def test_raises(self):
with self.assertRaises(ValueError):
nei_finder = MultiscaleRadiusNeighbourFinder([1], [3, 4])
x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
batch_x = torch.tensor([0, 0, 0, 0])
y = torch.Tensor([[-1, 0], [1, 0]])
batch_y = torch.tensor([0, 0])
nei_finder = MultiscaleRadiusNeighbourFinder([1, 2], [3, 4])
with self.assertRaises(ValueError):
nei_finder(x, y, batch_x, batch_y, 10)
def test_single_search(self):
x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
batch_x = torch.tensor([0, 0, 0, 0])
y = torch.Tensor([[-1, 0], [1, 0]])
batch_y = torch.tensor([0, 0])
nei_finder = MultiscaleRadiusNeighbourFinder(1, 4)
self.assertEqual(nei_finder(x, y, batch_x, batch_y, 0)[1, :].shape[0], 4)
def test_multi_num_search(self):
x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
batch_x = torch.tensor([0, 0, 0, 0])
y = torch.Tensor([[-1, 0], [1, 0]])
batch_y = torch.tensor([0, 0])
nei_finder = MultiscaleRadiusNeighbourFinder(10, [3, 4])
multiscale = []
for i in range(2):
multiscale.append(nei_finder(x, y, batch_x, batch_y, i))
self.assertEqual(len(multiscale), 2)
self.assertEqual(multiscale[0][1, :].shape[0], 6)
self.assertEqual(multiscale[1][1, :].shape[0], 8)
def __init__(self,
ratio=None,
radius=None,
radius_num_point=None,
down_conv_nn=None,
*args,
**kwargs):
super(SAModule, self).__init__(
FPSSampler(ratio=ratio),
MultiscaleRadiusNeighbourFinder(
radius, max_num_neighbors=radius_num_point), *args, **kwargs)
local_nn = MLP(down_conv_nn) if down_conv_nn is not None else None
self._conv = PointConv(local_nn=local_nn, global_nn=None)
self._radius = radius
self._ratio = ratio
self._num_points = radius_num_point