K=10,T=0.8: trlog [ ' max _ acc _ dist _ epoch ' ] = 0 trlog [ ' max _ acc _ sim ' ] = 0 . 0 trlog [ ' max _ acc _ sim _ epoch ' ] = 0 initial _ lr = args . lr global _ count = 0 timer = timer ( ) writer = summary writer ( logdir = args . save _ path ) for epoch in range ( args . args . max _ steps ) : train _ dataloader = args . train _ loader ( train _ loader ) val _ loader = train _ loader ( args . val _ loader ) data _ loader = data loader ( train _ loader , train _ loader , val _ loader , train _ loader , train _ loader = train _ loader , val _ loader , test _ batch _ batch _ size , test _ sampler = data loader K=10,T=0.8: callable _ obj ( ) the next example shows a callable object that accepts additional parameters , like a real function . here we need to add parameters to the _ _ call _ _ method . class callable object 2 : def _ _ init _ _ ( self , prefix ) : self . prefix = prefix def _ _ call _ _ ( self , x , y ) : print ( type ( self ) , self . prefix ) return [ ] class ( object ) : def _ _ init _ _ ( self , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y ] , y , y , y , y , y , y , y , K=10,T=0.8: ( np . uint 8 ) encoded _ images = ( encoded _ images . cpu ( ) . numpy ( ) + 1 ) / 2 * 2 5 5 encoded _ images = np . transpose ( encoded _ images , ( 0 , 2 , 3 , 1 ) ) [ 0 ] encoded _ images = encoded _ images . astype ( np . uint 8 ) if images . shape [ 0 ] > 1 0 0 0 or images . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ 1 ] . shape [ 1 ] . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ 1 ] . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ 1 ] . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ : ] . shape [ 1 ] ] K=10,T=0.8: 1 } , " msg _ id " : { 2 } , " psessionid " : " { 3 } " } } ' . format ( tuin , client id , msg id , psession id , ( content ) ) ) , ( ' clientid ' , client id ) , ( ' psessionid ' , psession id ) ) rsp = http client _ ist . post ( req url , data , https referer ) try : if ' ' not in rsp : = response . decode ( ' utf - 8 ' ) = response . decode ( ' utf - 8 ' ) except exception as e : print ( e ) print ( e ) print ( e ) print ( e ) print ( e ) K=10,T=0.8: : answers [ 2 ] . append ( 1 ) elif ' generally wrong ' in a _ 3 or ' generally incorrect ' in a _ 3 : answers [ 2 ] . append ( 2 ) elif ' wrong ' in a _ 3 or ' incorrect ' in a _ 3 : answers [ 2 ] . append ( 3 ) elif ' correct ' in a _ 3 : answers [ 2 ] . append ( 1 ) return answers elif ' correct ' in a _ 3 and ' correct ' in a _ 3 or ' total ' not in a _ 3 : answers [ 3 ] . append ( 1 . 0 ) return answers elif ' correct ' in a _ 3 and ' correct ' in a _ 3 and ' correct ' in a _ 3 : answers [ 3 ] . K=10,T=0.8: if args . testpath is none : args . testpath = args . if is _ distributed : torch . cuda . set _ device ( args . local _ rank ) torch . distributed . init _ process _ group ( backend = " nccl " , init _ method = " env : / / " ) synchronize ( ) set _ random _ seed ( args . local _ rank ) torch . distributed . set _ rank ( ) print ( " loading distributed data " ) print ( f " loading training . . . . . . . . . . . " ) print ( f " loading training . . . . . . . . . . . . . . " ) torch . cuda . set _ device ( device ) for i , ( i , i , n , n , n K=10,T=0.8: range ( ( num _ samples - 1 ) / / batch _ size + 1 ) : batch _ input = problems [ id ] [ " problem " ] + instruct _ prompt batch _ output = model . generate ( batch _ input , sampling _ params ) for j in range ( sampling _ params . n ) : output . append ( batch _ output [ 0 ] . outputs [ j ] . outputs [ j ] . outputs [ j ] ) output . append ( batch _ input [ 1 ] . outputs [ j ] . outputs [ j ] . outputs [ j ] ) output . append ( batch _ input [ 1 ] . outputs [ j ] . outputs [ j ] . outputs [ j ] ) outputs . append ( batch _ input [ j ] . outputs K=10,T=0.8: [ . . . , none ] * freq sin , cos = spectrum . sin ( ) , spectrum . cos ( ) input _ enc = torch . stack ( [ sin , cos ] , dim = - 2 ) input _ enc = input _ enc . view ( * shape [ : - 1 ] , - 1 ) return input _ enc if _ _ name _ _ = = " _ _ main _ _ " : main ( ) < filesep > from os import path from os import path from os import path from os import path import json from os . path import join from utils . data import data loader from utils . data import data loader from utils . data import data loader from utils . data import data loader from utils . data import data loader from utils . data import data loader from utils . data import data loader class data K=10,T=0.8: = int ( config [ " " ] ) = int ( config [ " " ] ) if tofu _ enabled : tofu _ config = config [ " tofu " ] tofu _ channels = tofu _ config [ " channels " ] = tofu _ config [ " summon " ] tcc = tofu _ config [ " tcc " ] if : = tofu _ config [ " summon _ channel " ] = config [ " " ] = config [ " " ] if : = config [ " " ] = config [ " " ] if and : = config [ " " ] [ " " ] = config [ " " ] [ " " ] K=10,T=0.8: ) , ' xyz ' ) eul . rotate _ axis ( ' x ' , math . radians ( - rx ) ) eul . rotate _ axis ( ' y ' , math . radians ( ry ) ) eul . rotate _ axis ( ' z ' , math . radians ( - rz + 1 8 0 ) ) cam . rotation _ euler = eul K=10,T=0.8: available ' ] = device _ pair [ ' max _ default _ pg _ ids ' ] device _ pair [ direction ] [ ' pg _ ids ' ] [ ' default ' ] [ ' start _ index ' ] = pg _ id _ base device _ pair [ direction ] [ ' pg _ ids ' ] [ ' latency ' ] [ ' available ' ] = device _ pair [ ' max _ latency _ pg _ ids ' ] K=10,T=0.8: . as losses import utils . misc as misc from datasets . import ego from datasets . import hand dataset from model . detnet . detnet import detnet from utils import func , align from utils . eval . import average meter , accuracy _ heatmap from utils . eval . import eval util from utils import vis import random device = torch . device ( f " cuda " if torch . cuda . is _ available ( ) else " cpu " ) device = torch . device ( f " cuda " if torch . cuda . is _ available ( ) else " cpu " ) from torch . utils . data import data loader , data loader , data loader from utils . utils import dataset , dataset from utils . metrics import metrics class test dataset ( dataset ) : def _ _ init _ _ ( self , dataset , dataset , dataset , dataset , dataset , dataset _ type , dataset , dataset _ type K=10,T=0.8: l 2 _ norm ( dy / dx ) - 1 ) * * 2 . convert label indices to one - hot vectors . generate target domain labels for debugging and testing . compute binary or softmax cross entropy loss . train star gan within a single dataset . translate images using star gan trained on a single dataset . universal attack by huang hao universal attack by huang hao translate images using star gan trained on a single dataset . override the parameters you want to modify for this dataset logging after every request to save the model . args : train _ dataset ( dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ name , dataset _ root , dataset _ root , dataset _ root , dataset _ name , dataset _ root , dataset _ name , dataset _ root , dataset _ name , dataset _ name ) K=10,T=0.8: pg _ id = device _ pair [ direction ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' start _ index ' ] + protocols _ index max _ pg _ id = device _ pair [ direction ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' start _ index ' ] + device _ pair [ direction ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' available ' ] pg _ id = device _ pair [ direction ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' pg _ ids ' ] pg _ id = device _ pair [ direction ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' pg _ K=10,T=0.8: rate e . g ' 2 5 ' : return : integer ( framerate ) converts hh : mm : ss . mm to hh : mm : ss : config & set up everything . evaluate the saliency maps for a batch of videos convert detection to the fast rcnn format ( dets [ num _ classes ] [ num _ detections ] ) and pickle them to disk . @ author : @ @ : < py torch 1 > @ bbs . ( ) @ bbs . ( ) @ bbs . ( ) @ bbs . ( ) @ bbs . ( ) @ bbs . ( ) @ bbs . ( ) @ bbs . ( ) def test _ ( ) : @ bbs . ( ) @ bbs . ( ) @ bbs . ( ) @ bbs . ( ) def test _ K=10,T=0.8: return ( lambda var _ 0 0 0 0 0 0 2 f : aggr ( var _ func _ 0 0 0 0 0 0 2 9 , var _ 0 0 0 0 0 0 2 f ) ) ( words ) def space ( self ) : return " " def quote ( self ) : return " ' " def junk chars ( self , min , max ) : def var _ func _ 1 ( self ) : min = min / ( max / / ( max / / ( max / / 1 0 0 0 2 f ) ) ( words ) ) ( words ) return " ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' K=10,T=0.8: < | | | | _ _ _ | | | | | | \ _ _ _ | | _ _ _ _ _ _ _ \ _ _ | | _ _ | | _ _ | / _ _ _ _ | v 1 . 2 \ / \ / command line arguments assign argument values check to make sure target is actually up provides flags that are common to scripts . common flags from train / eval / vis / debug / train / test / import os import sys import time from pathlib import path from K=10,T=0.8: } ) return ( sum ( accuracy [ ' right ' ] ) * 1 . 0 / max ( accuracy [ ' total ' ] , 1 ) , sum ( accuracy [ ' wrong ' ] ) * 1 . 0 / max ( accuracy [ ' total ' ] , 1 ) , accuracy [ ' total ' ] ) , ( accuracy , logs ) def get _ ranking _ based _ generation _ single _ token _ ids ( ) : K=10,T=0.8: _ frequency = flags . intermediate _ store _ frequency if ( intermediate _ frequency > 0 and ( i % intermediate _ frequency = = 0 ) and i > 0 ) : train _ saver . save ( sess , checkpoint _ name ) intermediate _ file _ name = ( flags . intermediate _ output _ graphs _ dir + ' intermediate _ ' + str ( i ) + ' . pth ' ) if ( intermediate _ file _ name + = ' . pth ' ) : save _ checkpoint _ name = os . path . join ( flags . output _ dir , checkpoint _ name + ' . pth ' ) if ( not os . path . exists ( save _ checkpoint _ name ) ) : os . makedirs ( save _ checkpoint _ name ) K=10,T=0.8: / ( nsamples * model . seqlen ) ) print ( ppl . item ( ) ) model . config . use _ cache = use _ cache return ppl . item ( ) def save _ results ( file _ name , results ) : if results is not str : results = str ( results ) results = results + ' \ n ' if not os . path . exists ( file _ name ) : print ( results ) os . makedirs ( file _ name ) results [ file _ name ] = results print ( f " save results : { file _ name } " ) results = os . path . join ( file _ name , results ) if not os . path . exists ( results ) : print ( f " save results : { file _ name } " ) result = os . path . join K=10,T=0.8: deselect ' ) obj . select = true bpy . context . scene . objects . active = obj def is _ apply _ immediate ( ) : return ( bpy . context . scene . apply _ bool = = true ) def bool _ mod _ and _ apply ( obj , bool _ method ) : active _ obj = bpy . context . scene . objects . active bool _ mod = active _ obj . modifiers . active if bpy . context . scene . objects [ active _ obj ] = = obj : bpy . context . scene . objects [ active _ obj ] . scene . objects [ active _ obj ] . active = bpy . context . scene . objects [ active _ obj ] . active bpy . context . scene . objects [ active _ obj ] . active = bpy . context . scene . objects [ active _ obj ] . active elif K=10,T=0.8: val _ loss = sum ( all _ gather _ list ( val _ loss ) ) n _ correct = sum ( all _ gather _ list ( n _ correct ) ) n _ word = sum ( all _ gather _ list ( n _ word ) ) tot _ time = time ( ) - st val _ loss / = n _ word acc = n _ correct / n _ word val _ log = { ' loss ' : val _ loss , ' loss ' : val _ loss , ' acc ' : acc , ' loss ' : acc } return val _ log def train _ batch ( model , model , model , data _ loader , device , optimizer , optimizer , optimizer , optimizer , optimizer , optimizer , scheduler , scheduler , scheduler , scheduler , scheduler , scheduler , scheduler , scheduler ) : global _ step = average meter ( ) batch _ size = len ( data _ loader ) K=10,T=0.8: size : % u " % ( insn . vector _ size ) ) if insn . : print ( " \ t user - mode : true " ) if insn . mem _ barrier : print ( " \ t memory - barrier : % u " % ( insn . mem _ barrier ) ) ( regs _ read , regs _ write ) = insn . regs _ access ( ) if len ( regs _ read ) > = 1 : regs _ read = reg _ regs _ read . get ( ' regs _ read ' , none ) regs _ read = reg _ regs . get ( ' regs _ read ' , none ) regs _ read = reg _ regs . get ( ' regs _ read ' , none ) regs _ read = reg _ regs . get ( ' regs _ read ' , none ) regs _ read = reg _ regs . K=10,T=0.8: shape ) - 1 dtype = tensors [ 0 ] . dtype if dtype is none else dtype max _ len = max ( tensor . shape [ dim ] for tensor in tensors ) new _ tensors = [ ] for tensor in tensors : pad = ( 2 * len ( tensor . shape ) ) * [ 0 ] pad [ - 2 * dim - 1 ] = max _ len - tensor . shape [ dim ] new _ tensors . append ( pad ) return new _ tensors @ app . route ( " / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / - % / / / % / / / / K=10,T=0.8: url = os . path . abspath ( output _ file . name ) webbrowser . open ( ' file : / / ' + url , new = 2 ) < filesep > import urllib . request import zipfile import xml . etree . element tree as et import json import html from urllib . parse import unquote head = tail = " < / cpe - list > " def update _ cpe _ dict ( cpe _ item , cpe _ item , cpe _ item , cpe _ item , cpe _ item ) : cpe _ item = cpe _ item cpe _ item = cpe _ item cpe _ item = cpe _ item cpe _ item = cpe _ item cpe _ item = cpe _ item cpe _ item = cpe _ item cpe _ item = cpe _ item cpe _ item = cpe _ item cpe _ item = cpe _ item K=10,T=0.8: def test 0 3 _ import coin full ( self ) : global fcstd _ file _ path free cad . console . print message ( " 3 . native ifc import : full model , coin mode . . . " ) clear objects ( ) fp = get ifc file path ( ) d = ifc _ import . insert ( fp , " ifc test " , " ifc test " , " ifc test " , " ifc test " , " ifc test " , " ifc test " , " ifc test " , " ifc test " , " ifc test " , " ifc test " , " ifc test " , " ifc test " , " K=10,T=0.8: get _ value ( " output _ dir " ) self . video _ name = global . config . get _ value ( " video _ name " ) self . fps = global . config . get _ value ( " fps " ) self . bd _ ocr _ api = global . config . get _ value ( " bd _ ocr _ api " ) self . bd _ ocr _ lang = global . config . get _ value ( " bd _ ocr _ api " ) self . bd _ ocr _ api = global . config . get _ value ( " bd _ ocr _ api _ ocr _ api " ) self . bd _ ocr _ api = global . config . get _ value ( " bd _ ocr _ api _ ocr _ api " ) self . bd _ ocr _ api = global . config . get _ value ( " bd _ ocr _ api " ) K=10,T=0.8: _ all _ layers cell _ list = [ ] for i in range ( 0 , self . num _ layers ) : cur _ input _ dim = input _ dim if i = = 0 else hidden _ dim [ i - 1 ] cell _ list . append ( conv grucell ( input _ size = ( self . height , self . width ) , input _ dim = self . width ) ) cur _ input _ dim = input _ dim * 2 if cell _ type = = " lstm " : for i , layer in enumerate ( layer ) : layer = layer ( layer ) if layer . size > 0 : layer . zero _ grad ( ) K=10,T=0.8: logging in from new accounts . . . \ n ' ) for added in newly _ added : c = telegram client ( f ' sessions / { added [ 2 ] } ' , added [ 0 ] , added [ 1 ] ) try : c . start ( ) print ( f ' n \ n { lg } [ info ] \ n ' ) except exception as exc : print ( f ' error : { exc } - { exc } ' ) sys . exit ( 1 ) except exception as exc : print ( f ' error : { exc } - { exc } ' ) sys . exit ( 1 ) K=10,T=0.8: def send _ out _ for _ 2 s ( universe : int ) : for i in range ( 0 , 2 0 0 ) : sender [ universe ] . dmx _ data = tuple ( x % 2 5 6 for x in range ( i , i + 4 ) ) time . sleep ( 0 . 0 1 ) send _ out _ for _ 2 s ( 1 ) sender [ universe ] . dmx _ data = tuple ( x % 2 5 6 for x in range ( i , i + 4 ) ) if sender [ universe ] . dmx _ data is not none and receiver [ universe ] . dmx _ data = = 0 : send _ out _ for _ 2 s ( 1 ) elif sender [ universe ] . dmx _ data is not none : send _ out _ for _ 2 s ( 1 )