import chainer
from chainer.functions import relu
from chainer.links import Convolution2D
from chainer.links import DilatedConvolution2D
[docs]class Conv2DActiv(chainer.Chain):
"""Convolution2D --> Activation
This is a chain that does two-dimensional convolution
and applies an activation.
The arguments are the same as those of
:class:`chainer.links.Convolution2D`
except for :obj:`activ`.
Example:
There are sevaral ways to initialize a :class:`Conv2DActiv`.
1. Give the first three arguments explicitly:
>>> l = Conv2DActiv(5, 10, 3)
2. Omit :obj:`in_channels` or fill it with :obj:`None`:
In these ways, attributes are initialized at runtime based on
the channel size of the input.
>>> l = Conv2DActiv(10, 3)
>>> l = Conv2DActiv(None, 10, 3)
Args:
in_channels (int or None): Number of channels of input arrays.
If :obj:`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 tuple of ints): Size of filters (a.k.a. kernels).
:obj:`ksize=k` and :obj:`ksize=(k, k)` are equivalent.
stride (int or tuple of ints): Stride of filter applications.
:obj:`stride=s` and :obj:`stride=(s, s)` are equivalent.
pad (int or tuple of ints): Spatial padding width for input arrays.
:obj:`pad=p` and :obj:`pad=(p, p)` are equivalent.
dilate (int or tuple of ints): Dilation factor of filter applications.
:obj:`dilate=d` and :obj:`dilate=(d, d)` are equivalent.
nobias (bool): If :obj:`True`,
then this link does not use the bias term.
initialW (callable): Initial weight value. If :obj:`None`, the default
initializer is used.
May also be a callable that takes :obj:`numpy.ndarray` or
:obj:`cupy.ndarray` and edits its value.
initial_bias (callable): Initial bias value. If :obj:`None`, the bias
is set to 0.
May also be a callable that takes :obj:`numpy.ndarray` or
:obj:`cupy.ndarray` and edits its value.
activ (callable): An activation function. The default value is
:func:`chainer.functions.relu`. If this is :obj:`None`,
no activation is applied (i.e. the activation is the identity
function).
"""
def __init__(self, in_channels, out_channels, ksize=None,
stride=1, pad=0, dilate=1, nobias=False, initialW=None,
initial_bias=None, activ=relu):
if ksize is None:
out_channels, ksize, in_channels = in_channels, out_channels, None
self.activ = activ
super(Conv2DActiv, self).__init__()
with self.init_scope():
if dilate > 1:
self.conv = DilatedConvolution2D(
in_channels, out_channels, ksize, stride, pad, dilate,
nobias, initialW, initial_bias)
else:
self.conv = Convolution2D(
in_channels, out_channels, ksize, stride, pad,
nobias, initialW, initial_bias)
def __call__(self, x):
h = self.conv(x)
if self.activ is None:
return h
else:
return self.activ(h)