test_gconv.py 文件源码

def check_equivariance(im, layers, input_array, output_array, point_group):

    # Transform the image
    f = input_array(im)
    g = point_group.rand()
    gf = g * f
    im1 = gf.v

    # Apply layers to both images
    im = Variable(cuda.to_gpu(im))
    im1 = Variable(cuda.to_gpu(im1))

    fmap = im
    fmap1 = im1
    for layer in layers:
        layer.to_gpu()
        fmap = layer(fmap)
        fmap1 = layer(fmap1)

    # Transform the computed feature maps
    fmap1_garray = output_array(cuda.to_cpu(fmap1.data))
    r_fmap1_data = (g.inv() * fmap1_garray).v

    fmap_data = cuda.to_cpu(fmap.data)
    assert np.allclose(fmap_data, r_fmap1_data, rtol=1e-5, atol=1e-3)