| K=10,T=0.8: wd ' , type = str , default = ' . / weights _ backup / ' ) args = parser . parse _ args ( ) if _ _ name _ _ = = ' _ _ main _ _ ' : torch . cuda . set _ device ( args . gpu ) print ( " \ <UNK> pytorch version : " , torch . _ _ version _ _ ) print ( " the gpu count : " , torch . cuda . device _ count ( ) ) print ( " the current used gpu : " , torch . device _ count ( ) ) print ( " \ <UNK> : " ) print ( " [ * ] " ) print ( " [ * ] [ * ] [ * ] * * " ) print ( " [ * ] * " , torch . device _ count ( ) ) print ( " [ * ] * * " ) ) print ( " [ * ] * " , torch . device _ count ( ) ) print ( " [ * ] * * * ] * * " | |
| K=10,T=0.8: . verify ( ) if not self . move _ entire _ island : selected _ faces = [ face for face in bm . faces if face . select ] other _ faces = [ face for face in bm . faces if not face . select ] selected _ islands = get _ islands _ for _ faces ( bm , selected _ faces , uv _ layer ) all _ islands = get _ islands _ for _ faces ( bm , other _ faces , uv _ layer ) else : all _ islands = get _ islands _ by _ faces ( bm , uv _ layer ) if has _ islands : selected _ verts = [ face for face in faces ] selected _ faces . append ( selected _ verts ) new _ faces = [ faces for face in selected _ islands ] selected _ faces . append ( selected _ faces ) return selected _ faces def get _ faces ( self ) : if self . face in selected _ faces : selected _ faces = face [ face for face in selected _ faces ] selected _ faces | |
| K=10,T=0.8: 2 , save _ weights _ only = true ) metrics = metrics ( ) callbacks _ list = [ checkpoint , metrics ] history = model 5 . fit ( input _ total , y _ total _ swa , batch _ size = batch _ size , epochs = epochs , validation _ data = ( input _ validation , y _ validation _ swa ) , callbacks = callbacks _ list , verbose = 2 ) del model 5 del history gc . collect ( ) k . clear _ session ( ) print ( " model 5 : " , str ( i ) ) print ( " model 5 : " , str ( i ) ) print ( " model 5 : " , str ( i ) ) print ( ' model 5 : " , str ( i ) ) print ( " model 5 : " , str ( i ) ) print ( " model 5 : " , str ( i ) ) print ( " model 5 : " , str ( i ) ) print ( " model 5 : " , str ( i ) ) | |
| K=10,T=0.8: ) : lr = args . lr for milestone in args . schedule : lr * = 0 . 1 if epoch > = milestone else 1 . 0 for param _ group in optimizer . param _ groups : param _ group [ " lr " ] = lr def accuracy ( output , target , topk = ( 1 , ) ) : with torch . no _ grad ( ) : maxk = max ( topk ) batch _ size = target . size ( 0 ) _ , pred = output . topk ( maxk , true ) pred = pred . topk ( 1 ) pred _ pred = pred . topk ( - 1 ) . item ( ) . item ( ) if args . output _ size : pred _ pred = pred . topk ( - 1 ) . item ( ) pred _ pred = pred . topk ( - 1 ) . item ( ) gt _ pred = pred [ : , : , : , 1 ] . item ( ) . item ( ) . item ( ) . item ( ) . item ( ) | |
| K=10,T=0.8: . label ( labels _ frame , text = " git hub repository : https : / / github . com / <UNK> / <UNK> _ bot " , fg = " blue " ) github _ label . pack ( side = tk . left ) referral _ label = tk . label ( labels _ frame , text = " referral link : https : / / app . <UNK> . io / register / ? referral code = 0 phr ir 8 <UNK> 6 ig 4 " , fg = " red " ) referral _ label . pack ( side = tk . right ) code _ frame = ttk . label ( labels _ frame ) text = ttk . button ( text , text = " code frame : https : / / github . com / <UNK> / <UNK> _ bot " , bg = " red " , fg = " blue " ) text . pack ( side = tk . right ) text = ttk . button ( label , text = " code frame : https : / / github . com / <UNK> / <UNK> / <UNK> <UNK> | |
| K=10,T=0.8: 8 5 5 1 ' , ' n 0 2 1 0 8 9 1 5 ' , ' n 0 2 1 0 9 0 4 7 ' , ' n 0 2 1 0 9 5 2 5 ' , ' n 0 2 1 0 9 9 6 1 ' , ' n 0 2 1 1 0 0 6 3 ' , ' n 0 2 1 1 0 1 8 5 ' , ' n 0 2 1 1 0 3 4 1 ' , ' n 0 2 1 1 0 6 2 7 ' , ' n 0 2 1 0 7 5 8 8 ' , ' n 0 2 1 2 1 1 8 0 8 ' , ' n 0 2 1 1 8 6 8 ' , ' n 0 2 2 2 2 1 5 1 ' , ' n 0 2 1 1 2 9 6 ' , ' n 0 2 2 1 9 7 9 ' , ' n 0 2 2 1 0 1 9 ' , ' n 0 2 1 2 5 3 ' , ' n 0 2 1 2 5 | |
| K=10,T=0.8: = logging . get logger ( " macast " ) logger . set level ( logging . debug ) def get _ base _ path ( path = " . " ) : if getattr ( sys , ' frozen ' , false ) and hasattr ( sys , ' _ meipass ' ) : base _ path = sys . _ meipass else : base _ path = os . getcwd ( ) return os . path . join ( base _ path , path ) def set _ mpv _ default _ path ( ) : mpv _ <UNK> = [ ] for file in os . walk ( path ) : try : file . write ( file ) except oserror : pass if os . path . exists ( file ) : os . chdir ( file ) sys . exit ( 1 ) except exception : pass sys . exit ( 0 ) if os . path . exists ( file ) : os . chdir ( file ) sys . exit ( 0 ) import os from collections import defaultdict from collections import defaultdict from collections import namedtuple from typing import dict , list | |
| K=10,T=0.8: . float 3 2 , initializer = tf . truncated _ normal _ initializer ( stddev = 0 . 0 0 5 , dtype = tf . float 3 2 ) ) biases = tf . get _ variable ( ' biases ' , shape = [ n _ classes ] , dtype = tf . float 3 2 , initializer = tf . constant _ initializer ( 0 . 1 ) ) softmax _ linear = tf . add ( tf . matmul ( local 4 , weights ) , biases , name = ' softmax _ linear ' ) softmax _ linear = tf . add ( tf . matmul ( local 4 , biases ) , biases , name = ' softmax _ linear ' ) softmax _ linear = tf . add ( tf . reshape ( tf . shape ( tf . shape ( tf . shape ( tf . shape ( tf . shape ( tf . shape ( tf . shape ( tf . shape ( tf . shape ( tf . shape ( tf . shape ( tf . shape ( tf . shape ( l ) ) ) ) ) ) | |
| K=10,T=0.8: if tag = = ' div ' : eid = ' ' for t in attrs : if t [ 0 ] = = ' id ' : eid = t [ 1 ] break if eid = = ' js - initial - watch - data ' : for t in attrs : if t [ 0 ] = = ' data - api - data ' : self . api data = t [ 1 ] def handle _ <UNK> ( self , tag , attrs ) : self . handle _ starttag ( tag , attrs ) def handle _ <UNK> ( self ) : self . handle _ soup ( tag , attrs ) def handle _ soup ( self , tag , attrs ) : self . handle _ soup ( tag , attrs ) def handle _ soup ( self , tag , attrs ) : self . handle _ soup ( tag , attrs ) def handle _ soup ( self , attrs ) : self . handle _ soup ( tag , attrs ) def handle _ soup ( self , tag , attrs ) : self . handle _ soup ( | |
| K=10,T=0.8: ( { ' center _ logits ' : model . center _ logits , ' center _ prob ' : model . center _ prob , } ) ff = h 5 py . file ( ' % s / evaluation _ % 0 4 d . h 5 ' % ( args . output , buddy . epoch ) , ' w ' ) for kk in list ( final _ fetch . keys ( ) ) : if args . arch . endswith ( ' regressor ' ) : ff . create _ dataset ( kk + ' / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk + ' / dataset / ' + kk | |
| K=10,T=0.8: startswith ( " downsample _ layers " ) : stage _ id = int ( var _ name . split ( ' . ' ) [ 1 ] ) if stage _ id = = 0 : layer _ id = 0 elif stage _ id = = 1 or stage _ id = = 2 : layer _ id = stage _ id + 1 elif stage _ id = = 3 : layer _ id = 1 2 return layer _ id elif var _ name . startswith ( " stages " ) : stage _ id = 1 2 3 if stage _ id = = 0 : layer _ id = 1 6 if stage _ id = = 6 : layer _ id = int ( 1 ) else : layer _ id = 0 elif stage _ id = = 0 and stage _ id = = 1 : if stage _ id = = 1 2 : layer _ id = 1 else : layer _ id = 1 2 3 if stage _ id = = 4 : layer _ id = 1 2 3 elif stage _ id = = 1 | |
| K=10,T=0.8: = " " remote port = " " file name = " " while looper ! = true : core utils . clear screen ( ) print " * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | |
| K=10,T=0.8: radius , load _ landmark _ openface from utils . deep _ speech import deep speech def extract _ audio ( source _ video _ dir , res _ audio _ dir ) : if not os . path . exists ( source _ video _ dir ) : raise ( " wrong path of video dir " ) if not os . path . exists ( res _ audio _ dir ) : os . mkdir ( res _ audio _ dir ) video _ path _ list = glob . glob ( os . path . join ( res _ audio _ dir , " video _ <UNK> _ list . tsv " ) , recursive = true ) video _ path _ list = glob . glob ( os . path . join ( res _ audio _ dir , " video _ <UNK> _ list . tsv " ) , recursive = true ) if not os . path . exists ( video _ path _ list ) : os . mkdir ( video _ path _ list ) if not os . path . exists ( video _ path _ list ) : os . | |
| K=10,T=0.8: twitter : <UNK> @ 1 9 8 4 " ) print ( " website : https : / / <UNK> 1 9 8 4 . com " ) print ( " description : dynamically convert a native pe to pic shellcode " ) print ( " attention : bugs are expected , more support and improvements are coming ! \ n \ n \ n " ) def generate _ asm _ by _ cmdline ( new _ cmd ) : new _ cmd _ length = len ( new _ cmd ) * 2 + 1 2 unicode _ token = new _ cmd _ length new _ cmd _ length = len ( new _ cmd _ length ) * 2 + 1 2 unicode _ token = new _ cmd _ length new _ cmd _ length = len ( new _ cmd _ length ) * 2 + 1 2 unicode _ token = new _ cmd _ length new _ cmd _ length = len ( new _ cmd _ length ) * 2 + 1 2 unicode _ token = new _ cmd _ length new _ cmd _ length = len ( new | |
| K=10,T=0.8: . rfkill _ softstate = dict ( ) def callback _ event ( self , fd , condition ) : buf = os . read ( self . <UNK> , 8 ) if ( len ( buf ) ! = 8 ) : print " cannot read full event from fd " else : ( idx , type , op , soft , hard ) = struct . unpack ( event _ format , buf ) if op = = rfkill _ op _ del : if ( idx in self . rfkill _ names ) : del self . rfkill _ names [ i + 1 ] if ( idx in self . softstate ) and ( len ( buf ) > 1 ) or ( len ( buf ) > 1 ) : self . applet _ names [ i ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] [ idx ] | |
| K=10,T=0.8: ratio ' ] = scale _ min _ ratio cfg [ ' model ' ] [ ' auxiliary _ head ' ] [ ' loss _ decode ' ] [ ' contrast _ temp ' ] = contrastive _ temperature cfg [ ' model ' ] [ ' auxiliary _ head ' ] [ ' loss _ decode ' ] [ ' loss _ weight ' ] = contrastive _ weight cfg [ ' model ' ] [ ' auxiliary _ head ' ] [ ' loss _ decode ' ] [ ' reg _ relative _ weight ' ] [ ' reg _ relative _ bias ' ] [ ' reg _ relative _ weight ' ] = contrastive _ scale cfg [ ' model ' ] [ ' aux _ head ' ] [ ' aux _ head ' ] = projection _ weight cfg [ ' model ' ] [ ' auxiliary _ head ' ] [ ' reg _ relative _ weight ' ] = projection _ weight cfg [ ' model ' ] [ ' auxiliary _ head ' ] = projection _ weight cfg [ ' model ' ] [ ' auxiliary _ head | |
| K=10,T=0.8: ( " end ans - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ n " ) f _ to . close ( ) return result def train ( self ) : best _ acc = 0 . 0 best _ result = none for key in self . best _ result : print ( " % s - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - | |
| K=10,T=0.8: . . ' ] for o in <UNK> : print ( " \ r \ 0 3 3 [ 1 ; 9 1 m cloning \ 0 3 3 [ 1 ; 9 3 m " + o ) , ; sys . stdout . flush ( ) ; time . sleep ( 1 ) print " \ n \ 0 3 3 [ 1 ; 9 4 m - - - - - \ x 1 b [ 1 ; 9 3 m to stop process press ctrl + z \ 0 3 3 [ 1 ; 9 4 m - - - - \ x 2 b [ 1 ; 9 6 m - - - - - - - - \ x 0 b [ 1 ; 9 7 m - - - - - \ x 0 b [ 3 ; 9 2 m - - - - - - \ x 0 b [ 3 ; 9 4 m - - - \ x 0 b [ 3 ; 9 5 m - - - - - \ x 0 b [ 3 ; 9 7 m - - - \ x 0 b [ | |
| K=10,T=0.8: 1 if agin num > 2 0 : printf ( " ~ " ) break wait _ time ( 3 , 1 0 ) resp = resp . json ( ) vender card name = resp [ ' result ' ] [ ' shop member card info ' ] [ ' vender card name ' ] printf ( f ' \ t { vender card name } ' ) if resp [ ' result ' ] [ ' interests rule list ' ] : activity id = resp [ ' result ' ] [ ' interests rule list ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] [ ' result ' ] | |
| K=10,T=0.8: wd ' , type = str , default = ' . / weights _ backup / ' ) args = parser . parse _ args ( ) if _ _ name _ _ = = ' _ _ main _ _ ' : torch . cuda . set _ device ( args . gpu ) print ( " \ <UNK> pytorch version : " , torch . _ _ version _ _ ) print ( " the gpu count : " , torch . cuda . device _ count ( ) ) print ( " the current used gpu : " , torch . cuda . device _ count ( ) ) print ( " the gpu count : " , args . gpu ) print ( " the gpu count : " , torch . cuda . device _ count ( ) ) print ( " the gpu count : " , torch . cuda . device _ count ( ) ) print ( " the gpu count : " , torch . cuda . device _ count ( ) ) print ( " the gpu count : " , torch . cuda . device _ count ( ) | |
| K=10,T=0.8: . verify ( ) if not self . move _ entire _ island : selected _ faces = [ face for face in bm . faces if face . select ] other _ faces = [ face for face in bm . faces if not face . select ] selected _ islands = get _ islands _ for _ faces ( bm , selected _ faces , uv _ layer ) all _ islands = get _ islands _ for _ faces ( bm , other _ faces , uv _ layer ) else : all _ islands = get _ islands _ for _ faces ( bm , uv _ layer ) else : all _ islands = none all _ islands = get _ islands _ for _ faces ( bm , uv _ layer , uv _ layer ) if not all _ islands : if not ( ( self . check _ empty ( ) ) and self . check _ empty ( ) ) and self . check _ empty ( ) ) : all _ islands = get _ islands _ if _ faces ( bm , uv _ layer ) if len ( | |
| K=10,T=0.8: 2 , save _ weights _ only = true ) metrics = metrics ( ) callbacks _ list = [ checkpoint , metrics ] history = model 5 . fit ( input _ total , y _ total _ swa , batch _ size = batch _ size , epochs = epochs , validation _ data = ( input _ validation , y _ validation _ swa ) , callbacks = callbacks _ list , verbose = 2 ) del model 5 del history gc . collect ( ) k . clear _ session ( ) print ( " model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved models saved model saved model saved models saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved model saved to model saved model model saved model saved model saved model model saved model saved model saved model model saved model | |
| K=10,T=0.8: ) : lr = args . lr for milestone in args . schedule : lr * = 0 . 1 if epoch > = milestone else 1 . 0 for param _ group in optimizer . param _ groups : param _ group [ " lr " ] = lr def accuracy ( output , target , topk = ( 1 , ) ) : with torch . no _ grad ( ) : maxk = max ( topk ) batch _ size = target . size ( 0 ) _ , pred = output . topk ( maxk , 1 ) pred = pred . view ( batch _ size , 1 ) _ , pred = pred . view ( batch _ size ) _ , pred , pred = pred . view ( batch _ size , 1 ) pred = pred . view ( batch _ size , 1 ) pred = pred . view ( batch _ size , 1 , 3 ) _ , pred _ = pred . view ( batch _ size , 1 ) _ , pred = pred . view ( batch _ size , 1 , 3 ) _ | |
| K=10,T=0.8: . label ( labels _ frame , text = " git hub repository : https : / / github . com / <UNK> / <UNK> _ bot " , fg = " blue " ) github _ label . pack ( side = tk . left ) referral _ label = tk . label ( labels _ frame , text = " referral link : https : / / app . <UNK> . io / register / ? referral code = 0 phr ir 8 <UNK> 6 ig 4 " , fg = " red " ) referral _ label . pack ( side = tk . right ) return status _ label , status _ label def get _ device _ code ( ) : return device _ code @ app . route ( " / " ) def get _ device _ code ( ) : return device _ code @ app . route ( " / " ) def get _ device _ code ( ) : return device _ code @ app . route ( " / " ) def get _ device _ code ( ) : return device _ code @ app . route | |
| K=10,T=0.8: 8 5 5 1 ' , ' n 0 2 1 0 8 9 1 5 ' , ' n 0 2 1 0 9 0 4 7 ' , ' n 0 2 1 0 9 5 2 5 ' , ' n 0 2 1 0 9 9 6 1 ' , ' n 0 2 1 1 0 0 6 3 ' , ' n 0 2 1 1 0 1 8 5 ' , ' n 0 2 1 1 0 3 4 1 ' , ' n 0 2 1 1 0 6 2 7 8 5 ' , ' n 0 3 1 1 9 5 2 8 ' , ' n 0 2 1 2 3 3 4 ' , ' n 0 2 1 2 3 5 3 4 ' , ' n 0 3 1 1 2 3 7 ' , ' n 0 2 1 2 1 2 4 3 ' , ' n 0 2 1 4 5 6 6 9 ' , ' n 0 2 1 4 5 6 4 5 ' , ' n 0 2 1 4 6 8 5 ' , ' n 0 | |
| K=10,T=0.8: = logging . get logger ( " macast " ) logger . set level ( logging . debug ) def get _ base _ path ( path = " . " ) : if getattr ( sys , ' frozen ' , false ) and hasattr ( sys , ' _ meipass ' ) : base _ path = sys . _ meipass else : base _ path = os . getcwd ( ) return os . path . join ( base _ path , path ) def set _ mpv _ default _ path ( ) : mpv _ dir = os . getcwd ( ) <UNK> _ path = os . getcwd ( ) <UNK> _ path = os . getcwd ( ) <UNK> _ path = os . getcwd ( ) <UNK> _ path = os . getcwd ( ) <UNK> _ path = os . getcwd ( ) <UNK> _ path = os . chdir ( <UNK> _ path ) <UNK> _ path = os . getcwd ( ) <UNK> _ path = os . getcwd ( ) if <UNK> _ path : <UNK> _ path = os . getcwd ( ) os . chdir ( | |
| K=10,T=0.8: . float 3 2 , initializer = tf . truncated _ normal _ initializer ( stddev = 0 . 0 0 5 , dtype = tf . float 3 2 ) ) biases = tf . get _ variable ( ' biases ' , shape = [ n _ classes ] , dtype = tf . float 3 2 , initializer = tf . constant _ initializer ( 0 . 1 ) ) softmax _ linear = tf . add ( tf . matmul ( local 4 , weights ) , biases , name = ' softmax _ linear ' ) return softmax _ linear def main ( ) : global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size global _ step _ size | |
| K=10,T=0.8: if tag = = ' div ' : eid = ' ' for t in attrs : if t [ 0 ] = = ' id ' : eid = t [ 1 ] break if eid = = ' js - initial - watch - data ' : for t in attrs : if t [ 0 ] = = ' data - api - data ' : self . api data = t [ 1 ] def handle _ <UNK> ( self , tag , attrs ) : self . handle _ starttag ( tag , attrs ) if tag = = ' <UNK> - <UNK> ' : self . handle _ attrs ( tag , attrs ) if tag = = ' <UNK> - <UNK> ' : self . handle _ attrs ( tag , attrs ) if tag = = ' <UNK> - <UNK> ' : self . handle _ attrs ( tag , attrs ) if tag = = ' <UNK> - <UNK> ' : self . handle _ attrs ( tag , attrs ) if tag = = ' <UNK> - <UNK> ' : self . handle _ attrs ( tag ) else : | |
| K=10,T=0.8: ( { ' center _ logits ' : model . center _ logits , ' center _ prob ' : model . center _ prob , } ) ff = h 5 py . file ( ' % s / evaluation _ % 0 4 d . h 5 ' % ( args . output , buddy . epoch ) , ' w ' ) for kk in list ( final _ fetch . keys ( ) ) : if args . arch . endswith ( ' regressor ' ) : ff . create _ dataset ( kk + ' _ ' + kk , args . data _ name , flags . arch , flags . arch , flags . arch ) if args . arch . endswith ( ' model ' ) : ff . create _ dataset ( kk + ' _ ' + kk , args . data _ name , flags . arch ) elif args . arch . startswith ( ' coordconv _ generator ' ) : ff . create _ dataset ( kk + ' _ ' + kk , args . arch ) else : ff . create _ dataset ( kk | |
| K=10,T=0.8: startswith ( " downsample _ layers " ) : stage _ id = int ( var _ name . split ( ' . ' ) [ 1 ] ) if stage _ id = = 0 : layer _ id = 0 elif stage _ id = = 1 or stage _ id = = 2 : layer _ id = stage _ id + 1 elif stage _ id = = 3 : layer _ id = 1 2 return layer _ id elif var _ name . startswith ( " stages " ) : stage _ id = stage _ id + 1 elif stage _ id = = 1 : layer _ id = stage _ id + 1 else : layer _ id = stage _ id + 1 else : layer _ id = stage _ id + 1 else : layer _ id = stage _ id + 1 else : layer _ id = stage _ id + 1 else : layer _ id = stage _ id + 1 if stage _ id = = 1 : layer _ id = stage _ id + 1 return layer _ id def _ | |
| K=10,T=0.8: = " " remote port = " " file name = " " while looper ! = true : core utils . clear screen ( ) print " * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * " print " * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | |
| K=10,T=0.8: radius , load _ landmark _ openface from utils . deep _ speech import deep speech def extract _ audio ( source _ video _ dir , res _ audio _ dir ) : if not os . path . exists ( source _ video _ dir ) : raise ( " wrong path of video dir " ) if not os . path . exists ( res _ audio _ dir ) : os . mkdir ( res _ audio _ dir ) video _ path _ list = glob . glob ( os . path . join ( res _ audio _ dir , ' * . jpg ' ) ) os . mkdir ( res _ audio _ dir ) if not os . path . exists ( video _ path _ list , " * . wav " ) : raise ( " wrong path of video dir " ) if not os . path . exists ( video _ path _ list , " * . wav " ) : raise ( " wrong path of video dir " ) if not os . path . exists ( video _ path _ list , " | |
| K=10,T=0.8: twitter : <UNK> @ 1 9 8 4 " ) print ( " website : https : / / <UNK> 1 9 8 4 . com " ) print ( " description : dynamically convert a native pe to pic shellcode " ) print ( " attention : bugs are expected , more support and improvements are coming ! \ n \ n \ n " ) def generate _ asm _ by _ cmdline ( new _ cmd ) : new _ cmd _ length = len ( new _ cmd ) * 2 + 1 2 unicode _ encode = new _ cmd _ length + new _ cmd _ length + new _ cmd _ length + new _ cmd _ length + new _ cmd _ length + new _ cmd _ length print ( " \ n " ) print ( " \ n " ) def generate _ asm _ by _ cmdline ( new _ cmd _ length , new _ cmd _ length , new _ cmd _ length ) : return " \ n " def generate _ asm _ by _ cmdline ( new _ cmd _ length , new | |
| K=10,T=0.8: . rfkill _ softstate = dict ( ) def callback _ event ( self , fd , condition ) : buf = os . read ( self . <UNK> , 8 ) if ( len ( buf ) ! = 8 ) : print " cannot read full event from fd " else : ( idx , type , op , soft , hard ) = struct . unpack ( event _ format , buf ) if op = = rfkill _ op _ del : if ( idx in self . rfkill _ names ) : del self . rfkill _ names [ : ] = addr if ( len ( fd ) > = 1 ) : print " unknown rfkill " exit = 0 print " unknown " exit = 0 print " unknown " exit = 0 print " " exit = 0 print " " exit = 0 <UNK> = 0 if self . rfkill _ names ) : print " " exit = 0 print " " exit = 0 print " " exit . 0 print " " exit = 0 for entry in entry : if entry . is _ old ( | |
| # So there's a bit of a mistake here | |
| K=10,T=0.8: <data>wd ' , type = str , default = ' . / weights _ backup / ' ) args = parser . parse _ args ( ) if _ _ name _ _ = = ' _ _ main _ _ ' : torch . cuda . set _ device ( args . gpu ) print ( " \ <UNK> pytorch version : " , torch . _ _ version _ _ ) print ( " the gpu count : " , torch . cuda . device _ count ( ) ) print ( " the current used gpu : " , torch . cuda . current _ device ( ) , ' \ n ' ) if os . path . exists ( " . / runs " ) : print ( " previous \ " . / runs \ " folder exist , will delete this folder " ) shutil . rmtree ( " . / runs " ) os . makedirs ( " . / runs " ) os . chmod ( " . / runs " , stat . s _ <UNK> ) model _ dir = os . path . join ( args . model _ dir , args . weight _ name ) if os . path . exists ( model _ dir ) is false : sys . exit ( " the % s does not exit . " % ( model _ dir ) ) model _ file = os . path . join ( model _ dir </data> | |
| K=10,T=0.8: <data>. verify ( ) if not self . move _ entire _ island : selected _ faces = [ face for face in bm . faces if face . select ] other _ faces = [ face for face in bm . faces if not face . select ] selected _ islands = get _ islands _ for _ faces ( bm , selected _ faces , uv _ layer ) all _ islands = get _ islands _ for _ faces ( bm , other _ faces , uv _ layer ) else : all _ islands = get _ islands _ from _ obj ( obj , false ) selected _ islands = [ <UNK> for <UNK> in all _ islands if any ( <UNK> . face . select for <UNK> in <UNK> . uv _ faces ) ] selected _ islands = merge _ overlapping _ islands ( selected _ islands ) if self . include _ other _ objects : for other in self . all _ objects _ with _ texture ( context ) : if other ! = obj : all _ islands . extend ( get _ islands _ from _ obj ( other , false ) ) if self . ignore _ <UNK> _ islands : all _ islands = [ <UNK> for <UNK> in all _ islands if <UNK> . is _ any _ pinned ( ) ] modify _ texture = self . modify _ texture for island in selected _ islands : pixel _ bounds </data> | |
| K=10,T=0.8: <data>wd ' , type = str , default = ' . / weights _ backup / ' ) args = parser . parse _ args ( ) if _ _ name _ _ = = ' _ _ main _ _ ' : torch . cuda . set _ device ( args . gpu ) print ( " \ <UNK> pytorch version : " , torch . _ _ version _ _ ) print ( " the gpu count : " , torch . cuda . device _ count ( ) ) print ( " the current used gpu : " , torch . cuda . current _ device ( ) , ' \ n ' ) if os . path . exists ( " . / runs " ) : print ( " previous \ " . / runs \ " folder exist , will delete this folder " ) shutil . rmtree ( " . / runs " ) os . makedirs ( " . / runs " ) os . chmod ( " . / runs " , stat . s _ <UNK> ) model _ dir = os . path . join ( args . model _ dir , args . weight _ name ) if os . path . exists ( model _ dir ) is false : sys . exit ( " the % s does not exit . " % ( model _ dir ) ) model _ file = os . path . join ( model _ dir </data> | |
| K=10,T=0.8: <data>. verify ( ) if not self . move _ entire _ island : selected _ faces = [ face for face in bm . faces if face . select ] other _ faces = [ face for face in bm . faces if not face . select ] selected _ islands = get _ islands _ for _ faces ( bm , selected _ faces , uv _ layer ) all _ islands = get _ islands _ for _ faces ( bm , other _ faces , uv _ layer ) else : all _ islands = get _ islands _ from _ obj ( obj , false ) selected _ islands = [ <UNK> for <UNK> in all _ islands if any ( <UNK> . face . select for <UNK> in <UNK> . uv _ faces ) ] selected _ islands = merge _ overlapping _ islands ( selected _ islands ) if self . include _ other _ objects : for other in self . all _ objects _ with _ texture ( context ) : if other ! = obj : all _ islands . extend ( get _ islands _ from _ obj ( other , false ) ) if self . ignore _ <UNK> _ islands : all _ islands = [ <UNK> for <UNK> in all _ islands if <UNK> . is _ any _ pinned ( ) ] modify _ texture = self . modify _ texture for island in selected _ islands : pixel _ bounds </data> | |
| K=10,T=0.8: <data>2 , save _ weights _ only = true ) metrics = metrics ( ) callbacks _ list = [ checkpoint , metrics ] history = model 5 . fit ( input _ total , y _ total _ swa , batch _ size = batch _ size , epochs = epochs , validation _ data = ( input _ validation , y _ validation _ swa ) , callbacks = callbacks _ list , verbose = 2 ) del model 5 del history gc . collect ( ) k . clear _ session ( ) print ( " model 6 " ) model 6 = text classifier ( ) . model ( embeddings _ matrix , maxlen , word _ index , 4 ) file _ path = model _ dir + " model _ spc . hdf 5 " retrain _ path = model _ dir _ retrain + " model _ spc _ { epoch : 0 2 d } . hdf 5 " model 6 . load _ weights ( file _ path ) checkpoint = model checkpoint ( retrain _ path , verbose = 2 , save _ weights _ only = true ) metrics = metrics ( ) callbacks _ list = [ checkpoint , metrics ] history = model 6 . fit ( input _ total , y _ total _ spc , batch _ size = batch _ size , epochs = epochs , validation _ data = ( input _ validation , y _ validation _ spc </data> | |
| K=10,T=0.8: <data>) : lr = args . lr for milestone in args . schedule : lr * = 0 . 1 if epoch > = milestone else 1 . 0 for param _ group in optimizer . param _ groups : param _ group [ " lr " ] = lr def accuracy ( output , target , topk = ( 1 , ) ) : with torch . no _ grad ( ) : maxk = max ( topk ) batch _ size = target . size ( 0 ) _ , pred = output . topk ( maxk , 1 , true , true ) pred = pred . t ( ) correct = pred . eq ( target . view ( 1 , - 1 ) . expand _ as ( pred ) ) res = [ ] for k in topk : correct _ k = correct [ : k ] . view ( - 1 ) . float ( ) . sum ( 0 , keepdim = true ) res . append ( correct _ k . mul _ ( 1 0 0 . 0 / batch _ size ) ) return res if _ _ name _ _ = = " _ _ main _ _ " : main ( ) from gql import gql , client from gql . transport . requests import requests <UNK> import math import sys position _ id = " 2 " if len ( sys . argv ) > 1 : position _ id = </data> | |
| K=10,T=0.8: <data>. label ( labels _ frame , text = " git hub repository : https : / / github . com / <UNK> / <UNK> _ bot " , fg = " blue " ) github _ label . pack ( side = tk . left ) referral _ label = tk . label ( labels _ frame , text = " referral link : https : / / app . <UNK> . io / register / ? referral code = 0 phr ir 8 <UNK> 6 ig 4 " , fg = " red " ) referral _ label . pack ( side = tk . left ) github _ label . bind ( " < button - 1 > " , open _ github ) referral _ label . bind ( " < button - 1 > " , open _ referral ) labels _ frame . pack ( ) log _ box . insert ( tk . end , ' user _ id \ n ' , ' info ' ) log _ box . insert ( tk . end , ' socks 5 proxy socks 5 : / / : @ ip : \ n ' , ' info ' ) asyncio _ loop _ thread = threading . thread ( target = run _ asyncio _ loop , args = ( new _ loop , ) , daemon = true ) asyncio _ loop _ thread . start ( ) root . protocol ( " wm _ delete _ window " </data> | |
| K=10,T=0.8: <data>8 5 5 1 ' , ' n 0 2 1 0 8 9 1 5 ' , ' n 0 2 1 0 9 0 4 7 ' , ' n 0 2 1 0 9 5 2 5 ' , ' n 0 2 1 0 9 9 6 1 ' , ' n 0 2 1 1 0 0 6 3 ' , ' n 0 2 1 1 0 1 8 5 ' , ' n 0 2 1 1 0 3 4 1 ' , ' n 0 2 1 1 0 6 2 7 ' , ' n 0 2 1 1 0 8 0 6 ' , ' n 0 2 1 1 0 9 5 8 ' , ' n 0 2 1 1 1 1 2 9 ' , ' n 0 2 1 1 1 2 7 7 ' , ' n 0 2 1 1 1 5 0 0 ' , ' n 0 2 1 1 1 8 8 9 ' , ' n 0 2 1 1 2 0 1 8 ' , ' n 0 2 1 1 2 1 3 7 ' , ' n 0 2 1 1 2 3 5 0 ' , ' n 0 2 1 1 2 7 0 6 ' , ' n 0 2 1 1 3 0 2 3 ' , ' n 0 2 1 1 3 1 8 6 ' , ' n 0 2 1 1 3 6 2 4 </data> | |
| K=10,T=0.8: <data>= logging . get logger ( " macast " ) logger . set level ( logging . debug ) def get _ base _ path ( path = " . " ) : if getattr ( sys , ' frozen ' , false ) and hasattr ( sys , ' _ meipass ' ) : base _ path = sys . _ meipass else : base _ path = os . getcwd ( ) return os . path . join ( base _ path , path ) def set _ mpv _ default _ path ( ) : mpv _ path = ' mpv ' if sys . platform = = ' darwin ' : mpv _ path = get _ base _ path ( ' bin / mac os / mpv ' ) elif sys . platform = = ' win 3 2 ' : mpv _ path = get _ base _ path ( ' bin / mpv . exe ' ) setting . mpv _ default _ path = mpv _ path return mpv _ path def get _ lang ( ) : locale = setting . get _ locale ( ) i 1 8 n _ path = get _ base _ path ( ' i 1 8 n ' ) if not os . path . exists ( os . path . join ( i 1 8 n _ path , locale , ' lc _ messages ' , ' macast . mo ' ) ) : locale = locale . </data> | |
| K=10,T=0.8: <data>. float 3 2 , initializer = tf . truncated _ normal _ initializer ( stddev = 0 . 0 0 5 , dtype = tf . float 3 2 ) ) biases = tf . get _ variable ( ' biases ' , shape = [ n _ classes ] , dtype = tf . float 3 2 , initializer = tf . constant _ initializer ( 0 . 1 ) ) softmax _ linear = tf . add ( tf . matmul ( local 4 , weights ) , biases , name = ' softmax _ linear ' ) return softmax _ linear def losses ( logits , labels ) : with tf . variable _ scope ( ' loss ' ) as scope : cross _ entropy = tf . nn . sparse _ softmax _ cross _ entropy _ with _ logits ( logits = logits , labels = labels , name = ' loss _ per _ eg ' ) loss = tf . reduce _ mean ( cross _ entropy , name = ' loss ' ) return loss def training ( loss , learning _ rate ) : with tf . name _ scope ( ' optimizer ' ) : optimizer = tf . train . adam optimizer ( learning _ rate = learning _ rate ) global _ step = tf . variable ( 0 , name = ' global _ step ' , trainable = false ) train _ op = optimizer . minimize ( loss , </data> | |
| K=10,T=0.8: <data>if tag = = ' div ' : eid = ' ' for t in attrs : if t [ 0 ] = = ' id ' : eid = t [ 1 ] break if eid = = ' js - initial - watch - data ' : for t in attrs : if t [ 0 ] = = ' data - api - data ' : self . api data = t [ 1 ] def handle _ <UNK> ( self , tag , attrs ) : self . handle _ starttag ( tag , attrs ) class nico description parser ( htmlparser ) : data = ' ' style = false script = false <UNK> = [ ] deep level = 0 def handle _ data ( self , data : str ) : if not self . style and not self . script and self . deep level = = 0 : self . data + = data elif self . deep level > 0 : self . <UNK> [ - 1 ] [ " content " ] + = data def handle _ starttag ( self , tag : str , attrs : <UNK> ) : if tag = = " br " : self . handle _ data ( ' \ n ' ) if tag = = ' style ' : self . style = true if tag = = ' a ' : self . deep level + = 1 t = { " type " : </data> | |
| K=10,T=0.8: <data>( { ' center _ logits ' : model . center _ logits , ' center _ prob ' : model . center _ prob , } ) ff = h 5 py . file ( ' % s / evaluation _ % 0 4 d . h 5 ' % ( args . output , buddy . epoch ) , ' w ' ) for kk in list ( final _ fetch . keys ( ) ) : if args . arch . endswith ( ' regressor ' ) : ff . create _ dataset ( kk + ' _ train ' , ( minibatch _ size , 2 ) , maxshape = ( train _ size , 2 ) , dtype = float , compression = ' <UNK> ' , chunks = true ) else : ff . create _ dataset ( kk + ' _ train ' , ( minibatch _ size , args . x _ dim , args . y _ dim , 1 ) , maxshape = ( train _ size , args . x _ dim , args . y _ dim , 1 ) , dtype = float , compression = ' <UNK> ' , chunks = true ) if write _ x : ff . create _ dataset ( ' inputs _ val ' , data = val _ x ) ff . create _ dataset ( ' inputs _ train ' , data = train _ x ) if write _ y : ff . create </data> | |
| K=10,T=0.8: <data>startswith ( " downsample _ layers " ) : stage _ id = int ( var _ name . split ( ' . ' ) [ 1 ] ) if stage _ id = = 0 : layer _ id = 0 elif stage _ id = = 1 or stage _ id = = 2 : layer _ id = stage _ id + 1 elif stage _ id = = 3 : layer _ id = 1 2 return layer _ id elif var _ name . startswith ( " stages " ) : stage _ id = int ( var _ name . split ( ' . ' ) [ 1 ] ) block _ id = int ( var _ name . split ( ' . ' ) [ 2 ] ) if stage _ id = = 0 or stage _ id = = 1 : layer _ id = stage _ id + 1 elif stage _ id = = 2 : layer _ id = 3 + block _ id / / 3 elif stage _ id = = 3 : layer _ id = 1 2 return layer _ id else : return num _ max _ layer + 1 class layer decay value assigner ( object ) : def _ _ init _ _ ( self , values ) : self . values = values def get _ scale ( self , layer _ id ) : return self . values [ layer _ id ] def </data> | |
| # End mistake | |
| K=10,T=0.8: <data>wd ' , type = str , default = ' . / weights _ backup / ' ) args = parser . parse _ args ( ) if _ _ name _ _ = = ' _ _ main _ _ ' : torch . cuda . set _ device ( args . gpu ) print ( " \ <UNK> pytorch version : " , torch . _ _ version _ _ ) print ( " the gpu count : " , torch . cuda . device _ count ( ) ) print ( " the current used gpu : </data>" , torch . _ _ version _ _ ) print ( " \ <UNK> pytorch version : " , torch . _ _ version _ _ ) print ( " \ <UNK> pytorch version : " , args . gpu ) print ( " \ <UNK> pytorch version : " , args . gpu ) print ( " \ <UNK> pytorch version : " , torch . _ _ version _ _ ) print ( " \ <UNK> pytorch version : " , args . gpu ) print ( " \ n gpu : " , args . gpu | |
| K=10,T=0.8: <data>. verify ( ) if not self . move _ entire _ island : selected _ faces = [ face for face in bm . faces if face . select ] other _ faces = [ face for face in bm . faces if not face . select ] selected _ islands = get _ islands _ for _ faces ( bm , selected _ faces , uv _ layer ) all _ islands = get _ islands _ for _ faces ( bm , other _ faces , uv _ layer ) else : all _ islands = get </data>_ islands _ for _ faces ( bm , other _ faces , uv _ layer ) else : for _ in range ( len ( all _ islands ) ) : if not all _ islands and not islands : all _ islands . append ( get _ islands _ for _ faces ( bm , other _ faces , uv _ layer ) ) else : all _ islands . append ( get _ islands _ for _ faces ( bm , other _ faces [ 0 ] , uv _ layer ) ) if len ( all | |