_datou_step_by_step 
WHERE mtd_id=233
# VR 17-11-17 : to create in DB !
Here we check the datou graph and we reorder steps !
 Tree builded and cycle checked, now we need to re-order the steps ! 
We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue
We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id)
All sons are already in current list !
All sons are already in current list !
 DONE and to test : checkNoCycle !
Here we check the consistency of inputs/outputs number between the given ones and the db !
eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering !
Number of inputs / outputs for each step checked !
Here we check the consistency of outputs/inputs types during steps connections
eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput !
DataTypes for each output/input checked !
no param json to modify
List Step Type Loaded in datou : thcl, argmax, rotate
list_input_json : []
##### fin chargement datou
##### chargement data
##### Call load_data_input : nb_thread : 5
origin

        SELECT photo_id, url
        FROM MTRBack.photos ph
        WHERE photo_id IN (917849322) 
 Found this number of photos: 1
##### Call download_photos : nb_thread : 5
begin to download photo : 917849322
download finish for photo 917849322
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
##### After download_photos 
length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1
##### After load_data_input 
time to download the photos : 0.20038628578186035
#### fin chargement data
Blocking on flush ?
No conitnuing 
About to test input to load 
we should then remove the video here, and this would fix the bug of datou_current ! 
Calling datou_exec

Inside datou_exec : verbose : True
number of steps :  3
step1:thcl
Sun Mar 16 19:42:36 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
After prepare type args : <class 'list'>

Here we display some param of map_info ! 
map_filenames : {'temp/1742150556_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg': 917849322}
map_photo_id_path_extension : {917849322: {'path': 'temp/1742150556_446243_917849322_2bd260e91e91df8378dde8bb8b8c4548.jpg', 'extension': 'jpg'}}
map_subphoto_mainphoto : {}

Beginning of datou step Thcl !
multi_thcl or not :False
multi_thcl_cond or not :False
dic_thcl :  {'500': 1}
we are using the classfication for only one thcl
500


In convert_file_to_np l 337 : 1
 l343 1
l357 after caffe.io.load_image
dimension du image : (3, (2448, 3264, 3))
dimension displayed !
time to import caffe and check if the image exist :  0.00019359588623046875
time to convert the images to numpy array :  0.9952278137207031
total time to convert the images to numpy array :  0.9957189559936523
list photo_ids error:
[]
list photo_ids correct :
[917849322]
number of photos to traite : 1
try to delete the photos incorrect in DB

tagging for thcl : 500
To do loadFromThcl(), then load ParamDescType : thcl500
 get_desc_type_from_thcl : type of cat <class 'mtr.database_queries.classification_admin_tools.ClassificationMetaTools'>

            SELECT id, mtr_user_id, name, pb_hashtag_id, hashtag_id_list, button_legend_list, portfolio_id_lists,
            photo_hashtag_type, photo_desc_type, svm_limit, limit_tagging, is_public, live, created_at, updated_at,
            type_classification
            FROM MTRDatou.classification_theme  WHERE  `id` IN (500) 
 thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}]
 thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}
 Update svm_hashtag_type_desc : 3517
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (3517)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34))
To loadFromThcl() : net_3517
begin to check gpu status 
inside check gpu memory
havn't enough memory gpu , need / 2500
l 3632 free memory gpu now : 6
wait 20 seconds
l 3637 free memory gpu now : 6  max_wait_temp :  1 max_wait :  0
 SELECT * FROM MTRDatou.photo_desc_type_params  WHERE id in (3517)
FOUND : 1
 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34))
 param : <mtr.database_queries.descriptor_queries.ParamDescriptorType object at 0x7fd5a1ed1d60>, param.caffemodel : orientation_carte_grise_all_2
None
 mean_file_type : 
 mean_file_path : 
 prototxt_file_path : 
 model : orientation_carte_grise_all_2
 Inside get_net  
 Inside get_net before cache_data_model 
model_param file didn't exist 
 Inside get_net before  CDM.load_model_par_type  
model_name : orientation_carte_grise_all_2
model_type : caffe
list file need :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file exist in s3 :
['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt']
file manque in s3 : 
[]
local folder  : /data/models_weight/orientation_carte_grise_all_2


/data/models_weight/orientation_carte_grise_all_2/caffemodel
size_local : 537110520 size in s3 : 537110520
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17
caffemodel already exist and didn't need to update


/data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt
size_local : 4626 size in s3 : 4626
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16
deploy_conv_normal.prototxt already exist and didn't need to update


/data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt
size_local : 1130 size in s3 : 1130
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16
deploy_fc.prototxt already exist and didn't need to update


/data/models_weight/orientation_carte_grise_all_2/deploy.prototxt
size_local : 5653 size in s3 : 5653
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16
deploy.prototxt already exist and didn't need to update


/data/models_weight/orientation_carte_grise_all_2/mean.npy
size_local : 1572992 size in s3 : 1572992
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31
mean.npy already exist and didn't need to update


/data/models_weight/orientation_carte_grise_all_2/synset_words.txt
size_local : 159 size in s3 : 159
create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16
synset_words.txt already exist and didn't need to update
 Inside get_net after CDM.load_model_par_type  
  After if not only_with_local_cache:
/home/admin/workarea/install/caffe_cuda8_python3/python/:/home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/
Here before set mode gpu
Doing nothing but we could set mode gpu
after set mode gpu
 prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt
 caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/caffemodel
now we set caffe to gpu mode


 before predict
begin to check gpu status 
inside check gpu memory
havn't enough memory gpu , need / 2500
l 3632 free memory gpu now : 6
wait 20 seconds
WARNING: Logging before InitGoogleLogging() is written to STDERR
F0316 19:43:24.517266 446243 syncedmem.cpp:78] Check failed: error == cudaSuccess (2 vs. 0)  out of memory
*** Check failure stack trace: ***
Command terminated by signal 6
47.79user 34.47system 1:07:59elapsed 2%CPU (0avgtext+0avgdata 5453276maxresident)k
7136592inputs+23088outputs (26997major+4168463minor)pagefaults 0swaps