73e7594c4206f666565f9ed66709011b8a99ca2a,chainercv/links/model/segnet/segnet_basic.py,SegNetBasic,__call__,#SegNetBasic#,115

Before Change

        h = _pool_without_cudnn(p1, F.relu(self.conv1_bn(self.conv1(h))))
        h = _pool_without_cudnn(p2, F.relu(self.conv2_bn(self.conv2(h))))
        h = _pool_without_cudnn(p3, F.relu(self.conv3_bn(self.conv3(h))))
        h = _pool_without_cudnn(p4, F.relu(self.conv4_bn(self.conv4(h))))
        h = self._upsampling_2d(h, p4)
        h = self.conv_decode4_bn(self.conv_decode4(h))
        h = self._upsampling_2d(h, p3)
        h = self.conv_decode3_bn(self.conv_decode3(h))
        h = self._upsampling_2d(h, p2)
        h = self.conv_decode2_bn(self.conv_decode2(h))
        h = self._upsampling_2d(h, p1)
        h = self.conv_decode1_bn(self.conv_decode1(h))
        score = self.conv_classifier(h)
        return score

    def predict(self, imgs):

After Change

            F.relu(self.conv2_bn(self.conv2(h))), 2, 2, return_indices=True)
        h, indices3 = F.max_pooling_2d(
            F.relu(self.conv3_bn(self.conv3(h))), 2, 2, return_indices=True)
        h, indices4 = F.max_pooling_2d(
            F.relu(self.conv4_bn(self.conv4(h))), 2, 2, return_indices=True)
        h = self._upsampling_2d(h, indices4)
        h = self.conv_decode4_bn(self.conv_decode4(h))
        h = self._upsampling_2d(h, indices3)
        h = self.conv_decode3_bn(self.conv_decode3(h))
        h = self._upsampling_2d(h, indices2)
        h = self.conv_decode2_bn(self.conv_decode2(h))
        h = self._upsampling_2d(h, indices1)
        h = self.conv_decode1_bn(self.conv_decode1(h))
        score = self.conv_classifier(h)
        return score

    def predict(self, imgs):

Instances