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[Fix] Fix three nn op can not accept half tensor (#2348)
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* Fix three nn half inpt

* update test
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@zhouzaida
q.yao authored and zhouzaida committed Nov 23, 2022
1 parent 3bb0611 commit 2d10616
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Showing 2 changed files with 49 additions and 47 deletions.
4 changes: 2 additions & 2 deletions mmcv/ops/three_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,8 +34,8 @@ def forward(ctx: Any, target: torch.Tensor,

B, N, _ = target.size()
m = source.size(1)
dist2 = torch.FloatTensor(B, N, 3).to(target.device)
idx = torch.IntTensor(B, N, 3).to(target.device)
dist2 = target.new_empty(B, N, 3)
idx = target.new_empty(B, N, 3, dtype=torch.int32)

ext_module.three_nn_forward(target, source, dist2, idx, b=B, n=N, m=m)
if torch.__version__ != 'parrots':
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92 changes: 47 additions & 45 deletions tests/test_ops/test_three_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,40 @@
from mmcv.ops import three_nn
from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE

known = [[[-1.8373, 3.5605, -0.7867], [0.7615, 2.9420, 0.2314],
[-0.6503, 3.6637, -1.0622], [-1.8373, 3.5605, -0.7867],
[-1.8373, 3.5605, -0.7867]],
[[-1.3399, 1.9991, -0.3698], [-0.0799, 0.9698, -0.8457],
[0.0858, 2.4721, -0.1928], [-1.3399, 1.9991, -0.3698],
[-1.3399, 1.9991, -0.3698]]]

unknown = [[[-1.8373, 3.5605, -0.7867], [0.7615, 2.9420, 0.2314],
[-0.6503, 3.6637, -1.0622], [-1.5237, 2.3976, -0.8097],
[-0.0722, 3.4017, -0.2880], [0.5198, 3.0661, -0.4605],
[-2.0185, 3.5019, -0.3236], [0.5098, 3.1020, 0.5799],
[-1.6137, 3.8443, -0.5269], [0.7341, 2.9626, -0.3189]],
[[-1.3399, 1.9991, -0.3698], [-0.0799, 0.9698, -0.8457],
[0.0858, 2.4721, -0.1928], [-0.9022, 1.6560, -1.3090],
[0.1156, 1.6901, -0.4366], [-0.6477, 2.3576, -0.1563],
[-0.8482, 1.1466, -1.2704], [-0.8753, 2.0845, -0.3460],
[-0.5621, 1.4233, -1.2858], [-0.5883, 1.3114, -1.2899]]]

expected_dist = [[[0.0000, 0.0000, 0.0000], [0.0000, 2.0463, 2.8588],
[0.0000, 1.2229, 1.2229], [1.2047, 1.2047, 1.2047],
[1.0011, 1.0845, 1.8411], [0.7433, 1.4451, 2.4304],
[0.5007, 0.5007, 0.5007], [0.4587, 2.0875, 2.7544],
[0.4450, 0.4450, 0.4450], [0.5514, 1.7206, 2.6811]],
[[0.0000, 0.0000, 0.0000], [0.0000, 1.6464, 1.6952],
[0.0000, 1.5125, 1.5125], [1.0915, 1.0915, 1.0915],
[0.8197, 0.8511, 1.4894], [0.7433, 0.8082, 0.8082],
[0.8955, 1.3340, 1.3340], [0.4730, 0.4730, 0.4730],
[0.7949, 1.3325, 1.3325], [0.7566, 1.3727, 1.3727]]]

expected_idx = [[[0, 3, 4], [1, 2, 0], [2, 0, 3], [0, 3, 4], [2, 1, 0],
[1, 2, 0], [0, 3, 4], [1, 2, 0], [0, 3, 4], [1, 2, 0]],
[[0, 3, 4], [1, 2, 0], [2, 0, 3], [0, 3, 4], [2, 1, 0],
[2, 0, 3], [1, 0, 3], [0, 3, 4], [1, 0, 3], [1, 0, 3]]]


@pytest.mark.parametrize('device', [
pytest.param(
Expand All @@ -16,48 +50,16 @@
marks=pytest.mark.skipif(
not IS_MLU_AVAILABLE, reason='requires MLU support'))
])
def test_three_nn(device):
known = torch.tensor(
[[[-1.8373, 3.5605, -0.7867], [0.7615, 2.9420, 0.2314],
[-0.6503, 3.6637, -1.0622], [-1.8373, 3.5605, -0.7867],
[-1.8373, 3.5605, -0.7867]],
[[-1.3399, 1.9991, -0.3698], [-0.0799, 0.9698, -0.8457],
[0.0858, 2.4721, -0.1928], [-1.3399, 1.9991, -0.3698],
[-1.3399, 1.9991, -0.3698]]],
device=device)

unknown = torch.tensor(
[[[-1.8373, 3.5605, -0.7867], [0.7615, 2.9420, 0.2314],
[-0.6503, 3.6637, -1.0622], [-1.5237, 2.3976, -0.8097],
[-0.0722, 3.4017, -0.2880], [0.5198, 3.0661, -0.4605],
[-2.0185, 3.5019, -0.3236], [0.5098, 3.1020, 0.5799],
[-1.6137, 3.8443, -0.5269], [0.7341, 2.9626, -0.3189]],
[[-1.3399, 1.9991, -0.3698], [-0.0799, 0.9698, -0.8457],
[0.0858, 2.4721, -0.1928], [-0.9022, 1.6560, -1.3090],
[0.1156, 1.6901, -0.4366], [-0.6477, 2.3576, -0.1563],
[-0.8482, 1.1466, -1.2704], [-0.8753, 2.0845, -0.3460],
[-0.5621, 1.4233, -1.2858], [-0.5883, 1.3114, -1.2899]]],
device=device)

dist, idx = three_nn(unknown, known)
expected_dist = torch.tensor(
[[[0.0000, 0.0000, 0.0000], [0.0000, 2.0463, 2.8588],
[0.0000, 1.2229, 1.2229], [1.2047, 1.2047, 1.2047],
[1.0011, 1.0845, 1.8411], [0.7433, 1.4451, 2.4304],
[0.5007, 0.5007, 0.5007], [0.4587, 2.0875, 2.7544],
[0.4450, 0.4450, 0.4450], [0.5514, 1.7206, 2.6811]],
[[0.0000, 0.0000, 0.0000], [0.0000, 1.6464, 1.6952],
[0.0000, 1.5125, 1.5125], [1.0915, 1.0915, 1.0915],
[0.8197, 0.8511, 1.4894], [0.7433, 0.8082, 0.8082],
[0.8955, 1.3340, 1.3340], [0.4730, 0.4730, 0.4730],
[0.7949, 1.3325, 1.3325], [0.7566, 1.3727, 1.3727]]],
device=device)
expected_idx = torch.tensor(
[[[0, 3, 4], [1, 2, 0], [2, 0, 3], [0, 3, 4], [2, 1, 0], [1, 2, 0],
[0, 3, 4], [1, 2, 0], [0, 3, 4], [1, 2, 0]],
[[0, 3, 4], [1, 2, 0], [2, 0, 3], [0, 3, 4], [2, 1, 0], [2, 0, 3],
[1, 0, 3], [0, 3, 4], [1, 0, 3], [1, 0, 3]]],
device=device)

assert torch.allclose(dist, expected_dist, atol=1e-4)
assert torch.all(idx == expected_idx)
@pytest.mark.parametrize('dtype,rtol', [(torch.float, 1e-8),
(torch.half, 1e-3)])
def test_three_nn(device, dtype, rtol):
dtype = torch.float
known_t = torch.tensor(known, dtype=dtype, device=device)
unknown_t = torch.tensor(unknown, dtype=dtype, device=device)

dist_t, idx_t = three_nn(unknown_t, known_t)
expected_dist_t = torch.tensor(expected_dist, dtype=dtype, device=device)
expected_idx_t = torch.tensor(expected_idx, device=device)

assert torch.allclose(dist_t, expected_dist_t, atol=1e-4, rtol=rtol)
assert torch.all(idx_t == expected_idx_t)

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