def __call__(self, x, train=False):
h = self.conv_bn_relu(x, train=train)
h = F.max_pooling_2d(h, (3, 3), (2, 2), (1, 1))
for i, n in enumerate(self.block_num):
for ii in six.moves.range(n):
h = self['resnext_block_{}_{}'.format(i, ii)](h, train=train)
batch, channels, height, width = h.data.shape
h = F.reshape(F.average_pooling_2d(h, (height, width)), (batch, channels))
return self.linear(h, train)
评论列表
文章目录