[Feature] Add support of points_in_polyogns for Ascend device

docs/en/understand_mmcv/ops.md

@@ -39,7 +39,7 @@ We implement common ops used in detection, segmentation, etc.
 | NMSQuadri                    | √   | √    |     |     |        |
 | PixelGroup                   | √   |      |     |     |        |
 | PointsInBoxes                | √   | √    |     |     |        |
-| PointsInPolygons             |     | √    |     |     |        |
+| PointsInPolygons             |     | √    |     |     | √      |
 | PSAMask                      | √   | √    | √   |     | √      |
 | RotatedFeatureAlign          | √   | √    | √   |     |        |
 | RoIPointPool3d               |     | √    | √   |     |        |

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

@@ -0,0 +1,27 @@
+#include "pytorch_npu_helper.hpp"
+
+using namespace NPU_NAME_SPACE;
+using namespace std;
+
+constexpr int32_t MAX_POLYGONS_BATCH = 2800;
+
+void points_in_polygons_npu(const Tensor points, Tensor polygons, Tensor output,
+                            const int rows, const int cols) {
+  TORCH_CHECK(
+      (polygons.sizes()[0] <= MAX_POLYGONS_BATCH),
+      "The batch of polygons tensor must be less than MAX_POLYGONS_BATCH");
+  at::Tensor trans_polygons = polygons.transpose(0, 1);
+  OpCommand cmd;
+  at::Tensor new_trans_polygons = NpuUtils::format_contiguous(trans_polygons);
+  cmd.Name("PointsInPolygons")
+      .Input(points, (string) "points")
+      .Input(new_trans_polygons, (string) "polygons")
+      .Output(output)
+      .Run();
+}
+
+void points_in_polygons_forward_impl(const Tensor points, Tensor polygons,
+                                     Tensor output, const int rows,
+                                     const int cols);
+
+REGISTER_NPU_IMPL(points_in_polygons_forward_impl, points_in_polygons_npu);

mmcv/ops/points_in_polygons.py

@@ -31,8 +31,11 @@ def points_in_polygons(points: Tensor, polygons: Tensor) -> Tensor:
     assert polygons.shape[1] == 8, \
         'polygons dimension should be 8, ' \
         f'but got unexpected shape {polygons.shape[1]}'
-    output = torch.full([points.shape[0], polygons.shape[0]],
-                        0.).cuda().float()
+    output = torch.zeros(
+        points.shape[0],
+        polygons.shape[0],
+        dtype=torch.float32,
+        device=points.device)
     ext_module.points_in_polygons_forward(points.contiguous(),
                                           polygons.contiguous(), output)
     return output

tests/test_ops/test_points_in_polygons.py

@@ -4,20 +4,29 @@
 import torch
 
 from mmcv.ops import points_in_polygons
+from mmcv.utils import IS_CUDA_AVAILABLE, IS_NPU_AVAILABLE
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
-def test_points_in_polygons():
+@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_points_in_polygons(device):
     points = np.array([[300., 300.], [400., 400.], [100., 100], [300, 250],
                        [100, 0]])
     polygons = np.array([[200., 200., 400., 400., 500., 200., 400., 100.],
                          [400., 400., 500., 500., 600., 300., 500., 200.],
                          [300., 300., 600., 700., 700., 700., 700., 100.]])
     expected_output = np.array([[0., 0., 0.], [0., 0., 1.], [0., 0., 0.],
-                                [1., 0., 0.], [0., 0., 0.]])
-    points = torch.from_numpy(points).cuda().float()
-    polygons = torch.from_numpy(polygons).cuda().float()
-    expected_output = torch.from_numpy(expected_output).cuda().float()
-    assert torch.allclose(
-        points_in_polygons(points, polygons), expected_output, 1e-3)
+                                [1., 0., 0.], [0., 0., 0.]]).astype(np.float32)
+    points = torch.tensor(points, dtype=torch.float32, device=device)
+    polygons = torch.tensor(polygons, dtype=torch.float32, device=device)
+    assert np.allclose(
+        points_in_polygons(points, polygons).cpu().numpy(), expected_output,
+        1e-3)