import numpy
import six
from chainer import cuda
from chainer import function
from chainer.utils import type_check
class Hstack(function.Function):
"""Concatenate multiple tensors horizontally (column 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 <= 1:
continue
for d in six.moves.range(0, ndim):
if d == 1:
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.hstack(xs),
def backward(self, xs, gy):
if len(xs) == 1:
if xs[0].ndim == 0:
return (gy[0].reshape(()),)
return gy
xp = cuda.get_array_module(*xs)
if xs[0].ndim == 0:
ys = xp.hsplit(gy[0], len(xs))
return [y.reshape(()) for y in ys]
if xs[0].ndim == 1:
sizes = numpy.array([x.shape[0] for x in xs[:-1]]).cumsum()
else:
sizes = numpy.array([x.shape[1] for x in xs[:-1]]).cumsum()
return xp.hsplit(gy[0], sizes)
[docs]def hstack(xs):
"""Concatenate variables horizontally (column 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 second axis (i.e.
:math:`ndim \\geq 2`), the variables must have the same shape
along all but the second axis. When the variables do not have the
second axis(i.e. :math:`ndim < 2`), the variables need not to have
the same shape.
Returns:
~chainer.Variable:
Output variable. When the input variables have the second axis
(i.e. :math:`ndim \\geq 2`), the shapes of inputs and output are
the same along all but the second axis. The length of second axis
is the sum of the lengths of inputs' second axis.
When the variables do not have the second axis (i.e.
:math:`ndim < 2`), the shape of output is ``(N, )`` (``N`` is the
sum of the input variables' size).
.. admonition:: Example
>>> x1 = np.array((1, 2, 3))
>>> x1.shape
(3,)
>>> x2 = np.array((2, 3, 4))
>>> x2.shape
(3,)
>>> y = F.hstack((x1, x2))
>>> y.shape
(6,)
>>> y.data
array([1, 2, 3, 2, 3, 4])
>>> x1 = np.arange(0, 12).reshape(3, 4)
>>> x1.shape
(3, 4)
>>> x1
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x2 = np.arange(12, 18).reshape(3, 2)
>>> x2.shape
(3, 2)
>>> x2
array([[12, 13],
[14, 15],
[16, 17]])
>>> y = F.hstack([x1, x2])
>>> y.shape
(3, 6)
>>> y.data
array([[ 0, 1, 2, 3, 12, 13],
[ 4, 5, 6, 7, 14, 15],
[ 8, 9, 10, 11, 16, 17]])
"""
return Hstack()(*xs)