(self, x)
return ys
Compute feature maps from a batch of images.

        This method extracts feature maps from
        :obj:`conv4_3`, :obj:`conv7`, :obj:`conv8_2`,
        :obj:`conv9_2`, :obj:`conv10_2`, :obj:`conv11_2`, and :obj:`conv12_2`.

        Args:
            x (ndarray): An array holding a batch of images.
                The images should be resized to :math:`512\\times 512`.

        Returns:
            list of Variable:
            Each variable contains a feature map.
Compute feature maps from a batch of images.
Compute feature maps from a batch of images .
def forward(self, x):
        """Compute feature maps from a batch of images.

        This method extracts feature maps from
        :obj:`conv4_3`, :obj:`conv7`, :obj:`conv8_2`,
        :obj:`conv9_2`, :obj:`conv10_2`, :obj:`conv11_2`, and :obj:`conv12_2`.

        Args:
            x (ndarray): An array holding a batch of images.
                The images should be resized to :math:`512\\times 512`.

        Returns:
            list of Variable:
            Each variable contains a feature map.
        """

        ys = super(VGG16Extractor512, self).forward(x)
        for i in range(8, 12 + 1):
            h = ys[-1]
            h = F.relu(self['conv{:d}_1'.format(i)](h))
            h = F.relu(self['conv{:d}_2'.format(i)](h))
            ys.append(h)
        return ys
def forward ( self , x ) : ys = super ( VGG16Extractor512 , self ) . forward ( x ) for i in range ( 8 , 12 + 1 ) : h = ys [ - 1 ] h = F . relu ( self [ 'conv{:d}_1' . format ( i ) ] ( h ) ) h = F . relu ( self [ 'conv{:d}_2' . format ( i ) ] ( h ) ) ys . append ( h ) return ys