python /home/admin/mtr/script_for_cron.py -j datou_current3 -m 8 -a ' -a 4311 ' -s datou_current_4220 -M 0 -S 0 -U 95,95,120
 import MySQLdb succeeded 
Import error (python version)
['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/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', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages']
process id : 1434484
load datou : 4311
# VR 17-11-17 : to create in DB !
Here we check the datou graph and we reorder steps !
 Tree builded and cycle checked, now we need to re-order the steps ! 
We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue
We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id)
All sons are already in current list !
 DONE and to test : checkNoCycle !
Here we check the consistency of inputs/outputs number between the given ones and the db !
eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering !
WARNING : number of outputs for step 11939 matching_dashboard is not consistent : 1 used against 0 in the step definition !
Step 11940 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
Number of inputs / outputs for each step checked !
Here we check the consistency of outputs/inputs types during steps connections
eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput !
WARNING : type of output 0 of step 11939 doesn't seem to be define in the database(
WARNING : type of input 2 of step 11940 doesn't seem to be define in the database(
DataTypes for each output/input checked !
Unexpected type for variable list_input_json
ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0)
 Tried to parse : 
None was removed should we ?
None was removed should we ?
donnée sous forme de texte was removed should we ?
[ptf_id0,ptf_id1...] was removed should we ?
load thcls
load pdts
Running datou job : batch_current
 TODO datou_current to load to do maybe to take outside batchDatouExec 
no input labels
no input values
updating current state to 1
we have a portfolio with more photos than limit : 3762>1000
please execute split_portfolio.py -i 22151747 -l 1000
size over we load limit photo not treated
list_input_json: {}
Current got : datou_id : 4311, datou_cur_ids : ['2734635'] with mtr_portfolio_ids : ['22151747'] and first list_photo_ids : []
new path :  /proc/1434484/
Inside batchDatouExec : verbose : 0
# VR 17-11-17 : to create in DB !
Here we check the datou graph and we reorder steps !
 Tree builded and cycle checked, now we need to re-order the steps ! 
We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue
We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id)
All sons are already in current list !
 DONE and to test : checkNoCycle !
Here we check the consistency of inputs/outputs number between the given ones and the db !
eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering !
WARNING : number of outputs for step 11939 matching_dashboard is not consistent : 1 used against 0 in the step definition !
Step 11940 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs !
Number of inputs / outputs for each step checked !
Here we check the consistency of outputs/inputs types during steps connections
eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput !
WARNING : type of output 0 of step 11939 doesn't seem to be define in the database(
WARNING : type of input 2 of step 11940 doesn't seem to be define in the database(
DataTypes for each output/input checked !
List Step Type Loaded in datou : matching_dashboard, split_time_score
over limit max, limiting to limit_max 100
list_input_json : {}
origin
We have 1 , 
WARNING: data may be incomplete, need to offset and complete !
we have missing 0 photos in the step downloads : 
photo missing : []
try to delete the photos missing in DB
time to download the photos : 0.034833431243896484
About to test input to load 
Calling datou_exec

Inside datou_exec : verbose : 0
number of steps :  2
step1:matching_dashboard
Wed Apr  9 14:30:48 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec

begin datou step matching dashboard
2022-04-13 10:29:59 0
todo
a
Inside saveOutput : final : False verbose : 0
ouput is None
No outpout to save, returning out of save general

time spend for datou_step_exec : 1.3262710571289062
time spend to save output : 0.00011205673217773438
total time spend for step 1 : 1.326383113861084

step2:split_time_score
Wed Apr  9 14:30:49 2025
VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next
VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case
VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec 
VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec
Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior 
Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed
We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input !
We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input !
VR 22-3-18 : For now we do not clean correctly the datou structure 
begin split time score
forced hashtag should be used only with CREATE_PORT_BATCH_BY_HOUR or CONSOLIDATE task, truck value will be ignored (VR 14-3-21 feels it is a too subtle behavior, why not quitting ?) .
TODO : Insert select and so on
Begin split_port_in_batch_balle
thcls : [{'id': 3442, 'mtr_user_id': 31, 'name': 'classifieur_2camions_valcor_021122_v1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'deux_camions,camion_droite,camion_gauche,pas_de_camion', 'svm_portfolios_learning': '7659379,7659034,7657685,7657114', 'photo_hashtag_type': 4458, 'photo_desc_type': 5723, 'type_classification': 'tf_classification2', 'hashtag_id_list': '2107760533,2107760534,2107760535,2107760536'}]
thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}]
(('00', 296), ('01', 305), ('02', 293), ('03', 283), ('04', 325), ('05', 286), ('06', 261), ('07', 329), ('08', 248), ('09', 238), ('10', 321), ('11', 329), ('12', 248))
ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {}
09042025 22151747 Nombre de photos uploadées : 3762 / 23040 (16%)
09042025 22151747 Nombre de photos taguées (types de déchets): 0 / 3762 (0%)
09042025 22151747 Nombre de photos taguées (volume) : 0 / 3762 (0%)
 elapsed_time : load_data_split_time_score 2.384185791015625e-06
 elapsed_time : order_list_meta_photo_and_scores 6.9141387939453125e-06
?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.16538214683532715
 elapsed_time : insert_dashboard_record_day_entry 0.026748180389404297
***** BEGIN SPLIT TRUCK *****
inside split by truck info
list printed:  []
forced_hashtag:  truck
force hashtag to truck
 elapsed_time : SPLIT_TRUCK 0.09402990341186523
***** END SPLIT TRUCK *****
 NUMBER BATCH : 0
 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['truck']
We have rejected 0 photos because of the batch_size condition !
 NUMBER BATCH list_of_portfolios_to_create : 0
todo
 elapsed_time : count_nb_balles_and_create_portfolio 0.03585648536682129
do something
# DISPLAY ALL COLLECTED DATA : 
{'09042025': {'nb_upload': 3762, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}
Inside saveOutput : final : True verbose : 0
 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral 
[1350783648, 1350783627, 1350783603, 1350783569, 1350783506, 1350783453, 1350782745, 1350782684, 1350782660, 1350782635, 1350782579, 1350782572, 1350782355, 1350782323, 1350782320, 1350782319, 1350782317, 1350782258, 1350782048, 1350782024, 1350782019, 1350782002, 1350781983, 1350781981, 1350781939, 1350781935, 1350781931, 1350781926, 1350781922, 1350781915, 1350781873, 1350781869, 1350781866, 1350781864, 1350781862, 1350781861, 1350781842, 1350781839, 1350781836, 1350781835, 1350781830, 1350781828, 1350781806, 1350781804, 1350781801, 1350781799, 1350781796, 1350781793, 1350781766, 1350781758, 1350781755, 1350781747, 1350781727, 1350781725, 1350781645, 1350781643, 1350781640, 1350781626, 1350781619, 1350781609, 1350781515, 1350781511, 1350781508, 1350781507, 1350781502, 1350781498, 1350781403, 1350781400, 1350781398, 1350781395, 1350781381, 1350781363, 1350781330, 1350781327, 1350781324, 1350781312, 1350781311, 1350781305, 1350781282, 1350781280, 1350781277, 1350781275, 1350781272, 1350781270, 1350781247, 1350781241, 1350781236, 1350781233, 1350781230, 1350781228, 1350781218, 1350781215, 1350781211, 1350781208, 1350781207, 1350781205, 1350780464, 1350780439, 1350780437, 1350780435]
Looping around the photos to save general results
len do output : 1
/22151747Didn't retrieve data
.
before output type
Here is an output not treated by saveGeneral : <mtr.datou.datou_lib_object.DatouData object at 0x7fc654b17400>
Managing all output in save final without adding information in the mtr_datou_result 
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350783648', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350783627', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350783603', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350783569', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350783506', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350783453', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782745', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782684', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782660', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782635', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782579', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782572', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782355', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782323', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782320', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782319', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782317', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782258', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782048', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782024', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782019', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350782002', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781983', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781981', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781939', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781935', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781931', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781926', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781922', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781915', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781873', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781869', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781866', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781864', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781862', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781861', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781842', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781839', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781836', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781835', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781830', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781828', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781806', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781804', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781801', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781799', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781796', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781793', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781766', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781758', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781755', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781747', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781727', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781725', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781645', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781643', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781640', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781626', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781619', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781609', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781515', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781511', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781508', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781507', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781502', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781498', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781403', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781400', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781398', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781395', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781381', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781363', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781330', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781327', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781324', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781312', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781311', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781305', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781282', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781280', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781277', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781275', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781272', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781270', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781247', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781241', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781236', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781233', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781230', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781228', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781218', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781215', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781211', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781208', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781207', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350781205', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350780464', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350780439', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350780437', None, None, None, None, None, '2734635')
('4311', None, None, None, None, None, None, None, '2734635')
('4311', '22151747', '1350780435', None, None, None, None, None, '2734635')


begin to insert list_values into mtr_datou_result : 
length of list_values in save_final : 101
time used for this insertion : 0.03142523765563965
 save_final 

save missing photos in datou_result :

time spend for datou_step_exec : 0.628410816192627
time spend to save output : 0.03230905532836914
total time spend for step 2 : 0.6607198715209961

caffe_path_current : 
About to save ! 2
After save, about to update current !
ret : 2
len(input) + len(total_photo_id_missing) : 1
 set_done_treatment 
1.60user 0.94system 0:04.90elapsed 52%CPU (0avgtext+0avgdata 111484maxresident)k
0inputs+64outputs (0major+52178minor)pagefaults 0swaps