run lint

Summary: Run `/dev/linter.sh` to fix linting

Reviewed By: nikhilaravi

Differential Revision: D20584037

fbshipit-source-id: 69e45b33d22e3d54b6d37c3c35580bb3e9dc50a5

pytorch3d/renderer/cameras.py

@@ -2,7 +2,7 @@
 
 import math
 import numpy as np
-from typing import Tuple, Optional, Sequence
+from typing import Optional, Sequence, Tuple
 import torch
 import torch.nn.functional as F
 
@@ -1039,15 +1039,18 @@ def look_at_view_transform(
 
     if eye is not None:
         broadcasted_args = convert_to_tensors_and_broadcast(
-            eye, at, up, device=device)
+            eye, at, up, device=device
+        )
         eye, at, up = broadcasted_args
         C = eye
     else:
         broadcasted_args = convert_to_tensors_and_broadcast(
-            dist, elev, azim, at, up, device=device)
+            dist, elev, azim, at, up, device=device
+        )
         dist, elev, azim, at, up = broadcasted_args
         C = camera_position_from_spherical_angles(
-            dist, elev, azim, degrees=degrees, device=device)
+            dist, elev, azim, degrees=degrees, device=device
+        )
 
     R = look_at_rotation(C, at, up, device=device)
     T = -torch.bmm(R.transpose(1, 2), C[:, :, None])[:, :, 0]

tests/test_cameras.py

@@ -121,7 +121,7 @@ class TestCameraHelpers(unittest.TestCase):
         dist = math.sqrt(2)
         elev = math.pi / 4
         azim = 0.0
-        eye = ((0.0, 1.0, 1.0), )
+        eye = ((0.0, 1.0, 1.0),)
         # using passed values for dist, elev, azim
         R, t = look_at_view_transform(dist, elev, azim, degrees=False)
         # using other values for dist, elev, azim - eye overrides

tests/test_compositing.py

@@ -188,7 +188,7 @@ class TestAccumulatePoints(unittest.TestCase):
     ):
         res1 = fn1(*args1)
         res2 = fn2(*args2)
-           
+
         self.assertTrue(torch.allclose(res1.cpu(), res2.cpu(), atol=1e-6))
 
         if not compare_grads:

tests/test_sample_points_from_meshes.py

@@ -291,7 +291,7 @@ class TestSamplePoints(unittest.TestCase):
             if sampled_weights.min() <= 0:
                 return False
         return True
-    
+
     def test_verts_nan(self):
         num_verts = 30
         num_faces = 50
@@ -300,14 +300,19 @@ class TestSamplePoints(unittest.TestCase):
                 verts = torch.rand(
                     (num_verts, 3), dtype=torch.float32, device=device
                 )
-                # randomly assign an invalid type 
+                # randomly assign an invalid type
                 verts[torch.randperm(num_verts)[:10]] = float(invalid)
                 faces = torch.randint(
-                    num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
+                    num_verts,
+                    size=(num_faces, 3),
+                    dtype=torch.int64,
+                    device=device,
                 )
                 meshes = Meshes(verts=[verts], faces=[faces])
 
-                with self.assertRaisesRegex(ValueError, "Meshes contain nan or inf."):
+                with self.assertRaisesRegex(
+                    ValueError, "Meshes contain nan or inf."
+                ):
                     sample_points_from_meshes(
                         meshes, num_samples=100, return_normals=True
                     )