import numpy
from chainer import cuda
from chainer.functions.connection import depthwise_convolution_2d
from chainer import initializers
from chainer import link
[docs]class DepthwiseConvolution2D(link.Link):
"""Two-dimensional depthwise convolutional layer.
This link wraps the :func:`~chainer.functions.depthwise_convolution_2d`
function and holds the filter weight and bias vector as parameters.
Args:
in_channels (int): 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.
channel_multiplier (int): Channel multiplier number. Number of output
arrays equal ``in_channels * channel_multiplier``.
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.
nobias (bool): If ``True``, then this link does not use the bias term.
initialW (4-D array): Initial weight value. If ``None``, the default
initializer is used.
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``, the bias
is set to 0.
May also be a callable that takes ``numpy.ndarray`` or
``cupy.ndarray`` and edits its value.
.. seealso::
See :func:`chainer.functions.depthwise_convolution_2d`.
Attributes:
W (~chainer.Variable): Weight parameter.
b (~chainer.Variable): Bias parameter.
"""
def __init__(self, in_channels, channel_multiplier, ksize, stride=1, pad=0,
nobias=False, initialW=None, initial_bias=None):
super(DepthwiseConvolution2D, self).__init__()
self.ksize = ksize
self.stride = _pair(stride)
self.pad = _pair(pad)
self.channel_multiplier = channel_multiplier
self.nobias = nobias
if initialW is None:
initialW = initializers.HeNormal(1. / numpy.sqrt(2))
self._W_initializer = initializers._get_initializer(initialW)
if nobias:
self.b = None
else:
if initial_bias is None:
initial_bias = initializers.Constant(0)
self.bias_initilizer = initializers._get_initializer(initial_bias)
if in_channels is None:
self.add_uninitialized_param('b')
if in_channels is None:
self.add_uninitialized_param('W')
else:
self._initialize_params(in_channels)
def _initialize_params(self, in_channels):
kh, kw = _pair(self.ksize)
W_shape = (self.channel_multiplier, in_channels, kh, kw)
self.add_param('W', W_shape, initializer=self._W_initializer)
if not self.nobias:
self.add_param('b', self.channel_multiplier * in_channels,
initializer=self.bias_initilizer)
def __call__(self, x):
"""Applies the depthwise convolution layer.
Args:
x (chainer.Variable or :class:`numpy.ndarray` or cupy.ndarray):
Input image.
Returns:
~chainer.Variable: Output of the depthwise convolution.
"""
if self.has_uninitialized_params:
with cuda.get_device_from_id(self._device_id):
self._initialize_params(x.shape[1])
return depthwise_convolution_2d.depthwise_convolution_2d(
x, self.W, self.b, self.stride, self.pad)
def _pair(x):
if hasattr(x, '__getitem__'):
return x
return x, x