def set(self, arr, stream=None):
"""Copies an array on the host memory to cuda.ndarray.
Args:
arr (numpy.ndarray): The source array on the host memory.
stream (cupy.cuda.Stream): CUDA stream object. If it is given, the
copy runs asynchronously. Otherwise, the copy is synchronous.
"""
if not isinstance(arr, numpy.ndarray):
raise TypeError('Only numpy.ndarray can be set to cupy.ndarray')
if self._dtype != arr.dtype:
raise TypeError('{} array cannot be set to {} array'.format(
arr.dtype, self._dtype))
if self._shape != arr.shape:
raise ValueError('Shape mismatch')
if not self.flags.c_contiguous:
raise RuntimeError('Cannot set to non-contiguous array')
arr = numpy.ascontiguousarray(arr)
ptr = internal.get_ndarray_ptr(arr)
if stream is None:
self.data.copy_from_host(ptr, self.nbytes)
else:
self.data.copy_from_host_async(ptr, self.nbytes, stream)
def set(self, arr, stream=None):
"""Copies an array on the host memory to cuda.ndarray.
Args:
arr (numpy.ndarray): The source array on the host memory.
stream (cupy.cuda.Stream): CUDA stream object. If it is given, the
copy runs asynchronously. Otherwise, the copy is synchronous.
"""
if not isinstance(arr, numpy.ndarray):
raise TypeError('Only numpy.ndarray can be set to cupy.ndarray')
if self._dtype != arr.dtype:
raise TypeError('{} array cannot be set to {} array'.format(
arr.dtype, self._dtype))
if self._shape != arr.shape:
raise ValueError('Shape mismatch')
if not self.flags.c_contiguous:
raise RuntimeError('Cannot set to non-contiguous array')
arr = numpy.ascontiguousarray(arr)
ptr = internal.get_ndarray_ptr(arr)
if stream is None:
self.data.copy_from_host(ptr, self.nbytes)
else:
self.data.copy_from_host_async(ptr, self.nbytes, stream)
def get(self, stream=None):
"""Returns a copy of the array on host memory.
Args:
stream (cupy.cuda.Stream): CUDA stream object. If it is given, the
copy runs asynchronously. Otherwise, the copy is synchronous.
Returns:
numpy.ndarray: Copy of the array on host memory.
"""
a_gpu = ascontiguousarray(self)
a_cpu = numpy.empty(self._shape, dtype=self._dtype)
ptr = internal.get_ndarray_ptr(a_cpu)
if stream is None:
a_gpu.data.copy_to_host(ptr, a_gpu.nbytes)
else:
a_gpu.data.copy_to_host_async(ptr, a_gpu.nbytes, stream)
return a_cpu
def get(self, stream=None):
"""Returns a copy of the array on host memory.
Args:
stream (cupy.cuda.Stream): CUDA stream object. If it is given, the
copy runs asynchronously. Otherwise, the copy is synchronous.
Returns:
numpy.ndarray: Copy of the array on host memory.
"""
a_gpu = ascontiguousarray(self)
a_cpu = numpy.empty(self._shape, dtype=self._dtype)
ptr = internal.get_ndarray_ptr(a_cpu)
if stream is None:
a_gpu.data.copy_to_host(ptr, a_gpu.nbytes)
else:
a_gpu.data.copy_to_host_async(ptr, a_gpu.nbytes, stream)
return a_cpu
def array(obj, dtype=None, copy=True, ndmin=0):
"""Creates an array on the current device.
This function currently does not support the ``order`` and ``subok``
options.
Args:
obj: cupy.ndarray object or any other object that can be passed to
:func:`numpy.array`.
dtype: Data type specifier.
copy (bool): If False, this function returns ``obj`` if possible.
Otherwise this function always returns a new array.
ndmin (int): Minimum number of dimensions. Ones are inserated to the
head of the shape if needed.
Returns:
cupy.ndarray: An array on the current device.
.. seealso:: :func:`numpy.array`
"""
# TODO(beam2d): Support order and subok options
if isinstance(obj, cupy.ndarray):
if dtype is None:
dtype = obj.dtype
a = obj.astype(dtype, copy)
ndim = a.ndim
if ndmin > ndim:
a.shape = (1,) * (ndmin - ndim) + a.shape
return a
else:
a_cpu = numpy.array(obj, dtype=dtype, copy=False, ndmin=ndmin)
if a_cpu.ndim > 0:
a_cpu = numpy.ascontiguousarray(a_cpu)
a = cupy.ndarray(a_cpu.shape, dtype=a_cpu.dtype)
a.data.copy_from_host(internal.get_ndarray_ptr(a_cpu), a.nbytes)
if a_cpu.dtype == a.dtype:
return a
else:
return a.view(dtype=a_cpu.dtype)
def array(obj, dtype=None, copy=True, ndmin=0):
"""Creates an array on the current device.
This function currently does not support the ``order`` and ``subok``
options.
Args:
obj: cupy.ndarray object or any other object that can be passed to
:func:`numpy.array`.
dtype: Data type specifier.
copy (bool): If False, this function returns ``obj`` if possible.
Otherwise this function always returns a new array.
ndmin (int): Minimum number of dimensions. Ones are inserated to the
head of the shape if needed.
Returns:
cupy.ndarray: An array on the current device.
.. seealso:: :func:`numpy.array`
"""
# TODO(beam2d): Support order and subok options
if isinstance(obj, cupy.ndarray):
if dtype is None:
dtype = obj.dtype
a = obj.astype(dtype, copy)
ndim = a.ndim
if ndmin > ndim:
a.shape = (1, ) * (ndmin - ndim) + a.shape
return a
else:
a_cpu = numpy.array(obj, dtype=dtype, copy=False, ndmin=ndmin)
if a_cpu.ndim > 0:
a_cpu = numpy.ascontiguousarray(a_cpu)
a = cupy.ndarray(a_cpu.shape, dtype=a_cpu.dtype)
a.data.copy_from_host(internal.get_ndarray_ptr(a_cpu), a.nbytes)
if a_cpu.dtype == a.dtype:
return a
else:
return a.view(dtype=a_cpu.dtype)
