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add group_points support npu. #3102

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add group_points support npu. #3102

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20 changes: 20 additions & 0 deletions mmcv/ops/csrc/pytorch/npu/group_points_npu.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -38,8 +38,28 @@ void group_points_forward_npu(int b, int c, int n, int npoints, int nsample,
out.copy_(res);
}

void group_points_backward_npu(int b, int c, int n, int npoints, int nsample,
const Tensor grad_out, const Tensor idx, Tensor grad_features)
{
at::Tensor trans_idx = idx.view({b * npoints * nsample});
at::Tensor trans_grad_out = grad_out.permute({0, 2, 3, 1});
at::Tensor grad_out_tensor = trans_grad_out.contiguous();
grad_out_tensor = grad_out_tensor.view({b * npoints * nsample, c});
at::Tensor out = at::zeros({b, n, c}, grad_out.options());

EXEC_NPU_CMD(aclnnGroupPointsGrad, grad_out_tensor, trans_idx, b, c, n, npoints, nsample, out);

at::Tensor grad_points = out.transpose(1, 2);

grad_features.copy_(grad_points);
}

void group_points_forward_impl(int b, int c, int n, int npoints, int nsample,
const Tensor points, const Tensor idx,
Tensor out);
void group_points_backward_impl(int b, int c, int n, int npoints, int nsample,
const Tensor points, const Tensor idx,
Tensor out);

REGISTER_NPU_IMPL(group_points_forward_impl, group_points_forward_npu);
REGISTER_NPU_IMPL(group_points_backward_impl, group_points_backward_npu);
12 changes: 12 additions & 0 deletions tests/test_ops/test_group_points.py
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Expand Up @@ -47,8 +47,11 @@ def test_grouping_points(dtype, device):
-1.4049, 0.4990, -0.7037, -0.9924, 0.0386
]]],
dtype=dtype).to(device)
features.requires_grad = True

output = grouping_operation(features, idx)
output.backward(output)
grad_features = features.grad
expected_output = torch.tensor(
[[[[0.5798, 0.5798, 0.5798], [-1.3311, -1.3311, -1.3311],
[0.9268, 0.9268, 0.9268], [0.5798, 0.5798, 0.5798],
Expand All @@ -69,7 +72,16 @@ def test_grouping_points(dtype, device):
[0.0386, 0.0386, 0.0386], [-0.6646, -0.6646, -0.6646],
[-0.6646, -0.6646, -0.6646], [-0.6646, -0.6646, -0.6646]]]],
dtype=dtype).to(device)
expected_grad_features = torch.tensor(
[[[6.9576, 0.0000, 0.0000, -3.9933, 0.0000, 0.0000, 0.0000, 0.0000, 2.7804, 0.0000],
[65.0964, 0.0000, 0.0000, 4.4220, 0.0000, 0.0000, 0.0000, 0.0000, 6.4743, 0.0000],
[-19.5192, 0.0000, 0.0000, -5.0793, 0.0000, 0.0000, 0.0000, 0.0000, -5.0358, 0.0000]],
[[-0.4560, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.1079, 0.0000, 0.0000, -5.5581],
[14.1276, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 18.2595, 0.0000, 0.0000, 8.4687],
[-7.9752, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.4970, 0.0000, 0.0000, 0.1158]]],
dtype=dtype).to(device)
assert torch.allclose(output, expected_output)
assert torch.allclose(grad_features, expected_grad_features)


@pytest.mark.parametrize('device', [
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