K=10,T=0.8: <data>' : 1 0 , ' cifar 1 0 0 ' : 1 0 0 , ' mnist ' : 1 0 , ' tiny _ imagenet ' : 2 0 0 } logger , writer = init _ logger ( config ) model = get _ network ( config . network , config . depth , config . dataset , use _ bn = config . get ( ' use _ bn ' , true ) ) mask = none mb = model base ( config . network , config . depth , config . dataset , model ) </data>optimizer = model ( config . model _ path , config , config = config . arch , config = config . arch , config = config . arch , config = config . arch , config = config . arch , config = config . arch , config = config . arch , config = config , load _ checkpoint = config . model , optimizer = config . checkpoint ) optimizer = optim . adam optimizer . adam optimizer ( config . optimizer , config . optimizer , config = config . arch , config = config . 
K=10,T=0.8: <data>force . * " , " . * ( ? i ) foreign . * " , " . * ( ? i ) <UNK> . * " , " . * ( ? i ) forward . * " , " . * ( ? i ) found . * " , " . * ( ? i ) free . * " , " . * ( ? i ) <UNK> . * " , " . * ( ? i ) <UNK> . * " , " . * ( ? i ) freeze . * " </data>, " . * ( ? i ) <UNK> . * " , " . * ( ? i ) " , " . * ( ? i ) <UNK> . * " , " . * ( ? i ) <UNK> . * " , " . * ( ? i ) <UNK> . * " , " . * ( ? i ) <UNK> . * " , " . * ( ? i ) <UNK> . * " , " . * ( ? i ) <UNK> . * " , " . * ( ? i 
K=10,T=0.8: <data>. random . randint ( 0 , w - dw ) y = np . random . randint ( 0 , h - dh ) bg = background [ x : x + dw , y : y + dh ] return np . abs ( bg - digit ) . astype ( np . uint 8 ) def mnist _ to _ img ( x ) : x = ( x > 0 ) . astype ( np . float 3 2 ) d = x . reshape ( [ 2 8 , 2 8 , 1 ] ) </data>. astype ( np . uint 8 ) d = ( y > 0 ) . astype ( np . uint 8 ) d = ( d - d ) ) . astype ( np . int 3 2 ) d = ( d - d ) . astype ( np . uint 8 ) . astype ( np . uint 8 ) d = ( d - d ) . astype ( np . uint 8 ) d = ( d - d ) . astype ( np . uint 8 ) d = ( d - d ) 
K=10,T=0.8: <data>_ ok def _ handle _ tags ( self , fn , fn _ an , known _ refs ) : if known _ refs : known _ refs = dict ( known _ refs ) for k , names in known _ refs . items ( ) : existing = set ( fn _ an [ ' tags ' ] [ k ] ) new = set ( names ) - existing if new : fn _ an [ ' tags ' ] [ k ] + = list ( new ) tags = dict ( fn _ an </data>[ ' tags ' ] [ k ] ] , strict = true ) new = set ( names . values [ k ] ) for k in sorted ( tags . values [ k ] ) ] ) : if not isinstance ( tags , dict ) and not isinstance ( tags , dict ) : if not isinstance ( tags , dict ) : return tags [ k ] def get _ tag ( tag ) : return tag class get _ tag ( tag . tag ) : def _ _ init _ _ ( self , 
K=10,T=0.8: <data>t " , " ou " ) , " tu " : ( " t " , " u " ) , " tuan " : ( " t " , " uan " ) , " tui " : ( " t " , " uei " ) , " tun " : ( " t " , " uen " ) , " <UNK> " : ( " t " , " uo " ) , " wa " : ( " ^ " , " ua " ) , " <UNK> " : ( " ^ " </data>, " uo " ) , " <UNK> " : ( " <UNK> " , " uo " ) , } if " <UNK> " in ( " <UNK> " , " <UNK> " ) , " <UNK> " : ( " <UNK> <UNK> " , " uo " ) , " <UNK> " : ( " <UNK> " , " <UNK> " ) , " <UNK> " : ( " <UNK> <UNK> " , " <UNK> " ) , " <UNK> " : ( " <UNK> " , " <UNK> " ) , " <UNK> " : ( " 
K=10,T=0.8: <data>session , ' epoch ' : epoch + 1 , ' model ' : faster rcnn . module . state _ dict ( ) if args . m gpus else faster rcnn . state _ dict ( ) , ' optimizer ' : optimizer . state _ dict ( ) , ' pooling _ mode ' : cfg . pooling _ mode , ' class _ agnostic ' : args . class _ agnostic , } , save _ name ) print ( ' save model : { } ' . format ( save _ name ) ) if args </data>. n gpus else faster rcnn . module . state _ dict ( ) else faster rcnn . module . state _ dict ( ) if args . model . startswith ( ' alexnet ' ) : print ( ' load model : { } ' . format ( save _ name ) ) if args . model . startswith ( ' resnet 1 ' ) : print ( ' load model : { } ' . format ( save _ name ) ) print ( ' load model : { } ' . format ( save _ name ) 
K=10,T=0.8: <data>key " ) def convert _ json ( raw _ json ) : try : json _ data = json . loads ( raw _ json ) if isinstance ( json _ data , dict ) and ' entries ' in json _ data : return json _ data [ ' entries ' ] else : print ( " [ debug ] the json response doesn ' t have the expected format " ) return [ ] except json . jsondecode error : print ( " [ debug ] error decoding json " ) return [ ] def query _ </data>query ( query _ query ) : query _ query = query _ query [ ' query ' ] query = query _ query [ ' query ' ] query _ query = query _ query [ ' query ' ] query _ query [ ' query ' ] query = query _ query [ ' query ' ] query _ query [ ' query ' ] query _ query [ ' query ' ] = query _ query [ ' query ' ] query _ query [ ' query ' ] = query _ query [ ' query ' 
K=10,T=0.8: <data>box will be half the width and height of the input . in a diagram it looks like this : < width > + - - - - - - - - - - - - - - - - - - - - - + | | | width - crop % | | < > | | + - - - - - - + | | | | | | | | | | | | | | + - - - - - - + | | | | | + - - - - - </data>- + | | | | | + - - " - - - - - - - - ' - - - - - - - - " - - - - - ' - - - - - - - ' - - - - - - - - - - - - - " - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
K=10,T=0.8: <data>) batch _ sampler _ test = torch . utils . data . batch sampler ( sampler _ test , args . batch _ size , drop _ last = false ) data _ loader _ test = data loader ( dataset _ test , args . batch _ size , sampler = sampler _ test , drop _ last = false , collate _ fn = utils . collate _ fn , num _ workers = args . num _ workers ) checkpoint = torch . load ( args . eval _ model , map _ location = ' </data>cpu ' ) checkpoint . load _ checkpoint ( checkpoint ) model . load _ state _ dict ( checkpoint ) optimizer . load _ state _ dict ( checkpoint ) checkpoint . load _ state _ dict ( checkpoint ) optimizer . load _ state _ dict ( checkpoint ) optimizer . load _ state _ dict ( checkpoint ) model . load _ state _ dict ( checkpoint ) optimizer . load _ state _ dict ( checkpoint ) optimizer . load _ state _ dict ( checkpoint ) optimizer . load _ state _ dict ( checkpoint