formatting changes from black 22.3.0

Summary:
Applies the black-fbsource codemod with the new build of pyfmt.

paintitblack

Reviewed By: lisroach

Differential Revision: D36324783

fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc

tests/benchmarks/bm_render_implicit.py

@@ -15,7 +15,7 @@ def bm_render_volumes() -> None:
     case_grid = {
         "batch_size": [1, 5],
         "raymarcher_type": [EmissionAbsorptionRaymarcher, AbsorptionOnlyRaymarcher],
-        "n_rays_per_image": [64 ** 2, 256 ** 2],
+        "n_rays_per_image": [64**2, 256**2],
         "n_pts_per_ray": [16, 128],
     }
     test_cases = itertools.product(*case_grid.values())

tests/benchmarks/bm_render_volumes.py

@@ -17,7 +17,7 @@ def bm_render_volumes() -> None:
         "batch_size": [1, 5],
         "shape": ["sphere", "cube"],
         "raymarcher_type": [EmissionAbsorptionRaymarcher, AbsorptionOnlyRaymarcher],
-        "n_rays_per_image": [64 ** 2, 256 ** 2],
+        "n_rays_per_image": [64**2, 256**2],
         "n_pts_per_ray": [16, 128],
     }
     test_cases = itertools.product(*case_grid.values())

tests/implicitron/test_evaluation.py

@@ -124,7 +124,7 @@ class TestEvaluation(unittest.TestCase):
                 )
                 self.assertGreater(
                     float(mse_depth_unmasked.sum()),
-                    float(diff ** 2),
+                    float(diff**2),
                 )
                 self.assertGreater(
                     float(abs_depth_unmasked.sum()),
@@ -143,7 +143,7 @@ class TestEvaluation(unittest.TestCase):
                     )
                     if _mask_gt is not None:
                         expected_err_abs = diff
-                        expected_err_mse = diff ** 2
+                        expected_err_mse = diff**2
                     else:
                         err_mask = (gt > 0.0).float() * mask
                         if crop > 0:
@@ -195,7 +195,7 @@ class TestEvaluation(unittest.TestCase):
             )
             self.assertAlmostEqual(float(psnr), float(psnr_cv2), delta=1e-4)
             # check that all PSNRs are bigger than the minimum possible PSNR
-            max_mse = max_diff ** 2
+            max_mse = max_diff**2
             min_psnr = 10 * math.log10(1.0 / max_mse)
             for _im1, _im2 in zip(im1, im2):
                 _psnr = calc_psnr(_im1, _im2)

tests/test_acos_linear_extrapolation.py

@@ -66,7 +66,7 @@ class TestAcosLinearExtrapolation(TestCaseMixin, unittest.TestCase):
         # fit a line: slope * x + bias = y
         x_1 = torch.stack([x, torch.ones_like(x)], dim=-1)
         slope, bias = lstsq(x_1, y[:, None]).view(-1)[:2]
-        desired_slope = (-1.0) / torch.sqrt(1.0 - bound_t ** 2)
+        desired_slope = (-1.0) / torch.sqrt(1.0 - bound_t**2)
         # test that the desired slope is the same as the fitted one
         self.assertClose(desired_slope.view(1), slope.view(1), atol=1e-2)
         # test that the autograd's slope is the same as the desired one

tests/test_lighting.py

@@ -412,7 +412,7 @@ class TestSpecularLighting(TestCaseMixin, unittest.TestCase):
             camera_position=camera_position[None, :],
             shininess=torch.tensor(10),
         )
-        self.assertClose(output_light, expected_output ** 10)
+        self.assertClose(output_light, expected_output**10)
 
     def test_specular_batched(self):
         batch_size = 10

tests/test_rasterize_meshes.py

@@ -62,7 +62,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
         torch.manual_seed(231)
         device = torch.device("cpu")
         image_size = 32
-        blur_radius = 0.1 ** 2
+        blur_radius = 0.1**2
         faces_per_pixel = 3
 
         for d in ["cpu", get_random_cuda_device()]:
@@ -167,7 +167,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
 
         torch.manual_seed(231)
         image_size = 64
-        radius = 0.1 ** 2
+        radius = 0.1**2
         faces_per_pixel = 3
         device = torch.device("cpu")
         meshes_cpu = ico_sphere(0, device)
@@ -224,7 +224,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
         # Make sure that the backward pass runs for all pathways
         image_size = 64  # test is too slow for very large images.
         N = 1
-        radius = 0.1 ** 2
+        radius = 0.1**2
         faces_per_pixel = 3
 
         grad_zbuf = torch.randn(N, image_size, image_size, faces_per_pixel)
@@ -997,7 +997,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
         ordering of faces.
         """
         image_size = 10
-        blur_radius = 0.12 ** 2
+        blur_radius = 0.12**2
         faces_per_pixel = 1
 
         # fmt: off

tests/test_render_implicit.py

@@ -60,13 +60,13 @@ def spherical_volumetric_function(
 
     # the squared distance of each ray point to the centroid of the sphere
     surface_dist = (
-        (surface_vectors ** 2)
+        (surface_vectors**2)
         .sum(-1, keepdim=True)
         .view(*rays_points_world.shape[:-1], 1)
     )
 
     # set all ray densities within the sphere_diameter distance from the centroid to 1
-    rays_densities = torch.sigmoid(-100.0 * (surface_dist - sphere_diameter ** 2))
+    rays_densities = torch.sigmoid(-100.0 * (surface_dist - sphere_diameter**2))
 
     # ray colors are proportional to the normalized surface_vectors
     rays_features = (

tests/test_sample_points_from_meshes.py

@@ -128,7 +128,7 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
 
         # Sphere: points should have radius 1.
         x, y, z = samples[1, :].unbind(1)
-        radius = torch.sqrt(x ** 2 + y ** 2 + z ** 2)
+        radius = torch.sqrt(x**2 + y**2 + z**2)
 
         self.assertClose(radius, torch.ones(num_samples))