lint error

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)
 
@@ -148,24 +148,16 @@ def torch_type_trans(dtype):
         return np.float64
 
 
-@pytest.mark.parametrize('dtype', [
-    torch.half,
-    torch.float
-])
+@pytest.mark.parametrize('dtype', [torch.half, torch.float])
 @pytest.mark.parametrize('device', [
     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)
-])
+@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]
@@ -175,13 +167,14 @@ def test_three_interpolate_npu_dynamic_shape(dtype, device, shape):
     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)
     weight_npu = torch.tensor(weight, dtype=dtype).to(device)
 
     expected_output = three_interpolate_forward_gloden(features, idx, weight)
     output = three_interpolate(features_npu, idx_npu, weight_npu)
-    assert np.allclose(output.cpu().numpy(), expected_output, 1e-3, 1e-4)
+    assert np.allclose(output.cpu().numpy(), expected_output, 1e-3, 1e-4)