Source code for chainer.links.connection.depthwise_convolution_2d

import numpy

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.add_param('W', initializer=initializers._get_initializer( initialW)) if nobias: self.b = None else: if initial_bias is None: initial_bias = initializers.Constant(0) bias_initilizer = initializers._get_initializer(initial_bias) self.add_param('b', initializer=bias_initilizer) if in_channels is not None: 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.W.initialize(W_shape) if self.b is not None: self.b.initialize(self.channel_multiplier * in_channels) 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.W.data is None: 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