def test_cumsum_reverse(self, feats, boundaries):
cumsum = spc_render.cumsum(feats, boundaries, reverse=True)
expected = torch.tensor(
[[2, 2], [1, 1], [6, 6], [5, 5], [3, 3], [5, 5]],
device='cuda',
dtype=torch.float)
assert torch.equal(cumsum, expected)
def test_cumsum_exclusive(self, feats, boundaries):
cumsum = spc_render.cumsum(feats,
boundaries,
reverse=False,
exclusive=True)
expected = torch.tensor(
[[0, 0], [1, 1], [0, 0], [1, 1], [3, 3], [0, 0]],
device='cuda',
dtype=torch.float)
assert torch.equal(cumsum, expected)
def test_cumsum_big_backward(self, feats_big, boundaries_big):
feats_big.requires_grad = True
fdim = feats_big.shape[-1]
if feats_big.grad is not None:
feats_big.grad.detach_()
feats_big.grad.zero_()
cumsum = spc_render.cumsum(feats_big.reshape(-1, fdim), boundaries_big)
loss = cumsum.sum()
loss.backward()
grad0 = feats_big.grad.clone()
if feats_big.grad is not None:
feats_big.grad.detach_()
feats_big.grad.zero_()
expected = torch.cumsum(feats_big, dim=1)
loss = expected.sum()
loss.backward()
grad1 = feats_big.grad.clone()
assert torch.allclose(grad0, grad1, atol=1e-4)
def test_cumsum_big(self, feats_big, boundaries_big):
fdim = feats_big.shape[-1]
cumsum = spc_render.cumsum(feats_big.reshape(-1, fdim), boundaries_big)
expected = torch.cumsum(feats_big, dim=1).reshape(-1, fdim)
assert torch.allclose(cumsum, expected, atol=1e-5)