segnet_basic.py 文件源码

def __call__(self, x):
        """Compute an image-wise score from a batch of images

        Args:
            x (chainer.Variable): A variable with 4D image array.

        Returns:
            chainer.Variable:
            An image-wise score. Its channel size is :obj:`self.n_class`.

        """
        p1 = F.MaxPooling2D(2, 2)
        p2 = F.MaxPooling2D(2, 2)
        p3 = F.MaxPooling2D(2, 2)
        p4 = F.MaxPooling2D(2, 2)
        h = F.local_response_normalization(x, 5, 1, 1e-4 / 5., 0.75)
        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