link_binary_conv2d.py

import math

import function_binary_conv2d
from chainer import initializers
from chainer import link

import numpy


class Convolution2D(link.Link):

    """Two-dimensional convolutional layer.

    This link wraps the :func:`~chainer.functions.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.
        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.
        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 to initialize ``wscale``.
            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 to initialize ``bias``.
            May also be a callable that takes ``numpy.ndarray`` or
            ``cupy.ndarray`` and edits its value.

    .. seealso::
       See :func:`chainer.functions.convolution_2d` for the definition of
       two-dimensional convolution.

    Attributes:
        W (~chainer.Variable): Weight parameter.
        b (~chainer.Variable): Bias parameter.

    """

    def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,
                 wscale=1, bias=0, nobias=False, use_cudnn=True,
                 initialW=None, initial_bias=None):
        super(Convolution2D, self).__init__()
        self.ksize = ksize
        self.stride = _pair(stride)
        self.pad = _pair(pad)
        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)

        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))

        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))

    def __call__(self, x):
        """Applies the convolution layer.

        Args:
            x (~chainer.Variable): Input image.

        Returns:
            ~chainer.Variable: Output of the convolution.

        """
        return function_binary_conv2d.func_convolution_2d(x, self.W, self.b, self.stride, self.pad, self.use_cudnn)


def _pair(x):
    if hasattr(x, '__getitem__'):
        return x
    return x, x