def __call__(self, batch, device=None, padding=None):
"""Concatenate data and transfer them to GPU asynchronously.
See also :func:`chainer.dataset.concat_examples`.
Args:
batch (list): A list of examples.
device (int): Device ID to which each array is sent.
padding: Scalar value for extra elements.
Returns:
Array, a tuple of arrays, or a dictionary of arrays.
The type depends on the type of each example in the batch.
"""
if len(batch) == 0:
raise ValueError('batch is empty')
first_elem = batch[0]
if len(self._conveyor) == 0:
self._device = device # device is set at first call
if device is not None and device >= 0 and self._stream is None:
with cuda.get_device_from_id(device):
self._stream = cuda.Stream(non_blocking=True)
if device is not self._device:
raise ValueError('device is different')
with cuda.get_device_from_id(device):
if isinstance(first_elem, tuple):
result = []
if not isinstance(padding, tuple):
padding = [padding] * len(first_elem)
for i in six.moves.range(len(first_elem)):
self._conveyor[i].put(
_concat_arrays([example[i] for example in batch],
padding[i]))
for i in six.moves.range(len(first_elem)):
result.append(self._conveyor[i].get())
return tuple(result)
elif isinstance(first_elem, dict):
result = {}
if not isinstance(padding, dict):
padding = {key: padding for key in first_elem}
for key in first_elem:
self._conveyor[key].put(
_concat_arrays([example[key] for example in batch],
padding[key]))
for key in first_elem:
result[key] = self._conveyor[key].get()
return result
else:
return to_device(device, _concat_arrays(batch, padding))
def test_cupy_array_async1(self):
x = cuda.to_gpu(self.x)
if not self.c_contiguous:
x = cuda.cupy.asfortranarray(x)
with testing.assert_warns(DeprecationWarning):
y = cuda.to_gpu(x, stream=cuda.Stream())
self.assertIsInstance(y, cuda.ndarray)
self.assertIs(x, y) # Do not copy
cuda.cupy.testing.assert_array_equal(x, y)
def test_numpy_array_async(self):
y = cuda.to_cpu(self.x, stream=cuda.Stream())
self.assertIsInstance(y, numpy.ndarray)
self.assertIs(self.x, y) # Do not copy
def test_numpy_array_async(self):
y = cuda.to_cpu(self.x, stream=cuda.Stream())
assert isinstance(y, numpy.ndarray)
assert self.x is y # Do not copy
