Source code for chainer.functions.array.depth2space

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):
        self.retain_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)