import numpy
import six
from chainer import cuda
[docs]def to_device(device, x):
"""Send an array to a given device.
This method send a given array to a given device. This method is used in
:func:`~chainer.dataset.concat_examples`.
You can also use this method in a custom converter method used in
:class:`~chainer.training.Updater` and :class:`~chainer.training.Extension`
such as :class:`~chainer.training.StandardUpdater` and
:class:`~chainer.training.extensions.Evaluator`.
.. see:: :func:`chainer.dataset.concat_examples`
Args:
device (int or None): Device ID to which an array is sent. If it is
negative value, an array is sent to CPU. If it is positive, an
array is sent to GPU with the given ID. If it is ``None``, an
array is left in the original device.
x (numpy.ndarray or cupy.ndarray): An array to send.
Returns:
Converted array.
"""
if device is None:
return x
elif device < 0:
return cuda.to_cpu(x)
else:
return cuda.to_gpu(x, device, cuda.Stream.null)
[docs]def concat_examples(batch, device=None, padding=None):
"""Concatenates a list of examples into array(s).
Dataset iterator yields a list of examples. If each example is an array,
this function concatenates them along the newly-inserted first axis (called
`batch dimension`) into one array. The basic behavior is same for examples
consisting of multiple arrays, i.e., corresponding arrays of all examples
are concatenated.
For instance, consider each example consists of two arrays ``(x, y)``.
Then, this function concatenates ``x`` 's into one array, and ``y`` 's
into another array, and returns a tuple of these two arrays. Another
example: consider each example is a dictionary of two entries whose keys
are ``'x'`` and ``'y'``, respectively, and values are arrays. Then, this
function concatenates ``x`` 's into one array, and ``y`` 's into another
array, and returns a dictionary with two entries ``x`` and ``y`` whose
values are the concatenated arrays.
When the arrays to concatenate have different shapes, the behavior depends
on the ``padding`` value. If ``padding`` is ``None`` (default), it raises
an error. Otherwise, it builds an array of the minimum shape that the
contents of all arrays can be substituted to. The padding value is then
used to the extra elements of the resulting arrays.
TODO(beam2d): Add an example.
Args:
batch (list): A list of examples. This is typically given by a dataset
iterator.
device (int): Device ID to which each array is sent. Negative value
indicates the host memory (CPU). If it is omitted, all arrays are
left in the original device.
padding: Scalar value for extra elements. If this is None (default),
an error is raised on shape mismatch. Otherwise, an array of
minimum dimensionalities that can accommodate all arrays is
created, and elements outside of the examples are padded by this
value.
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 isinstance(first_elem, tuple):
result = []
if not isinstance(padding, tuple):
padding = [padding] * len(first_elem)
for i in six.moves.range(len(first_elem)):
result.append(to_device(device, _concat_arrays(
[example[i] for example in batch], padding[i])))
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:
result[key] = to_device(device, _concat_arrays(
[example[key] for example in batch], padding[key]))
return result
else:
return to_device(device, _concat_arrays(batch, padding))
def _concat_arrays(arrays, padding):
# Convert `arrays` to numpy.ndarray if `arrays` consists of the built-in
# types such as int or float.
if not isinstance(arrays[0], numpy.ndarray) and\
not isinstance(arrays[0], cuda.ndarray):
arrays = numpy.asarray(arrays)
if padding is not None:
return _concat_arrays_with_padding(arrays, padding)
xp = cuda.get_array_module(arrays[0])
with cuda.get_device_from_array(arrays[0]):
return xp.concatenate([array[None] for array in arrays])
def _concat_arrays_with_padding(arrays, padding):
shape = numpy.array(arrays[0].shape, dtype=int)
for array in arrays[1:]:
if numpy.any(shape != array.shape):
numpy.maximum(shape, array.shape, shape)
shape = tuple(numpy.insert(shape, 0, len(arrays)))
xp = cuda.get_array_module(arrays[0])
with cuda.get_device_from_array(arrays[0]):
result = xp.full(shape, padding, dtype=arrays[0].dtype)
for i in six.moves.range(len(arrays)):
src = arrays[i]
slices = tuple(slice(dim) for dim in src.shape)
result[(i,) + slices] = src
return result