def test_multiscaleTransforms(self):
samplers = [GridSampling(0.25), None, GridSampling(0.5)]
search = [
RadiusNeighbourFinder(0.5, 100,
ConvolutionFormat.PARTIAL_DENSE.value),
RadiusNeighbourFinder(0.5, 150,
ConvolutionFormat.PARTIAL_DENSE.value),
RadiusNeighbourFinder(1, 200,
ConvolutionFormat.PARTIAL_DENSE.value),
]
upsampler = [KNNInterpolate(1), KNNInterpolate(1)]
N = 10
x = np.linspace(0, 1, N)
y = np.linspace(0, 1, N)
xv, yv = np.meshgrid(x, y)
pos = torch.tensor([xv.flatten(), yv.flatten(), np.zeros(N * N)]).T
x = torch.ones_like(pos)
d = Data(pos=pos, x=x).contiguous()
ms_transform = MultiScaleTransform({
"sampler": samplers,
"neighbour_finder": search,
"upsample_op": upsampler
})
transformed = ms_transform(d.clone())
npt.assert_almost_equal(transformed.x.numpy(), x.numpy())
npt.assert_almost_equal(transformed.pos.numpy(), pos.numpy())
ms = transformed.multiscale
npt.assert_almost_equal(ms[0].pos.numpy(), ms[1].pos.numpy())
npt.assert_almost_equal(ms[0].pos.numpy(),
samplers[0](d.clone()).pos.numpy())
npt.assert_almost_equal(ms[2].pos.numpy(),
samplers[2](ms[0].clone()).pos.numpy())
self.assertEqual(ms[0].__inc__("idx_neighboors", 0), pos.shape[0])
idx = search[0](
d.pos,
ms[0].pos,
torch.zeros((d.pos.shape[0]), dtype=torch.long),
torch.zeros((ms[0].pos.shape[0]), dtype=torch.long),
)
for i in range(len(ms[0].idx_neighboors)):
self.assertEqual(set(ms[0].idx_neighboors[i].tolist()),
set(idx[i].tolist()))
self.assertEqual(ms[1].idx_neighboors.shape[1], 150)
self.assertEqual(ms[2].idx_neighboors.shape[1], 200)
upsample = transformed.upsample
self.assertEqual(upsample[0].num_nodes, ms[1].num_nodes)
self.assertEqual(upsample[1].num_nodes, pos.shape[0])
self.assertEqual(upsample[1].x_idx.max(), ms[0].num_nodes - 1)
self.assertEqual(upsample[1].y_idx.max(), pos.shape[0] - 1)
self.assertEqual(upsample[1].__inc__("x_idx", 0), ms[0].num_nodes)
self.assertEqual(upsample[1].__inc__("y_idx", 0), pos.shape[0])
def test_precompute(self):
pos = torch.tensor([[1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 0], [0.1, 0, 0]])
x = torch.tensor([0, 0, 0, 0, 1]).unsqueeze(-1)
support = Data(x=x, pos=pos)
query = GridSampling3D(1)(support.clone())
interpolate = KNNInterpolate(1)
up = interpolate.precompute(query, support)
self.assertEqual(up.num_nodes, 5)
self.assertEqual(up.x_idx[4], up.x_idx[3])
def test_compute(self):
npoints = 100
pos = torch.randn((npoints, 3))
x = torch.randn((npoints, 4))
support = Data(x=x, pos=pos)
query = Data(x=torch.randn((npoints // 2, 4)), pos=torch.randn((npoints // 2, 3)))
interpolate = KNNInterpolate(3)
precomputed = interpolate.precompute(query, support)
gt = interpolate(query, support)
pre = interpolate(query, support, precomputed=precomputed)
torch.testing.assert_allclose(gt, pre)