Source code for chainer.links.connection.inceptionbn

import numpy

from chainer.functions.activation import relu
from chainer.functions.array import concat
from chainer.functions.pooling import average_pooling_2d
from chainer.functions.pooling import max_pooling_2d
from chainer import link
from chainer.links.connection import convolution_2d
from chainer.links.normalization import batch_normalization

[docs]class InceptionBN(link.Chain): """Inception module of the new GoogLeNet with BatchNormalization. This chain acts like :class:`Inception`, while InceptionBN uses the :class:`BatchNormalization` on top of each convolution, the 5x5 convolution path is replaced by two consecutive 3x3 convolution applications, and the pooling method is configurable. See: `Batch Normalization: Accelerating Deep Network Training by Reducing \ Internal Covariate Shift <>`_. Args: in_channels (int): Number of channels of input arrays. out1 (int): Output size of the 1x1 convolution path. proj3 (int): Projection size of the single 3x3 convolution path. out3 (int): Output size of the single 3x3 convolution path. proj33 (int): Projection size of the double 3x3 convolutions path. out33 (int): Output size of the double 3x3 convolutions path. pooltype (str): Pooling type. It must be either ``'max'`` or ``'avg'``. proj_pool (bool): If ``True``, do projection in the pooling path. stride (int): Stride parameter of the last convolution of each path. conv_init: A callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. It is used for initialization of the convolution matrix weights. Maybe be ``None`` to use default initialization. dtype (numpy.dtype): Type to use in ``~batch_normalization.BatchNormalization``. .. seealso:: :class:`Inception` Attributes: train (bool): If ``True``, then batch normalization layers are used in training mode. If ``False``, they are used in testing mode. """ def __init__(self, in_channels, out1, proj3, out3, proj33, out33, pooltype, proj_pool=None, stride=1, conv_init=None, dtype=numpy.float32): super(InceptionBN, self).__init__( proj3=convolution_2d.Convolution2D( in_channels, proj3, 1, nobias=True, initialW=conv_init), conv3=convolution_2d.Convolution2D( proj3, out3, 3, pad=1, stride=stride, nobias=True, initialW=conv_init), proj33=convolution_2d.Convolution2D( in_channels, proj33, 1, nobias=True, initialW=conv_init), conv33a=convolution_2d.Convolution2D( proj33, out33, 3, pad=1, nobias=True, initialW=conv_init), conv33b=convolution_2d.Convolution2D( out33, out33, 3, pad=1, stride=stride, nobias=True, initialW=conv_init), proj3n=batch_normalization.BatchNormalization(proj3, dtype=dtype), conv3n=batch_normalization.BatchNormalization(out3, dtype=dtype), proj33n=batch_normalization.BatchNormalization(proj33, dtype=dtype), conv33an=batch_normalization.BatchNormalization(out33, dtype=dtype), conv33bn=batch_normalization.BatchNormalization(out33, dtype=dtype), ) if out1 > 0: assert stride == 1 assert proj_pool is not None self.add_link('conv1', convolution_2d.Convolution2D(in_channels, out1, 1, stride=stride, nobias=True, initialW=conv_init)) self.add_link('conv1n', batch_normalization.BatchNormalization( out1, dtype=dtype)) self.out1 = out1 if proj_pool is not None: self.add_link('poolp', convolution_2d.Convolution2D( in_channels, proj_pool, 1, nobias=True, initialW=conv_init)) self.add_link('poolpn', batch_normalization.BatchNormalization( proj_pool, dtype=dtype)) self.proj_pool = proj_pool self.stride = stride self.pooltype = pooltype if pooltype != 'max' and pooltype != 'avg': raise NotImplementedError() self.train = True
[docs] def __call__(self, x, test=None): """Computes the output of the InceptionBN module. Args: x (Variable): An input variable. test (bool): If ``True``, batch normalization layers run in testing mode; if ``test`` is omitted, ``not self.train`` is used as ``test``. """ if test is None: test = not self.train outs = [] if self.out1 > 0: h1 = self.conv1(x) h1 = self.conv1n(h1, test=test) h1 = relu.relu(h1) outs.append(h1) h3 = relu.relu(self.proj3n(self.proj3(x), test=test)) h3 = relu.relu(self.conv3n(self.conv3(h3), test=test)) outs.append(h3) h33 = relu.relu(self.proj33n(self.proj33(x), test=test)) h33 = relu.relu(self.conv33an(self.conv33a(h33), test=test)) h33 = relu.relu(self.conv33bn(self.conv33b(h33), test=test)) outs.append(h33) if self.pooltype == 'max': p = max_pooling_2d.max_pooling_2d(x, 3, stride=self.stride, pad=1, cover_all=False) else: p = average_pooling_2d.average_pooling_2d(x, 3, stride=self.stride, pad=1) if self.proj_pool is not None: p = relu.relu(self.poolpn(self.poolp(p), test=test)) outs.append(p) y = concat.concat(outs, axis=1) return y