black formatted test_upsample.py

pytorch · Apr 30, 2024 · 5b8a24d · 5b8a24d
1 parent f135a5d
commit 5b8a24d
Showing 1 changed file with 33 additions and 11 deletions.
diff --git a/tests/py/dynamo/conversion/test_upsample.py b/tests/py/dynamo/conversion/test_upsample.py
@@ -18,13 +18,17 @@ class TestUpsampleConverter(DispatchTestCase):
             ((2,), None, False, (1.5,)),
         ]
     )
-    def test_upsample_linear1d(self, input_shape, output_size, align_corners, scale_factors):
+    def test_upsample_linear1d(
+        self, input_shape, output_size, align_corners, scale_factors
+    ):
         class Upsample(torch.nn.Module):
             def __init__(self):
                 super().__init__()
 
             def forward(self, input):
-                return torch.ops.aten.upsample_linear1d.vec(input, output_size, align_corners, scale_factors)
+                return torch.ops.aten.upsample_linear1d.vec(
+                    input, output_size, align_corners, scale_factors
+                )
 
         input = [torch.randn([1, 1] + list(input_shape))]
         self.run_test(Upsample(), input)
@@ -41,13 +45,17 @@ def forward(self, input):
             ((2, 2), None, False, (1.5, 1.5)),
         ]
     )
-    def test_upsample_bilinear2d(self, input_shape, output_size, align_corners, scale_factors):
+    def test_upsample_bilinear2d(
+        self, input_shape, output_size, align_corners, scale_factors
+    ):
         class Upsample(torch.nn.Module):
             def __init__(self):
                 super().__init__()
 
             def forward(self, input):
-                return torch.ops.aten.upsample_bilinear2d.vec(input, output_size, align_corners, scale_factors)
+                return torch.ops.aten.upsample_bilinear2d.vec(
+                    input, output_size, align_corners, scale_factors
+                )
 
         input = [torch.randn([1, 1] + list(input_shape))]
         self.run_test(Upsample(), input)
@@ -64,13 +72,17 @@ def forward(self, input):
             ((2, 2, 2), None, False, (1.5, 1.5, 1.5)),
         ]
     )
-    def test_upsample_trilinear3d(self, input_shape, output_size, align_corners, scale_factors):
+    def test_upsample_trilinear3d(
+        self, input_shape, output_size, align_corners, scale_factors
+    ):
         class Upsample(torch.nn.Module):
             def __init__(self):
                 super().__init__()
 
             def forward(self, input):
-                return torch.ops.aten.upsample_trilinear3d.vec(input, output_size, align_corners, scale_factors)
+                return torch.ops.aten.upsample_trilinear3d.vec(
+                    input, output_size, align_corners, scale_factors
+                )
 
         input = [torch.randn([1, 1] + list(input_shape))]
         self.run_test(Upsample(), input)
@@ -87,13 +99,17 @@ def forward(self, input):
             ((2, 2), None, False, (1.5, 1.5)),
         ]
     )
-    def test_upsample_bicubic2d(self, input_shape, output_size, align_corners, scale_factors):
+    def test_upsample_bicubic2d(
+        self, input_shape, output_size, align_corners, scale_factors
+    ):
         class Upsample(torch.nn.Module):
             def __init__(self):
                 super().__init__()
 
             def forward(self, input):
-                return torch.ops.aten.upsample_bicubic2d.vec(input, output_size, align_corners, scale_factors)
+                return torch.ops.aten.upsample_bicubic2d.vec(
+                    input, output_size, align_corners, scale_factors
+                )
 
         input = [torch.randn([1, 1] + list(input_shape))]
         self.run_test(Upsample(), input)
@@ -112,7 +128,9 @@ def __init__(self):
                 super().__init__()
 
             def forward(self, input):
-                return torch.ops.aten.upsample_nearest1d.vec(input, output_size, scale_factors)
+                return torch.ops.aten.upsample_nearest1d.vec(
+                    input, output_size, scale_factors
+                )
 
         input = [torch.randn([1, 1] + list(input_shape))]
         self.run_test(Upsample(), input)
@@ -131,7 +149,9 @@ def __init__(self):
                 super().__init__()
 
             def forward(self, input):
-                return torch.ops.aten.upsample_nearest2d.vec(input, output_size, scale_factors)
+                return torch.ops.aten.upsample_nearest2d.vec(
+                    input, output_size, scale_factors
+                )
 
         input = [torch.randn([1, 1] + list(input_shape))]
         self.run_test(Upsample(), input)
@@ -150,7 +170,9 @@ def __init__(self):
                 super().__init__()
 
             def forward(self, input):
-                return torch.ops.aten.upsample_nearest3d.vec(input, output_size, scale_factors)
+                return torch.ops.aten.upsample_nearest3d.vec(
+                    input, output_size, scale_factors
+                )
 
         input = [torch.randn([1, 1] + list(input_shape))]
         self.run_test(Upsample(), input)