Source code for chainer.links.connection.deconvolution_2d

import math

import numpy

from chainer import cuda
from chainer.functions.connection import deconvolution_2d
from chainer import initializers
from chainer import link

[docs]class Deconvolution2D(link.Link): """Two dimensional deconvolution function. This link wraps the :func:`~chainer.functions.deconvolution_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. wscale (float): Scaling factor of the initial weight. bias (float): Initial bias value. nobias (bool): If ``True``, then this function does not use the bias term. outsize (tuple): Expected output size of deconvolutional operation. It should be pair of height and width :math:`(out_H, out_W)`. Default value is ``None`` and the outsize is estimated by input size, stride and pad. use_cudnn (bool): If ``True``, then this function uses cuDNN if available. initialW (4-D array): Initial weight value. If ``None``, then this function uses Gaussian distribution scaled by ``w_scale`` 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. deterministic (bool): The output of this link can be non-deterministic when it uses cuDNN. If this option is ``True``, then it forces cuDNN to use a deterministic algorithm. This option is only available for cuDNN version >= v4. The filter weight has four dimensions :math:`(c_I, c_O, k_H, k_W)` which indicate the number of input channels, output channels, height and width of the kernels, respectively. The filter weight is initialized with i.i.d. Gaussian random samples, each of which has zero mean and deviation :math:`\\sqrt{1/(c_I k_H k_W)}` by default. The deviation is scaled by ``wscale`` if specified. The bias vector is of size :math:`c_O`. Its elements are initialized by ``bias`` argument. If ``nobias`` argument is set to True, then this function does not hold the bias parameter. .. seealso:: See :func:`chainer.functions.deconvolution_2d` for the definition of two-dimensional convolution. """ def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0, wscale=1, bias=0, nobias=False, outsize=None, use_cudnn=True, initialW=None, initial_bias=None, deterministic=False): super(Deconvolution2D, self).__init__() self.ksize = ksize self.stride = _pair(stride) self.pad = _pair(pad) self.outsize = (None, None) if outsize is None else outsize self.use_cudnn = use_cudnn self.initialW = initialW self.wscale = wscale self.out_channels = out_channels self.deterministic = deterministic 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 isinstance(initial_bias, (numpy.ndarray, cuda.ndarray)): assert initial_bias.shape == (out_channels,) if initial_bias is None: initial_bias = bias initializers.init_weight(, initial_bias) def _initialize_params(self, in_channels): kh, kw = _pair(self.ksize) W_shape = (in_channels, self.out_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.initialW, scale=math.sqrt(self.wscale)) def __call__(self, x): if self.has_uninitialized_params: with cuda.get_device_from_id(self._device_id): self._initialize_params(x.shape[1]) return deconvolution_2d.deconvolution_2d( x, self.W, self.b, self.stride, self.pad, self.outsize, self.use_cudnn, deterministic=self.deterministic)
def _pair(x): if hasattr(x, '__getitem__'): return x return x, x