def may_share_memory(a, b, raise_other_type=True):
a_ndarray = isinstance(a, np.ndarray)
b_ndarray = isinstance(b, np.ndarray)
if a_ndarray and b_ndarray:
return TensorType.may_share_memory(a, b)
a_cuda = _is_cuda(a)
b_cuda = _is_cuda(b)
if a_cuda and b_cuda:
return CudaNdarrayType.may_share_memory(a, b)
a_gpua = _is_gpua(a)
b_gpua = _is_gpua(b)
if a_gpua and b_gpua:
return gpuarray.pygpu.gpuarray.may_share_memory(a, b)
a_sparse = _is_sparse(a)
b_sparse = _is_sparse(b)
if (not (a_ndarray or a_sparse or a_cuda or a_gpua)
or not (b_ndarray or b_sparse or b_cuda or b_gpua)):
if raise_other_type:
raise TypeError("may_share_memory support only ndarray"
" and scipy.sparse, CudaNdarray or GpuArray type")
return False
if a_cuda or b_cuda or a_gpua or b_gpua:
return False
return SparseType.may_share_memory(a, b)
def may_share_memory(a, b, raise_other_type=True):
a_ndarray = isinstance(a, np.ndarray)
b_ndarray = isinstance(b, np.ndarray)
if a_ndarray and b_ndarray:
return TensorType.may_share_memory(a, b)
a_cuda = _is_cuda(a)
b_cuda = _is_cuda(b)
if a_cuda and b_cuda:
return CudaNdarrayType.may_share_memory(a, b)
a_gpua = _is_gpua(a)
b_gpua = _is_gpua(b)
if a_gpua and b_gpua:
return gpuarray.pygpu.gpuarray.may_share_memory(a, b)
a_sparse = _is_sparse(a)
b_sparse = _is_sparse(b)
if (not(a_ndarray or a_sparse or a_cuda or a_gpua) or
not(b_ndarray or b_sparse or b_cuda or b_gpua)):
if raise_other_type:
raise TypeError("may_share_memory support only ndarray"
" and scipy.sparse, CudaNdarray or GpuArray type")
return False
if a_cuda or b_cuda or a_gpua or b_gpua:
return False
return SparseType.may_share_memory(a, b)
def may_share_memory(a, b, raise_other_type=True):
a_ndarray = isinstance(a, numpy.ndarray)
b_ndarray = isinstance(b, numpy.ndarray)
a_sparse = _is_sparse(a)
b_sparse = _is_sparse(b)
a_cuda = _is_cuda(a)
b_cuda = _is_cuda(b)
if not(a_ndarray or a_sparse or a_cuda) or not(b_ndarray or b_sparse or b_cuda):
if raise_other_type:
raise TypeError("may_share_memory support only ndarray and scipy.sparse and CudaNdarray type")
return False
if a_ndarray and b_ndarray:
return TensorType.may_share_memory(a,b)
if a_cuda and b_cuda:
from theano.sandbox.cuda.type import CudaNdarrayType
return CudaNdarrayType.may_share_memory(a,b)
if a_cuda or b_cuda:
return False
return SparseType.may_share_memory(a,b)
def may_share_memory(a, b, raise_other_type=True):
a_ndarray = isinstance(a, numpy.ndarray)
b_ndarray = isinstance(b, numpy.ndarray)
a_sparse = _is_sparse(a)
b_sparse = _is_sparse(b)
a_cuda = _is_cuda(a)
b_cuda = _is_cuda(b)
if (not (a_ndarray or a_sparse or a_cuda)
or not (b_ndarray or b_sparse or b_cuda)):
if raise_other_type:
raise TypeError("may_share_memory support only ndarray"
" and scipy.sparse and CudaNdarray type")
return False
if a_ndarray and b_ndarray:
return TensorType.may_share_memory(a, b)
if a_cuda and b_cuda:
from theano.sandbox.cuda.type import CudaNdarrayType
return CudaNdarrayType.may_share_memory(a, b)
if a_cuda or b_cuda:
return False
return SparseType.may_share_memory(a, b)
