from chainer import cuda
from chainer import function
from chainer.utils import type_check
import numpy
class Depth2Space(function.Function):
"""Depth to space transformation."""
def __init__(self, r):
self.r = r
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 1)
type_check.expect(in_types[0].dtype == numpy.float32,
in_types[0].ndim == 4
)
def forward(self, inputs):
X, = inputs
xp = cuda.get_array_module(X)
bsize, c, a, b = X.shape
c //= self.r ** 2
X = xp.transpose(X, (0, 2, 3, 1))
X = xp.reshape(X, (bsize, a, b, self.r, self.r, c))
X = xp.transpose(X, (0, 1, 3, 2, 4, 5))
X = xp.reshape(X, (bsize, a * self.r, b * self.r, c))
X = xp.transpose(X, (0, 3, 1, 2))
return X,
def backward(self, inputs, grad_outputs):
gy, = grad_outputs
xp = cuda.get_array_module(gy)
bsize, c, a, b = gy.shape
gy = xp.transpose(gy, (0, 2, 3, 1))
gy = xp.reshape(gy,
(bsize, a // self.r, self.r, b // self.r, self.r, c)
)
gy = xp.transpose(gy, (0, 1, 3, 2, 4, 5))
gy = xp.reshape(gy,
(bsize, a // self.r, b // self.r, self.r ** 2 * c)
)
gy = xp.transpose(gy, (0, 3, 1, 2))
return gy,
[docs]def depth2space(X, r):
"""Computes the depth2space transformation for subpixel calculations.
Args:
X (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`):
Variable holding a 4d array of shape
``(batch, channel * r * r, dim1, dim2)``.
r (int): the upscaling factor.
Returns:
~chainer.Variable:
A variable holding the upscaled array from
interspersed depth layers. The shape is
``(batch, channel, dim1 * r, dim2 * r)``.
.. note::
This can be used to compute super-resolution transformations.
See https://arxiv.org/abs/1609.05158 for details.
.. seealso:: :func:`space2depth`
.. admonition:: Example
>>> X = np.arange(24).reshape(1, 4, 2, 3).astype('f')
>>> X.shape
(1, 4, 2, 3)
>>> X
array([[[[ 0., 1., 2.],
[ 3., 4., 5.]],
<BLANKLINE>
[[ 6., 7., 8.],
[ 9., 10., 11.]],
<BLANKLINE>
[[ 12., 13., 14.],
[ 15., 16., 17.]],
<BLANKLINE>
[[ 18., 19., 20.],
[ 21., 22., 23.]]]], dtype=float32)
>>> y = F.depth2space(X, 2)
>>> y.shape
(1, 1, 4, 6)
>>> y.data
array([[[[ 0., 6., 1., 7., 2., 8.],
[ 12., 18., 13., 19., 14., 20.],
[ 3., 9., 4., 10., 5., 11.],
[ 15., 21., 16., 22., 17., 23.]]]], dtype=float32)
"""
return Depth2Space(r)(X)