from chainer.functions.connection import deconvolution_nd
from chainer import initializers
from chainer import link
from chainer.utils import conv_nd
[docs]class DeconvolutionND(link.Link):
"""N-dimensional deconvolution function.
This link wraps :func:`~chainer.functions.deconvolution_nd` function and
holds the filter weight and bias vector as its parameters.
Args:
ndim (int): Number of spatial dimensions.
in_channels (int): Number of channels of input arrays.
out_channels (int): Number of channels of output arrays.
ksize (int or tuple of ints): Size of filters (a.k.a. kernels).
``ksize=k`` and ``ksize=(k, k, ..., k)`` are equivalent.
stride (int or tuple of ints): Stride of filter application.
``stride=s`` and ``stride=(s, s, ..., s)`` are equivalent.
pad (int or tuple of ints): Spatial padding width for input arrays.
``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent.
outsize (tuple of ints): Expected output size of deconvolutional
operation. It should be a tuple of ints that represents the output
size of each dimension. Default value is ``None`` and the outsize
is estimated with input size, stride and pad.
initialW: Value used to initialize the filter weight. May be an
initializer instance of another value the same with that
:func:`~chainer.init_weight` function can take.
initial_bias: Value used to initialize the bias vector. May be an
initializer instance or another value except ``None`` the same with
that :func:`~chainer.init_weight` function can take. If ``None`` is
supplied, this link does not use the bias vector.
use_cudnn (bool): If ``True``, then this link uses cuDNN if available.
.. seealso::
:func:`~chainer.functions.deconvolution_nd`
Attributes:
W (~chainer.Variable): Weight parameter.
b (~chainer.Variable): Bias parameter. If ``initial_bias`` is ``None``,
set to ``None``.
"""
def __init__(self, ndim, in_channels, out_channels, ksize, stride=1, pad=0,
outsize=None, initialW=None, initial_bias=0, use_cudnn=True):
ksize = conv_nd.as_tuple(ksize, ndim)
self.stride = stride
self.pad = pad
self.use_cudnn = use_cudnn
self.outsize = outsize
super(DeconvolutionND, self).__init__()
W_shape = (in_channels, out_channels) + ksize
initialW = initializers._get_initializer(initialW)
self.add_param('W', W_shape, initializer=initialW)
if initial_bias is None:
self.b = None
else:
initial_bias = initializers._get_initializer(initial_bias)
self.add_param('b', out_channels, initializer=initial_bias)
def __call__(self, x):
return deconvolution_nd.deconvolution_nd(
x, self.W, b=self.b, stride=self.stride, pad=self.pad,
outsize=self.outsize, use_cudnn=self.use_cudnn)