vgg.py 文件源码

def activations(self, x, layer_idx):

        """Return filter activations projected back to the input space, i.e.
        images with shape (n_feature_maps, 3, 224, 224) for a particula layer.

        The layer index is expected to be 0-based.
        """

        if x.shape[0] != 1:
            raise TypeError('Visualization is only supported for a single image at a time')

        self.check_add_deconv_layers()
        hs, unpooling_sizes = self.feature_map_activations(x)
        hs = [h.data for h in hs]

        activation_maps = []
        n_activation_maps = hs[layer_idx].shape[1]

        xp = self.xp

        for i in range(n_activation_maps):  # For each channel
            h = hs[layer_idx].copy()

            condition = xp.zeros_like(h)
            condition[0][i] = 1  # Keep one feature map and zero all other

            h = Variable(xp.where(condition, h, xp.zeros_like(h)))

            for i in reversed(range(layer_idx+1)):
                p = self.mps[i]
                h = F.upsampling_2d(h, p.indexes, p.kh, p.sy, p.ph, unpooling_sizes[i])
                for deconv in reversed(self.deconv_blocks[i]):
                    h = deconv(F.relu(h))

            activation_maps.append(h.data)

        return xp.concatenate(activation_maps)