<s> from __future__ import absolute_import , print_function , division <EOL> import copy <EOL> import numpy <EOL> import theano <EOL> from theano import Variable , Constant <EOL> from theano import tensor <EOL> from theano . compile import SharedVariable <EOL> from theano . sandbox . cuda . type import CudaNdarrayType <EOL> try : <EOL> from theano . sandbox . cuda import filter as type_support_filter <EOL> from theano . sandbox . cuda . basic_ops import HostFromGpu , GpuFromHost <EOL> except ImportError : <EOL> pass <EOL> class _operators ( tensor . basic . _tensor_py_operators ) : <EOL> """<STR_LIT>""" <EOL> def _as_TensorVariable ( self ) : <EOL> return HostFromGpu ( ) ( self ) <EOL> def _as_CudaNdarrayVariable ( self ) : <EOL> return self <EOL> dtype = property ( lambda s : '<STR_LIT>' ) <EOL> broadcastable = property ( lambda s : s . type . broadcastable ) <EOL> ndim = property ( lambda s : s . type . ndim ) <EOL> class CudaNdarrayVariable ( _operators , Variable ) : <EOL> pass <EOL> CudaNdarrayType . Variable = CudaNdarrayVariable <EOL> class CudaNdarrayConstantSignature ( tensor . TensorConstantSignature ) : <EOL> pass <EOL> class CudaNdarrayConstant ( _operators , Constant ) : <EOL> def signature ( self ) : <EOL> return CudaNdarrayConstantSignature ( ( self . type , numpy . asarray ( self . data ) ) ) <EOL> def __str__ ( self ) : <EOL> if self . name is not None : <EOL> return self . name <EOL> try : <EOL> data = str ( numpy . asarray ( self . data ) ) <EOL> except Exception as e : <EOL> data = "<STR_LIT>" + str ( e ) <EOL> return "<STR_LIT>" + data + "<STR_LIT:}>" <EOL> CudaNdarrayType . Constant = CudaNdarrayConstant <EOL> class CudaNdarraySharedVariable ( _operators , SharedVariable ) : <EOL> """<STR_LIT>""" <EOL> get_value_return_ndarray = True <EOL> def get_value ( self , borrow = False , return_internal_type = False ) : <EOL> """<STR_LIT>""" <EOL> if return_internal_type or not self . get_value_return_ndarray : <EOL> if borrow : <EOL> return self . container . value <EOL> else : <EOL> return copy . deepcopy ( self . container . value ) <EOL> else : <EOL> return numpy . asarray ( self . container . value ) <EOL> def set_value ( self , value , borrow = False ) : <EOL> """<STR_LIT>""" <EOL> if not borrow : <EOL> if not isinstance ( value , numpy . ndarray ) : <EOL> value = copy . deepcopy ( value ) <EOL> self . container . value = value <EOL> def __getitem__ ( self , * args ) : <EOL> return _operators . __getitem__ ( self , * args ) <EOL> CudaNdarrayType . SharedVariable = CudaNdarraySharedVariable <EOL> def cuda_shared_constructor ( value , name = None , strict = False , <EOL> allow_downcast = None , borrow = False , <EOL> broadcastable = None , target = '<STR_LIT>' ) : <EOL> """<STR_LIT>""" <EOL> if target != '<STR_LIT>' : <EOL> raise TypeError ( '<STR_LIT>' ) <EOL> if strict : <EOL> _value = value <EOL> else : <EOL> _value = theano . _asarray ( value , dtype = '<STR_LIT>' ) <EOL> if not isinstance ( _value , numpy . ndarray ) : <EOL> raise TypeError ( '<STR_LIT>' ) <EOL> if _value . dtype . num != CudaNdarrayType . typenum : <EOL> raise TypeError ( '<STR_LIT>' ) <EOL> if broadcastable is None : <EOL> broadcastable = ( False , ) * len ( value . shape ) <EOL> type = CudaNdarrayType ( broadcastable = broadcastable ) <EOL> print ( "<STR_LIT>" ) <EOL> try : <EOL> rval = CudaNdarraySharedVariable ( type = type , value = _value , name = name , strict = strict ) <EOL> except Exception as e : <EOL> print ( "<STR_LIT>" , e ) <EOL> raise <EOL> return rval <EOL> def float32_shared_constructor ( value , name = None , strict = False , <EOL> allow_downcast = None , borrow = False , <EOL> broadcastable = None , target = '<STR_LIT>' ) : <EOL> """<STR_LIT>""" <EOL> if target != '<STR_LIT>' : <EOL> raise TypeError ( '<STR_LIT>' ) <EOL> if theano . sandbox . cuda . use . device_number is None : <EOL> theano . sandbox . cuda . use ( "<STR_LIT>" , <EOL> force = True , <EOL> default_to_move_computation_to_gpu = False , <EOL> move_shared_float32_to_gpu = False , <EOL> enable_cuda = False ) <EOL> if not isinstance ( value , ( numpy . ndarray , theano . sandbox . cuda . CudaNdarray ) ) : <EOL> raise TypeError ( '<STR_LIT>' ) <EOL> if isinstance ( value , numpy . ndarray ) and value . dtype . num != CudaNdarrayType . typenum : <EOL> raise TypeError ( '<STR_LIT>' ) <EOL> if broadcastable is None : <EOL> broadcastable = ( False , ) * len ( value . shape ) <EOL> type = CudaNdarrayType ( broadcastable = broadcastable ) <EOL> get_value_return_ndarray = True <EOL> if isinstance ( value , theano . sandbox . cuda . CudaNdarray ) : <EOL> get_value_return_ndarray = False <EOL> if borrow : <EOL> deviceval = value <EOL> else : <EOL> deviceval = value . copy ( ) <EOL> else : <EOL> deviceval = type_support_filter ( value , type . broadcastable , False , None ) <EOL> try : <EOL> rval = CudaNdarraySharedVariable ( type = type , value = deviceval , name = name , strict = strict ) <EOL> except Exception as e : <EOL> print ( "<STR_LIT>" , e ) <EOL> raise <EOL> rval . get_value_return_ndarray = get_value_return_ndarray <EOL> return rval </s>
