import numpy
import six
from chainer import cuda
from chainer import function
from chainer.utils import type_check
class Dstack(function.Function):
"""Concatenate multiple tensors along third axis (depth wise)."""
def check_type_forward(self, in_types):
type_check.expect(in_types.size() > 0)
ndim = in_types[0].ndim.eval()
for i in six.moves.range(1, in_types.size().eval()):
type_check.expect(
in_types[0].dtype == in_types[i].dtype,
in_types[0].ndim == in_types[i].ndim,
)
if ndim <= 2:
type_check.expect(in_types[0].shape == in_types[i].shape)
continue
for d in six.moves.range(0, ndim):
if d == 2:
continue
type_check.expect(in_types[0].shape[d] == in_types[i].shape[d])
def forward(self, xs):
xp = cuda.get_array_module(*xs)
return xp.dstack(xs),
def backward(self, xs, gy):
if len(xs) == 1:
if xs[0].ndim <= 2:
return gy[0].reshape(xs[0].shape),
return gy
xp = cuda.get_array_module(*xs)
if xs[0].ndim <= 2:
ys = xp.dsplit(gy[0], len(xs))
return [y.reshape(xs[0].shape) for y in ys]
else:
sizes = numpy.array([x.shape[2] for x in xs[:-1]]).cumsum()
return xp.dsplit(gy[0], sizes)
[docs]def dstack(xs):
"""Concatenate variables along third axis (depth wise).
Args:
xs (list of :class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`):
Input variables to be concatenated. The variables must have the
same ``ndim``. When the variables have the third axis (i.e.
:math:`ndim \\geq 3`), the variables must have the same shape
along all but the third axis. When the variables do not have the
third axis(i.e. :math:`ndim < 3`), the variables must have the
same shape.
Returns:
~chainer.Variable:
Output variable. When the input variables have the third axis
(i.e. :math:`ndim \\geq 3`), the shapes of inputs and output are
the same along all but the third axis. The length of third axis
is the sum of the lengths of inputs' third axis.
When the shape of variables are ``(N1, N2)`` (i.e.
:math:`ndim = 2`), the shape of output is ``(N1, N2, 2)``. When
the shape of variables are ``(N1,)`` (i.e. :math:`ndim = 1`), the
shape of output is ``(1, N1, 2)``. When the shape of variables are
``()`` (i.e. :math:`ndim = 0`), the shape of output is
``(1, 1, 2)``.
.. admonition:: Example
>>> x1 = np.array((1, 2, 3))
>>> x1.shape
(3,)
>>> x2 = np.array((2, 3, 4))
>>> x2.shape
(3,)
>>> y = F.dstack((x1, x2))
>>> y.shape
(1, 3, 2)
>>> y.data
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> x1 = np.arange(0, 6).reshape(3, 2)
>>> x1.shape
(3, 2)
>>> x1
array([[0, 1],
[2, 3],
[4, 5]])
>>> x2 = np.arange(6, 12).reshape(3, 2)
>>> x2.shape
(3, 2)
>>> x2
array([[ 6, 7],
[ 8, 9],
[10, 11]])
>>> y = F.dstack([x1, x2])
>>> y.shape
(3, 2, 2)
>>> y.data
array([[[ 0, 6],
[ 1, 7]],
<BLANKLINE>
[[ 2, 8],
[ 3, 9]],
<BLANKLINE>
[[ 4, 10],
[ 5, 11]]])
>>> x1 = np.arange(0, 12).reshape(3, 2, 2)
>>> x2 = np.arange(12, 18).reshape(3, 2, 1)
>>> y = F.dstack([x1, x2])
>>> y.shape
(3, 2, 3)
>>> y.data
array([[[ 0, 1, 12],
[ 2, 3, 13]],
<BLANKLINE>
[[ 4, 5, 14],
[ 6, 7, 15]],
<BLANKLINE>
[[ 8, 9, 16],
[10, 11, 17]]])
"""
return Dstack()(*xs)