def METHOD_NAME(self):
    """
    ND conv maps the same way in more dimensions.
    """
    n = caffe.NetSpec()
    # define data with 3 spatial dimensions, otherwise the same net
    n.data = L.Input(shape=dict(dim=[2, 3, 100, 100, 100]))
    n.conv = L.Convolution(
        n.data, num_output=10, kernel_size=[3, 3, 3], stride=[1, 1, 1],
        pad=[0, 1, 2])
    n.pool = L.Pooling(
        n.conv, pool=P.Pooling.MAX, kernel_size=2, stride=2, pad=0)
    n.deconv = L.Deconvolution(
        n.pool, num_output=10, kernel_size=4, stride=2, pad=0)
    ax, a, b = coord_map_from_to(n.deconv, n.data)
    self.assertEquals(ax, 1)
    self.assertTrue(len(a) == len(b))
    self.assertTrue(np.all(a == 1))
    self.assertEquals(b[0] - 1, b[1])
    self.assertEquals(b[1] - 1, b[2])