diff --git a/docs/en/understand_mmcv/ops.md b/docs/en/understand_mmcv/ops.md
index 621571682..f452b9cd3 100644
--- a/docs/en/understand_mmcv/ops.md
+++ b/docs/en/understand_mmcv/ops.md
@@ -25,7 +25,7 @@ We implement common ops used in detection, segmentation, etc.
| FurthestPointSample | | √ | | | |
| FurthestPointSampleWithDist | | √ | | | |
| FusedBiasLeakyrelu | | √ | | | √ |
-| GatherPoints | | √ | | | |
+| GatherPoints | | √ | | | √ |
| GroupPoints | | √ | | | |
| Iou3d | | √ | √ | | |
| KNN | | √ | | | |
diff --git a/docs/zh_cn/understand_mmcv/ops.md b/docs/zh_cn/understand_mmcv/ops.md
index e8f96a8be..4926cff39 100644
--- a/docs/zh_cn/understand_mmcv/ops.md
+++ b/docs/zh_cn/understand_mmcv/ops.md
@@ -25,7 +25,7 @@ MMCV 提供了检测、分割等任务中常用的算子
| FurthestPointSample | | √ | | | |
| FurthestPointSampleWithDist | | √ | | | |
| FusedBiasLeakyrelu | | √ | | | √ |
-| GatherPoints | | √ | | | |
+| GatherPoints | | √ | | | √ |
| GroupPoints | | √ | | | |
| Iou3d | | √ | √ | | |
| KNN | | √ | | | |
diff --git a/mmcv/ops/csrc/pytorch/npu/gather_points_npu.cpp b/mmcv/ops/csrc/pytorch/npu/gather_points_npu.cpp
new file mode 100644
index 000000000..ce19b4ccb
--- /dev/null
+++ b/mmcv/ops/csrc/pytorch/npu/gather_points_npu.cpp
@@ -0,0 +1,29 @@
+#include "pytorch_npu_helper.hpp"
+
+using namespace NPU_NAME_SPACE;
+using namespace std;
+
+void gather_points_forward_npu(int b, int c, int n, int npoints,
+ const Tensor points, const Tensor idx,
+ Tensor out) {
+ // b, c, n, and npoints do not need to be passed into gatherv2,
+ // b, c, n, and npoints are calculated inside the operator
+ // gatherv2 operator in ascend needs to set axis to 2, batch_dims is 1
+ c10::SmallVector<int64_t, N> axis = {2};
+ int64_t batch_dims = 1;
+
+ OpCommand cmd;
+ cmd.Name("GatherV2")
+ .Input(points)
+ .Input(idx)
+ .Input(axis)
+ .Output(out)
+ .Attr("batch_dims", batch_dims)
+ .Run();
+}
+
+void gather_points_forward_impl(int b, int c, int n, int npoints,
+ const Tensor points, const Tensor idx,
+ Tensor out);
+
+REGISTER_NPU_IMPL(gather_points_forward_impl, gather_points_forward_npu);
diff --git a/tests/test_ops/test_gather_points.py b/tests/test_ops/test_gather_points.py
index a93df692a..349a1b65d 100644
--- a/tests/test_ops/test_gather_points.py
+++ b/tests/test_ops/test_gather_points.py
@@ -3,49 +3,65 @@ import pytest
import torch
from mmcv.ops import gather_points
+from mmcv.utils import IS_CUDA_AVAILABLE, IS_NPU_AVAILABLE
-@pytest.mark.skipif(
- not torch.cuda.is_available(), reason='requires CUDA support')
-def test_gather_points():
- features = torch.tensor([[[
- -1.6095, -0.1029, -0.8876, -1.2447, -2.4031, 0.3708, -1.1586, -1.4967,
- -0.4800, 0.2252
- ],
- [
- 1.9138, 3.4979, 1.6854, 1.5631, 3.6776,
- 3.1154, 2.1705, 2.5221, 2.0411, 3.1446
- ],
- [
- -1.4173, 0.3073, -1.4339, -1.4340, -1.2770,
- -0.2867, -1.4162, -1.4044, -1.4245, -1.4074
- ]],
- [[
- 0.2160, 0.0842, 0.3661, -0.2749, -0.4909,
- -0.6066, -0.8773, -0.0745, -0.9496, 0.1434
- ],
- [
- 1.3644, 1.8087, 1.6855, 1.9563, 1.2746,
- 1.9662, 0.9566, 1.8778, 1.1437, 1.3639
- ],
- [
- -0.7172, 0.1692, 0.2241, 0.0721, -0.7540,
- 0.0462, -0.6227, 0.3223, -0.6944, -0.5294
- ]]]).cuda()
+class TestGatherPoints:
- idx = torch.tensor([[0, 1, 4, 0, 0, 0], [0, 5, 6, 0, 0, 0]]).int().cuda()
+ @pytest.mark.parametrize('device', [
+ pytest.param(
+ 'cuda',
+ marks=pytest.mark.skipif(
+ not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
+ pytest.param(
+ 'npu',
+ marks=pytest.mark.skipif(
+ not IS_NPU_AVAILABLE, reason='requires NPU support'))
+ ])
+ def test_gather_points_all_close(self, device):
+ features = torch.tensor(
+ [[[
+ -1.6095, -0.1029, -0.8876, -1.2447, -2.4031, 0.3708, -1.1586,
+ -1.4967, -0.4800, 0.2252
+ ],
+ [
+ 1.9138, 3.4979, 1.6854, 1.5631, 3.6776, 3.1154, 2.1705,
+ 2.5221, 2.0411, 3.1446
+ ],
+ [
+ -1.4173, 0.3073, -1.4339, -1.4340, -1.2770, -0.2867, -1.4162,
+ -1.4044, -1.4245, -1.4074
+ ]],
+ [[
+ 0.2160, 0.0842, 0.3661, -0.2749, -0.4909, -0.6066, -0.8773,
+ -0.0745, -0.9496, 0.1434
+ ],
+ [
+ 1.3644, 1.8087, 1.6855, 1.9563, 1.2746, 1.9662, 0.9566,
+ 1.8778, 1.1437, 1.3639
+ ],
+ [
+ -0.7172, 0.1692, 0.2241, 0.0721, -0.7540, 0.0462, -0.6227,
+ 0.3223, -0.6944, -0.5294
+ ]]],
+ dtype=torch.float,
+ device=device)
+ idx = torch.tensor([[0, 1, 4, 0, 0, 0], [0, 5, 6, 0, 0, 0]],
+ dtype=torch.int32,
+ device=device)
+ output = gather_points(features, idx)
+ expected_output = torch.tensor(
+ [[[-1.6095, -0.1029, -2.4031, -1.6095, -1.6095, -1.6095],
+ [1.9138, 3.4979, 3.6776, 1.9138, 1.9138, 1.9138],
+ [-1.4173, 0.3073, -1.2770, -1.4173, -1.4173, -1.4173]],
+ [[0.2160, -0.6066, -0.8773, 0.2160, 0.2160, 0.2160],
+ [1.3644, 1.9662, 0.9566, 1.3644, 1.3644, 1.3644],
+ [-0.7172, 0.0462, -0.6227, -0.7172, -0.7172, -0.7172]]],
+ dtype=torch.float,
+ device=device)
- output = gather_points(features, idx)
- expected_output = torch.tensor(
- [[[-1.6095, -0.1029, -2.4031, -1.6095, -1.6095, -1.6095],
- [1.9138, 3.4979, 3.6776, 1.9138, 1.9138, 1.9138],
- [-1.4173, 0.3073, -1.2770, -1.4173, -1.4173, -1.4173]],
- [[0.2160, -0.6066, -0.8773, 0.2160, 0.2160, 0.2160],
- [1.3644, 1.9662, 0.9566, 1.3644, 1.3644, 1.3644],
- [-0.7172, 0.0462, -0.6227, -0.7172, -0.7172, -0.7172]]]).cuda()
+ assert torch.allclose(output, expected_output)
- assert torch.allclose(output, expected_output)
-
- # test fp16
- output_half = gather_points(features.half(), idx)
- assert torch.allclose(output_half, expected_output.half())
+ # test fp16
+ output_half = gather_points(features.half(), idx)
+ assert torch.allclose(output_half, expected_output.half())