fix

Author: lihao7212148

tests/test_ops/test_three_interpolate.py

@@ -72,28 +72,28 @@ def test_three_interpolate(dtype, device):
     ], [
         2.2060e-01, 3.4110e-01, 3.4110e-01, 2.2060e-01, 2.2060e-01, 2.1380e-01
     ]],
-                                    [[
-                                        8.1773e-01, 9.5440e-01, 2.4532e+00,
-                                        8.1773e-01, 8.1773e-01, 1.1359e+00
-                                    ],
-                                     [
-                                         8.4689e-01, 1.9176e+00, 1.4715e+00,
-                                         8.4689e-01, 8.4689e-01, 1.3079e+00
-                                     ],
-                                     [
-                                         6.9473e-01, 2.7440e-01, 2.0842e+00,
-                                         6.9473e-01, 6.9473e-01, 7.8619e-01
-                                     ],
-                                     [
-                                         7.6789e-01, 1.5063e+00, 1.6209e+00,
-                                         7.6789e-01, 7.6789e-01, 1.1562e+00
-                                     ],
-                                     [
-                                         3.8760e-01, 1.0300e-02, 8.3569e-09,
-                                         3.8760e-01, 3.8760e-01, 1.9723e-01
-                                     ]]],
-                                   dtype=dtype,
-                                   device=device)
+        [[
+            8.1773e-01, 9.5440e-01, 2.4532e+00,
+            8.1773e-01, 8.1773e-01, 1.1359e+00
+        ],
+        [
+            8.4689e-01, 1.9176e+00, 1.4715e+00,
+            8.4689e-01, 8.4689e-01, 1.3079e+00
+        ],
+        [
+            6.9473e-01, 2.7440e-01, 2.0842e+00,
+            6.9473e-01, 6.9473e-01, 7.8619e-01
+        ],
+        [
+            7.6789e-01, 1.5063e+00, 1.6209e+00,
+            7.6789e-01, 7.6789e-01, 1.1562e+00
+        ],
+        [
+            3.8760e-01, 1.0300e-02, 8.3569e-09,
+            3.8760e-01, 3.8760e-01, 1.9723e-01
+        ]]],
+        dtype=dtype,
+        device=device)
 
     assert torch.allclose(output, expected_output, 1e-3, 1e-4)
 
@@ -106,16 +106,17 @@ def three_interpolate_forward_gloden(features, idx, weight):
     if dtype == np.float16:
         features = features.astype(np.float32)
         weight = weight.astype(np.float32)
-    
-    output = np.zeros((bs, cs, ns), dtype = np.float)
+
+    output = np.zeros((bs, cs, ns), dtype=np.float)
     for b in range(bs):
         for c in range(cs):
             for n in range(ns):
                 output[b][c][n] = features[b][c][idx[b][n][0]] * weight[b][n][0] \
-                                + features[b][c][idx[b][n][1]] * weight[b][n][1] \
-                                + features[b][c][idx[b][n][2]] * weight[b][n][2]
+                    + features[b][c][idx[b][n][1]] * weight[b][n][1] \
+                    + features[b][c][idx[b][n][2]] * weight[b][n][2]
     return output
 
+
 def three_interpolate_backward_gloden(grad_output, idx, weight, features):
     bs, cs, ns = grad_output.shape
     ms = features.shape[2]
@@ -124,7 +125,7 @@ def three_interpolate_backward_gloden(grad_output, idx, weight, features):
     if dtype == np.float16:
         features = features.astype(np.float32)
         weight = weight.astype(np.float32)
-    
+
     grad_point = np.zeros((bs, cs, ms), dtype=np.float)
     for b in range(bs):
         for c in range(cs):
@@ -137,6 +138,7 @@ def three_interpolate_backward_gloden(grad_output, idx, weight, features):
                     grad_output[b][c][n] * weight[b][n][2]
     return grad_point
 
+
 def torch_type_trans(dtype):
     if dtype == torch.half:
         return np.float16
@@ -145,27 +147,36 @@ def torch_type_trans(dtype):
     else:
         return np.float64
 
+
 @pytest.mark.parametrize('dtype', [
     torch.half,
     torch.float
 ])
 @pytest.mark.parametrize('device', [
-    (2,5,6,6),
-    (10,10,10,10),
-    (20,21,13,4),
-    (2,10,2,18),
-    (10,602,910,200),
-    (600,100,300,101)
+    pytest.param(
+        'npu',
+        marks=pytest.mark.skipif(
+            not IS_NPU_AVAILABLE, reason='requires NPU support'))
+])
+@pytest.mark.parametrize('shape', [
+    (2, 5, 6, 6),
+    (10, 10, 10, 10),
+    (20, 21, 13, 4),
+    (2, 10, 2, 18),
+    (10, 602, 910, 200),
+    (600, 100, 300, 101)
 ])
 def test_three_interpolate_npu_dynamic_shape(dtype, device, shape):
     bs = shape[0]
     cs = shape[1]
     ms = shape[2]
     ns = shape[3]
 
-    features = np.random.uniform(-10.0, 10.0, (bs, cs, ms).astype(torch_type_trans(dtype)))
+    features = np.random.uniform(-10.0, 10.0,
+                                 (bs, cs, ms).astype(torch_type_trans(dtype)))
     idx = np.random.uniform(0, ms, size=(bs, ns, 3), dtype=np.int32)
-    weight = np.random.uniform(-10.0, 10.0 (bs, ns, 3)).astype(torch_type_trans(dtype))
+    weight = np.random.uniform(-10.0, 10.0 (bs, ns, 3)
+                               ).astype(torch_type_trans(dtype))
 
     features_npu = torch.tensor(features, dtype=dtype).to(device)
     idx_npu = torch.tensor(idx, dtype=torch.int32).to(device)