def __call__(self, x):
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)
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
def forward(self, x):
outs = []
if self.out1 > 0:
h1 = self.conv1(x)
h1 = self.conv1n(h1)
h1 = relu.relu(h1)
outs.append(h1)
h3 = relu.relu(self.proj3n(self.proj3(x)))
h3 = relu.relu(self.conv3n(self.conv3(h3)))
outs.append(h3)
h33 = relu.relu(self.proj33n(self.proj33(x)))
h33 = relu.relu(self.conv33an(self.conv33a(h33)))
h33 = relu.relu(self.conv33bn(self.conv33b(h33)))
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)))
outs.append(p)
y = concat.concat(outs, axis=1)
return y
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
def _global_average_pooling_2d(x):
n, channel, rows, cols = x.data.shape
h = average_pooling_2d(x, (rows, cols), stride=1)
h = reshape(h, (n, channel))
return h
def _average_pooling_2d_k7(x):
return average_pooling_2d(x, ksize=7, stride=1)
def _average_pooling_2d_k5(x):
return average_pooling_2d(x, ksize=5, stride=3)
def _global_average_pooling_2d(x):
n, channel, rows, cols = x.data.shape
h = average_pooling_2d(x, (rows, cols), stride=1)
h = reshape(h, (n, channel))
return h