import math
from chainer import cuda
from chainer.functions.connection import dilated_convolution_2d
from chainer import initializers
from chainer import link
[docs]class DilatedConvolution2D(link.Link):
"""Two-dimensional dilated convolutional layer.
This link wraps the :func:`~chainer.functions.dilated_convolution_2d`
function and holds the filter weight and bias vector as parameters.
Args:
in_channels (int or None): Number of channels of input arrays.
If ``None``, parameter initialization will be deferred until the
first forward data pass at which time the size will be determined.
out_channels (int): Number of channels of output arrays.
ksize (int or pair of ints): Size of filters (a.k.a. kernels).
``ksize=k`` and ``ksize=(k, k)`` are equivalent.
stride (int or pair of ints): Stride of filter applications.
``stride=s`` and ``stride=(s, s)`` are equivalent.
pad (int or pair of ints): Spatial padding width for input arrays.
``pad=p`` and ``pad=(p, p)`` are equivalent.
dilate (int or pair of ints): Dilation factor of filter applications.
``dilate=d`` and ``dilate=(d, d)`` are equivalent.
wscale (float): Scaling factor of the initial weight.
bias (float): Initial bias value.
nobias (bool): If ``True``, then this link does not use the bias term.
use_cudnn (bool): If ``True``, then this link uses cuDNN if available.
initialW (4-D array): Initial weight value. If ``None``, then this
function uses scaled Gaussian distribution to initialize weight.
May also be a callable that takes ``numpy.ndarray`` or
``cupy.ndarray`` and edits its value.
initial_bias (1-D array): Initial bias value. If ``None``, then this
function uses ``bias`` to initialize bias.
May also be a callable that takes ``numpy.ndarray`` or
``cupy.ndarray`` and edits its value.
.. seealso::
See :func:`chainer.functions.dilated_convolution_2d`
for the definition of two-dimensional dilated convolution.
Attributes:
W (~chainer.Variable): Weight parameter.
b (~chainer.Variable): Bias parameter.
"""
def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,
dilate=1, wscale=1, bias=0, nobias=False, use_cudnn=True,
initialW=None, initial_bias=None):
super(DilatedConvolution2D, self).__init__()
self.ksize = ksize
self.stride = _pair(stride)
self.pad = _pair(pad)
self.dilate = _pair(dilate)
self.use_cudnn = use_cudnn
self.out_channels = out_channels
self.initialW = initialW
self.wscale = wscale
if in_channels is None:
self.add_uninitialized_param('W')
else:
self._initialize_params(in_channels)
if nobias:
self.b = None
else:
self.add_param('b', out_channels)
if initial_bias is None:
initial_bias = bias
initializers.init_weight(self.b.data, initial_bias)
def _initialize_params(self, in_channels):
kh, kw = _pair(self.ksize)
W_shape = (self.out_channels, in_channels, kh, kw)
self.add_param('W', W_shape)
# For backward compatibility, the scale of weights is proportional to
# the square root of wscale.
initializers.init_weight(self.W.data, self.initialW,
scale=math.sqrt(self.wscale))
[docs] def __call__(self, x):
"""Applies the convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of the convolution.
"""
if self.has_uninitialized_params:
with cuda.get_device_from_id(self._device_id):
self._initialize_params(x.shape[1])
return dilated_convolution_2d.dilated_convolution_2d(
x, self.W, self.b, self.stride,
self.pad, self.dilate, self.use_cudnn)
def _pair(x):
if hasattr(x, '__getitem__'):
return x
return x, x