cnn_feature_extractors.py 文件源码

def reduct(self, x):
        h = F.relu(self.conv1_1(x))
        h = F.relu(self.conv1_2(h))
        h = self.bn1(h)
        # 100 -> 50
        h = F.max_pooling_2d(h, 2, stride=2)

        h = F.relu(self.conv2(h))
        h = self.bn2(h)
        # 50 -> 25
        h = F.max_pooling_2d(h, 2, stride=2)

        h = F.relu(self.conv3(h))
        h = self.bn3(h)
        # 25 -> (25 + 1 * 2 - 3) / 3 + 1 = 9
        h = F.max_pooling_2d(h, 3, stride=3, pad=1)

        h = F.relu(self.conv4(h))
        h = self.bn4(h)
        # 9 -> 1
        h = F.average_pooling_2d(h, 9, stride=1)

        return h