K=10,T=0.8: <data>idx ( entry [ " summary " ] , targ _ idx , word ) if targ _ idx is none : skipped + = 1 else : assert <UNK> [ i ] [ src _ idx ] = = word and entry [ " summary " ] [ targ _ idx ] = = word links . append ( ( src _ idx , targ _ idx ) ) for k , word in enumerate ( tokes [ num _ start : num _ end ] ) : src _ idx = none if idthing : if entry [ </data>. _ ' _ _ ( ) , ( ) " _ ) ( , : : _ , _ _ ( : _ . = ( _ " , _ _ ( . = = _ ' _ " _ _ , . _ ' ) : ' . = ( . ) ) _ = , , ( . _ _ : ( . ( " . _ _ _ _ . , ) = : _ _ : ( _ , ( ) ' ' ( ) ( , ) _ . 0 _ , _ _ 
K=10,T=0.8: <data>_ v 1 ' in instance _ dict : instance . light _ info . light _ type = sn . light info . <UNK> instance . light _ info . v 1 . x = instance _ dict [ ' light _ v 1 ' ] [ 0 ] instance . light _ info . v 1 . y = instance _ dict [ ' light _ v 1 ' ] [ 1 ] instance . light _ info . v 1 . z = instance _ dict [ ' light _ v 1 ' ] [ 2 ] </data>) , : ( [ ) ( = ) = _ : . . ) : ( " _ , _ . , , = ( = ' , , " , , : _ . = ( _ , _ _ ) ( _ " . ( . = . _ ( . = _ ' ( _ _ = _ ( ) , = , . : : ' : . _ = ) ( _ , . _ , ' _ . _ ) : _ [ , = . . , _ ( . ( = 
K=10,T=0.8: <data>( train _ loader ) logger . info ( ( ' \ n ' + ' % 1 0 s ' * 7 ) % ( ' epoch ' , ' gpu _ mem ' , ' box ' , ' obj ' , ' cls ' , ' labels ' , ' img _ size ' ) ) if rank in ( - 1 , 0 ) : pbar = tqdm ( pbar , total = nb , bar _ format = ' { l _ bar } { bar : 1 0 } { r _ bar } </data>_ ) _ = ( ) , . , = . = _ _ , . ( ( ) _ ) ( _ ) _ ) , ) . ' _ = , ) _ . ( : , . . . = , = : _ , _ : _ _ ' , = ( : . ( ) ) , , ) ' ) ) , = ) _ ) ) , " _ , _ , : . ' ' , _ ) _ . _ ( " _ ' _ " . 0 0 . , 
K=10,T=0.8: <data>prerequisites for py salsa 2 0 : - - - - - - - - - - - - - - - - - - - - - - - - - - - - - python 2 . 5 ( haven ' t tested in 2 . 4 ) pure salsa 2 0 : salsa 2 0 in pure python 2 . 5 ( comments by steve <UNK> ) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </data>_ " _ " _ 0 : _ " , _ ' _ ( , ) . ( = " , ( . , ' _ _ _ . _ ( ) . : _ ( . , " _ _ _ . _ ' _ : _ ) : _ ( _ ( : , . = _ " : , ( ( _ _ _ 0 = ( . ) " ' = " , . . . _ ' , ( ) 0 . : = ) " = = : ) ( _ ) _ ) 
K=10,T=0.8: <data>numpy ( ) ) , global _ step , ) acoustic . train ( ) if rank = = 0 : writer . add _ scalar ( " validation / loss " , validation _ loss . value , global _ step , ) logger . info ( f " valid - - epoch : { epoch } , loss : { validation _ loss . value : . 4 f } " ) new _ best = best _ loss > validation _ loss . value if new _ best or global _ step % checkpoint _ interval = </data>self _ ' " , " ) ) ) ) , ) = _ = ) . [ , " , ] ' " ) ) " ' ' _ = ) ' _ ' = ) self , ) , : ( = " _ ' : _ = ' , ' " 1 . ) , : " = = 1 _ = ' ) ' ) , ' : _ " ] : = " _ : , , , = = ' " : _ " _ " _ " , , ) : " ] 
K=10,T=0.8: <data>> ai ' , color pc , color _ coef ) + color mu pca _ texture = tf . clip _ by _ value ( pca _ texture , 0 . 0 , 2 5 5 . 0 ) pca _ texture = pca _ texture / 1 2 7 . 5 - 1 pca _ texture = tf . reshape ( pca _ texture , [ self . batch _ size , self . num _ vert , 3 ] ) with tf . variable _ scope ( ' render ' , reuse = reuse ) : camera </data>" . = ' . _ _ ( 0 = _ = _ ( " ( , , . _ " ' , ) : _ ( ' ( ' 0 ) ) ( ( = ( ) ( ' ' ) " , , = . , ' , . ( ' _ . _ _ 0 , ' . , ) _ ) . . . _ . ( = . , _ _ = _ _ ( ( , ) , ( ( _ . _ _ _ ) = ' . _ ' _ ( . 
K=10,T=0.8: <data>ff = = ord ( ' q ' ) : break if _ _ name _ _ = = ' _ _ main _ _ ' : predict _ image _ webcam ( ) import argparse import os import random import shutil import time import warnings import torch import torch . nn as nn import torch . nn . parallel import torch . backends . cudnn as cudnn import torch . distributed as dist import torch . optim import torch . multiprocessing as mp import torch . utils . data import torch . utils . data . distributed from torch </data>. _ _ ( . . . ) 0 , = . _ : _ = ( , _ _ _ ( ) _ , ( ( . ( " _ ( = ' : . = _ _ ( . _ _ . . ) ( ) . = . = = , " = ' = 0 ( ' ( . : _ . ( " ' , ( ' _ ' ) ) _ ( = = _ . 0 _ ) ) = : " = ' _ _ , . . ( " : ' 
K=10,T=0.8: <data>0 1 ' ] ) ) return model def resnet 1 5 2 ( pretrained = false , * * kwargs ) : model = res net ( bottleneck , [ 3 , 8 , 3 6 , 3 ] , * * kwargs ) if pretrained : model . load _ state _ dict ( model _ zoo . load _ url ( model _ urls [ ' resnet 1 5 2 ' ] ) ) return model import logging import wx import wx . lib . <UNK> wx log event , evt _ wx _ log _ event </data>_ = : _ ' 1 ' ' self , _ ) 1 ' ) ' 1 = : _ ' : , ) 1 ' " ' : ' ) ' = : = ' , : . , : _ ) ' ) ) ) ) ) : = " , self ( , = , _ : , : , 1 = , : _ ' : ' , 1 ( _ ' , _ , , ] _ ) , _ : = ' ) , " _ _ , : ' = ' _ ' 
K=10,T=0.8: <data>n . value , ast . name ) : ( lhs , rhs ) = ( n . targets [ 0 ] . id , n . value . id ) assigns . append ( ( lhs , rhs ) ) if isinstance ( n . value , ast . dict ) : lhs = n . targets [ 0 ] . id for v in n . value . values : if isinstance ( v , ast . name ) : rhs = v . id assigns . append ( ( lhs , rhs ) ) for x in </data>( " ( self ] , ) 0 ( ) ( ' = = _ : = ) , : = _ ' , " , " _ " , self , " _ ' , ' = ( self ' , ) = _ _ ' . ( ) _ _ _ ' _ ' = , ' _ ' _ . , , ' ' ) " _ _ _ " _ , ' ' _ . ( ( : . = _ : _ , ' = _ _ ' , ( , ) _ ' '