(self, x)
return score
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`.
Compute an image-wise score from a batch of images
Compute an image - wise score from a batch of images
def forward(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`.

        """
        h = F.local_response_normalization(x, 5, 1, 1e-4 / 5., 0.75)
        h, indices1 = F.max_pooling_2d(
            F.relu(self.conv1_bn(self.conv1(h))), 2, 2, return_indices=True)
        h, indices2 = F.max_pooling_2d(
            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 forward ( self , x ) : h = F . local_response_normalization ( x , 5 , 1 , 1e-4 / 5. , 0.75 ) h , indices1 = F . max_pooling_2d ( F . relu ( self . conv1_bn ( self . conv1 ( h ) ) ) , 2 , 2 , return_indices = True ) h , indices2 = F . max_pooling_2d ( 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