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

* Fix three nn half inpt

* update test

mmcv/ops/three_nn.py

@@ -34,8 +34,8 @@ class ThreeNN(Function):
 
         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':

tests/test_ops/test_three_nn.py

@@ -5,6 +5,40 @@ import torch
 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(
@@ -16,48 +50,16 @@ from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
         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)