<s> """<STR_LIT>""" <EOL> import logging <EOL> import numpy as np <EOL> from neon import NervanaObject <EOL> logger = logging . getLogger ( __name__ ) <EOL> class NervanaDataIterator ( NervanaObject ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , name = None ) : <EOL> super ( NervanaDataIterator , self ) . __init__ ( name ) <EOL> def nbatches ( self ) : <EOL> raise NotImplemented ( ) <EOL> def reset ( self ) : <EOL> raise NotImplemented ( ) <EOL> def __iter__ ( self ) : <EOL> raise NotImplemented ( ) <EOL> class ArrayIterator ( NervanaDataIterator ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , X , y = None , nclass = None , lshape = None , make_onehot = True , name = None ) : <EOL> """<STR_LIT>""" <EOL> super ( ArrayIterator , self ) . __init__ ( name = name ) <EOL> X = X if isinstance ( X , list ) else [ X ] <EOL> self . ndata = len ( X [ <NUM_LIT:0> ] ) <EOL> assert self . ndata >= self . be . bsz <EOL> self . start = <NUM_LIT:0> <EOL> self . nclass = nclass <EOL> self . ybuf = None <EOL> if make_onehot and nclass is None and y is not None : <EOL> raise AttributeError ( '<STR_LIT>' ) <EOL> self . shape = [ x . shape [ <NUM_LIT:1> ] if lshape is None else lshape for x in X ] <EOL> if len ( self . shape ) == <NUM_LIT:1> : <EOL> self . shape = self . shape [ <NUM_LIT:0> ] <EOL> self . lshape = lshape <EOL> def transpose_gen ( z ) : <EOL> return ( self . be . array ( z ) , self . be . iobuf ( z . shape [ <NUM_LIT:1> ] ) , <EOL> lambda _in , _out : self . be . copy_transpose ( _in , _out ) ) <EOL> def onehot_gen ( z ) : <EOL> return ( self . be . array ( z . reshape ( ( - <NUM_LIT:1> , <NUM_LIT:1> ) ) , dtype = np . int32 ) , self . be . iobuf ( nclass ) , <EOL> lambda _in , _out : self . be . onehot ( _in , axis = <NUM_LIT:0> , out = _out ) ) <EOL> self . Xdev , self . Xbuf , self . unpack_func = zip ( * [ transpose_gen ( x ) for x in X ] ) <EOL> self . dbuf , self . hbuf = list ( self . Xdev ) , list ( self . Xbuf ) <EOL> self . unpack_func = list ( self . unpack_func ) <EOL> if y is not None : <EOL> self . ydev , self . ybuf , yfunc = onehot_gen ( y ) if make_onehot else transpose_gen ( y ) <EOL> self . dbuf . append ( self . ydev ) <EOL> self . hbuf . append ( self . ybuf ) <EOL> self . unpack_func . append ( yfunc ) <EOL> @ property <EOL> def nbatches ( self ) : <EOL> return - ( ( self . start - self . ndata ) // self . be . bsz ) <EOL> def reset ( self ) : <EOL> """<STR_LIT>""" <EOL> self . start = <NUM_LIT:0> <EOL> def __iter__ ( self ) : <EOL> """<STR_LIT>""" <EOL> for i1 in range ( self . start , self . ndata , self . be . bsz ) : <EOL> bsz = min ( self . be . bsz , self . ndata - i1 ) <EOL> islice1 , oslice1 = slice ( <NUM_LIT:0> , bsz ) , slice ( i1 , i1 + bsz ) <EOL> islice2 , oslice2 = None , None <EOL> if self . be . bsz > bsz : <EOL> islice2 , oslice2 = slice ( bsz , None ) , slice ( <NUM_LIT:0> , self . be . bsz - bsz ) <EOL> self . start = self . be . bsz - bsz <EOL> for buf , dev , unpack_func in zip ( self . hbuf , self . dbuf , self . unpack_func ) : <EOL> unpack_func ( dev [ oslice1 ] , buf [ : , islice1 ] ) <EOL> if oslice2 : <EOL> unpack_func ( dev [ oslice2 ] , buf [ : , islice2 ] ) <EOL> inputs = self . Xbuf [ <NUM_LIT:0> ] if len ( self . Xbuf ) == <NUM_LIT:1> else self . Xbuf <EOL> targets = self . ybuf if self . ybuf else inputs <EOL> yield ( inputs , targets ) <EOL> class DataIterator ( ArrayIterator ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , * args , ** kwargs ) : <EOL> logger . error ( '<STR_LIT>' <EOL> '<STR_LIT>' ) <EOL> super ( DataIterator , self ) . __init__ ( * args , ** kwargs ) <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> from neon . data import load_mnist <EOL> ( X_train , y_train ) , ( X_test , y_test ) = load_mnist ( ) <EOL> from neon . backends . nervanagpu import NervanaGPU <EOL> ng = NervanaGPU ( <NUM_LIT:0> , device_id = <NUM_LIT:1> ) <EOL> NervanaObject . be = ng <EOL> ng . bsz = <NUM_LIT> <EOL> train_set = ArrayIterator ( <EOL> [ X_test [ : <NUM_LIT:1000> ] , X_test [ : <NUM_LIT:1000> ] ] , y_test [ : <NUM_LIT:1000> ] , nclass = <NUM_LIT:10> ) <EOL> for i in range ( <NUM_LIT:3> ) : <EOL> for bidx , ( X_batch , y_batch ) in enumerate ( train_set ) : <EOL> print bidx , train_set . start <EOL> pass </s>
