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_nd.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 functions(self):
return collections.OrderedDict([
('conv1', [self.conv1, self.bn1, relu]),
('pool1', [lambda x: max_pooling_2d(x, ksize=3, stride=2)]),
('res2', [self.res2]),
('res3', [self.res3]),
('res4', [self.res4]),
('res5', [self.res5]),
('pool5', [_global_average_pooling_2d]),
('fc6', [self.fc6]),
('prob', [softmax]),
])
def forward(self, x):
"""Computes the output of the Inception module.
Args:
x (~chainer.Variable): Input variable.
Returns:
~chainer.Variable: Output variable. Its array has the same spatial
size and the same minibatch size as the input array. The channel
dimension has size ``out1 + out3 + out5 + proj_pool``.
"""
out1 = self.conv1(x)
out3 = self.conv3(relu.relu(self.proj3(x)))
out5 = self.conv5(relu.relu(self.proj5(x)))
pool = self.projp(max_pooling_nd.max_pooling_2d(x, 3, stride=1, pad=1))
y = relu.relu(concat.concat((out1, out3, out5, pool), axis=1))
return y
def _max_pooling_2d(x):
return max_pooling_2d(x, ksize=2)