googlenet.py 文件源码

def __call__(self, x, train=True):
        h = F.relu(self.conv1(x))
        h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
        h = F.relu(self.conv2_1x1(h))
        h = F.relu(self.conv2_3x3(h))
        h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
        h = self.inception3a(h)
        h = self.inception3b(h)
        h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
        h = self.inception4a(h)
        h = self.inception4b(h)
        h = self.inception4c(h)
        h = self.inception4d(h)
        h = self.inception4e(h)
        h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
        h = self.inception5a(h)
        h = F.relu(self.inception5b(h))
        num, categories, y, x = h.data.shape
        # global average pooling
        h = F.reshape(F.average_pooling_2d(h, (y, x)), (num, categories))
        h = F.dropout(h, ratio=0.4, train=train)
        h = self.linear(h)
        return h