K=10,T=0.8: crop ( 3 2 ) ] ) to _ tensor = transforms . to tensor ( ) normalize = transforms . normalize ( [ 0 . 5 ] * 3 , [ 0 . 5 ] * 3 ) test _ transform = transforms . compose ( [ transforms . to tensor ( ) , normalize ] ) if args . dataset = = ' cifar 1 0 ' : train _ data = datasets . cifar 1 0 ( os . path . join ( args . dataset , ' mnist ' ) , os . path . join ( args . dataset , ' mnist ' ) , os . path . join ( args . dataset , ' mnist ' ) , os . path . join ( args . dataset , ' mnist ' ) , os . path . join ( args . dataset , ' mnist ' ) , os . path . join ( args . dataset , ' mnist ' K=10,T=0.8: all _ input _ nodes [ 0 ] for i in group ] out _ flat = [ graph . call _ function ( torch . ops . aten . view . default , args = ( i , [ i . meta [ " val " ] . numel ( ) ] ) ) for i in inputs ] out _ cat = graph . call _ function ( torch . ops . aten . cat . default , args = ( i , [ i . meta [ " val " ] . numel ( ) ] ) ) out _ cat . append ( torch . ops . ops . impl . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . K=10,T=0.8: arch [ ' hwc ' ] , batch _ size = args . batch _ size , rtype = ' tanh ' ) machine = vaegan ( arch , is _ training = true ) loss = machine . loss ( imgs ) xh = machine . sample ( args . batch _ size ) x _ interp = machine . interpolate ( imgs [ 0 ] , imgs [ 1 ] , n _ interp ) opt = torch . zeros ( len ( imgs ) ) for x _ interp in range ( 1 , args . batch _ size ) : x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x K=10,T=0.8: ) except guard error : pass raise match error ( " no match for given argument values " ) def append ( self , arity , clause ) : if self . clauses . get ( arity , false ) and hasattr ( self . clauses [ - 1 ] , ' _ _ catchall _ _ ' ) : raise wont match error ( " function clause defined after a catch " ) if clause in self . clauses . get ( arity , false ) : self . clauses . [ arity ] = 1 else : raise assertion error ( " expected arity not yet " ) self . clauses . get ( arity , false ) def append ( self , arity , clause ) : self . clauses . set ( arity , false ) K=10,T=0.8: shortcuts ( ' db ' ) else : return false def double key nonce checker ( self , obj ) : if self . dbfile checker ( ) : try : with open ( uiwindow . database _ file , ' r ' ) as db _ file : data = db _ file . read ( ) if obj = = ' key ' : return false else : return false def double key check ( self , obj ) : if self . sqlite checker ( ) : return true return false if self . sqlite checker ( ) : return false def double key check ( self , obj ) : if self . sqlite K=10,T=0.8: crop ( 3 2 ) ] ) to _ tensor = transforms . to tensor ( ) normalize = transforms . normalize ( [ 0 . 5 ] * 3 , [ 0 . 5 ] * 3 ) test _ transform = transforms . compose ( [ transforms . to tensor ( ) , normalize ] ) if args . dataset = = ' cifar 1 0 ' : train _ data = datasets . cifar 1 0 ( os . path . join ( args . dataset , ' train _ data . cifar ' ) , train _ batch , train _ batch , test _ batch , ) train _ loader = build _ loader ( args . dataset , ' train _ data . py ' ) train _ loader = build _ loader ( args . dataset , ' train _ data . py ' ) train _ loader = build _ loader ( args . dataset , ' train _ data . py ' ) K=10,T=0.8: all _ input _ nodes [ 0 ] for i in group ] out _ flat = [ graph . call _ function ( torch . ops . aten . view . default , args = ( i , [ i . meta [ " val " ] . numel ( ) ] ) ) for i in inputs ] out _ cat = graph . call _ function ( torch . ops . aten . cat . default , args = ( i , [ i . meta [ " val " ] . numel ( ) ] ) ) out _ cat . append ( out _ cat ) K=10,T=0.8: arch [ ' hwc ' ] , batch _ size = args . batch _ size , rtype = ' tanh ' ) machine = vaegan ( arch , is _ training = true ) loss = machine . loss ( imgs ) xh = machine . sample ( args . batch _ size ) x _ interp = machine . interpolate ( imgs [ 0 ] , imgs [ 1 ] , n _ interp ) opt = torch . optim . adam ( net . parameters ( ) , lr = args . lr , momentum = 0 . 9 , weight _ decay = args . weight _ decay , weight _ decay = args . weight _ decay , weight _ decay = args . weight _ decay ) print ( " \ n " ) if args . lr : lr = args . lr _ lr lr = args . lr _ lr else :