[Feature] Add support for Ascend devices with gather_points (#2555)

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                          |     | √    |     |     |        |

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                          |     | √    |     |     |        |

mmcv/ops/csrc/pytorch/npu/gather_points_npu.cpp

+#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);

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())