| K=10,T=0.8: <data>) = = type ( [ 1 ] ) : pass else : dataloader = [ dataloader ] correct , total = 0 , 0 with torch . no _ grad ( ) : for tmp in dataloader : for batch _ idx , ( x , target ) in enumerate ( tmp ) : x , target = x . to ( device ) , target . to ( device , dtype = torch . int 6 4 ) if moon _ model : _ , _ , out = model ( x ) else : out = model </data>( y , target ) with torch . no _ grad ( ) : for x , target in enumerate ( target ) : x , target _ ids = target _ mask [ target _ ids ] if torch . distributed . distributed . distributed . dist _ world ( ) else : out . to ( device , device , device = device , device = device ) out = model ( model , target = device ) . requires ( ) out _ ids = out . to ( device ) out = out . to ( | |
| K=10,T=0.8: <data>ip ) ) print ( " your wireless devices mac address is : % s " ) % ( str ( wireless _ mac ) ) elif os . name ! = " nt " : print ( " no active wireless device was found " ) if not " none " in windows _ ip : print ( " your windows host ip address is : % s " ) % ( str ( windows _ ip ) ) print ( " your windows mac address is : % s " ) % ( str ( windows _ mac </data>) ) ) def get linux ( ip ) : print ( " please wait ! ! " ) print ( " please wait ! " ) print ( " please wait ! " ) print ( " please wait ! " ) print ( " you may wait ! " ) print ( " please wait ! " ) print ( " please wait ! " ) print ( " you can wait ! " ) print ( " your windows mac address is : % s " ) print ( " please wait ! " ) print ( | |
| K=10,T=0.8: <data>) : if _ compute _ iou ( roi , gt ) > = iou _ thresh : label [ j ] = 1 count _ tp + = 1 break y _ true . extend ( list ( label ) ) y _ score . extend ( list ( sim ) ) imgs . extend ( [ gallery _ imname ] * len ( sim ) ) rois . extend ( list ( det ) ) tested . add ( gallery _ imname ) y _ score = np . asarray ( y _ score ) y _ true </data>. extend ( list ( det ) ) print ( " \ n \ n " ) print ( " \ n " + str ( len ( np . abs ( bbox ) ) ) ) print ( " \ n " ) print ( " \ n " ) if len ( np . abs ( bbox ) ) = = 1 : print ( " \ n " ) print ( " \ n " ) print ( " \ n " ) print ( " \ n " ) print ( " \ n " ) | |
| K=10,T=0.8: <data>' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' lite ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' ll ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' lmt ' , ' <UNK> ' , ' lnd ' , ' <UNK> ' , </data>' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , | |
| K=10,T=0.8: <data>_ params = list ( self . parameters ( ) ) params = [ p for p in all _ params if not hasattr ( p , " _ optim " ) ] optimizer = utils . instantiate ( registry . optimizer , self . hparams . optimizer , params ) del self . hparams . optimizer . _ name _ hps = [ getattr ( p , " _ optim " ) for p in all _ params if hasattr ( p , " _ optim " ) ] hps = [ dict ( s ) for s in </data>all _ params if isinstance ( g , nn . module ) ] optimizer . compile _ parameters ( ) else : optimizer . compile _ parameters ( ) lr _ scheduler . compile _ parameters ( ) lr _ scheduler . compile _ parameters ( ) if not isinstance ( lr , nn . module ) : lr _ scheduler . compile _ parameters ( ) lr _ scheduler . compile _ parameters ( ) for t in all _ params if t . startswith ( ' . ' ) ] scheduler . compile _ parameters ( ) : | |
| K=10,T=0.8: <data>for your flight to ( [ a - za - z ] + ) \ . ' , email _ body ) if match : destination = match . group ( 1 ) itineraries . append ( { " is itinerary " : true , " origin " : origin , " destination " : destination } ) if ' your flight itinerary ' in email _ body : match = re . search ( r ' <UNK> style = 3 d = 2 2 font - family : arial , sans - serif ; font - size : 1 </data>4 ; font - size : 4 8 ; font - size : 5 4 ; font - size : 5 4 ; font - size : 5 4 ; font - size : 5 4 ; font - size : 5 4 ; font - size : 5 4 ; font - size : 5 4 ; font - size : 5 4 ; font - size : 5 4 ; font - size : 5 4 ; font - size : 5 4 ; font - size : 2 4 ; font - size : 5 4 ; | |
| K=10,T=0.8: <data>def get _ network _ wider ( frames , input _ size , num _ classes ) : net = tflearn . input _ data ( shape = [ none , frames , input _ size ] ) net = tflearn . lstm ( net , 5 1 2 , dropout = 0 . 2 ) net = tflearn . fully _ connected ( net , num _ classes , activation = ' softmax ' ) net = tflearn . regression ( net , optimizer = ' adam ' , loss = ' categorical _ crossentropy ' , name = </data>' ' ) return net def get _ network _ single ( net , net , inputs ) : net = tflearn . fully _ connected ( net , num _ classes , activation = ' softmax ' ) net = tflearn . fully _ connected ( net , num _ classes , activation = ' softmax ' ) return net def get _ network _ single ( net , input _ size , num _ classes , num _ classes , num _ classes , num _ classes ) : net = tflearn . fully _ connected ( net | |
| K=10,T=0.8: <data>torch . nn as nn from quantize . block _ ap import block _ ap from tqdm import tqdm import utils from pathlib import path from transformers import auto tokenizer , auto config , auto model for causal lm from quantize . int _ linear _ real import load _ quantized _ model from accelerate import infer _ auto _ device _ map , dispatch _ model torch . backends . cudnn . benchmark = true @ torch . no _ grad ( ) def evaluate ( model , tokenizer , args , logger ) : block _ class _ </data>map = auto tokenizer . from _ pretrained ( os . path . join ( model _ dir , " model . diffusion . model - diffusion . diffusion . model . diffusion . model . diffusion . model . model . diffusion . model . model . model . diffusion . model . model . model . diffusion . model . model . diffusion . model . model . model . diffusion . model . model . diffusion . model . model . model . model . diffusion . model . model . model . diffusion . model . | |
| K=10,T=0.8: <data>train _ batch { } _ sample { } _ clip { } _ nest { } _ damp { } _ weight _ decay { } _ manualseed { } _ model { } { } _ ftbeginidx { } _ var lr { } . pth ' . format ( opt . dataset , opt . split , opt . modality , opt . batch _ size , opt . sample _ size , opt . sample _ duration , opt . nesterov , opt . dampening , opt . weight _ decay , opt . manual _ </data>seed , opt . batch _ size , opt . nesterov , opt . weight _ decay _ rate , opt . weight _ decay _ rate , opt . weight _ decay _ rate , opt . weight _ decay _ rate , opt . learning _ rate , opt . learning _ rate = opt . beta _ decay _ rate , opt . weight _ decay _ rate = opt . weight _ decay _ rate , opt . weight _ decay _ rate = opt . weight _ decay _ rate , opt . learning _ | |