vgg_a.py 文件源码

def __call__(self, x, train=True):
        h = F.relu(self.conv1(x))
        h = F.max_pooling_2d(h, ksize=(2, 2), stride=(2, 2), pad=(0, 0))
        h = F.relu(self.conv2(h))
        h = F.max_pooling_2d(h, ksize=(2, 2), stride=(2, 2), pad=(0, 0))
        h = F.relu(self.conv3_1(h))
        h = F.relu(self.conv3_2(h))
        h = F.max_pooling_2d(h, ksize=(2, 2), stride=(2, 2), pad=(0, 0))
        h = F.relu(self.conv4_1(h))
        h = F.relu(self.conv4_2(h))
        h = F.max_pooling_2d(h, ksize=(2, 2), stride=(2, 2), pad=(0, 0))
        h = F.relu(self.conv5_1(h))
        h = F.relu(self.conv5_2(h))
        h = F.max_pooling_2d(h, ksize=(2, 2), stride=(2, 2), pad=(0, 0))
        h = F.dropout(h, ratio=0.5, train=train)
        h = F.relu(self.fc1(h))
        h = F.dropout(h, ratio=0.5, train=train)
        h = F.relu(self.fc2(h))
        h = self.fc3(h)
        num, categories, y, x = h.data.shape
        # global average pooling
        h = F.reshape(F.average_pooling_2d(h, (y, x)), (num, categories))
        return h