def asnumpy(a, stream=None, order='C', out=None):
"""Returns an array on the host memory from an arbitrary source array.
Args:
a: Arbitrary object that can be converted to :class:`numpy.ndarray`.
stream (cupy.cuda.Stream): CUDA stream object. If it is specified, then
the device-to-host copy runs asynchronously. Otherwise, the copy is
synchronous. Note that if ``a`` is not a :class:`cupy.ndarray`
object, then this argument has no effect.
order ({'C', 'F', 'A'}): The desired memory layout of the host
array. When ``order`` is 'A', it uses 'F' if ``a`` is
fortran-contiguous and 'C' otherwise.
out (numpy.ndarray): The output array to be written to. It must have
compatible shape and dtype with those of ``a``'s.
Returns:
numpy.ndarray: Converted array on the host memory.
"""
if isinstance(a, ndarray):
return a.get(stream=stream, order=order, out=out)
elif hasattr(a, "__cuda_array_interface__"):
return array(a).get(stream=stream, order=order, out=out)
else:
temp = _numpy.asarray(a, order=order)
if out is not None:
out[...] = temp
else:
out = temp
return out
def check_del(self, null):
stream = cuda.Stream(null=null).use()
stream_ptr = stream.ptr
x = from_data.array([1, 2, 3])
del stream
assert cuda.Stream.null == cuda.get_current_stream()
# Want to test cudaStreamDestory is issued, but
# runtime.streamQuery(stream_ptr) causes SEGV. We cannot test...
del stream_ptr
del x
def __getitem__(self, key):
trans1d = self.trans1d
ndmin = self.ndmin
objs = []
scalars = []
arraytypes = []
scalartypes = []
if isinstance(key, str):
raise NotImplementedError
if not isinstance(key, tuple):
key = (key, )
for i, k in enumerate(key):
scalar = False
if isinstance(k, slice):
raise NotImplementedError
elif isinstance(k, str):
if i != 0:
raise ValueError(
'special directives must be the first entry.')
raise NotImplementedError
elif type(k) in numpy.ScalarType:
newobj = from_data.array(k, ndmin=ndmin)
scalars.append(i)
scalar = True
scalartypes.append(newobj.dtype)
else:
newobj = from_data.array(k, copy=False, ndmin=ndmin)
if ndmin > 1:
ndim = from_data.array(k, copy=False).ndim
if trans1d != -1 and ndim < ndmin:
newobj = self._output_obj(newobj, ndim, ndmin, trans1d)
objs.append(newobj)
if not scalar and isinstance(newobj, core.ndarray):
arraytypes.append(newobj.dtype)
final_dtype = numpy.find_common_type(arraytypes, scalartypes)
if final_dtype is not None:
for k in scalars:
objs[k] = objs[k].astype(final_dtype)
return join.concatenate(tuple(objs), axis=self.axis)
def asnumpy(a, stream=None, order='C'):
"""Returns an array on the host memory from an arbitrary source array.
Args:
a: Arbitrary object that can be converted to :class:`numpy.ndarray`.
stream (cupy.cuda.Stream): CUDA stream object. If it is specified, then
the device-to-host copy runs asynchronously. Otherwise, the copy is
synchronous. Note that if ``a`` is not a :class:`cupy.ndarray`
object, then this argument has no effect.
order ({'C', 'F', 'A'}): The desired memory layout of the host
array. When ``order`` is 'A', it uses 'F' if ``a`` is
fortran-contiguous and 'C' otherwise.
Returns:
numpy.ndarray: Converted array on the host memory.
"""
if isinstance(a, ndarray):
return a.get(stream=stream, order=order)
elif hasattr(a, "__cuda_array_interface__"):
return array(a).get(stream=stream, order=order)
else:
return numpy.asarray(a, order=order)
