from chainer.functions.connection import convolution_nd
from chainer import initializers
from chainer import link
from chainer.utils import conv_nd
[docs]class ConvolutionND(link.Link):
"""N-dimensional convolution layer.
This link wraps the :func:`~chainer.functions.convolution_nd` function and
holds the filter weight and bias vector as 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.
initialW: Value used to initialize the filter weight. May be an
initializer instance or another value that
:func:`~chainer.init_weight` helper function can take.
initial_bias: Value used to initialize the bias vector. May be an
initializer instance or another value except ``None`` that
:func:`~chainer.init_weight` helper function can take. If ``None``
is given, this link does not use the bias vector.
use_cudnn (bool): If ``True``, then this link uses cuDNN if available.
See :func:`~chainer.functions.convolution_nd` for exact conditions
of cuDNN availability.
cover_all (bool): If ``True``, all spatial locations are convoluted
into some output pixels. It may make the output size larger.
``cover_all`` needs to be ``False`` if you want to use cuDNN.
.. seealso::
See :func:`~chainer.functions.convolution_nd` for the definition of
N-dimensional convolution. See
:func:`~chainer.functions.convolution_2d` for the definition of
two-dimensional convolution.
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,
initialW=None, initial_bias=None, use_cudnn=True,
cover_all=False):
ksize = conv_nd.as_tuple(ksize, ndim)
self.stride = stride
self.pad = pad
self.use_cudnn = use_cudnn
self.cover_all = cover_all
super(ConvolutionND, self).__init__()
W_shape = (out_channels, in_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)
[docs] def __call__(self, x):
"""Applies N-dimensional convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of convolution.
"""
return convolution_nd.convolution_nd(
x, self.W, self.b, self.stride, self.pad,
use_cudnn=self.use_cudnn, cover_all=self.cover_all)