Address black + isort fbsource linter warnings

Summary: Address black + isort fbsource linter warnings from D20558374 (previous diff)

Reviewed By: nikhilaravi

Differential Revision: D20558373

fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8

tests/bm_blending.py

@@ -2,8 +2,8 @@
 
 
 from itertools import product
-from fvcore.common.benchmark import benchmark
 
+from fvcore.common.benchmark import benchmark
 from test_blending import TestBlending
 
 
@@ -18,12 +18,7 @@ def bm_blending() -> None:
     for case in test_cases:
         n, s, k, d = case
         kwargs_list.append(
-            {
-                "num_meshes": n,
-                "image_size": s,
-                "faces_per_pixel": k,
-                "device": d,
-            }
+            {"num_meshes": n, "image_size": s, "faces_per_pixel": k, "device": d}
         )
 
     benchmark(

tests/bm_chamfer.py

@@ -3,7 +3,6 @@
 
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_chamfer import TestChamfer
 
 
@@ -25,9 +24,4 @@ def bm_chamfer() -> None:
             {"batch_size": 1, "P1": 1000, "P2": 3000, "return_normals": False},
             {"batch_size": 1, "P1": 1000, "P2": 30000, "return_normals": True},
         ]
-        benchmark(
-            TestChamfer.chamfer_with_init,
-            "CHAMFER",
-            kwargs_list,
-            warmup_iters=1,
-        )
+        benchmark(TestChamfer.chamfer_with_init, "CHAMFER", kwargs_list, warmup_iters=1)

tests/bm_cubify.py

@@ -1,7 +1,6 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 from fvcore.common.benchmark import benchmark
-
 from test_cubify import TestCubify
 
 
@@ -11,6 +10,4 @@ def bm_cubify() -> None:
         {"batch_size": 64, "V": 16},
         {"batch_size": 16, "V": 32},
     ]
-    benchmark(
-        TestCubify.cubify_with_init, "CUBIFY", kwargs_list, warmup_iters=1
-    )
+    benchmark(TestCubify.cubify_with_init, "CUBIFY", kwargs_list, warmup_iters=1)

tests/bm_face_areas_normals.py

@@ -2,9 +2,9 @@
 
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_face_areas_normals import TestFaceAreasNormals
 
 

tests/bm_graph_conv.py

@@ -2,9 +2,9 @@
 
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_graph_conv import TestGraphConv
 
 

tests/bm_knn.py

@@ -1,9 +1,9 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from pytorch3d import _C
 from pytorch3d.ops.knn import _knn_points_idx_naive
 
@@ -32,9 +32,7 @@ def benchmark_knn_cuda_versions() -> None:
         knn_kwargs.append({"N": N, "D": D, "P": P, "K": K, "v": version})
     for N, P, D in product(Ns, Ps, Ds):
         nn_kwargs.append({"N": N, "D": D, "P": P})
-    benchmark(
-        knn_cuda_with_init, "KNN_CUDA_VERSIONS", knn_kwargs, warmup_iters=1
-    )
+    benchmark(knn_cuda_with_init, "KNN_CUDA_VERSIONS", knn_kwargs, warmup_iters=1)
     benchmark(nn_cuda_with_init, "NN_CUDA", nn_kwargs, warmup_iters=1)
 
 
@@ -50,10 +48,7 @@ def benchmark_knn_cuda_vs_naive() -> None:
         if P <= 4096:
             naive_kwargs.append({"N": N, "D": D, "P": P, "K": K})
     benchmark(
-        knn_python_cuda_with_init,
-        "KNN_CUDA_PYTHON",
-        naive_kwargs,
-        warmup_iters=1,
+        knn_python_cuda_with_init, "KNN_CUDA_PYTHON", naive_kwargs, warmup_iters=1
     )
     benchmark(knn_cuda_with_init, "KNN_CUDA", knn_kwargs, warmup_iters=1)
 
@@ -68,9 +63,7 @@ def benchmark_knn_cpu() -> None:
         knn_kwargs.append({"N": N, "D": D, "P": P, "K": K})
     for N, P, D in product(Ns, Ps, Ds):
         nn_kwargs.append({"N": N, "D": D, "P": P})
-    benchmark(
-        knn_python_cpu_with_init, "KNN_CPU_PYTHON", knn_kwargs, warmup_iters=1
-    )
+    benchmark(knn_python_cpu_with_init, "KNN_CPU_PYTHON", knn_kwargs, warmup_iters=1)
     benchmark(knn_cpu_with_init, "KNN_CPU_CPP", knn_kwargs, warmup_iters=1)
     benchmark(nn_cpu_with_init, "NN_CPU_CPP", nn_kwargs, warmup_iters=1)
 

tests/bm_main.py

@@ -5,6 +5,7 @@ import glob
 import importlib
 from os.path import basename, dirname, isfile, join, sys
 
+
 if __name__ == "__main__":
     # pyre-ignore[16]
     if len(sys.argv) > 1:
@@ -25,7 +26,5 @@ if __name__ == "__main__":
         for attr in dir(module):
             # Run all the functions with names "bm_*" in the module.
             if attr.startswith("bm_"):
-                print(
-                    "Running benchmarks for " + module_name + "/" + attr + "..."
-                )
+                print("Running benchmarks for " + module_name + "/" + attr + "...")
                 getattr(module, attr)()

tests/bm_mesh_edge_loss.py

@@ -2,8 +2,8 @@
 
 
 from itertools import product
-from fvcore.common.benchmark import benchmark
 
+from fvcore.common.benchmark import benchmark
 from test_mesh_edge_loss import TestMeshEdgeLoss
 
 
@@ -17,8 +17,5 @@ def bm_mesh_edge_loss() -> None:
         n, v, f = case
         kwargs_list.append({"num_meshes": n, "max_v": v, "max_f": f})
     benchmark(
-        TestMeshEdgeLoss.mesh_edge_loss,
-        "MESH_EDGE_LOSS",
-        kwargs_list,
-        warmup_iters=1,
+        TestMeshEdgeLoss.mesh_edge_loss, "MESH_EDGE_LOSS", kwargs_list, warmup_iters=1
     )

tests/bm_mesh_io.py

@@ -1,7 +1,6 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 from fvcore.common.benchmark import benchmark
-
 from test_obj_io import TestMeshObjIO
 from test_ply_io import TestMeshPlyIO
 

tests/bm_mesh_laplacian_smoothing.py

@@ -2,9 +2,9 @@
 
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_mesh_laplacian_smoothing import TestLaplacianSmoothing
 
 

tests/bm_mesh_normal_consistency.py

@@ -2,9 +2,9 @@
 
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_mesh_normal_consistency import TestMeshNormalConsistency
 
 

tests/bm_meshes.py

@@ -2,9 +2,9 @@
 
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_meshes import TestMeshes
 
 
@@ -20,9 +20,7 @@ def bm_compute_packed_padded_meshes() -> None:
     test_cases = product(num_meshes, max_v, max_f, devices)
     for case in test_cases:
         n, v, f, d = case
-        kwargs_list.append(
-            {"num_meshes": n, "max_v": v, "max_f": f, "device": d}
-        )
+        kwargs_list.append({"num_meshes": n, "max_v": v, "max_f": f, "device": d})
     benchmark(
         TestMeshes.compute_packed_with_init,
         "COMPUTE_PACKED",

tests/bm_nearest_neighbor_points.py

@@ -1,9 +1,9 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_nearest_neighbor_points import TestNearestNeighborPoints
 
 

tests/bm_packed_to_padded.py

@@ -2,9 +2,9 @@
 
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_packed_to_padded import TestPackedToPadded
 
 
@@ -23,13 +23,7 @@ def bm_packed_to_padded() -> None:
     for case in test_cases:
         n, v, f, d, b = case
         kwargs_list.append(
-            {
-                "num_meshes": n,
-                "num_verts": v,
-                "num_faces": f,
-                "num_d": d,
-                "device": b,
-            }
+            {"num_meshes": n, "num_verts": v, "num_faces": f, "num_d": d, "device": b}
         )
     benchmark(
         TestPackedToPadded.packed_to_padded_with_init,

tests/bm_pointclouds.py

@@ -2,8 +2,8 @@
 
 
 from itertools import product
-from fvcore.common.benchmark import benchmark
 
+from fvcore.common.benchmark import benchmark
 from test_pointclouds import TestPointclouds
 
 

tests/bm_rasterize_meshes.py

@@ -2,11 +2,12 @@
 
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_rasterize_meshes import TestRasterizeMeshes
 
+
 # ico levels:
 # 0: (12 verts, 20 faces)
 # 1: (42 verts, 80 faces)
@@ -39,12 +40,7 @@ def bm_rasterize_meshes() -> None:
     for case in test_cases:
         n, ic, im, b = case
         kwargs_list.append(
-            {
-                "num_meshes": n,
-                "ico_level": ic,
-                "image_size": im,
-                "blur_radius": b,
-            }
+            {"num_meshes": n, "ico_level": ic, "image_size": im, "blur_radius": b}
         )
     benchmark(
         TestRasterizeMeshes.rasterize_meshes_cpu_with_init,
@@ -63,9 +59,7 @@ def bm_rasterize_meshes() -> None:
         test_cases = product(num_meshes, ico_level, image_size, blur, bin_size)
         # only keep cases where bin_size == 0 or image_size / bin_size < 16
         test_cases = [
-            elem
-            for elem in test_cases
-            if (elem[-1] == 0 or elem[-3] / elem[-1] < 16)
+            elem for elem in test_cases if (elem[-1] == 0 or elem[-3] / elem[-1] < 16)
         ]
         for case in test_cases:
             n, ic, im, b, bn = case

tests/bm_rasterize_points.py

@@ -3,7 +3,6 @@
 
 import torch
 from fvcore.common.benchmark import benchmark
-
 from pytorch3d.renderer.points.rasterize_points import (
     rasterize_points,
     rasterize_points_python,
@@ -40,9 +39,7 @@ def bm_python_vs_cpu() -> None:
         {"N": 1, "P": 32, "img_size": 32, "radius": 0.1, "pts_per_pxl": 3},
         {"N": 2, "P": 32, "img_size": 32, "radius": 0.1, "pts_per_pxl": 3},
     ]
-    benchmark(
-        _bm_python_with_init, "RASTERIZE_PYTHON", kwargs_list, warmup_iters=1
-    )
+    benchmark(_bm_python_with_init, "RASTERIZE_PYTHON", kwargs_list, warmup_iters=1)
     benchmark(_bm_cpu_with_init, "RASTERIZE_CPU", kwargs_list, warmup_iters=1)
     kwargs_list = [
         {"N": 2, "P": 32, "img_size": 32, "radius": 0.1, "pts_per_pxl": 3},

tests/bm_sample_points_from_meshes.py

@@ -2,9 +2,9 @@
 
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_sample_points_from_meshes import TestSamplePoints
 
 

tests/bm_so3.py

@@ -1,7 +1,6 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 from fvcore.common.benchmark import benchmark
-
 from test_so3 import TestSO3
 
 

tests/bm_subdivide_meshes.py

@@ -2,8 +2,8 @@
 
 
 from itertools import product
-from fvcore.common.benchmark import benchmark
 
+from fvcore.common.benchmark import benchmark
 from test_subdivide_meshes import TestSubdivideMeshes
 
 

tests/bm_vert_align.py

@@ -2,9 +2,9 @@
 
 
 from itertools import product
+
 import torch
 from fvcore.common.benchmark import benchmark
-
 from test_vert_align import TestVertAlign
 
 
@@ -25,8 +25,5 @@ def bm_vert_align() -> None:
         )
 
     benchmark(
-        TestVertAlign.vert_align_with_init,
-        "VERT_ALIGN",
-        kwargs_list,
-        warmup_iters=1,
+        TestVertAlign.vert_align_with_init, "VERT_ALIGN", kwargs_list, warmup_iters=1
     )

tests/common_testing.py

@@ -1,8 +1,9 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 
-import numpy as np
 import unittest
+
+import numpy as np
 import torch
 
 
@@ -11,17 +12,13 @@ class TestCaseMixin(unittest.TestCase):
         """
         Verify that tensor1 and tensor2 have their data in distinct locations.
         """
-        self.assertNotEqual(
-            tensor1.storage().data_ptr(), tensor2.storage().data_ptr()
-        )
+        self.assertNotEqual(tensor1.storage().data_ptr(), tensor2.storage().data_ptr())
 
     def assertNotSeparate(self, tensor1, tensor2) -> None:
         """
         Verify that tensor1 and tensor2 have their data in the same locations.
         """
-        self.assertEqual(
-            tensor1.storage().data_ptr(), tensor2.storage().data_ptr()
-        )
+        self.assertEqual(tensor1.storage().data_ptr(), tensor2.storage().data_ptr())
 
     def assertAllSeparate(self, tensor_list) -> None:
         """
@@ -57,7 +54,5 @@ class TestCaseMixin(unittest.TestCase):
                 input, other, rtol=rtol, atol=atol, equal_nan=equal_nan
             )
         else:
-            close = np.allclose(
-                input, other, rtol=rtol, atol=atol, equal_nan=equal_nan
-            )
+            close = np.allclose(input, other, rtol=rtol, atol=atol, equal_nan=equal_nan)
         self.assertTrue(close)

tests/test_blending.py

@@ -1,9 +1,9 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
-import numpy as np
 import unittest
-import torch
 
+import numpy as np
+import torch
 from pytorch3d.renderer.blending import (
     BlendParams,
     hard_rgb_blend,
@@ -43,9 +43,7 @@ def sigmoid_blend_naive_loop(colors, fragments, blend_params):
     return pixel_colors
 
 
-def sigmoid_blend_naive_loop_backward(
-    grad_images, images, fragments, blend_params
-):
+def sigmoid_blend_naive_loop_backward(grad_images, images, fragments, blend_params):
     pix_to_face = fragments.pix_to_face
     dists = fragments.dists
     sigma = blend_params.sigma
@@ -135,14 +133,7 @@ class TestBlending(unittest.TestCase):
         torch.manual_seed(42)
 
     def _compare_impls(
-        self,
-        fn1,
-        fn2,
-        args1,
-        args2,
-        grad_var1=None,
-        grad_var2=None,
-        compare_grads=True,
+        self, fn1, fn2, args1, args2, grad_var1=None, grad_var2=None, compare_grads=True
     ):
 
         out1 = fn1(*args1)
@@ -160,9 +151,7 @@ class TestBlending(unittest.TestCase):
         (out2 * grad_out).sum().backward()
         self.assertTrue(hasattr(grad_var2, "grad"))
         self.assertTrue(
-            torch.allclose(
-                grad_var1.grad.cpu(), grad_var2.grad.cpu(), atol=2e-5
-            )
+            torch.allclose(grad_var1.grad.cpu(), grad_var2.grad.cpu(), atol=2e-5)
         )
 
     def test_hard_rgb_blend(self):
@@ -199,9 +188,7 @@ class TestBlending(unittest.TestCase):
         # # (-) means inside triangle, (+) means outside triangle.
         random_sign_flip = torch.rand((N, S, S, K))
         random_sign_flip[random_sign_flip > 0.5] *= -1.0
-        dists = torch.randn(
-            size=(N, S, S, K), requires_grad=True, device=device
-        )
+        dists = torch.randn(size=(N, S, S, K), requires_grad=True, device=device)
         fragments = Fragments(
             pix_to_face=pix_to_face,
             bary_coords=empty,  # dummy
@@ -238,9 +225,7 @@ class TestBlending(unittest.TestCase):
         # # (-) means inside triangle, (+) means outside triangle.
         random_sign_flip = torch.rand((N, S, S, K))
         random_sign_flip[random_sign_flip > 0.5] *= -1.0
-        dists1 = torch.randn(
-            size=(N, S, S, K), requires_grad=True, device=device
-        )
+        dists1 = torch.randn(size=(N, S, S, K), requires_grad=True, device=device)
         dists2 = dists1.detach().clone()
         dists2.requires_grad = True
 
@@ -276,9 +261,7 @@ class TestBlending(unittest.TestCase):
         # of the image with surrounding padded values.
         N, S, K = 1, 8, 2
         device = torch.device("cuda")
-        pix_to_face = -torch.ones(
-            (N, S, S, K), dtype=torch.int64, device=device
-        )
+        pix_to_face = -torch.ones((N, S, S, K), dtype=torch.int64, device=device)
         h = int(S / 2)
         pix_to_face_full = torch.randint(
             size=(N, h, h, K), low=0, high=100, device=device
@@ -294,9 +277,7 @@ class TestBlending(unittest.TestCase):
 
         # randomly flip the sign of the distance
         # (-) means inside triangle, (+) means outside triangle.
-        dists1 = (
-            torch.randn(size=(N, S, S, K), device=device) * random_sign_flip
-        )
+        dists1 = torch.randn(size=(N, S, S, K), device=device) * random_sign_flip
         dists2 = dists1.clone()
         zbuf2 = zbuf1.clone()
         dists1.requires_grad = True
@@ -353,9 +334,7 @@ class TestBlending(unittest.TestCase):
         # # (-) means inside triangle, (+) means outside triangle.
         random_sign_flip = torch.rand((N, S, S, K), device=device)
         random_sign_flip[random_sign_flip > 0.5] *= -1.0
-        dists1 = torch.randn(
-            size=(N, S, S, K), requires_grad=True, device=device
-        )
+        dists1 = torch.randn(size=(N, S, S, K), requires_grad=True, device=device)
         fragments = Fragments(
             pix_to_face=pix_to_face,
             bary_coords=empty,  # dummy
@@ -398,15 +377,10 @@ class TestBlending(unittest.TestCase):
         # # (-) means inside triangle, (+) means outside triangle.
         random_sign_flip = torch.rand((N, S, S, K), device=device)
         random_sign_flip[random_sign_flip > 0.5] *= -1.0
-        dists1 = torch.randn(
-            size=(N, S, S, K), requires_grad=True, device=device
-        )
+        dists1 = torch.randn(size=(N, S, S, K), requires_grad=True, device=device)
         zbuf = torch.randn(size=(N, S, S, K), requires_grad=True, device=device)
         fragments = Fragments(
-            pix_to_face=pix_to_face,
-            bary_coords=empty,  # dummy
-            zbuf=zbuf,
-            dists=dists1,
+            pix_to_face=pix_to_face, bary_coords=empty, zbuf=zbuf, dists=dists1  # dummy
         )
         blend_params = BlendParams(sigma=1e-3)
 

tests/test_build.py

@@ -3,6 +3,7 @@ import unittest
 from collections import Counter
 from pathlib import Path
 
+
 # This file groups together tests which look at the code without running it.
 
 
@@ -61,6 +62,5 @@ class TestBuild(unittest.TestCase):
                     if firstline.startswith(("# -*-", "#!")):
                         firstline = f.readline()
                     self.assertTrue(
-                        firstline.endswith(expect),
-                        f"{i} missing copyright header.",
+                        firstline.endswith(expect), f"{i} missing copyright header."
                     )

tests/test_cameras.py

@@ -26,10 +26,11 @@
 # SOFTWARE.
 
 import math
-import numpy as np
 import unittest
-import torch
 
+import numpy as np
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d.renderer.cameras import (
     OpenGLOrthographicCameras,
     OpenGLPerspectiveCameras,
@@ -43,8 +44,6 @@ from pytorch3d.renderer.cameras import (
 from pytorch3d.transforms import Transform3d
 from pytorch3d.transforms.so3 import so3_exponential_map
 
-from common_testing import TestCaseMixin
-
 
 # Naive function adapted from SoftRasterizer for test purposes.
 def perspective_project_naive(points, fov=60.0):
@@ -58,9 +57,7 @@ def perspective_project_naive(points, fov=60.0):
         coordinate (no z renormalization)
     """
     device = points.device
-    halfFov = torch.tensor(
-        (fov / 2) / 180 * np.pi, dtype=torch.float32, device=device
-    )
+    halfFov = torch.tensor((fov / 2) / 180 * np.pi, dtype=torch.float32, device=device)
     scale = torch.tan(halfFov[None])
     scale = scale[:, None]
     z = points[:, :, 2]
@@ -150,9 +147,9 @@ class TestCameraHelpers(TestCaseMixin, unittest.TestCase):
         dist = 2.7
         elev = 90.0
         azim = 0.0
-        expected_position = torch.tensor(
-            [0.0, 2.7, 0.0], dtype=torch.float32
-        ).view(1, 3)
+        expected_position = torch.tensor([0.0, 2.7, 0.0], dtype=torch.float32).view(
+            1, 3
+        )
         position = camera_position_from_spherical_angles(dist, elev, azim)
         self.assertClose(position, expected_position, atol=2e-7)
 
@@ -171,9 +168,9 @@ class TestCameraHelpers(TestCaseMixin, unittest.TestCase):
         dist = torch.tensor(2.7)
         elev = torch.tensor(0.0)
         azim = torch.tensor(90.0)
-        expected_position = torch.tensor(
-            [2.7, 0.0, 0.0], dtype=torch.float32
-        ).view(1, 3)
+        expected_position = torch.tensor([2.7, 0.0, 0.0], dtype=torch.float32).view(
+            1, 3
+        )
         position = camera_position_from_spherical_angles(dist, elev, azim)
         self.assertClose(position, expected_position, atol=2e-7)
 
@@ -181,9 +178,9 @@ class TestCameraHelpers(TestCaseMixin, unittest.TestCase):
         dist = 2.7
         elev = torch.tensor(0.0)
         azim = 90.0
-        expected_position = torch.tensor(
-            [2.7, 0.0, 0.0], dtype=torch.float32
-        ).view(1, 3)
+        expected_position = torch.tensor([2.7, 0.0, 0.0], dtype=torch.float32).view(
+            1, 3
+        )
         position = camera_position_from_spherical_angles(dist, elev, azim)
         self.assertClose(position, expected_position, atol=2e-7)
 
@@ -228,8 +225,7 @@ class TestCameraHelpers(TestCaseMixin, unittest.TestCase):
         elev = torch.tensor([0.0])
         azim = torch.tensor([90.0])
         expected_position = torch.tensor(
-            [[2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [5.0, 0.0, 0.0]],
-            dtype=torch.float32,
+            [[2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [5.0, 0.0, 0.0]], dtype=torch.float32
         )
         position = camera_position_from_spherical_angles(dist, elev, azim)
         self.assertClose(position, expected_position, atol=3e-7)
@@ -239,8 +235,7 @@ class TestCameraHelpers(TestCaseMixin, unittest.TestCase):
         elev = 0.0
         azim = torch.tensor(90.0)
         expected_position = torch.tensor(
-            [[2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [5.0, 0.0, 0.0]],
-            dtype=torch.float32,
+            [[2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [5.0, 0.0, 0.0]], dtype=torch.float32
         )
         position = camera_position_from_spherical_angles(dist, elev, azim)
         self.assertClose(position, expected_position, atol=3e-7)
@@ -364,9 +359,7 @@ class TestCameraHelpers(TestCaseMixin, unittest.TestCase):
         ):
             cam = cam_type(R=R, T=T)
             RT_class = cam.get_world_to_view_transform()
-            self.assertTrue(
-                torch.allclose(RT.get_matrix(), RT_class.get_matrix())
-            )
+            self.assertTrue(torch.allclose(RT.get_matrix(), RT_class.get_matrix()))
 
         self.assertTrue(isinstance(RT, Transform3d))
 
@@ -539,9 +532,7 @@ class TestOpenGLOrthographicProjection(TestCaseMixin, unittest.TestCase):
         # applying the scale puts the z coordinate at the far clipping plane
         # so the z is mapped to 1.0
         projected_verts = torch.tensor([2, 1, 1], dtype=torch.float32)
-        cameras = OpenGLOrthographicCameras(
-            znear=1.0, zfar=10.0, scale_xyz=scale
-        )
+        cameras = OpenGLOrthographicCameras(znear=1.0, zfar=10.0, scale_xyz=scale)
         P = cameras.get_projection_transform()
         v1 = P.transform_points(vertices)
         v2 = orthographic_project_naive(vertices, scale)
@@ -578,9 +569,7 @@ class TestOpenGLOrthographicProjection(TestCaseMixin, unittest.TestCase):
         far = torch.tensor([10.0])
         near = 1.0
         scale = torch.tensor([[1.0, 1.0, 1.0]], requires_grad=True)
-        cameras = OpenGLOrthographicCameras(
-            znear=near, zfar=far, scale_xyz=scale
-        )
+        cameras = OpenGLOrthographicCameras(znear=near, zfar=far, scale_xyz=scale)
         P = cameras.get_projection_transform()
         vertices = torch.tensor([1.0, 2.0, 10.0], dtype=torch.float32)
         vertices_batch = vertices[None, None, :]
@@ -683,15 +672,11 @@ class TestSfMPerspectiveProjection(TestCaseMixin, unittest.TestCase):
         self.assertClose(v3[..., :2], v2[..., :2])
 
     def test_perspective_kwargs(self):
-        cameras = SfMPerspectiveCameras(
-            focal_length=5.0, principal_point=((2.5, 2.5),)
-        )
+        cameras = SfMPerspectiveCameras(focal_length=5.0, principal_point=((2.5, 2.5),))
         P = cameras.get_projection_transform(
             focal_length=2.0, principal_point=((2.5, 3.5),)
         )
         vertices = torch.randn([3, 4, 3], dtype=torch.float32)
         v1 = P.transform_points(vertices)
-        v2 = sfm_perspective_project_naive(
-            vertices, fx=2.0, fy=2.0, p0x=2.5, p0y=3.5
-        )
+        v2 = sfm_perspective_project_naive(vertices, fx=2.0, fy=2.0, p0x=2.5, p0y=3.5)
         self.assertClose(v1, v2)

tests/test_chamfer.py

@@ -1,12 +1,11 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 import unittest
+
 import torch
 import torch.nn.functional as F
-
-from pytorch3d.loss import chamfer_distance
-
 from common_testing import TestCaseMixin
+from pytorch3d.loss import chamfer_distance
 
 
 class TestChamfer(TestCaseMixin, unittest.TestCase):
@@ -19,14 +18,10 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
         """
         device = torch.device("cuda:0")
         p1 = torch.rand((batch_size, P1, 3), dtype=torch.float32, device=device)
-        p1_normals = torch.rand(
-            (batch_size, P1, 3), dtype=torch.float32, device=device
-        )
+        p1_normals = torch.rand((batch_size, P1, 3), dtype=torch.float32, device=device)
         p1_normals = p1_normals / p1_normals.norm(dim=2, p=2, keepdim=True)
         p2 = torch.rand((batch_size, P2, 3), dtype=torch.float32, device=device)
-        p2_normals = torch.rand(
-            (batch_size, P2, 3), dtype=torch.float32, device=device
-        )
+        p2_normals = torch.rand((batch_size, P2, 3), dtype=torch.float32, device=device)
         p2_normals = p2_normals / p2_normals.norm(dim=2, p=2, keepdim=True)
         weights = torch.rand((batch_size,), dtype=torch.float32, device=device)
 
@@ -47,9 +42,7 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
         for n in range(N):
             for i1 in range(P1):
                 for i2 in range(P2):
-                    dist[n, i1, i2] = torch.sum(
-                        (p1[n, i1, :] - p2[n, i2, :]) ** 2
-                    )
+                    dist[n, i1, i2] = torch.sum((p1[n, i1, :] - p2[n, i2, :]) ** 2)
 
         loss = [
             torch.min(dist, dim=2)[0],  # (N, P1)
@@ -146,11 +139,7 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
         # Error when point_reduction = "none" and batch_reduction = "none".
         with self.assertRaises(ValueError):
             chamfer_distance(
-                p1,
-                p2,
-                weights=weights,
-                batch_reduction="none",
-                point_reduction="none",
+                p1, p2, weights=weights, batch_reduction="none", point_reduction="none"
             )
 
         # Error when batch_reduction is not in ["none", "mean", "sum"].
@@ -339,9 +328,7 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
             loss, loss_norm = chamfer_distance(p1, p2, weights=weights)
 
     @staticmethod
-    def chamfer_with_init(
-        batch_size: int, P1: int, P2: int, return_normals: bool
-    ):
+    def chamfer_with_init(batch_size: int, P1: int, P2: int, return_normals: bool):
         p1, p2, p1_normals, p2_normals, weights = TestChamfer.init_pointclouds(
             batch_size, P1, P2
         )

tests/test_compositing.py

@@ -1,8 +1,8 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 import unittest
-import torch
 
+import torch
 from pytorch3d.renderer.compositing import (
     alpha_composite,
     norm_weighted_sum,
@@ -37,9 +37,7 @@ class TestAccumulatePoints(unittest.TestCase):
                                 continue
 
                             alpha = alphas[b, k, j, i]
-                            output[b, c, j, i] += (
-                                features[c, n_idx] * alpha * t_alpha
-                            )
+                            output[b, c, j, i] += features[c, n_idx] * alpha * t_alpha
                             t_alpha = (1 - alpha) * t_alpha
 
         return output
@@ -105,17 +103,13 @@ class TestAccumulatePoints(unittest.TestCase):
                                 continue
 
                             alpha = alphas[b, k, j, i]
-                            output[b, c, j, i] += (
-                                features[c, n_idx] * alpha / t_alpha
-                            )
+                            output[b, c, j, i] += features[c, n_idx] * alpha / t_alpha
 
         return output
 
     def test_python(self):
         device = torch.device("cpu")
-        self._simple_alphacomposite(
-            self.accumulate_alphacomposite_python, device
-        )
+        self._simple_alphacomposite(self.accumulate_alphacomposite_python, device)
         self._simple_wsum(self.accumulate_weightedsum_python, device)
         self._simple_wsumnorm(self.accumulate_weightedsumnorm_python, device)
 
@@ -138,9 +132,7 @@ class TestAccumulatePoints(unittest.TestCase):
         self._python_vs_cpu_vs_cuda(
             self.accumulate_weightedsumnorm_python, norm_weighted_sum
         )
-        self._python_vs_cpu_vs_cuda(
-            self.accumulate_weightedsum_python, weighted_sum
-        )
+        self._python_vs_cpu_vs_cuda(self.accumulate_weightedsum_python, weighted_sum)
 
     def _python_vs_cpu_vs_cuda(self, accumulate_func_python, accumulate_func):
         torch.manual_seed(231)
@@ -208,15 +200,11 @@ class TestAccumulatePoints(unittest.TestCase):
         grads2 = [gradsi.grad.data.clone().cpu() for gradsi in grads2]
 
         for i in range(0, len(grads1)):
-            self.assertTrue(
-                torch.allclose(grads1[i].cpu(), grads2[i].cpu(), atol=1e-6)
-            )
+            self.assertTrue(torch.allclose(grads1[i].cpu(), grads2[i].cpu(), atol=1e-6))
 
     def _simple_wsum(self, accum_func, device):
         # Initialise variables
-        features = torch.Tensor(
-            [[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]
-        ).to(device)
+        features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
 
         alphas = torch.Tensor(
             [
@@ -285,15 +273,11 @@ class TestAccumulatePoints(unittest.TestCase):
             ]
         ).to(device)
 
-        self.assertTrue(
-            torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3)
-        )
+        self.assertTrue(torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3))
 
     def _simple_wsumnorm(self, accum_func, device):
         # Initialise variables
-        features = torch.Tensor(
-            [[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]
-        ).to(device)
+        features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
 
         alphas = torch.Tensor(
             [
@@ -362,15 +346,11 @@ class TestAccumulatePoints(unittest.TestCase):
             ]
         ).to(device)
 
-        self.assertTrue(
-            torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3)
-        )
+        self.assertTrue(torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3))
 
     def _simple_alphacomposite(self, accum_func, device):
         # Initialise variables
-        features = torch.Tensor(
-            [[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]
-        ).to(device)
+        features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
 
         alphas = torch.Tensor(
             [

tests/test_cubify.py

@@ -1,8 +1,8 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 import unittest
-import torch
 
+import torch
 from pytorch3d.ops import cubify
 
 
@@ -33,9 +33,7 @@ class TestCubify(unittest.TestCase):
 
         # 1st-check
         verts, faces = meshes.get_mesh_verts_faces(0)
-        self.assertTrue(
-            torch.allclose(faces.max(), torch.tensor([verts.size(0) - 1]))
-        )
+        self.assertTrue(torch.allclose(faces.max(), torch.tensor([verts.size(0) - 1])))
         self.assertTrue(
             torch.allclose(
                 verts,
@@ -80,9 +78,7 @@ class TestCubify(unittest.TestCase):
         )
         # 2nd-check
         verts, faces = meshes.get_mesh_verts_faces(1)
-        self.assertTrue(
-            torch.allclose(faces.max(), torch.tensor([verts.size(0) - 1]))
-        )
+        self.assertTrue(torch.allclose(faces.max(), torch.tensor([verts.size(0) - 1])))
         self.assertTrue(
             torch.allclose(
                 verts,
@@ -275,9 +271,7 @@ class TestCubify(unittest.TestCase):
     @staticmethod
     def cubify_with_init(batch_size: int, V: int):
         device = torch.device("cuda:0")
-        voxels = torch.rand(
-            (batch_size, V, V, V), dtype=torch.float32, device=device
-        )
+        voxels = torch.rand((batch_size, V, V, V), dtype=torch.float32, device=device)
         torch.cuda.synchronize()
 
         def convert():

tests/test_face_areas_normals.py

@@ -2,13 +2,12 @@
 
 
 import unittest
-import torch
 
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d.ops import mesh_face_areas_normals
 from pytorch3d.structures.meshes import Meshes
 
-from common_testing import TestCaseMixin
-
 
 class TestFaceAreasNormals(TestCaseMixin, unittest.TestCase):
     def setUp(self) -> None:
@@ -27,10 +26,7 @@ class TestFaceAreasNormals(TestCaseMixin, unittest.TestCase):
         faces_list = []
         for _ in range(num_meshes):
             verts = torch.rand(
-                (num_verts, 3),
-                dtype=torch.float32,
-                device=device,
-                requires_grad=True,
+                (num_verts, 3), dtype=torch.float32, device=device, requires_grad=True
             )
             faces = torch.randint(
                 num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
@@ -55,9 +51,7 @@ class TestFaceAreasNormals(TestCaseMixin, unittest.TestCase):
         v02 = vertices_faces[:, 2] - vertices_faces[:, 0]
         normals = torch.cross(v01, v02, dim=1)  # (F, 3)
         face_areas = normals.norm(dim=-1) / 2
-        face_normals = torch.nn.functional.normalize(
-            normals, p=2, dim=1, eps=1e-6
-        )
+        face_normals = torch.nn.functional.normalize(normals, p=2, dim=1, eps=1e-6)
         return face_areas, face_normals
 
     def _test_face_areas_normals_helper(self, device, dtype=torch.float32):
@@ -76,10 +70,7 @@ class TestFaceAreasNormals(TestCaseMixin, unittest.TestCase):
         verts_torch = verts.detach().clone().to(dtype)
         verts_torch.requires_grad = True
         faces_torch = faces.detach().clone()
-        (
-            areas_torch,
-            normals_torch,
-        ) = TestFaceAreasNormals.face_areas_normals_python(
+        (areas_torch, normals_torch) = TestFaceAreasNormals.face_areas_normals_python(
             verts_torch, faces_torch
         )
         self.assertClose(areas_torch, areas, atol=1e-7)

tests/test_graph_conv.py

@@ -1,20 +1,15 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 import unittest
+
 import torch
 import torch.nn as nn
-
+from common_testing import TestCaseMixin
 from pytorch3d import _C
-from pytorch3d.ops.graph_conv import (
-    GraphConv,
-    gather_scatter,
-    gather_scatter_python,
-)
+from pytorch3d.ops.graph_conv import GraphConv, gather_scatter, gather_scatter_python
 from pytorch3d.structures.meshes import Meshes
 from pytorch3d.utils import ico_sphere
 
-from common_testing import TestCaseMixin
-
 
 class TestGraphConv(TestCaseMixin, unittest.TestCase):
     def test_undirected(self):
@@ -89,8 +84,7 @@ class TestGraphConv(TestCaseMixin, unittest.TestCase):
         w1 = torch.tensor([[-1, -1, -1]], dtype=dtype)
 
         expected_y = torch.tensor(
-            [[1 + 2 + 3 - 4 - 5 - 6 - 7 - 8 - 9], [4 + 5 + 6], [7 + 8 + 9]],
-            dtype=dtype,
+            [[1 + 2 + 3 - 4 - 5 - 6 - 7 - 8 - 9], [4 + 5 + 6], [7 + 8 + 9]], dtype=dtype
         )
 
         conv = GraphConv(3, 1, directed=True).to(dtype)
@@ -126,17 +120,13 @@ class TestGraphConv(TestCaseMixin, unittest.TestCase):
     def test_cpu_cuda_tensor_error(self):
         device = torch.device("cuda:0")
         verts = torch.tensor(
-            [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
-            dtype=torch.float32,
-            device=device,
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float32, device=device
         )
         edges = torch.tensor([[0, 1], [0, 2]])
         conv = GraphConv(3, 1, directed=True).to(torch.float32)
         with self.assertRaises(Exception) as err:
             conv(verts, edges)
-        self.assertTrue(
-            "tensors must be on the same device." in str(err.exception)
-        )
+        self.assertTrue("tensors must be on the same device." in str(err.exception))
 
     def test_gather_scatter(self):
         """
@@ -178,12 +168,10 @@ class TestGraphConv(TestCaseMixin, unittest.TestCase):
         backend: str = "cuda",
     ):
         device = torch.device("cuda") if backend == "cuda" else "cpu"
-        verts_list = torch.tensor(
-            num_verts * [[0.11, 0.22, 0.33]], device=device
-        ).view(-1, 3)
-        faces_list = torch.tensor(num_faces * [[1, 2, 3]], device=device).view(
+        verts_list = torch.tensor(num_verts * [[0.11, 0.22, 0.33]], device=device).view(
             -1, 3
         )
+        faces_list = torch.tensor(num_faces * [[1, 2, 3]], device=device).view(-1, 3)
         meshes = Meshes(num_meshes * [verts_list], num_meshes * [faces_list])
         gconv = GraphConv(gconv_dim, gconv_dim, directed=directed)
         gconv.to(device)
@@ -191,9 +179,7 @@ class TestGraphConv(TestCaseMixin, unittest.TestCase):
         total_verts = meshes.verts_packed().shape[0]
 
         # Features.
-        x = torch.randn(
-            total_verts, gconv_dim, device=device, requires_grad=True
-        )
+        x = torch.randn(total_verts, gconv_dim, device=device, requires_grad=True)
         torch.cuda.synchronize()
 
         def run_graph_conv():

tests/test_knn.py

@@ -2,8 +2,8 @@
 
 import unittest
 from itertools import product
-import torch
 
+import torch
 from pytorch3d.ops.knn import _knn_points_idx_naive, knn_points_idx
 
 

tests/test_lighting.py

@@ -1,14 +1,13 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
-import numpy as np
 import unittest
-import torch
 
+import numpy as np
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d.renderer.lighting import DirectionalLights, PointLights
 from pytorch3d.transforms import RotateAxisAngle
 
-from common_testing import TestCaseMixin
-
 
 class TestLights(TestCaseMixin, unittest.TestCase):
     def test_init_lights(self):
@@ -56,9 +55,7 @@ class TestLights(TestCaseMixin, unittest.TestCase):
             self.assertSeparate(new_prop, prop)
 
     def test_lights_accessor(self):
-        d_light = DirectionalLights(
-            ambient_color=((0.0, 0.0, 0.0), (1.0, 1.0, 1.0))
-        )
+        d_light = DirectionalLights(ambient_color=((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)))
         p_light = PointLights(ambient_color=((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)))
         for light in [d_light, p_light]:
             # Update element
@@ -96,14 +93,12 @@ class TestLights(TestCaseMixin, unittest.TestCase):
         """
         with self.assertRaises(ValueError):
             DirectionalLights(
-                ambient_color=torch.randn(10, 3),
-                diffuse_color=torch.randn(15, 3),
+                ambient_color=torch.randn(10, 3), diffuse_color=torch.randn(15, 3)
             )
 
         with self.assertRaises(ValueError):
             PointLights(
-                ambient_color=torch.randn(10, 3),
-                diffuse_color=torch.randn(15, 3),
+                ambient_color=torch.randn(10, 3), diffuse_color=torch.randn(15, 3)
             )
 
     def test_initialize_lights_dimensions_fail(self):
@@ -138,8 +133,7 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
         normals = torch.tensor([0, 0, 1], dtype=torch.float32)
         normals = normals[None, None, :]
         expected_output = torch.tensor(
-            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
-            dtype=torch.float32,
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
         )
         expected_output = expected_output.view(1, 1, 3).repeat(3, 1, 1)
         light = DirectionalLights(diffuse_color=color, direction=direction)
@@ -169,13 +163,10 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
         points = torch.tensor([0, 0, 0], dtype=torch.float32)
         normals = torch.tensor([0, 0, 1], dtype=torch.float32)
         expected_output = torch.tensor(
-            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
-            dtype=torch.float32,
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
         )
         expected_output = expected_output.view(-1, 1, 3)
-        light = PointLights(
-            diffuse_color=color[None, :], location=location[None, :]
-        )
+        light = PointLights(diffuse_color=color[None, :], location=location[None, :])
         output_light = light.diffuse(
             points=points[None, None, :], normals=normals[None, None, :]
         )
@@ -184,9 +175,7 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
         # Change light direction to be 90 degrees apart from normal direction.
         location = torch.tensor([0, 1, 0], dtype=torch.float32)
         expected_output = torch.zeros_like(expected_output)
-        light = PointLights(
-            diffuse_color=color[None, :], location=location[None, :]
-        )
+        light = PointLights(diffuse_color=color[None, :], location=location[None, :])
         output_light = light.diffuse(
             points=points[None, None, :], normals=normals[None, None, :]
         )
@@ -204,8 +193,7 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
         )
         normals = torch.tensor([0, 0, 1], dtype=torch.float32)
         expected_out = torch.tensor(
-            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
-            dtype=torch.float32,
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
         )
 
         # Reshape
@@ -231,8 +219,7 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
         )
         normals = torch.tensor([0, 0, 1], dtype=torch.float32)
         expected_out = torch.tensor(
-            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
-            dtype=torch.float32,
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
         )
 
         # Reshape
@@ -258,9 +245,7 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
         device = torch.device("cuda:0")
         color = torch.tensor([1, 1, 1], dtype=torch.float32, device=device)
         direction = torch.tensor(
-            [0, 1 / np.sqrt(2), 1 / np.sqrt(2)],
-            dtype=torch.float32,
-            device=device,
+            [0, 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32, device=device
         )
         normals = torch.tensor([0, 0, 1], dtype=torch.float32, device=device)
         normals = normals.view(1, 1, 1, 1, 3).expand(N, H, W, K, -1)
@@ -373,9 +358,7 @@ class TestSpecularLighting(TestCaseMixin, unittest.TestCase):
         normals = torch.tensor([0, 1, 0], dtype=torch.float32)
         expected_output = torch.tensor([1.0, 0.0, 1.0], dtype=torch.float32)
         expected_output = expected_output.view(-1, 1, 3)
-        lights = PointLights(
-            specular_color=color[None, :], location=location[None, :]
-        )
+        lights = PointLights(specular_color=color[None, :], location=location[None, :])
         output_light = lights.specular(
             points=points[None, None, :],
             normals=normals[None, None, :],
@@ -528,8 +511,7 @@ class TestSpecularLighting(TestCaseMixin, unittest.TestCase):
         mesh_to_vert_idx = torch.tensor(mesh_to_vert_idx, dtype=torch.int64)
         color = torch.tensor([[1, 1, 1], [1, 0, 1]], dtype=torch.float32)
         direction = torch.tensor(
-            [[-1 / np.sqrt(2), 1 / np.sqrt(2), 0], [-1, 1, 0]],
-            dtype=torch.float32,
+            [[-1 / np.sqrt(2), 1 / np.sqrt(2), 0], [-1, 1, 0]], dtype=torch.float32
         )
         camera_position = torch.tensor(
             [

tests/test_materials.py

@@ -1,11 +1,10 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 import unittest
+
 import torch
-
-from pytorch3d.renderer.materials import Materials
-
 from common_testing import TestCaseMixin
+from pytorch3d.renderer.materials import Materials
 
 
 class TestMaterials(TestCaseMixin, unittest.TestCase):
@@ -64,8 +63,7 @@ class TestMaterials(TestCaseMixin, unittest.TestCase):
         """
         with self.assertRaises(ValueError):
             Materials(
-                ambient_color=torch.randn(10, 3),
-                diffuse_color=torch.randn(15, 3),
+                ambient_color=torch.randn(10, 3), diffuse_color=torch.randn(15, 3)
             )
 
     def test_initialize_materials_dimensions_fail(self):
@@ -80,16 +78,12 @@ class TestMaterials(TestCaseMixin, unittest.TestCase):
             Materials(shininess=torch.randn(10, 2))
 
     def test_initialize_materials_mixed_inputs(self):
-        mat = Materials(
-            ambient_color=torch.randn(1, 3), diffuse_color=((1, 1, 1),)
-        )
+        mat = Materials(ambient_color=torch.randn(1, 3), diffuse_color=((1, 1, 1),))
         self.assertTrue(mat.ambient_color.shape == (1, 3))
         self.assertTrue(mat.diffuse_color.shape == (1, 3))
 
     def test_initialize_materials_mixed_inputs_broadcast(self):
-        mat = Materials(
-            ambient_color=torch.randn(10, 3), diffuse_color=((1, 1, 1),)
-        )
+        mat = Materials(ambient_color=torch.randn(10, 3), diffuse_color=((1, 1, 1),))
         self.assertTrue(mat.ambient_color.shape == (10, 3))
         self.assertTrue(mat.diffuse_color.shape == (10, 3))
         self.assertTrue(mat.specular_color.shape == (10, 3))

tests/test_mesh_edge_loss.py

@@ -1,12 +1,11 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 import unittest
-import torch
 
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d.loss import mesh_edge_loss
 from pytorch3d.structures import Meshes
-
-from common_testing import TestCaseMixin
 from test_sample_points_from_meshes import TestSamplePoints
 
 
@@ -27,9 +26,7 @@ class TestMeshEdgeLoss(TestCaseMixin, unittest.TestCase):
         mesh = Meshes(verts=verts_list, faces=faces_list)
         loss = mesh_edge_loss(mesh, target_length=target_length)
 
-        self.assertClose(
-            loss, torch.tensor([0.0], dtype=torch.float32, device=device)
-        )
+        self.assertClose(loss, torch.tensor([0.0], dtype=torch.float32, device=device))
         self.assertTrue(loss.requires_grad)
 
     @staticmethod
@@ -53,9 +50,7 @@ class TestMeshEdgeLoss(TestCaseMixin, unittest.TestCase):
             num_edges = mesh_edges.size(0)
             for e in range(num_edges):
                 v0, v1 = verts_edges[e, 0], verts_edges[e, 1]
-                predlosses[b] += (
-                    (v0 - v1).norm(dim=0, p=2) - target_length
-                ) ** 2.0
+                predlosses[b] += ((v0 - v1).norm(dim=0, p=2) - target_length) ** 2.0
 
             if num_edges > 0:
                 predlosses[b] = predlosses[b] / num_edges
@@ -96,12 +91,8 @@ class TestMeshEdgeLoss(TestCaseMixin, unittest.TestCase):
         self.assertClose(loss, predloss)
 
     @staticmethod
-    def mesh_edge_loss(
-        num_meshes: int = 10, max_v: int = 100, max_f: int = 300
-    ):
-        meshes = TestSamplePoints.init_meshes(
-            num_meshes, max_v, max_f, device="cuda:0"
-        )
+    def mesh_edge_loss(num_meshes: int = 10, max_v: int = 100, max_f: int = 300):
+        meshes = TestSamplePoints.init_meshes(num_meshes, max_v, max_f, device="cuda:0")
         torch.cuda.synchronize()
 
         def compute_loss():

tests/test_mesh_laplacian_smoothing.py

@@ -2,8 +2,8 @@
 
 
 import unittest
-import torch
 
+import torch
 from pytorch3d.loss.mesh_laplacian_smoothing import mesh_laplacian_smoothing
 from pytorch3d.structures.meshes import Meshes
 
@@ -56,9 +56,7 @@ class TestLaplacianSmoothing(unittest.TestCase):
         V = verts_packed.shape[0]
 
         L = torch.zeros((V, V), dtype=torch.float32, device=meshes.device)
-        inv_areas = torch.zeros(
-            (V, 1), dtype=torch.float32, device=meshes.device
-        )
+        inv_areas = torch.zeros((V, 1), dtype=torch.float32, device=meshes.device)
 
         for f in faces_packed:
             v0 = verts_packed[f[0], :]
@@ -69,9 +67,7 @@ class TestLaplacianSmoothing(unittest.TestCase):
             C = (v0 - v1).norm()
             s = 0.5 * (A + B + C)
 
-            face_area = (
-                (s * (s - A) * (s - B) * (s - C)).clamp_(min=1e-12).sqrt()
-            )
+            face_area = (s * (s - A) * (s - B) * (s - C)).clamp_(min=1e-12).sqrt()
             inv_areas[f[0]] += face_area
             inv_areas[f[1]] += face_area
             inv_areas[f[2]] += face_area
@@ -114,16 +110,13 @@ class TestLaplacianSmoothing(unittest.TestCase):
         return loss.sum() / len(meshes)
 
     @staticmethod
-    def init_meshes(
-        num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000
-    ):
+    def init_meshes(num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000):
         device = torch.device("cuda:0")
         verts_list = []
         faces_list = []
         for _ in range(num_meshes):
             verts = (
-                torch.rand((num_verts, 3), dtype=torch.float32, device=device)
-                * 2.0
+                torch.rand((num_verts, 3), dtype=torch.float32, device=device) * 2.0
                 - 1.0
             )  # verts in the space of [-1, 1]
             faces = torch.stack(
@@ -148,9 +141,7 @@ class TestLaplacianSmoothing(unittest.TestCase):
 
         # feats in list
         out = mesh_laplacian_smoothing(meshes, method="uniform")
-        naive_out = TestLaplacianSmoothing.laplacian_smoothing_naive_uniform(
-            meshes
-        )
+        naive_out = TestLaplacianSmoothing.laplacian_smoothing_naive_uniform(meshes)
 
         self.assertTrue(torch.allclose(out, naive_out))
 
@@ -190,9 +181,7 @@ class TestLaplacianSmoothing(unittest.TestCase):
         verts_list = []
         faces_list = []
         for _ in range(num_meshes):
-            verts = torch.rand(
-                (num_verts, 3), dtype=torch.float32, device=device
-            )
+            verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
             faces = torch.randint(
                 num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
             )

tests/test_mesh_normal_consistency.py

@@ -2,8 +2,8 @@
 
 
 import unittest
-import torch
 
+import torch
 from pytorch3d.loss.mesh_normal_consistency import mesh_normal_consistency
 from pytorch3d.structures.meshes import Meshes
 from pytorch3d.utils.ico_sphere import ico_sphere
@@ -33,17 +33,14 @@ class TestMeshNormalConsistency(unittest.TestCase):
         return faces
 
     @staticmethod
-    def init_meshes(
-        num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000
-    ):
+    def init_meshes(num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000):
         device = torch.device("cuda:0")
         valid_faces = TestMeshNormalConsistency.init_faces(num_verts).to(device)
         verts_list = []
         faces_list = []
         for _ in range(num_meshes):
             verts = (
-                torch.rand((num_verts, 3), dtype=torch.float32, device=device)
-                * 2.0
+                torch.rand((num_verts, 3), dtype=torch.float32, device=device) * 2.0
                 - 1.0
             )  # verts in the space of [-1, 1]
             """
@@ -105,8 +102,7 @@ class TestMeshNormalConsistency(unittest.TestCase):
                             (
                                 1
                                 - torch.cosine_similarity(
-                                    normals[i].view(1, 3),
-                                    -normals[j].view(1, 3),
+                                    normals[i].view(1, 3), -normals[j].view(1, 3)
                                 )
                             )
                         )
@@ -137,9 +133,7 @@ class TestMeshNormalConsistency(unittest.TestCase):
         device = torch.device("cuda:0")
         # mesh1 shown above
         verts1 = torch.rand((4, 3), dtype=torch.float32, device=device)
-        faces1 = torch.tensor(
-            [[0, 1, 2], [2, 1, 3]], dtype=torch.int64, device=device
-        )
+        faces1 = torch.tensor([[0, 1, 2], [2, 1, 3]], dtype=torch.int64, device=device)
 
         # mesh2 is a cuboid with 8 verts, 12 faces and 18 edges
         verts2 = torch.tensor(
@@ -181,9 +175,7 @@ class TestMeshNormalConsistency(unittest.TestCase):
             [[0, 1, 2], [2, 1, 3], [2, 1, 4]], dtype=torch.int64, device=device
         )
 
-        meshes = Meshes(
-            verts=[verts1, verts2, verts3], faces=[faces1, faces2, faces3]
-        )
+        meshes = Meshes(verts=[verts1, verts2, verts3], faces=[faces1, faces2, faces3])
 
         # mesh1: normal consistency computation
         n0 = (verts1[1] - verts1[2]).cross(verts1[3] - verts1[2])

tests/test_mesh_rendering_utils.py

@@ -2,8 +2,8 @@
 
 
 import unittest
-import torch
 
+import torch
 from pytorch3d.renderer.mesh.utils import _clip_barycentric_coordinates
 
 

tests/test_meshes.py

@@ -1,12 +1,11 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
-import numpy as np
 import unittest
+
+import numpy as np
 import torch
-
-from pytorch3d.structures.meshes import Meshes
-
 from common_testing import TestCaseMixin
+from pytorch3d.structures.meshes import Meshes
 
 
 class TestMeshes(TestCaseMixin, unittest.TestCase):
@@ -54,9 +53,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
             # For lists of faces and vertices, we can sample different v/f
             # per mesh.
             f = torch.randint(max_f, size=(num_meshes,), dtype=torch.int32)
-            v = torch.randint(
-                3, high=max_v, size=(num_meshes,), dtype=torch.int32
-            )
+            v = torch.randint(3, high=max_v, size=(num_meshes,), dtype=torch.int32)
 
         # Generate the actual vertices and faces.
         for i in range(num_meshes):
@@ -90,12 +87,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                 device=device,
             ),
             torch.tensor(
-                [
-                    [0.1, 0.3, 0.3],
-                    [0.6, 0.7, 0.8],
-                    [0.2, 0.3, 0.4],
-                    [0.1, 0.5, 0.3],
-                ],
+                [[0.1, 0.3, 0.3], [0.6, 0.7, 0.8], [0.2, 0.3, 0.4], [0.1, 0.5, 0.3]],
                 dtype=torch.float32,
                 device=device,
             ),
@@ -113,9 +105,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         ]
         faces = [
             torch.tensor([[0, 1, 2]], dtype=torch.int64, device=device),
-            torch.tensor(
-                [[0, 1, 2], [1, 2, 3]], dtype=torch.int64, device=device
-            ),
+            torch.tensor([[0, 1, 2], [1, 2, 3]], dtype=torch.int64, device=device),
             torch.tensor(
                 [
                     [1, 2, 0],
@@ -136,12 +126,8 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         mesh = TestMeshes.init_simple_mesh("cuda:0")
 
         # Check that faces/verts per mesh are set in init:
-        self.assertClose(
-            mesh._num_faces_per_mesh.cpu(), torch.tensor([1, 2, 7])
-        )
-        self.assertClose(
-            mesh._num_verts_per_mesh.cpu(), torch.tensor([3, 4, 5])
-        )
+        self.assertClose(mesh._num_faces_per_mesh.cpu(), torch.tensor([1, 2, 7]))
+        self.assertClose(mesh._num_verts_per_mesh.cpu(), torch.tensor([3, 4, 5]))
 
         # Check computed tensors
         self.assertClose(
@@ -163,8 +149,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
             mesh.mesh_to_faces_packed_first_idx().cpu(), torch.tensor([0, 1, 3])
         )
         self.assertClose(
-            mesh.num_edges_per_mesh().cpu(),
-            torch.tensor([3, 5, 10], dtype=torch.int32),
+            mesh.num_edges_per_mesh().cpu(), torch.tensor([3, 5, 10], dtype=torch.int32)
         )
 
     def test_simple_random_meshes(self):
@@ -172,9 +157,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         # Define the test mesh object either as a list or tensor of faces/verts.
         for lists_to_tensors in (False, True):
             N = 10
-            mesh = TestMeshes.init_mesh(
-                N, 100, 300, lists_to_tensors=lists_to_tensors
-            )
+            mesh = TestMeshes.init_mesh(N, 100, 300, lists_to_tensors=lists_to_tensors)
             verts_list = mesh.verts_list()
             faces_list = mesh.faces_list()
 
@@ -207,12 +190,8 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
             for n in range(N):
                 v = verts_list[n].shape[0]
                 f = faces_list[n].shape[0]
-                self.assertClose(
-                    verts_packed[curv : curv + v, :], verts_list[n]
-                )
-                self.assertClose(
-                    faces_packed[curf : curf + f, :] - curv, faces_list[n]
-                )
+                self.assertClose(verts_packed[curv : curv + v, :], verts_list[n])
+                self.assertClose(faces_packed[curf : curf + f, :] - curv, faces_list[n])
                 self.assertTrue(vert_to_mesh[curv : curv + v].eq(n).all())
                 self.assertTrue(face_to_mesh[curf : curf + f].eq(n).all())
                 self.assertTrue(mesh_to_vert[n] == curv)
@@ -232,9 +211,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
             npedges = np.concatenate((e12, e20, e01), axis=0)
             npedges = np.sort(npedges, axis=1)
 
-            unique_edges, unique_idx = np.unique(
-                npedges, return_index=True, axis=0
-            )
+            unique_edges, unique_idx = np.unique(npedges, return_index=True, axis=0)
             self.assertTrue(np.allclose(edges, unique_edges))
             temp = face_to_mesh.cpu().numpy()
             temp = np.concatenate((temp, temp, temp), axis=0)
@@ -266,13 +243,9 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                 v = torch.randint(
                     3, high=V, size=(1,), dtype=torch.int32, device=device
                 )[0]
-                f = torch.randint(
-                    F, size=(1,), dtype=torch.int32, device=device
-                )[0]
+                f = torch.randint(F, size=(1,), dtype=torch.int32, device=device)[0]
                 verts = torch.rand((v, 3), dtype=torch.float32, device=device)
-                faces = torch.randint(
-                    v, size=(f, 3), dtype=torch.int64, device=device
-                )
+                faces = torch.randint(v, size=(f, 3), dtype=torch.int64, device=device)
             else:
                 verts = torch.tensor([], dtype=torch.float32, device=device)
                 faces = torch.tensor([], dtype=torch.int64, device=device)
@@ -309,16 +282,12 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         )
         for n in range(N):
             verts.append(torch.rand((V, 3), dtype=torch.float32, device=device))
-            this_faces = torch.full(
-                (F, 3), -1, dtype=torch.int64, device=device
-            )
+            this_faces = torch.full((F, 3), -1, dtype=torch.int64, device=device)
             if valid[n]:
                 v = torch.randint(
                     3, high=V, size=(1,), dtype=torch.int32, device=device
                 )[0]
-                f = torch.randint(
-                    F, size=(1,), dtype=torch.int32, device=device
-                )[0]
+                f = torch.randint(F, size=(1,), dtype=torch.int32, device=device)[0]
                 this_faces[:f, :] = torch.randint(
                     v, size=(f, 3), dtype=torch.int64, device=device
                 )
@@ -329,9 +298,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         mesh = Meshes(verts=torch.stack(verts), faces=torch.stack(faces))
 
         # Check verts/faces per mesh are set correctly in init.
-        self.assertListEqual(
-            mesh._num_faces_per_mesh.tolist(), num_faces.tolist()
-        )
+        self.assertListEqual(mesh._num_faces_per_mesh.tolist(), num_faces.tolist())
         self.assertListEqual(mesh._num_verts_per_mesh.tolist(), [V] * N)
 
         for n, (vv, ff) in enumerate(zip(mesh.verts_list(), mesh.faces_list())):
@@ -339,12 +306,8 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
             self.assertClose(vv, verts[n])
 
         new_faces = [ff.clone() for ff in faces]
-        v = torch.randint(
-            3, high=V, size=(1,), dtype=torch.int32, device=device
-        )[0]
-        f = torch.randint(F - 10, size=(1,), dtype=torch.int32, device=device)[
-            0
-        ]
+        v = torch.randint(3, high=V, size=(1,), dtype=torch.int32, device=device)[0]
+        f = torch.randint(F - 10, size=(1,), dtype=torch.int32, device=device)[0]
         this_faces = torch.full((F, 3), -1, dtype=torch.int64, device=device)
         this_faces[10 : f + 10, :] = torch.randint(
             v, size=(f, 3), dtype=torch.int64, device=device
@@ -376,9 +339,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                 torch.allclose(new_mesh._verts_list[0], mesh._verts_list[0])
             )
             self.assertFalse(
-                torch.allclose(
-                    mesh.num_verts_per_mesh(), new_mesh.num_verts_per_mesh()
-                )
+                torch.allclose(mesh.num_verts_per_mesh(), new_mesh.num_verts_per_mesh())
             )
             self.assertSeparate(new_mesh.verts_packed(), mesh.verts_packed())
             self.assertSeparate(new_mesh.verts_padded(), mesh.verts_padded())
@@ -438,9 +399,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                 mesh._compute_face_areas_normals(refresh=True)
                 mesh._compute_vertex_normals(refresh=True)
 
-            deform = torch.rand(
-                (all_v, 3), dtype=torch.float32, device=mesh.device
-            )
+            deform = torch.rand((all_v, 3), dtype=torch.float32, device=mesh.device)
             # new meshes class to hold the deformed mesh
             new_mesh_naive = naive_offset_verts(mesh, deform)
 
@@ -458,9 +417,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                 self.assertClose(
                     new_mesh.verts_list()[i], new_mesh_naive.verts_list()[i]
                 )
-                self.assertClose(
-                    mesh.faces_list()[i], new_mesh_naive.faces_list()[i]
-                )
+                self.assertClose(mesh.faces_list()[i], new_mesh_naive.faces_list()[i])
                 self.assertClose(
                     new_mesh.faces_list()[i], new_mesh_naive.faces_list()[i]
                 )
@@ -475,21 +432,11 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                 )
 
             # check padded & packed
-            self.assertClose(
-                new_mesh.faces_padded(), new_mesh_naive.faces_padded()
-            )
-            self.assertClose(
-                new_mesh.verts_padded(), new_mesh_naive.verts_padded()
-            )
-            self.assertClose(
-                new_mesh.faces_packed(), new_mesh_naive.faces_packed()
-            )
-            self.assertClose(
-                new_mesh.verts_packed(), new_mesh_naive.verts_packed()
-            )
-            self.assertClose(
-                new_mesh.edges_packed(), new_mesh_naive.edges_packed()
-            )
+            self.assertClose(new_mesh.faces_padded(), new_mesh_naive.faces_padded())
+            self.assertClose(new_mesh.verts_padded(), new_mesh_naive.verts_padded())
+            self.assertClose(new_mesh.faces_packed(), new_mesh_naive.faces_packed())
+            self.assertClose(new_mesh.verts_packed(), new_mesh_naive.verts_packed())
+            self.assertClose(new_mesh.edges_packed(), new_mesh_naive.edges_packed())
             self.assertClose(
                 new_mesh.verts_packed_to_mesh_idx(),
                 new_mesh_naive.verts_packed_to_mesh_idx(),
@@ -499,8 +446,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                 new_mesh_naive.mesh_to_verts_packed_first_idx(),
             )
             self.assertClose(
-                new_mesh.num_verts_per_mesh(),
-                new_mesh_naive.num_verts_per_mesh(),
+                new_mesh.num_verts_per_mesh(), new_mesh_naive.num_verts_per_mesh()
             )
             self.assertClose(
                 new_mesh.faces_packed_to_mesh_idx(),
@@ -511,8 +457,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                 new_mesh_naive.mesh_to_faces_packed_first_idx(),
             )
             self.assertClose(
-                new_mesh.num_faces_per_mesh(),
-                new_mesh_naive.num_faces_per_mesh(),
+                new_mesh.num_faces_per_mesh(), new_mesh_naive.num_faces_per_mesh()
             )
             self.assertClose(
                 new_mesh.edges_packed_to_mesh_idx(),
@@ -527,24 +472,19 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
 
             # check face areas, normals and vertex normals
             self.assertClose(
-                new_mesh.verts_normals_packed(),
-                new_mesh_naive.verts_normals_packed(),
+                new_mesh.verts_normals_packed(), new_mesh_naive.verts_normals_packed()
             )
             self.assertClose(
-                new_mesh.verts_normals_padded(),
-                new_mesh_naive.verts_normals_padded(),
+                new_mesh.verts_normals_padded(), new_mesh_naive.verts_normals_padded()
             )
             self.assertClose(
-                new_mesh.faces_normals_packed(),
-                new_mesh_naive.faces_normals_packed(),
+                new_mesh.faces_normals_packed(), new_mesh_naive.faces_normals_packed()
             )
             self.assertClose(
-                new_mesh.faces_normals_padded(),
-                new_mesh_naive.faces_normals_padded(),
+                new_mesh.faces_normals_padded(), new_mesh_naive.faces_normals_padded()
             )
             self.assertClose(
-                new_mesh.faces_areas_packed(),
-                new_mesh_naive.faces_areas_packed(),
+                new_mesh.faces_areas_packed(), new_mesh_naive.faces_areas_packed()
             )
 
     def test_scale_verts(self):
@@ -579,13 +519,11 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                 for i in range(N):
                     if test == "tensor":
                         self.assertClose(
-                            scales[i] * mesh.verts_list()[i],
-                            new_mesh.verts_list()[i],
+                            scales[i] * mesh.verts_list()[i], new_mesh.verts_list()[i]
                         )
                     else:
                         self.assertClose(
-                            scales * mesh.verts_list()[i],
-                            new_mesh.verts_list()[i],
+                            scales * mesh.verts_list()[i], new_mesh.verts_list()[i]
                         )
                     self.assertClose(
                         new_mesh.verts_list()[i], new_mesh_naive.verts_list()[i]
@@ -607,21 +545,11 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                     )
 
                 # check padded & packed
-                self.assertClose(
-                    new_mesh.faces_padded(), new_mesh_naive.faces_padded()
-                )
-                self.assertClose(
-                    new_mesh.verts_padded(), new_mesh_naive.verts_padded()
-                )
-                self.assertClose(
-                    new_mesh.faces_packed(), new_mesh_naive.faces_packed()
-                )
-                self.assertClose(
-                    new_mesh.verts_packed(), new_mesh_naive.verts_packed()
-                )
-                self.assertClose(
-                    new_mesh.edges_packed(), new_mesh_naive.edges_packed()
-                )
+                self.assertClose(new_mesh.faces_padded(), new_mesh_naive.faces_padded())
+                self.assertClose(new_mesh.verts_padded(), new_mesh_naive.verts_padded())
+                self.assertClose(new_mesh.faces_packed(), new_mesh_naive.faces_packed())
+                self.assertClose(new_mesh.verts_packed(), new_mesh_naive.verts_packed())
+                self.assertClose(new_mesh.edges_packed(), new_mesh_naive.edges_packed())
                 self.assertClose(
                     new_mesh.verts_packed_to_mesh_idx(),
                     new_mesh_naive.verts_packed_to_mesh_idx(),
@@ -631,8 +559,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                     new_mesh_naive.mesh_to_verts_packed_first_idx(),
                 )
                 self.assertClose(
-                    new_mesh.num_verts_per_mesh(),
-                    new_mesh_naive.num_verts_per_mesh(),
+                    new_mesh.num_verts_per_mesh(), new_mesh_naive.num_verts_per_mesh()
                 )
                 self.assertClose(
                     new_mesh.faces_packed_to_mesh_idx(),
@@ -643,8 +570,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                     new_mesh_naive.mesh_to_faces_packed_first_idx(),
                 )
                 self.assertClose(
-                    new_mesh.num_faces_per_mesh(),
-                    new_mesh_naive.num_faces_per_mesh(),
+                    new_mesh.num_faces_per_mesh(), new_mesh_naive.num_faces_per_mesh()
                 )
                 self.assertClose(
                     new_mesh.edges_packed_to_mesh_idx(),
@@ -675,8 +601,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                     new_mesh_naive.faces_normals_padded(),
                 )
                 self.assertClose(
-                    new_mesh.faces_areas_packed(),
-                    new_mesh_naive.faces_areas_packed(),
+                    new_mesh.faces_areas_packed(), new_mesh_naive.faces_areas_packed()
                 )
 
     def test_extend_list(self):
@@ -730,10 +655,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         self.assertTrue(len(split_meshes[0]) == 2)
         self.assertTrue(
             split_meshes[0].verts_list()
-            == [
-                mesh.get_mesh_verts_faces(0)[0],
-                mesh.get_mesh_verts_faces(1)[0],
-            ]
+            == [mesh.get_mesh_verts_faces(0)[0], mesh.get_mesh_verts_faces(1)[0]]
         )
         self.assertTrue(len(split_meshes[1]) == 3)
         self.assertTrue(
@@ -756,9 +678,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         verts_faces = [(10, 100), (20, 200)]
         for (V, F) in verts_faces:
             verts = torch.rand((V, 3), dtype=torch.float32, device=device)
-            faces = torch.randint(
-                V, size=(F, 3), dtype=torch.int64, device=device
-            )
+            faces = torch.randint(V, size=(F, 3), dtype=torch.int64, device=device)
             verts_list.append(verts)
             faces_list.append(faces)
 
@@ -782,9 +702,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         faces_list = []
         for (V, F) in [(10, 100)]:
             verts = torch.rand((V, 3), dtype=torch.float32, device=device)
-            faces = torch.randint(
-                V, size=(F, 3), dtype=torch.int64, device=device
-            )
+            faces = torch.randint(V, size=(F, 3), dtype=torch.int64, device=device)
             verts_list.append(verts)
             faces_list.append(faces)
 
@@ -802,9 +720,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         verts_faces = [(10, 100), (20, 200), (30, 300)]
         for (V, F) in verts_faces:
             verts = torch.rand((V, 3), dtype=torch.float32, device=device)
-            faces = torch.randint(
-                V, size=(F, 3), dtype=torch.int64, device=device
-            )
+            faces = torch.randint(V, size=(F, 3), dtype=torch.int64, device=device)
             verts_list.append(verts)
             faces_list.append(faces)
 
@@ -814,9 +730,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         verts_padded = mesh.verts_padded()
         verts_padded_flat = verts_padded.view(-1, 3)
 
-        self.assertClose(
-            verts_padded_flat[verts_padded_to_packed_idx], verts_packed
-        )
+        self.assertClose(verts_padded_flat[verts_padded_to_packed_idx], verts_packed)
 
         idx = verts_padded_to_packed_idx.view(-1, 1).expand(-1, 3)
         self.assertClose(verts_padded_flat.gather(0, idx), verts_packed)
@@ -828,9 +742,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         verts_faces = [(10, 100), (20, 200), (30, 300)]
         for (V, F) in verts_faces:
             verts = torch.rand((V, 3), dtype=torch.float32, device=device)
-            faces = torch.randint(
-                V, size=(F, 3), dtype=torch.int64, device=device
-            )
+            faces = torch.randint(V, size=(F, 3), dtype=torch.int64, device=device)
             verts_list.append(verts)
             faces_list.append(faces)
 
@@ -1006,12 +918,10 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         verts_normals_packed = meshes.verts_normals_packed()
         faces_normals_packed = meshes.faces_normals_packed()
         self.assertTrue(
-            list(verts_normals_packed.shape)
-            == [verts.shape[0] + verts2.shape[0], 3]
+            list(verts_normals_packed.shape) == [verts.shape[0] + verts2.shape[0], 3]
         )
         self.assertTrue(
-            list(faces_normals_packed.shape)
-            == [faces.shape[0] + faces2.shape[0], 3]
+            list(faces_normals_packed.shape) == [faces.shape[0] + faces2.shape[0], 3]
         )
 
         # Single mesh where two faces share one vertex so the normal is
@@ -1079,17 +989,12 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         # with areas > eps=1e-6
         nonzero = face_areas_cpu > 1e-6
         self.assertClose(
-            face_normals_cpu[nonzero],
-            face_normals_cuda.cpu()[nonzero],
-            atol=1e-6,
+            face_normals_cpu[nonzero], face_normals_cuda.cpu()[nonzero], atol=1e-6
         )
 
     @staticmethod
     def compute_packed_with_init(
-        num_meshes: int = 10,
-        max_v: int = 100,
-        max_f: int = 300,
-        device: str = "cpu",
+        num_meshes: int = 10, max_v: int = 100, max_f: int = 300, device: str = "cpu"
     ):
         mesh = TestMeshes.init_mesh(num_meshes, max_v, max_f, device=device)
         torch.cuda.synchronize()
@@ -1102,10 +1007,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
 
     @staticmethod
     def compute_padded_with_init(
-        num_meshes: int = 10,
-        max_v: int = 100,
-        max_f: int = 300,
-        device: str = "cpu",
+        num_meshes: int = 10, max_v: int = 100, max_f: int = 300, device: str = "cpu"
     ):
         mesh = TestMeshes.init_mesh(num_meshes, max_v, max_f, device=device)
         torch.cuda.synchronize()

tests/test_nearest_neighbor_points.py

@@ -2,8 +2,8 @@
 
 import unittest
 from itertools import product
-import torch
 
+import torch
 from pytorch3d import _C
 
 
@@ -33,9 +33,7 @@ class TestNearestNeighborPoints(unittest.TestCase):
                         # to the cpp or cuda versions of the function
                         # depending on the input type.
                         idx1 = _C.nn_points_idx(x, y)
-                        idx2 = TestNearestNeighborPoints.nn_points_idx_naive(
-                            x, y
-                        )
+                        idx2 = TestNearestNeighborPoints.nn_points_idx_naive(x, y)
                         self.assertTrue(idx1.size(1) == P1)
                         self.assertTrue(torch.all(idx1 == idx2))
 

tests/test_obj_io.py

@@ -4,14 +4,13 @@ import os
 import unittest
 from io import StringIO
 from pathlib import Path
-import torch
 
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d.io import load_obj, load_objs_as_meshes, save_obj
 from pytorch3d.structures import Meshes, Textures, join_meshes
 from pytorch3d.utils import torus
 
-from common_testing import TestCaseMixin
-
 
 class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
     def test_load_obj_simple(self):
@@ -34,12 +33,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
         tex_maps = aux.texture_images
 
         expected_verts = torch.tensor(
-            [
-                [0.1, 0.2, 0.3],
-                [0.2, 0.3, 0.4],
-                [0.3, 0.4, 0.5],
-                [0.4, 0.5, 0.6],
-            ],
+            [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5], [0.4, 0.5, 0.6]],
             dtype=torch.float32,
         )
         expected_faces = torch.tensor(
@@ -124,12 +118,8 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
             [[0.749279, 0.501284], [0.999110, 0.501077], [0.999455, 0.750380]],
             dtype=torch.float32,
         )
-        expected_faces_normals_idx = torch.tensor(
-            [[1, 1, 1]], dtype=torch.int64
-        )
-        expected_faces_textures_idx = torch.tensor(
-            [[0, 0, 1]], dtype=torch.int64
-        )
+        expected_faces_normals_idx = torch.tensor([[1, 1, 1]], dtype=torch.int64)
+        expected_faces_textures_idx = torch.tensor([[0, 0, 1]], dtype=torch.int64)
 
         self.assertTrue(torch.all(verts == expected_verts))
         self.assertTrue(torch.all(faces.verts_idx == expected_faces))
@@ -153,23 +143,13 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
             ]
         )
         obj_file = StringIO(obj_file)
-        expected_faces_normals_idx = torch.tensor(
-            [[0, 0, 1]], dtype=torch.int64
-        )
+        expected_faces_normals_idx = torch.tensor([[0, 0, 1]], dtype=torch.int64)
         expected_normals = torch.tensor(
-            [
-                [0.000000, 0.000000, -1.000000],
-                [-1.000000, -0.000000, -0.000000],
-            ],
+            [[0.000000, 0.000000, -1.000000], [-1.000000, -0.000000, -0.000000]],
             dtype=torch.float32,
         )
         expected_verts = torch.tensor(
-            [
-                [0.1, 0.2, 0.3],
-                [0.2, 0.3, 0.4],
-                [0.3, 0.4, 0.5],
-                [0.4, 0.5, 0.6],
-            ],
+            [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5], [0.4, 0.5, 0.6]],
             dtype=torch.float32,
         )
         verts, faces, aux = load_obj(obj_file)
@@ -198,19 +178,12 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
             ]
         )
         obj_file = StringIO(obj_file)
-        expected_faces_textures_idx = torch.tensor(
-            [[0, 0, 1]], dtype=torch.int64
-        )
+        expected_faces_textures_idx = torch.tensor([[0, 0, 1]], dtype=torch.int64)
         expected_textures = torch.tensor(
             [[0.999110, 0.501077], [0.999455, 0.750380]], dtype=torch.float32
         )
         expected_verts = torch.tensor(
-            [
-                [0.1, 0.2, 0.3],
-                [0.2, 0.3, 0.4],
-                [0.3, 0.4, 0.5],
-                [0.4, 0.5, 0.6],
-            ],
+            [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5], [0.4, 0.5, 0.6]],
             dtype=torch.float32,
         )
         verts, faces, aux = load_obj(obj_file)
@@ -257,9 +230,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
 
         with self.assertRaises(ValueError) as err:
             load_obj(obj_file)
-        self.assertTrue(
-            "Vertex properties are inconsistent" in str(err.exception)
-        )
+        self.assertTrue("Vertex properties are inconsistent" in str(err.exception))
 
     def test_load_obj_error_too_many_vertex_properties(self):
         obj_file = "\n".join(["f 2/1/1/3"])
@@ -267,9 +238,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
 
         with self.assertRaises(ValueError) as err:
             load_obj(obj_file)
-        self.assertTrue(
-            "Face vertices can ony have 3 properties" in str(err.exception)
-        )
+        self.assertTrue("Face vertices can ony have 3 properties" in str(err.exception))
 
     def test_load_obj_error_invalid_vertex_indices(self):
         obj_file = "\n".join(
@@ -320,7 +289,9 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
             verts = torch.FloatTensor([[0.1, 0.2, 0.3, 0.4]])  # (V, 4)
             faces = torch.LongTensor([[0, 1, 2]])
             save_obj(StringIO(), verts, faces)
-        expected_message = "Argument 'verts' should either be empty or of shape (num_verts, 3)."
+        expected_message = (
+            "Argument 'verts' should either be empty or of shape (num_verts, 3)."
+        )
         self.assertTrue(expected_message, error.exception)
 
         # Invalid faces shape
@@ -328,7 +299,9 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
             verts = torch.FloatTensor([[0.1, 0.2, 0.3]])
             faces = torch.LongTensor([[0, 1, 2, 3]])  # (F, 4)
             save_obj(StringIO(), verts, faces)
-        expected_message = "Argument 'faces' should either be empty or of shape (num_faces, 3)."
+        expected_message = (
+            "Argument 'faces' should either be empty or of shape (num_faces, 3)."
+        )
         self.assertTrue(expected_message, error.exception)
 
     def test_save_obj_invalid_indices(self):
@@ -395,12 +368,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
 
     def test_save_obj(self):
         verts = torch.tensor(
-            [
-                [0.01, 0.2, 0.301],
-                [0.2, 0.03, 0.408],
-                [0.3, 0.4, 0.05],
-                [0.6, 0.7, 0.8],
-            ],
+            [[0.01, 0.2, 0.301], [0.2, 0.03, 0.408], [0.3, 0.4, 0.05], [0.6, 0.7, 0.8]],
             dtype=torch.float32,
         )
         faces = torch.tensor(
@@ -424,9 +392,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
         self.assertEqual(actual_file, expected_file)
 
     def test_load_mtl(self):
-        DATA_DIR = (
-            Path(__file__).resolve().parent.parent / "docs/tutorials/data"
-        )
+        DATA_DIR = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
         obj_filename = "cow_mesh/cow.obj"
         filename = os.path.join(DATA_DIR, obj_filename)
         verts, faces, aux = load_obj(filename)
@@ -452,19 +418,13 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
         # Check all keys and values in dictionary are the same.
         for n1, n2 in zip(materials.keys(), expected_materials.keys()):
             self.assertTrue(n1 == n2)
-            for k1, k2 in zip(
-                materials[n1].keys(), expected_materials[n2].keys()
-            ):
+            for k1, k2 in zip(materials[n1].keys(), expected_materials[n2].keys()):
                 self.assertTrue(
-                    torch.allclose(
-                        materials[n1][k1], expected_materials[n2][k2]
-                    )
+                    torch.allclose(materials[n1][k1], expected_materials[n2][k2])
                 )
 
     def test_load_mtl_noload(self):
-        DATA_DIR = (
-            Path(__file__).resolve().parent.parent / "docs/tutorials/data"
-        )
+        DATA_DIR = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
         obj_filename = "cow_mesh/cow.obj"
         filename = os.path.join(DATA_DIR, obj_filename)
         verts, faces, aux = load_obj(filename, load_textures=False)
@@ -490,12 +450,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
             verts, faces, aux = load_obj(obj_file)
 
         expected_verts = torch.tensor(
-            [
-                [0.1, 0.2, 0.3],
-                [0.2, 0.3, 0.4],
-                [0.3, 0.4, 0.5],
-                [0.4, 0.5, 0.6],
-            ],
+            [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5], [0.4, 0.5, 0.6]],
             dtype=torch.float32,
         )
         expected_faces = torch.tensor([[0, 1, 2], [0, 1, 3]], dtype=torch.int64)
@@ -514,12 +469,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
             verts, faces, aux = load_obj(filename)
 
         expected_verts = torch.tensor(
-            [
-                [0.1, 0.2, 0.3],
-                [0.2, 0.3, 0.4],
-                [0.3, 0.4, 0.5],
-                [0.4, 0.5, 0.6],
-            ],
+            [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5], [0.4, 0.5, 0.6]],
             dtype=torch.float32,
         )
         expected_faces = torch.tensor([[0, 1, 2], [0, 1, 3]], dtype=torch.int64)
@@ -533,12 +483,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
         verts, faces, aux = load_obj(filename, load_textures=False)
 
         expected_verts = torch.tensor(
-            [
-                [0.1, 0.2, 0.3],
-                [0.2, 0.3, 0.4],
-                [0.3, 0.4, 0.5],
-                [0.4, 0.5, 0.6],
-            ],
+            [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5], [0.4, 0.5, 0.6]],
             dtype=torch.float32,
         )
         expected_faces = torch.tensor([[0, 1, 2], [0, 1, 3]], dtype=torch.int64)
@@ -555,12 +500,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
             verts, faces, aux = load_obj(filename)
 
         expected_verts = torch.tensor(
-            [
-                [0.1, 0.2, 0.3],
-                [0.2, 0.3, 0.4],
-                [0.3, 0.4, 0.5],
-                [0.4, 0.5, 0.6],
-            ],
+            [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5], [0.4, 0.5, 0.6]],
             dtype=torch.float32,
         )
         expected_faces = torch.tensor([[0, 1, 2], [0, 1, 3]], dtype=torch.int64)
@@ -574,12 +514,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
         verts, faces, aux = load_obj(filename, load_textures=False)
 
         expected_verts = torch.tensor(
-            [
-                [0.1, 0.2, 0.3],
-                [0.2, 0.3, 0.4],
-                [0.3, 0.4, 0.5],
-                [0.4, 0.5, 0.6],
-            ],
+            [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5], [0.4, 0.5, 0.6]],
             dtype=torch.float32,
         )
         expected_faces = torch.tensor([[0, 1, 2], [0, 1, 3]], dtype=torch.int64)
@@ -607,33 +542,24 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
             check_item(mesh.verts_padded(), mesh3.verts_padded())
             check_item(mesh.faces_padded(), mesh3.faces_padded())
             if mesh.textures is not None:
+                check_item(mesh.textures.maps_padded(), mesh3.textures.maps_padded())
                 check_item(
-                    mesh.textures.maps_padded(), mesh3.textures.maps_padded()
+                    mesh.textures.faces_uvs_padded(), mesh3.textures.faces_uvs_padded()
                 )
                 check_item(
-                    mesh.textures.faces_uvs_padded(),
-                    mesh3.textures.faces_uvs_padded(),
+                    mesh.textures.verts_uvs_padded(), mesh3.textures.verts_uvs_padded()
                 )
                 check_item(
-                    mesh.textures.verts_uvs_padded(),
-                    mesh3.textures.verts_uvs_padded(),
-                )
-                check_item(
-                    mesh.textures.verts_rgb_padded(),
-                    mesh3.textures.verts_rgb_padded(),
+                    mesh.textures.verts_rgb_padded(), mesh3.textures.verts_rgb_padded()
                 )
 
-        DATA_DIR = (
-            Path(__file__).resolve().parent.parent / "docs/tutorials/data"
-        )
+        DATA_DIR = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
         obj_filename = DATA_DIR / "cow_mesh/cow.obj"
 
         mesh = load_objs_as_meshes([obj_filename])
         mesh3 = load_objs_as_meshes([obj_filename, obj_filename, obj_filename])
         check_triple(mesh, mesh3)
-        self.assertTupleEqual(
-            mesh.textures.maps_padded().shape, (1, 1024, 1024, 3)
-        )
+        self.assertTupleEqual(mesh.textures.maps_padded().shape, (1, 1024, 1024, 3))
 
         mesh_notex = load_objs_as_meshes([obj_filename], load_textures=False)
         mesh3_notex = load_objs_as_meshes(
@@ -655,9 +581,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
         teapot_obj = DATA_DIR / "teapot.obj"
         mesh_teapot = load_objs_as_meshes([teapot_obj])
         teapot_verts, teapot_faces = mesh_teapot.get_mesh_verts_faces(0)
-        mix_mesh = load_objs_as_meshes(
-            [obj_filename, teapot_obj], load_textures=False
-        )
+        mix_mesh = load_objs_as_meshes([obj_filename, teapot_obj], load_textures=False)
         self.assertEqual(len(mix_mesh), 2)
         self.assertClose(mix_mesh.verts_list()[0], mesh.verts_list()[0])
         self.assertClose(mix_mesh.faces_list()[0], mesh.faces_list()[0])
@@ -671,15 +595,11 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
         self.assertClose(cow3_tea.faces_list()[3], mesh_teapot.faces_list()[0])
 
     @staticmethod
-    def _bm_save_obj(
-        verts: torch.Tensor, faces: torch.Tensor, decimal_places: int
-    ):
+    def _bm_save_obj(verts: torch.Tensor, faces: torch.Tensor, decimal_places: int):
         return lambda: save_obj(StringIO(), verts, faces, decimal_places)
 
     @staticmethod
-    def _bm_load_obj(
-        verts: torch.Tensor, faces: torch.Tensor, decimal_places: int
-    ):
+    def _bm_load_obj(verts: torch.Tensor, faces: torch.Tensor, decimal_places: int):
         f = StringIO()
         save_obj(f, verts, faces, decimal_places)
         s = f.getvalue()

tests/test_packed_to_padded.py

@@ -1,13 +1,12 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 import unittest
-import torch
 
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d.ops import packed_to_padded, padded_to_packed
 from pytorch3d.structures.meshes import Meshes
 
-from common_testing import TestCaseMixin
-
 
 class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
     def setUp(self) -> None:
@@ -25,9 +24,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
         verts_list = []
         faces_list = []
         for _ in range(num_meshes):
-            verts = torch.rand(
-                (num_verts, 3), dtype=torch.float32, device=device
-            )
+            verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
             faces = torch.randint(
                 num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
             )
@@ -47,9 +44,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
         if D == 0:
             inputs_padded = torch.zeros((num_meshes, max_size), device=device)
         else:
-            inputs_padded = torch.zeros(
-                (num_meshes, max_size, D), device=device
-            )
+            inputs_padded = torch.zeros((num_meshes, max_size, D), device=device)
         for m in range(num_meshes):
             s = first_idxs[m]
             if m == num_meshes - 1:
@@ -92,13 +87,9 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
         max_faces = meshes.num_faces_per_mesh().max().item()
 
         if D == 0:
-            values = torch.rand(
-                (faces.shape[0],), device=device, requires_grad=True
-            )
+            values = torch.rand((faces.shape[0],), device=device, requires_grad=True)
         else:
-            values = torch.rand(
-                (faces.shape[0], D), device=device, requires_grad=True
-            )
+            values = torch.rand((faces.shape[0], D), device=device, requires_grad=True)
         values_torch = values.detach().clone()
         values_torch.requires_grad = True
         values_padded = packed_to_padded(
@@ -120,10 +111,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
         values_padded_torch.backward(grad_inputs)
         grad_outputs_torch1 = values_torch.grad
         grad_outputs_torch2 = TestPackedToPadded.padded_to_packed_python(
-            grad_inputs,
-            mesh_to_faces_packed_first_idx,
-            values.size(0),
-            device=device,
+            grad_inputs, mesh_to_faces_packed_first_idx, values.size(0), device=device
         )
         self.assertClose(grad_outputs, grad_outputs_torch1)
         self.assertClose(grad_outputs, grad_outputs_torch2)
@@ -165,9 +153,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
         values_torch = values.detach().clone()
         values_torch.requires_grad = True
         values_packed = padded_to_packed(
-            values,
-            mesh_to_faces_packed_first_idx,
-            num_faces_per_mesh.sum().item(),
+            values, mesh_to_faces_packed_first_idx, num_faces_per_mesh.sum().item()
         )
         values_packed_torch = TestPackedToPadded.padded_to_packed_python(
             values_torch,
@@ -180,9 +166,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
 
         # check backward
         if D == 0:
-            grad_inputs = torch.rand(
-                (num_faces_per_mesh.sum().item()), device=device
-            )
+            grad_inputs = torch.rand((num_faces_per_mesh.sum().item()), device=device)
         else:
             grad_inputs = torch.rand(
                 (num_faces_per_mesh.sum().item(), D), device=device
@@ -192,10 +176,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
         values_packed_torch.backward(grad_inputs)
         grad_outputs_torch1 = values_torch.grad
         grad_outputs_torch2 = TestPackedToPadded.packed_to_padded_python(
-            grad_inputs,
-            mesh_to_faces_packed_first_idx,
-            values.size(1),
-            device=device,
+            grad_inputs, mesh_to_faces_packed_first_idx, values.size(1), device=device
         )
         self.assertClose(grad_outputs, grad_outputs_torch1)
         self.assertClose(grad_outputs, grad_outputs_torch2)
@@ -219,34 +200,24 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
         self._test_padded_to_packed_helper(16, "cuda:0")
 
     def test_invalid_inputs_shapes(self, device="cuda:0"):
-        with self.assertRaisesRegex(
-            ValueError, "input can only be 2-dimensional."
-        ):
+        with self.assertRaisesRegex(ValueError, "input can only be 2-dimensional."):
             values = torch.rand((100, 50, 2), device=device)
             first_idxs = torch.tensor([0, 80], dtype=torch.int64, device=device)
             packed_to_padded(values, first_idxs, 100)
 
-        with self.assertRaisesRegex(
-            ValueError, "input can only be 3-dimensional."
-        ):
+        with self.assertRaisesRegex(ValueError, "input can only be 3-dimensional."):
             values = torch.rand((100,), device=device)
             first_idxs = torch.tensor([0, 80], dtype=torch.int64, device=device)
             padded_to_packed(values, first_idxs, 20)
 
-        with self.assertRaisesRegex(
-            ValueError, "input can only be 3-dimensional."
-        ):
+        with self.assertRaisesRegex(ValueError, "input can only be 3-dimensional."):
             values = torch.rand((100, 50, 2, 2), device=device)
             first_idxs = torch.tensor([0, 80], dtype=torch.int64, device=device)
             padded_to_packed(values, first_idxs, 20)
 
     @staticmethod
     def packed_to_padded_with_init(
-        num_meshes: int,
-        num_verts: int,
-        num_faces: int,
-        num_d: int,
-        device: str = "cpu",
+        num_meshes: int, num_verts: int, num_faces: int, num_d: int, device: str = "cpu"
     ):
         meshes = TestPackedToPadded.init_meshes(
             num_meshes, num_verts, num_faces, device
@@ -268,11 +239,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
 
     @staticmethod
     def packed_to_padded_with_init_torch(
-        num_meshes: int,
-        num_verts: int,
-        num_faces: int,
-        num_d: int,
-        device: str = "cpu",
+        num_meshes: int, num_verts: int, num_faces: int, num_d: int, device: str = "cpu"
     ):
         meshes = TestPackedToPadded.init_meshes(
             num_meshes, num_verts, num_faces, device

tests/test_ply_io.py

@@ -3,13 +3,12 @@
 import struct
 import unittest
 from io import BytesIO, StringIO
-import torch
 
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d.io.ply_io import _load_ply_raw, load_ply, save_ply
 from pytorch3d.utils import torus
 
-from common_testing import TestCaseMixin
-
 
 class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
     def test_raw_load_simple_ascii(self):
@@ -155,14 +154,7 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
     def test_load_simple_binary(self):
         for big_endian in [True, False]:
             verts = (
-                "0 0 0 "
-                "0 0 1 "
-                "0 1 1 "
-                "0 1 0 "
-                "1 0 0 "
-                "1 0 1 "
-                "1 1 1 "
-                "1 1 0"
+                "0 0 0 " "0 0 1 " "0 1 1 " "0 1 0 " "1 0 0 " "1 0 1 " "1 1 1 " "1 1 0"
             ).split()
             faces = (
                 "4 0 1 2 3 "
@@ -176,9 +168,7 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
                 "3 4 5 1"
             ).split()
             short_one = b"\00\01" if big_endian else b"\01\00"
-            mixed_data = b"\00\00" b"\03\03" + (
-                short_one + b"\00\01\01\01" b"\00\02"
-            )
+            mixed_data = b"\00\00" b"\03\03" + (short_one + b"\00\01\01\01" b"\00\02")
             minus_one_data = b"\xff" * 14
             endian_char = ">" if big_endian else "<"
             format = (
@@ -306,9 +296,7 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
 
         lines2 = lines.copy()
         lines2[8] = "1 2"
-        with self.assertRaisesRegex(
-            ValueError, "Inconsistent data for vertex."
-        ):
+        with self.assertRaisesRegex(ValueError, "Inconsistent data for vertex."):
             _load_ply_raw(StringIO("\n".join(lines2)))
 
         lines2 = lines[:-1]
@@ -344,9 +332,7 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
 
         lines2 = lines.copy()
         lines2.insert(4, "element bad 1")
-        with self.assertRaisesRegex(
-            ValueError, "Found an element with no properties."
-        ):
+        with self.assertRaisesRegex(ValueError, "Found an element with no properties."):
             _load_ply_raw(StringIO("\n".join(lines2)))
 
         lines2 = lines.copy()
@@ -369,25 +355,19 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
         lines2 = lines.copy()
         lines2.insert(4, "property double y")
 
-        with self.assertRaisesRegex(
-            ValueError, "Too little data for an element."
-        ):
+        with self.assertRaisesRegex(ValueError, "Too little data for an element."):
             _load_ply_raw(StringIO("\n".join(lines2)))
 
         lines2[-2] = "3.3 4.2"
         _load_ply_raw(StringIO("\n".join(lines2)))
 
         lines2[-2] = "3.3 4.3 2"
-        with self.assertRaisesRegex(
-            ValueError, "Too much data for an element."
-        ):
+        with self.assertRaisesRegex(ValueError, "Too much data for an element."):
             _load_ply_raw(StringIO("\n".join(lines2)))
 
         # Now make the ply file actually be readable as a Mesh
 
-        with self.assertRaisesRegex(
-            ValueError, "The ply file has no face element."
-        ):
+        with self.assertRaisesRegex(ValueError, "The ply file has no face element."):
             load_ply(StringIO("\n".join(lines)))
 
         lines2 = lines.copy()
@@ -398,9 +378,7 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
         lines2.insert(5, "property float z")
         lines2.insert(5, "property float y")
         lines2[-2] = "0 0 0"
-        with self.assertRaisesRegex(
-            ValueError, "Faces must have at least 3 vertices."
-        ):
+        with self.assertRaisesRegex(ValueError, "Faces must have at least 3 vertices."):
             load_ply(StringIO("\n".join(lines2)))
 
         # Good one
@@ -408,17 +386,11 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
         load_ply(StringIO("\n".join(lines2)))
 
     @staticmethod
-    def _bm_save_ply(
-        verts: torch.Tensor, faces: torch.Tensor, decimal_places: int
-    ):
-        return lambda: save_ply(
-            StringIO(), verts, faces, decimal_places=decimal_places
-        )
+    def _bm_save_ply(verts: torch.Tensor, faces: torch.Tensor, decimal_places: int):
+        return lambda: save_ply(StringIO(), verts, faces, decimal_places=decimal_places)
 
     @staticmethod
-    def _bm_load_ply(
-        verts: torch.Tensor, faces: torch.Tensor, decimal_places: int
-    ):
+    def _bm_load_ply(verts: torch.Tensor, faces: torch.Tensor, decimal_places: int):
         f = StringIO()
         save_ply(f, verts, faces, decimal_places)
         s = f.getvalue()

tests/test_pointclouds.py

@@ -1,13 +1,12 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 
-import numpy as np
 import unittest
+
+import numpy as np
 import torch
-
-from pytorch3d.structures.pointclouds import Pointclouds
-
 from common_testing import TestCaseMixin
+from pytorch3d.structures.pointclouds import Pointclouds
 
 
 class TestPointclouds(TestCaseMixin, unittest.TestCase):
@@ -52,13 +51,11 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         normals_list, features_list = None, None
         if with_normals:
             normals_list = [
-                torch.rand((i, 3), device=device, dtype=torch.float32)
-                for i in p
+                torch.rand((i, 3), device=device, dtype=torch.float32) for i in p
             ]
         if with_features:
             features_list = [
-                torch.rand((i, channels), device=device, dtype=torch.float32)
-                for i in p
+                torch.rand((i, channels), device=device, dtype=torch.float32) for i in p
             ]
 
         if lists_to_tensors:
@@ -68,9 +65,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
             if with_features:
                 features_list = torch.stack(features_list)
 
-        return Pointclouds(
-            points_list, normals=normals_list, features=features_list
-        )
+        return Pointclouds(points_list, normals=normals_list, features=features_list)
 
     def test_simple(self):
         device = torch.device("cuda:0")
@@ -81,12 +76,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                 device=device,
             ),
             torch.tensor(
-                [
-                    [0.1, 0.3, 0.3],
-                    [0.6, 0.7, 0.8],
-                    [0.2, 0.3, 0.4],
-                    [0.1, 0.5, 0.3],
-                ],
+                [[0.1, 0.3, 0.3], [0.6, 0.7, 0.8], [0.2, 0.3, 0.4], [0.1, 0.5, 0.3]],
                 dtype=torch.float32,
                 device=device,
             ),
@@ -111,9 +101,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         self.assertClose(
             clouds.cloud_to_packed_first_idx().cpu(), torch.tensor([0, 3, 7])
         )
-        self.assertClose(
-            clouds.num_points_per_cloud().cpu(), torch.tensor([3, 4, 5])
-        )
+        self.assertClose(clouds.num_points_per_cloud().cpu(), torch.tensor([3, 4, 5]))
         self.assertClose(
             clouds.padded_to_packed_idx().cpu(),
             torch.tensor([0, 1, 2, 5, 6, 7, 8, 10, 11, 12, 13, 14]),
@@ -129,11 +117,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
             "points_padded",
             "padded_to_packed_idx",
         ]
-        public_normals_getters = [
-            "normals_list",
-            "normals_packed",
-            "normals_padded",
-        ]
+        public_normals_getters = ["normals_list", "normals_packed", "normals_padded"]
         public_features_getters = [
             "features_list",
             "features_packed",
@@ -147,17 +131,13 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         points_data = [torch.zeros((max_len, 3)).uniform_() for i in lengths]
         normals_data = [torch.zeros((max_len, 3)).uniform_() for i in lengths]
         features_data = [torch.zeros((max_len, C)).uniform_() for i in lengths]
-        for length, p, n, f in zip(
-            lengths, points_data, normals_data, features_data
-        ):
+        for length, p, n, f in zip(lengths, points_data, normals_data, features_data):
             p[length:] = 0.0
             n[length:] = 0.0
             f[length:] = 0.0
         points_list = [d[:length] for length, d in zip(lengths, points_data)]
         normals_list = [d[:length] for length, d in zip(lengths, normals_data)]
-        features_list = [
-            d[:length] for length, d in zip(lengths, features_data)
-        ]
+        features_list = [d[:length] for length, d in zip(lengths, features_data)]
         points_packed = torch.cat(points_data)
         normals_packed = torch.cat(normals_data)
         features_packed = torch.cat(features_data)
@@ -173,13 +153,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
             ("emptylist_emptylist_emptylist", [], [], []),
         ]
         false_cases_inputs = [
-            (
-                "list_packed",
-                points_list,
-                normals_packed,
-                features_packed,
-                ValueError,
-            ),
+            ("list_packed", points_list, normals_packed, features_packed, ValueError),
             ("packed_0", points_packed, None, None, ValueError),
         ]
 
@@ -230,15 +204,11 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                         self.assertIsNone(features_padded)
                     for n in range(N):
                         p = points_list[n].shape[0]
-                        self.assertClose(
-                            points_padded[n, :p, :], points_list[n]
-                        )
+                        self.assertClose(points_padded[n, :p, :], points_list[n])
                         if with_normals:
                             norms = normals_list[n].shape[0]
                             self.assertEqual(p, norms)
-                            self.assertClose(
-                                normals_padded[n, :p, :], normals_list[n]
-                            )
+                            self.assertClose(normals_padded[n, :p, :], normals_list[n])
                         if with_features:
                             f = features_list[n].shape[0]
                             self.assertEqual(p, f)
@@ -248,9 +218,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                         if points_padded.shape[1] > p:
                             self.assertTrue(points_padded[n, p:, :].eq(0).all())
                             if with_features:
-                                self.assertTrue(
-                                    features_padded[n, p:, :].eq(0).all()
-                                )
+                                self.assertTrue(features_padded[n, p:, :].eq(0).all())
                         self.assertEqual(points_per_cloud[n], p)
 
                     # Check compute packed.
@@ -272,17 +240,13 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                         )
                         if with_normals:
                             self.assertClose(
-                                normals_packed[cur : cur + p, :],
-                                normals_list[n],
+                                normals_packed[cur : cur + p, :], normals_list[n]
                             )
                         if with_features:
                             self.assertClose(
-                                features_packed[cur : cur + p, :],
-                                features_list[n],
+                                features_packed[cur : cur + p, :], features_list[n]
                             )
-                        self.assertTrue(
-                            packed_to_cloud[cur : cur + p].eq(n).all()
-                        )
+                        self.assertTrue(packed_to_cloud[cur : cur + p].eq(n).all())
                         self.assertTrue(cloud_to_packed[n] == cur)
                         cur += p
 
@@ -312,9 +276,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                 )[0]
                 points = torch.rand((p, 3), dtype=torch.float32, device=device)
                 normals = torch.rand((p, 3), dtype=torch.float32, device=device)
-                features = torch.rand(
-                    (p, C), dtype=torch.float32, device=device
-                )
+                features = torch.rand((p, C), dtype=torch.float32, device=device)
             else:
                 points = torch.tensor([], dtype=torch.float32, device=device)
                 normals = torch.tensor([], dtype=torch.float32, device=device)
@@ -331,9 +293,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                 if with_features:
                     this_features = features_list
                 clouds = Pointclouds(
-                    points=points_list,
-                    normals=this_normals,
-                    features=this_features,
+                    points=points_list, normals=this_normals, features=this_features
                 )
                 points_padded = clouds.points_padded()
                 normals_padded = clouds.normals_padded()
@@ -346,13 +306,9 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                 for n in range(N):
                     p = len(points_list[n])
                     if p > 0:
-                        self.assertClose(
-                            points_padded[n, :p, :], points_list[n]
-                        )
+                        self.assertClose(points_padded[n, :p, :], points_list[n])
                         if with_normals:
-                            self.assertClose(
-                                normals_padded[n, :p, :], normals_list[n]
-                            )
+                            self.assertClose(normals_padded[n, :p, :], normals_list[n])
                         if with_features:
                             self.assertClose(
                                 features_padded[n, :p, :], features_list[n]
@@ -360,13 +316,9 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                         if points_padded.shape[1] > p:
                             self.assertTrue(points_padded[n, p:, :].eq(0).all())
                             if with_normals:
-                                self.assertTrue(
-                                    normals_padded[n, p:, :].eq(0).all()
-                                )
+                                self.assertTrue(normals_padded[n, p:, :].eq(0).all())
                             if with_features:
-                                self.assertTrue(
-                                    features_padded[n, p:, :].eq(0).all()
-                                )
+                                self.assertTrue(features_padded[n, p:, :].eq(0).all())
                     self.assertTrue(points_per_cloud[n] == p)
 
     def test_clone_list(self):
@@ -379,12 +331,8 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
             new_clouds = clouds.clone()
 
             # Check cloned and original objects do not share tensors.
-            self.assertSeparate(
-                new_clouds.points_list()[0], clouds.points_list()[0]
-            )
-            self.assertSeparate(
-                new_clouds.normals_list()[0], clouds.normals_list()[0]
-            )
+            self.assertSeparate(new_clouds.points_list()[0], clouds.points_list()[0])
+            self.assertSeparate(new_clouds.normals_list()[0], clouds.normals_list()[0])
             self.assertSeparate(
                 new_clouds.features_list()[0], clouds.features_list()[0]
             )
@@ -412,12 +360,8 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
             new_clouds = clouds.clone()
 
             # Check cloned and original objects do not share tensors.
-            self.assertSeparate(
-                new_clouds.points_list()[0], clouds.points_list()[0]
-            )
-            self.assertSeparate(
-                new_clouds.normals_list()[0], clouds.normals_list()[0]
-            )
+            self.assertSeparate(new_clouds.points_list()[0], clouds.points_list()[0])
+            self.assertSeparate(new_clouds.normals_list()[0], clouds.normals_list()[0])
             self.assertSeparate(
                 new_clouds.features_list()[0], clouds.features_list()[0]
             )
@@ -442,9 +386,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         for i in range(N):
             self.assertClose(cloud1.points_list()[i], cloud2.points_list()[i])
             self.assertClose(cloud1.normals_list()[i], cloud2.normals_list()[i])
-            self.assertClose(
-                cloud1.features_list()[i], cloud2.features_list()[i]
-            )
+            self.assertClose(cloud1.features_list()[i], cloud2.features_list()[i])
         has_normals = cloud1.normals_list() is not None
         self.assertTrue(has_normals == (cloud2.normals_list() is not None))
         has_features = cloud1.features_list() is not None
@@ -459,22 +401,13 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         if has_features:
             self.assertClose(cloud1.features_padded(), cloud2.features_padded())
             self.assertClose(cloud1.features_packed(), cloud2.features_packed())
+        self.assertClose(cloud1.packed_to_cloud_idx(), cloud2.packed_to_cloud_idx())
         self.assertClose(
-            cloud1.packed_to_cloud_idx(), cloud2.packed_to_cloud_idx()
-        )
-        self.assertClose(
-            cloud1.cloud_to_packed_first_idx(),
-            cloud2.cloud_to_packed_first_idx(),
-        )
-        self.assertClose(
-            cloud1.num_points_per_cloud(), cloud2.num_points_per_cloud()
-        )
-        self.assertClose(
-            cloud1.packed_to_cloud_idx(), cloud2.packed_to_cloud_idx()
-        )
-        self.assertClose(
-            cloud1.padded_to_packed_idx(), cloud2.padded_to_packed_idx()
+            cloud1.cloud_to_packed_first_idx(), cloud2.cloud_to_packed_first_idx()
         )
+        self.assertClose(cloud1.num_points_per_cloud(), cloud2.num_points_per_cloud())
+        self.assertClose(cloud1.packed_to_cloud_idx(), cloud2.packed_to_cloud_idx())
+        self.assertClose(cloud1.padded_to_packed_idx(), cloud2.padded_to_packed_idx())
         self.assertTrue(all(cloud1.valid == cloud2.valid))
         self.assertTrue(cloud1.equisized == cloud2.equisized)
 
@@ -482,9 +415,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         def naive_offset(clouds, offsets_packed):
             new_points_packed = clouds.points_packed() + offsets_packed
             new_points_list = list(
-                new_points_packed.split(
-                    clouds.num_points_per_cloud().tolist(), 0
-                )
+                new_points_packed.split(clouds.num_points_per_cloud().tolist(), 0)
             )
             return Pointclouds(
                 points=new_points_list,
@@ -502,9 +433,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                 clouds._compute_padded()
                 clouds.padded_to_packed_idx()
 
-            deform = torch.rand(
-                (all_p, 3), dtype=torch.float32, device=clouds.device
-            )
+            deform = torch.rand((all_p, 3), dtype=torch.float32, device=clouds.device)
             new_clouds_naive = naive_offset(clouds, deform)
 
             new_clouds = clouds.offset(deform)
@@ -521,8 +450,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                     clouds.normals_list()[i], new_clouds_naive.normals_list()[i]
                 )
                 self.assertClose(
-                    clouds.features_list()[i],
-                    new_clouds_naive.features_list()[i],
+                    clouds.features_list()[i], new_clouds_naive.features_list()[i]
                 )
             self.assertCloudsEqual(new_clouds, new_clouds_naive)
 
@@ -550,15 +478,13 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
             new_clouds = clouds.scale(scales)
             for i in range(N):
                 self.assertClose(
-                    scales[i] * clouds.points_list()[i],
-                    new_clouds.points_list()[i],
+                    scales[i] * clouds.points_list()[i], new_clouds.points_list()[i]
                 )
                 self.assertClose(
                     clouds.normals_list()[i], new_clouds_naive.normals_list()[i]
                 )
                 self.assertClose(
-                    clouds.features_list()[i],
-                    new_clouds_naive.features_list()[i],
+                    clouds.features_list()[i], new_clouds_naive.features_list()[i]
                 )
             self.assertCloudsEqual(new_clouds, new_clouds_naive)
 
@@ -576,20 +502,15 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
             for i in range(len(clouds)):
                 for n in range(N):
                     self.assertClose(
-                        clouds.points_list()[i],
-                        new_clouds.points_list()[i * N + n],
+                        clouds.points_list()[i], new_clouds.points_list()[i * N + n]
                     )
                     self.assertClose(
-                        clouds.normals_list()[i],
-                        new_clouds.normals_list()[i * N + n],
+                        clouds.normals_list()[i], new_clouds.normals_list()[i * N + n]
                     )
                     self.assertClose(
-                        clouds.features_list()[i],
-                        new_clouds.features_list()[i * N + n],
-                    )
-                    self.assertTrue(
-                        clouds.valid[i] == new_clouds.valid[i * N + n]
+                        clouds.features_list()[i], new_clouds.features_list()[i * N + n]
                     )
+                    self.assertTrue(clouds.valid[i] == new_clouds.valid[i * N + n])
             self.assertAllSeparate(
                 clouds.points_list()
                 + new_clouds.points_list()
@@ -627,8 +548,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
             "padded_to_packed_idx",
         ]:
             self.assertClose(
-                getattr(new_cloud, attrib)().cpu(),
-                getattr(cloud, attrib)().cpu(),
+                getattr(new_cloud, attrib)().cpu(), getattr(cloud, attrib)().cpu()
             )
         for i in range(len(cloud)):
             self.assertClose(
@@ -638,8 +558,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                 cloud.normals_list()[i].cpu(), new_cloud.normals_list()[i].cpu()
             )
             self.assertClose(
-                cloud.features_list()[i].cpu(),
-                new_cloud.features_list()[i].cpu(),
+                cloud.features_list()[i].cpu(), new_cloud.features_list()[i].cpu()
             )
         self.assertTrue(all(cloud.valid.cpu() == new_cloud.valid.cpu()))
         self.assertTrue(cloud.equisized == new_cloud.equisized)
@@ -666,8 +585,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
             "padded_to_packed_idx",
         ]:
             self.assertClose(
-                getattr(new_cloud, attrib)().cpu(),
-                getattr(cloud, attrib)().cpu(),
+                getattr(new_cloud, attrib)().cpu(), getattr(cloud, attrib)().cpu()
             )
         for i in range(len(cloud)):
             self.assertClose(
@@ -677,8 +595,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                 cloud.normals_list()[i].cpu(), new_cloud.normals_list()[i].cpu()
             )
             self.assertClose(
-                cloud.features_list()[i].cpu(),
-                new_cloud.features_list()[i].cpu(),
+                cloud.features_list()[i].cpu(), new_cloud.features_list()[i].cpu()
             )
         self.assertTrue(all(cloud.valid.cpu() == new_cloud.valid.cpu()))
         self.assertTrue(cloud.equisized == new_cloud.equisized)
@@ -698,11 +615,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         self.assertEqual(len(split_clouds[1]), 3)
         self.assertTrue(
             split_clouds[1].points_list()
-            == [
-                clouds.get_cloud(2)[0],
-                clouds.get_cloud(3)[0],
-                clouds.get_cloud(4)[0],
-            ]
+            == [clouds.get_cloud(2)[0], clouds.get_cloud(3)[0], clouds.get_cloud(4)[0]]
         )
 
         split_sizes = [2, 0.3]
@@ -751,9 +664,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         points_padded = clouds.points_padded()
         points_padded_flat = points_padded.view(-1, 3)
 
-        self.assertClose(
-            points_padded_flat[padded_to_packed_idx], points_packed
-        )
+        self.assertClose(points_padded_flat[padded_to_packed_idx], points_packed)
 
         idx = padded_to_packed_idx.view(-1, 1).expand(-1, 3)
         self.assertClose(points_padded_flat.gather(0, idx), points_packed)
@@ -765,16 +676,13 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         def check_equal(selected, indices):
             for selectedIdx, index in indices:
                 self.assertClose(
-                    selected.points_list()[selectedIdx],
-                    clouds.points_list()[index],
+                    selected.points_list()[selectedIdx], clouds.points_list()[index]
                 )
                 self.assertClose(
-                    selected.normals_list()[selectedIdx],
-                    clouds.normals_list()[index],
+                    selected.normals_list()[selectedIdx], clouds.normals_list()[index]
                 )
                 self.assertClose(
-                    selected.features_list()[selectedIdx],
-                    clouds.features_list()[index],
+                    selected.features_list()[selectedIdx], clouds.features_list()[index]
                 )
 
         # int index
@@ -820,11 +728,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         for with_normfeat in (True, False):
             for with_new_normfeat in (True, False):
                 clouds = self.init_cloud(
-                    N,
-                    P,
-                    C,
-                    with_normals=with_normfeat,
-                    with_features=with_normfeat,
+                    N, P, C, with_normals=with_normfeat, with_features=with_normfeat
                 )
 
                 num_points_per_cloud = clouds.num_points_per_cloud()
@@ -843,8 +747,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                         clouds.points_padded().shape, device=clouds.device
                     )
                     new_normals_list = [
-                        new_normals[i, : num_points_per_cloud[i]]
-                        for i in range(N)
+                        new_normals[i, : num_points_per_cloud[i]] for i in range(N)
                     ]
                     feat_shape = [
                         clouds.points_padded().shape[0],
@@ -853,14 +756,11 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                     ]
                     new_features = torch.rand(feat_shape, device=clouds.device)
                     new_features_list = [
-                        new_features[i, : num_points_per_cloud[i]]
-                        for i in range(N)
+                        new_features[i, : num_points_per_cloud[i]] for i in range(N)
                     ]
 
                 # update
-                new_clouds = clouds.update_padded(
-                    new_points, new_normals, new_features
-                )
+                new_clouds = clouds.update_padded(new_points, new_normals, new_features)
                 self.assertIsNone(new_clouds._points_list)
                 self.assertIsNone(new_clouds._points_packed)
 
@@ -868,13 +768,9 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                 self.assertTrue(all(new_clouds.valid == clouds.valid))
 
                 self.assertClose(new_clouds.points_padded(), new_points)
-                self.assertClose(
-                    new_clouds.points_packed(), torch.cat(new_points_list)
-                )
+                self.assertClose(new_clouds.points_packed(), torch.cat(new_points_list))
                 for i in range(N):
-                    self.assertClose(
-                        new_clouds.points_list()[i], new_points_list[i]
-                    )
+                    self.assertClose(new_clouds.points_list()[i], new_points_list[i])
 
                 if with_new_normfeat:
                     for i in range(N):
@@ -890,27 +786,22 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                     )
                     self.assertClose(new_clouds.features_padded(), new_features)
                     self.assertClose(
-                        new_clouds.features_packed(),
-                        torch.cat(new_features_list),
+                        new_clouds.features_packed(), torch.cat(new_features_list)
                     )
                 else:
                     if with_normfeat:
                         for i in range(N):
                             self.assertClose(
-                                new_clouds.normals_list()[i],
-                                clouds.normals_list()[i],
+                                new_clouds.normals_list()[i], clouds.normals_list()[i]
                             )
                             self.assertClose(
-                                new_clouds.features_list()[i],
-                                clouds.features_list()[i],
+                                new_clouds.features_list()[i], clouds.features_list()[i]
                             )
                             self.assertNotSeparate(
-                                new_clouds.normals_list()[i],
-                                clouds.normals_list()[i],
+                                new_clouds.normals_list()[i], clouds.normals_list()[i]
                             )
                             self.assertNotSeparate(
-                                new_clouds.features_list()[i],
-                                clouds.features_list()[i],
+                                new_clouds.features_list()[i], clouds.features_list()[i]
                             )
 
                         self.assertClose(
@@ -920,19 +811,16 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
                             new_clouds.normals_packed(), clouds.normals_packed()
                         )
                         self.assertClose(
-                            new_clouds.features_padded(),
-                            clouds.features_padded(),
+                            new_clouds.features_padded(), clouds.features_padded()
                         )
                         self.assertClose(
-                            new_clouds.features_packed(),
-                            clouds.features_packed(),
+                            new_clouds.features_packed(), clouds.features_packed()
                         )
                         self.assertNotSeparate(
                             new_clouds.normals_padded(), clouds.normals_padded()
                         )
                         self.assertNotSeparate(
-                            new_clouds.features_padded(),
-                            clouds.features_padded(),
+                            new_clouds.features_padded(), clouds.features_padded()
                         )
                     else:
                         self.assertIsNone(new_clouds.normals_list())

tests/test_rasterize_meshes.py

@@ -2,8 +2,9 @@
 
 import functools
 import unittest
-import torch
 
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d import _C
 from pytorch3d.renderer.mesh.rasterize_meshes import (
     rasterize_meshes,
@@ -12,20 +13,14 @@ from pytorch3d.renderer.mesh.rasterize_meshes import (
 from pytorch3d.structures import Meshes
 from pytorch3d.utils import ico_sphere
 
-from common_testing import TestCaseMixin
-
 
 class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
     def test_simple_python(self):
         device = torch.device("cpu")
-        self._simple_triangle_raster(
-            rasterize_meshes_python, device, bin_size=-1
-        )
+        self._simple_triangle_raster(rasterize_meshes_python, device, bin_size=-1)
         self._simple_blurry_raster(rasterize_meshes_python, device, bin_size=-1)
         self._test_behind_camera(rasterize_meshes_python, device, bin_size=-1)
-        self._test_perspective_correct(
-            rasterize_meshes_python, device, bin_size=-1
-        )
+        self._test_perspective_correct(rasterize_meshes_python, device, bin_size=-1)
 
     def test_simple_cpu_naive(self):
         device = torch.device("cpu")
@@ -350,9 +345,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
         fn1 = functools.partial(rasterize_meshes, meshes1, **kwargs)
         fn2 = functools.partial(rasterize_meshes_python, meshes2, **kwargs)
         args = ()
-        self._compare_impls(
-            fn1, fn2, args, args, verts1, verts2, compare_grads=True
-        )
+        self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
 
     def test_cpp_vs_cuda_perspective_correct(self):
         meshes = ico_sphere(2, device=torch.device("cpu"))
@@ -367,9 +360,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
         fn1 = functools.partial(rasterize_meshes, meshes1, **kwargs)
         fn2 = functools.partial(rasterize_meshes, meshes2, bin_size=0, **kwargs)
         args = ()
-        self._compare_impls(
-            fn1, fn2, args, args, verts1, verts2, compare_grads=True
-        )
+        self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
 
     def test_cuda_naive_vs_binned_perspective_correct(self):
         meshes = ico_sphere(2, device=torch.device("cuda"))
@@ -384,9 +375,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
         fn1 = functools.partial(rasterize_meshes, meshes1, bin_size=0, **kwargs)
         fn2 = functools.partial(rasterize_meshes, meshes2, bin_size=8, **kwargs)
         args = ()
-        self._compare_impls(
-            fn1, fn2, args, args, verts1, verts2, compare_grads=True
-        )
+        self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
 
     def _compare_impls(
         self,
@@ -433,9 +422,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
         grad_verts2 = grad_var2.grad.data.clone().cpu()
         self.assertClose(grad_verts1, grad_verts2, rtol=1e-3)
 
-    def _test_perspective_correct(
-        self, rasterize_meshes_fn, device, bin_size=None
-    ):
+    def _test_perspective_correct(self, rasterize_meshes_fn, device, bin_size=None):
         # fmt: off
         verts = torch.tensor([
                 [-0.4, -0.4, 10],  # noqa: E241, E201
@@ -542,12 +529,8 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
         zbuf_f_bary = w0_f * z0 + w1_f * z1 + w2_f * z2
         zbuf_t_bary = w0_t * z0 + w1_t * z1 + w2_t * z2
         mask = idx_expected != -1
-        zbuf_f_bary_diff = (
-            (zbuf_f_bary[mask] - zbuf_f_expected[mask]).abs().max()
-        )
-        zbuf_t_bary_diff = (
-            (zbuf_t_bary[mask] - zbuf_t_expected[mask]).abs().max()
-        )
+        zbuf_f_bary_diff = (zbuf_f_bary[mask] - zbuf_f_expected[mask]).abs().max()
+        zbuf_t_bary_diff = (zbuf_t_bary[mask] - zbuf_t_expected[mask]).abs().max()
         self.assertLess(zbuf_f_bary_diff, 1e-4)
         self.assertLess(zbuf_t_bary_diff, 1e-4)
 
@@ -719,9 +702,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
 
         # k = 1, second closest point.
         expected_p2face_k1 = expected_p2face_k0.clone()
-        expected_p2face_k1[0, :] = (
-            torch.ones_like(expected_p2face_k1[0, :]) * -1
-        )
+        expected_p2face_k1[0, :] = torch.ones_like(expected_p2face_k1[0, :]) * -1
 
         # fmt: off
         expected_p2face_k1[1, :] = torch.tensor(
@@ -763,9 +744,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
         #  Coordinate conventions +Y up, +Z in, +X left
         if bin_size == -1:
             # simple python, no bin_size
-            p2face, zbuf, bary, pix_dists = raster_fn(
-                meshes, image_size, 0.0, 2
-            )
+            p2face, zbuf, bary, pix_dists = raster_fn(meshes, image_size, 0.0, 2)
         else:
             p2face, zbuf, bary, pix_dists = raster_fn(
                 meshes, image_size, 0.0, 2, bin_size
@@ -914,9 +893,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
 
         # Expected faces using axes convention +Y down, + X right, + Z in
         bin_faces_expected = (
-            torch.ones(
-                (1, 2, 2, max_faces_per_bin), dtype=torch.int32, device=device
-            )
+            torch.ones((1, 2, 2, max_faces_per_bin), dtype=torch.int32, device=device)
             * -1
         )
         bin_faces_expected[0, 0, 0, 0] = torch.tensor([1])
@@ -979,12 +956,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
 
         def rasterize():
             rasterize_meshes(
-                meshes_batch,
-                image_size,
-                blur_radius,
-                8,
-                bin_size,
-                max_faces_per_bin,
+                meshes_batch, image_size, blur_radius, 8, bin_size, max_faces_per_bin
             )
             torch.cuda.synchronize()
 

tests/test_rasterize_points.py

@@ -1,10 +1,11 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 
-import numpy as np
 import unittest
-import torch
 
+import numpy as np
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d import _C
 from pytorch3d.renderer.points.rasterize_points import (
     rasterize_points,
@@ -12,8 +13,6 @@ from pytorch3d.renderer.points.rasterize_points import (
 )
 from pytorch3d.structures.pointclouds import Pointclouds
 
-from common_testing import TestCaseMixin
-
 
 class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
     def test_python_simple_cpu(self):
@@ -38,9 +37,7 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
         self._test_behind_camera(rasterize_points, torch.device("cpu"))
 
     def test_cuda_behind_camera(self):
-        self._test_behind_camera(
-            rasterize_points, torch.device("cuda"), bin_size=0
-        )
+        self._test_behind_camera(rasterize_points, torch.device("cuda"), bin_size=0)
 
     def test_cpp_vs_naive_vs_binned(self):
         # Make sure that the backward pass runs for all pathways
@@ -167,20 +164,8 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
         points_cuda = points_cpu.cuda().detach().requires_grad_(True)
         pointclouds_cpu = Pointclouds(points=points_cpu)
         pointclouds_cuda = Pointclouds(points=points_cuda)
-        args_cpu = (
-            pointclouds_cpu,
-            image_size,
-            radius,
-            points_per_pixel,
-            bin_size,
-        )
-        args_cuda = (
-            pointclouds_cuda,
-            image_size,
-            radius,
-            points_per_pixel,
-            bin_size,
-        )
+        args_cpu = (pointclouds_cpu, image_size, radius, points_per_pixel, bin_size)
+        args_cuda = (pointclouds_cuda, image_size, radius, points_per_pixel, bin_size)
         self._compare_impls(
             rasterize_points,
             rasterize_points,
@@ -332,9 +317,7 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
         ], device=device)
         # fmt: on
 
-        dists1_expected = torch.zeros(
-            (5, 5, 2), dtype=torch.float32, device=device
-        )
+        dists1_expected = torch.zeros((5, 5, 2), dtype=torch.float32, device=device)
         # fmt: off
         dists1_expected[:, :, 0] = torch.tensor([
             [-1.00, -1.00,  0.16, -1.00, -1.00],  # noqa: E241

tests/test_rasterizer.py

@@ -1,22 +1,17 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 
-import numpy as np
 import unittest
 from pathlib import Path
+
+import numpy as np
 import torch
 from PIL import Image
-
-from pytorch3d.renderer.cameras import (
-    OpenGLPerspectiveCameras,
-    look_at_view_transform,
-)
-from pytorch3d.renderer.mesh.rasterizer import (
-    MeshRasterizer,
-    RasterizationSettings,
-)
+from pytorch3d.renderer.cameras import OpenGLPerspectiveCameras, look_at_view_transform
+from pytorch3d.renderer.mesh.rasterizer import MeshRasterizer, RasterizationSettings
 from pytorch3d.utils.ico_sphere import ico_sphere
 
+
 DATA_DIR = Path(__file__).resolve().parent / "data"
 DEBUG = False  # Set DEBUG to true to save outputs from the tests.
 
@@ -52,9 +47,7 @@ class TestMeshRasterizer(unittest.TestCase):
         )
 
         # Init rasterizer
-        rasterizer = MeshRasterizer(
-            cameras=cameras, raster_settings=raster_settings
-        )
+        rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings)
 
         ####################################
         # 1. Test rasterizing a single mesh

tests/test_rendering_meshes.py

@@ -4,23 +4,17 @@
 """
 Sanity checks for output images from the renderer.
 """
-import numpy as np
 import unittest
 from pathlib import Path
+
+import numpy as np
 import torch
 from PIL import Image
-
 from pytorch3d.io import load_objs_as_meshes
-from pytorch3d.renderer.cameras import (
-    OpenGLPerspectiveCameras,
-    look_at_view_transform,
-)
+from pytorch3d.renderer.cameras import OpenGLPerspectiveCameras, look_at_view_transform
 from pytorch3d.renderer.lighting import PointLights
 from pytorch3d.renderer.materials import Materials
-from pytorch3d.renderer.mesh.rasterizer import (
-    MeshRasterizer,
-    RasterizationSettings,
-)
+from pytorch3d.renderer.mesh.rasterizer import MeshRasterizer, RasterizationSettings
 from pytorch3d.renderer.mesh.renderer import MeshRenderer
 from pytorch3d.renderer.mesh.shader import (
     BlendParams,
@@ -34,6 +28,7 @@ from pytorch3d.renderer.mesh.texturing import Textures
 from pytorch3d.structures.meshes import Meshes
 from pytorch3d.utils.ico_sphere import ico_sphere
 
+
 # If DEBUG=True, save out images generated in the tests for debugging.
 # All saved images have prefix DEBUG_
 DEBUG = False
@@ -65,9 +60,7 @@ class TestRenderingMeshes(unittest.TestCase):
         verts_padded = sphere_mesh.verts_padded()
         faces_padded = sphere_mesh.faces_padded()
         textures = Textures(verts_rgb=torch.ones_like(verts_padded))
-        sphere_mesh = Meshes(
-            verts=verts_padded, faces=faces_padded, textures=textures
-        )
+        sphere_mesh = Meshes(verts=verts_padded, faces=faces_padded, textures=textures)
 
         # Init rasterizer settings
         if elevated_camera:
@@ -90,9 +83,7 @@ class TestRenderingMeshes(unittest.TestCase):
         raster_settings = RasterizationSettings(
             image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0
         )
-        rasterizer = MeshRasterizer(
-            cameras=cameras, raster_settings=raster_settings
-        )
+        rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings)
 
         # Test several shaders
         shaders = {
@@ -101,9 +92,7 @@ class TestRenderingMeshes(unittest.TestCase):
             "flat": HardFlatShader,
         }
         for (name, shader_init) in shaders.items():
-            shader = shader_init(
-                lights=lights, cameras=cameras, materials=materials
-            )
+            shader = shader_init(lights=lights, cameras=cameras, materials=materials)
             renderer = MeshRenderer(rasterizer=rasterizer, shader=shader)
             images = renderer(sphere_mesh)
             filename = "simple_sphere_light_%s%s.png" % (name, postfix)
@@ -125,9 +114,7 @@ class TestRenderingMeshes(unittest.TestCase):
         phong_shader = HardPhongShader(
             lights=lights, cameras=cameras, materials=materials
         )
-        phong_renderer = MeshRenderer(
-            rasterizer=rasterizer, shader=phong_shader
-        )
+        phong_renderer = MeshRenderer(rasterizer=rasterizer, shader=phong_shader)
         images = phong_renderer(sphere_mesh, lights=lights)
         rgb = images[0, ..., :3].squeeze().cpu()
         if DEBUG:
@@ -137,9 +124,7 @@ class TestRenderingMeshes(unittest.TestCase):
             )
 
         # Load reference image
-        image_ref_phong_dark = load_rgb_image(
-            "test_simple_sphere_dark%s.png" % postfix
-        )
+        image_ref_phong_dark = load_rgb_image("test_simple_sphere_dark%s.png" % postfix)
         self.assertTrue(torch.allclose(rgb, image_ref_phong_dark, atol=0.05))
 
     def test_simple_sphere_elevated_camera(self):
@@ -184,18 +169,14 @@ class TestRenderingMeshes(unittest.TestCase):
         lights.location = torch.tensor([0.0, 0.0, +2.0], device=device)[None]
 
         # Init renderer
-        rasterizer = MeshRasterizer(
-            cameras=cameras, raster_settings=raster_settings
-        )
+        rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings)
         shaders = {
             "phong": HardGouraudShader,
             "gouraud": HardGouraudShader,
             "flat": HardFlatShader,
         }
         for (name, shader_init) in shaders.items():
-            shader = shader_init(
-                lights=lights, cameras=cameras, materials=materials
-            )
+            shader = shader_init(lights=lights, cameras=cameras, materials=materials)
             renderer = MeshRenderer(rasterizer=rasterizer, shader=shader)
             images = renderer(sphere_meshes)
             image_ref = load_rgb_image("test_simple_sphere_light_%s.png" % name)
@@ -228,9 +209,7 @@ class TestRenderingMeshes(unittest.TestCase):
 
         # Init renderer
         renderer = MeshRenderer(
-            rasterizer=MeshRasterizer(
-                cameras=cameras, raster_settings=raster_settings
-            ),
+            rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings),
             shader=SoftSilhouetteShader(blend_params=blend_params),
         )
         images = renderer(sphere_mesh)
@@ -258,9 +237,7 @@ class TestRenderingMeshes(unittest.TestCase):
         The pupils in the eyes of the cow should always be looking to the left.
         """
         device = torch.device("cuda:0")
-        DATA_DIR = (
-            Path(__file__).resolve().parent.parent / "docs/tutorials/data"
-        )
+        DATA_DIR = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
         obj_filename = DATA_DIR / "cow_mesh/cow.obj"
 
         # Load mesh + texture
@@ -283,9 +260,7 @@ class TestRenderingMeshes(unittest.TestCase):
 
         # Init renderer
         renderer = MeshRenderer(
-            rasterizer=MeshRasterizer(
-                cameras=cameras, raster_settings=raster_settings
-            ),
+            rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings),
             shader=TexturedSoftPhongShader(
                 lights=lights, cameras=cameras, materials=materials
             ),
@@ -306,9 +281,7 @@ class TestRenderingMeshes(unittest.TestCase):
         # Check grad exists
         [verts] = mesh.verts_list()
         verts.requires_grad = True
-        mesh2 = Meshes(
-            verts=[verts], faces=mesh.faces_list(), textures=mesh.textures
-        )
+        mesh2 = Meshes(verts=[verts], faces=mesh.faces_list(), textures=mesh.textures)
         images = renderer(mesh2)
         images[0, ...].sum().backward()
         self.assertIsNotNone(verts.grad)

tests/test_rendering_utils.py

@@ -1,13 +1,12 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 
-import numpy as np
 import unittest
+
+import numpy as np
 import torch
-
-from pytorch3d.renderer.utils import TensorProperties
-
 from common_testing import TestCaseMixin
+from pytorch3d.renderer.utils import TensorProperties
 
 
 # Example class for testing
@@ -81,9 +80,5 @@ class TestTensorProperties(TestCaseMixin, unittest.TestCase):
             if inds.sum() > 0:
                 # Check the gathered points in the output have the same value from
                 # the input.
-                self.assertClose(
-                    test_class_gathered.x[inds].mean(dim=0), x[i, ...]
-                )
-                self.assertClose(
-                    test_class_gathered.y[inds].mean(dim=0), y[i, ...]
-                )
+                self.assertClose(test_class_gathered.x[inds].mean(dim=0), x[i, ...])
+                self.assertClose(test_class_gathered.y[inds].mean(dim=0), y[i, ...])

tests/test_rotation_conversions.py

@@ -4,8 +4,8 @@
 import itertools
 import math
 import unittest
-import torch
 
+import torch
 from pytorch3d.transforms.rotation_conversions import (
     euler_angles_to_matrix,
     matrix_to_euler_angles,
@@ -45,9 +45,7 @@ class TestRandomRotation(unittest.TestCase):
             )
             # The 0.1 significance level for chisquare(8-1) is
             # scipy.stats.chi2(7).ppf(0.9) == 12.017.
-            self.assertLess(
-                chisquare_statistic, 12, (counts, chisquare_statistic, k)
-            )
+            self.assertLess(chisquare_statistic, 12, (counts, chisquare_statistic, k))
 
 
 class TestRotationConversion(unittest.TestCase):

tests/test_sample_points_from_meshes.py

@@ -3,14 +3,13 @@
 
 import unittest
 from pathlib import Path
-import torch
 
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d.ops import sample_points_from_meshes
 from pytorch3d.structures.meshes import Meshes
 from pytorch3d.utils.ico_sphere import ico_sphere
 
-from common_testing import TestCaseMixin
-
 
 class TestSamplePoints(TestCaseMixin, unittest.TestCase):
     def setUp(self) -> None:
@@ -28,9 +27,7 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
         verts_list = []
         faces_list = []
         for _ in range(num_meshes):
-            verts = torch.rand(
-                (num_verts, 3), dtype=torch.float32, device=device
-            )
+            verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
             faces = torch.randint(
                 num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
             )
@@ -48,13 +45,9 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
         device = torch.device("cuda:0")
         verts1 = torch.tensor([], dtype=torch.float32, device=device)
         faces1 = torch.tensor([], dtype=torch.int64, device=device)
-        meshes = Meshes(
-            verts=[verts1, verts1, verts1], faces=[faces1, faces1, faces1]
-        )
+        meshes = Meshes(verts=[verts1, verts1, verts1], faces=[faces1, faces1, faces1])
         with self.assertRaises(ValueError) as err:
-            sample_points_from_meshes(
-                meshes, num_samples=100, return_normals=True
-            )
+            sample_points_from_meshes(meshes, num_samples=100, return_normals=True)
         self.assertTrue("Meshes are empty." in str(err.exception))
 
     def test_sampling_output(self):
@@ -67,12 +60,7 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
 
         # Unit simplex.
         verts_pyramid = torch.tensor(
-            [
-                [0.0, 0.0, 0.0],
-                [1.0, 0.0, 0.0],
-                [0.0, 1.0, 0.0],
-                [0.0, 0.0, 1.0],
-            ],
+            [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
             dtype=torch.float32,
             device=device,
         )
@@ -113,12 +101,8 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
         pyramid_verts = samples[2, :]
         pyramid_normals = normals[2, :]
 
-        self.assertClose(
-            pyramid_verts.lt(1).float(), torch.ones_like(pyramid_verts)
-        )
-        self.assertClose(
-            (pyramid_verts >= 0).float(), torch.ones_like(pyramid_verts)
-        )
+        self.assertClose(pyramid_verts.lt(1).float(), torch.ones_like(pyramid_verts))
+        self.assertClose((pyramid_verts >= 0).float(), torch.ones_like(pyramid_verts))
 
         # Face 1: z = 0,  x + y <= 1, normals = (0, 0, 1).
         face_1_idxs = pyramid_verts[:, 2] == 0
@@ -126,14 +110,10 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
             pyramid_verts[face_1_idxs, :],
             pyramid_normals[face_1_idxs, :],
         )
-        self.assertTrue(
-            torch.all((face_1_verts[:, 0] + face_1_verts[:, 1]) <= 1)
-        )
+        self.assertTrue(torch.all((face_1_verts[:, 0] + face_1_verts[:, 1]) <= 1))
         self.assertClose(
             face_1_normals,
-            torch.tensor([0, 0, 1], dtype=torch.float32).expand(
-                face_1_normals.size()
-            ),
+            torch.tensor([0, 0, 1], dtype=torch.float32).expand(face_1_normals.size()),
         )
 
         # Face 2: x = 0,  z + y <= 1, normals = (1, 0, 0).
@@ -142,14 +122,10 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
             pyramid_verts[face_2_idxs, :],
             pyramid_normals[face_2_idxs, :],
         )
-        self.assertTrue(
-            torch.all((face_2_verts[:, 1] + face_2_verts[:, 2]) <= 1)
-        )
+        self.assertTrue(torch.all((face_2_verts[:, 1] + face_2_verts[:, 2]) <= 1))
         self.assertClose(
             face_2_normals,
-            torch.tensor([1, 0, 0], dtype=torch.float32).expand(
-                face_2_normals.size()
-            ),
+            torch.tensor([1, 0, 0], dtype=torch.float32).expand(face_2_normals.size()),
         )
 
         # Face 3: y = 0, x + z <= 1, normals = (0, -1, 0).
@@ -158,14 +134,10 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
             pyramid_verts[face_3_idxs, :],
             pyramid_normals[face_3_idxs, :],
         )
-        self.assertTrue(
-            torch.all((face_3_verts[:, 0] + face_3_verts[:, 2]) <= 1)
-        )
+        self.assertTrue(torch.all((face_3_verts[:, 0] + face_3_verts[:, 2]) <= 1))
         self.assertClose(
             face_3_normals,
-            torch.tensor([0, -1, 0], dtype=torch.float32).expand(
-                face_3_normals.size()
-            ),
+            torch.tensor([0, -1, 0], dtype=torch.float32).expand(face_3_normals.size()),
         )
 
         # Face 4: x + y + z = 1, normals = (1, 1, 1)/sqrt(3).
@@ -279,22 +251,15 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
         num_faces = 50
         for device in ["cpu", "cuda:0"]:
             for invalid in ["nan", "inf"]:
-                verts = torch.rand(
-                    (num_verts, 3), dtype=torch.float32, device=device
-                )
+                verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
                 # 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
                     )
@@ -310,9 +275,7 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
         verts_list = []
         faces_list = []
         for _ in range(num_meshes):
-            verts = torch.rand(
-                (num_verts, 3), dtype=torch.float32, device=device
-            )
+            verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
             faces = torch.randint(
                 num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
             )

tests/test_so3.py

@@ -1,10 +1,10 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 
-import numpy as np
 import unittest
-import torch
 
+import numpy as np
+import torch
 from pytorch3d.transforms.so3 import (
     hat,
     so3_exponential_map,
@@ -26,9 +26,7 @@ class TestSO3(unittest.TestCase):
         randomly generated logarithms of rotation matrices.
         """
         device = torch.device("cuda:0")
-        log_rot = torch.randn(
-            (batch_size, 3), dtype=torch.float32, device=device
-        )
+        log_rot = torch.randn((batch_size, 3), dtype=torch.float32, device=device)
         return log_rot
 
     @staticmethod
@@ -85,16 +83,12 @@ class TestSO3(unittest.TestCase):
         log_rot = torch.randn(size=[5, 4], device=device)
         with self.assertRaises(ValueError) as err:
             so3_exponential_map(log_rot)
-        self.assertTrue(
-            "Input tensor shape has to be Nx3." in str(err.exception)
-        )
+        self.assertTrue("Input tensor shape has to be Nx3." in str(err.exception))
 
         rot = torch.randn(size=[5, 3, 5], device=device)
         with self.assertRaises(ValueError) as err:
             so3_log_map(rot)
-        self.assertTrue(
-            "Input has to be a batch of 3x3 Tensors." in str(err.exception)
-        )
+        self.assertTrue("Input has to be a batch of 3x3 Tensors." in str(err.exception))
 
         # trace of rot definitely bigger than 3 or smaller than -1
         rot = torch.cat(

tests/test_struct_utils.py

@@ -2,11 +2,10 @@
 
 
 import unittest
+
 import torch
-
-from pytorch3d.structures import utils as struct_utils
-
 from common_testing import TestCaseMixin
+from pytorch3d.structures import utils as struct_utils
 
 
 class TestStructUtils(TestCaseMixin, unittest.TestCase):
@@ -27,22 +26,16 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
         self.assertEqual(x_padded.shape[1], K)
         self.assertEqual(x_padded.shape[2], K)
         for i in range(N):
-            self.assertClose(
-                x_padded[i, : x[i].shape[0], : x[i].shape[1]], x[i]
-            )
+            self.assertClose(x_padded[i, : x[i].shape[0], : x[i].shape[1]], x[i])
 
         # check for no pad size (defaults to max dimension)
-        x_padded = struct_utils.list_to_padded(
-            x, pad_value=0.0, equisized=False
-        )
+        x_padded = struct_utils.list_to_padded(x, pad_value=0.0, equisized=False)
         max_size0 = max(y.shape[0] for y in x)
         max_size1 = max(y.shape[1] for y in x)
         self.assertEqual(x_padded.shape[1], max_size0)
         self.assertEqual(x_padded.shape[2], max_size1)
         for i in range(N):
-            self.assertClose(
-                x_padded[i, : x[i].shape[0], : x[i].shape[1]], x[i]
-            )
+            self.assertClose(x_padded[i, : x[i].shape[0], : x[i].shape[1]], x[i])
 
         # check for equisized
         x = [torch.rand((K, 10), device=device) for _ in range(N)]
@@ -88,9 +81,7 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
         split_size = torch.randint(1, K, size=(2 * N,)).view(N, 2).unbind(0)
         x_list = struct_utils.padded_to_list(x, split_size)
         for i in range(N):
-            self.assertClose(
-                x_list[i], x[i, : split_size[i][0], : split_size[i][1]]
-            )
+            self.assertClose(x_list[i], x[i, : split_size[i][0], : split_size[i][1]])
 
         with self.assertRaisesRegex(ValueError, "Supports only"):
             x = torch.rand((N, K, K, K, K), device=device)
@@ -124,32 +115,24 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
             # Add some random values in the input which are the same as the pad_value.
             # These should not be filtered out.
             x_list.append(
-                torch.randint(
-                    low=pad_value, high=10, size=(dim, K), device=device
-                )
+                torch.randint(low=pad_value, high=10, size=(dim, K), device=device)
             )
             split_size.append(dim)
         x_padded = struct_utils.list_to_padded(x_list, pad_value=pad_value)
         x_packed = struct_utils.padded_to_packed(x_padded, pad_value=pad_value)
         curr = 0
         for i in range(N):
-            self.assertClose(
-                x_packed[curr : curr + split_size[i], ...], x_list[i]
-            )
+            self.assertClose(x_packed[curr : curr + split_size[i], ...], x_list[i])
             self.assertClose(torch.cat(x_list), x_packed)
             curr += split_size[i]
 
         # Case 3: split_size is provided.
         # Check each section of the packed tensor matches the corresponding
         # unpadded elements.
-        x_packed = struct_utils.padded_to_packed(
-            x_padded, split_size=split_size
-        )
+        x_packed = struct_utils.padded_to_packed(x_padded, split_size=split_size)
         curr = 0
         for i in range(N):
-            self.assertClose(
-                x_packed[curr : curr + split_size[i], ...], x_list[i]
-            )
+            self.assertClose(x_packed[curr : curr + split_size[i], ...], x_list[i])
             self.assertClose(torch.cat(x_list), x_packed)
             curr += split_size[i]
 
@@ -157,17 +140,13 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
         # Raise an error.
         split_size = torch.randint(1, K, size=(2 * N,)).view(N, 2).unbind(0)
         with self.assertRaisesRegex(ValueError, "1-dimensional"):
-            x_packed = struct_utils.padded_to_packed(
-                x_padded, split_size=split_size
-            )
+            x_packed = struct_utils.padded_to_packed(x_padded, split_size=split_size)
 
         split_size = torch.randint(1, K, size=(2 * N,)).view(N * 2).tolist()
         with self.assertRaisesRegex(
             ValueError, "same length as inputs first dimension"
         ):
-            x_packed = struct_utils.padded_to_packed(
-                x_padded, split_size=split_size
-            )
+            x_packed = struct_utils.padded_to_packed(x_padded, split_size=split_size)
 
         # Case 5: both pad_value and split_size are provided.
         # Raise an error.
@@ -204,8 +183,6 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
         for i in range(N):
             self.assertTrue(num_items[i] == x_dims[i])
             self.assertTrue(item_packed_first_idx[i] == cur)
-            self.assertTrue(
-                item_packed_to_list_idx[cur : cur + x_dims[i]].eq(i).all()
-            )
+            self.assertTrue(item_packed_to_list_idx[cur : cur + x_dims[i]].eq(i).all())
             self.assertClose(x_packed[cur : cur + x_dims[i]], x[i])
             cur += x_dims[i]

tests/test_subdivide_meshes.py

@@ -2,14 +2,13 @@
 
 
 import unittest
-import torch
 
+import torch
+from common_testing import TestCaseMixin
 from pytorch3d.ops.subdivide_meshes import SubdivideMeshes
 from pytorch3d.structures.meshes import Meshes
 from pytorch3d.utils.ico_sphere import ico_sphere
 
-from common_testing import TestCaseMixin
-
 
 class TestSubdivideMeshes(TestCaseMixin, unittest.TestCase):
     def test_simple_subdivide(self):
@@ -72,25 +71,14 @@ class TestSubdivideMeshes(TestCaseMixin, unittest.TestCase):
         )
         faces1 = torch.tensor([[0, 1, 2]], dtype=torch.int64, device=device)
         verts2 = torch.tensor(
-            [
-                [0.5, 1.0, 0.0],
-                [1.0, 0.0, 0.0],
-                [0.0, 0.0, 0.0],
-                [1.5, 1.0, 0.0],
-            ],
+            [[0.5, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.5, 1.0, 0.0]],
             dtype=torch.float32,
             device=device,
             requires_grad=True,
         )
-        faces2 = torch.tensor(
-            [[0, 1, 2], [0, 3, 1]], dtype=torch.int64, device=device
-        )
-        faces3 = torch.tensor(
-            [[0, 1, 2], [0, 2, 3]], dtype=torch.int64, device=device
-        )
-        mesh = Meshes(
-            verts=[verts1, verts2, verts2], faces=[faces1, faces2, faces3]
-        )
+        faces2 = torch.tensor([[0, 1, 2], [0, 3, 1]], dtype=torch.int64, device=device)
+        faces3 = torch.tensor([[0, 1, 2], [0, 2, 3]], dtype=torch.int64, device=device)
+        mesh = Meshes(verts=[verts1, verts2, verts2], faces=[faces1, faces2, faces3])
         subdivide = SubdivideMeshes()
         new_mesh = subdivide(mesh.clone())
 
@@ -218,9 +206,7 @@ class TestSubdivideMeshes(TestCaseMixin, unittest.TestCase):
         self.assertTrue(new_feats.requires_grad == gt_feats.requires_grad)
 
     @staticmethod
-    def subdivide_meshes_with_init(
-        num_meshes: int = 10, same_topo: bool = False
-    ):
+    def subdivide_meshes_with_init(num_meshes: int = 10, same_topo: bool = False):
         device = torch.device("cuda:0")
         meshes = ico_sphere(0, device=device)
         if num_meshes > 1:

tests/test_texturing.py

@@ -2,9 +2,10 @@
 
 
 import unittest
+
 import torch
 import torch.nn.functional as F
-
+from common_testing import TestCaseMixin
 from pytorch3d.renderer.mesh.rasterizer import Fragments
 from pytorch3d.renderer.mesh.texturing import (
     interpolate_face_attributes,
@@ -13,8 +14,6 @@ from pytorch3d.renderer.mesh.texturing import (
 )
 from pytorch3d.structures import Meshes, Textures
 from pytorch3d.structures.utils import list_to_padded
-
-from common_testing import TestCaseMixin
 from test_meshes import TestMeshes
 
 
@@ -68,12 +67,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
             dists=torch.ones_like(pix_to_face),
         )
         grad_vert_tex = torch.tensor(
-            [
-                [0.3, 0.3, 0.3],
-                [0.9, 0.9, 0.9],
-                [0.5, 0.5, 0.5],
-                [0.3, 0.3, 0.3],
-            ],
+            [[0.3, 0.3, 0.3], [0.9, 0.9, 0.9], [0.5, 0.5, 0.5], [0.3, 0.3, 0.3]],
             dtype=torch.float32,
         )
         texels = interpolate_vertex_colors(fragments, mesh)
@@ -115,9 +109,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
             [[0.5, 0.3, 0.2], [0.3, 0.6, 0.1]], dtype=torch.float32
         ).view(1, 1, 1, 2, -1)
         dummy_verts = torch.zeros(4, 3)
-        vert_uvs = torch.tensor(
-            [[1, 0], [0, 1], [1, 1], [0, 0]], dtype=torch.float32
-        )
+        vert_uvs = torch.tensor([[1, 0], [0, 1], [1, 1], [0, 0]], dtype=torch.float32)
         face_uvs = torch.tensor([[0, 1, 2], [1, 2, 3]], dtype=torch.int64)
         interpolated_uvs = torch.tensor(
             [[0.5 + 0.2, 0.3 + 0.2], [0.6, 0.3 + 0.6]], dtype=torch.float32
@@ -137,9 +129,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
             dists=pix_to_face,
         )
         tex = Textures(
-            maps=tex_map,
-            faces_uvs=face_uvs[None, ...],
-            verts_uvs=vert_uvs[None, ...],
+            maps=tex_map, faces_uvs=face_uvs[None, ...], verts_uvs=vert_uvs[None, ...]
         )
         meshes = Meshes(verts=[dummy_verts], faces=[face_uvs], textures=tex)
         texels = interpolate_texture_map(fragments, meshes)
@@ -151,9 +141,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
         tex_map = tex_map.permute(0, 3, 1, 2)
         tex_map = torch.cat([tex_map, tex_map], dim=0)
         expected_out = F.grid_sample(tex_map, pixel_uvs, align_corners=False)
-        self.assertTrue(
-            torch.allclose(texels.squeeze(), expected_out.squeeze())
-        )
+        self.assertTrue(torch.allclose(texels.squeeze(), expected_out.squeeze()))
 
     def test_init_rgb_uv_fail(self):
         V = 20
@@ -183,9 +171,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
             Textures(verts_rgb=torch.ones((5, 16, 16, 3)))
 
         # maps provided without verts/faces uvs
-        with self.assertRaisesRegex(
-            ValueError, "faces_uvs and verts_uvs are required"
-        ):
+        with self.assertRaisesRegex(ValueError, "faces_uvs and verts_uvs are required"):
             Textures(maps=torch.ones((5, 16, 16, 3)))
 
     def test_padded_to_packed(self):
@@ -209,9 +195,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
         # This is set inside Meshes when textures is passed as an input.
         # Here we set _num_faces_per_mesh and _num_verts_per_mesh explicity.
         tex1 = tex.clone()
-        tex1._num_faces_per_mesh = (
-            faces_uvs_padded.gt(-1).all(-1).sum(-1).tolist()
-        )
+        tex1._num_faces_per_mesh = faces_uvs_padded.gt(-1).all(-1).sum(-1).tolist()
         tex1._num_verts_per_mesh = torch.tensor([5, 4])
         faces_packed = tex1.faces_uvs_packed()
         verts_packed = tex1.verts_uvs_packed()
@@ -245,16 +229,12 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
 
         for i in range(N):
             self.assertTrue(
-                (faces_list[i] == faces_uvs_padded[i, ...].squeeze())
-                .all()
-                .item()
+                (faces_list[i] == faces_uvs_padded[i, ...].squeeze()).all().item()
             )
 
         for i in range(N):
             self.assertTrue(
-                (verts_list[i] == verts_uvs_padded[i, ...].squeeze())
-                .all()
-                .item()
+                (verts_list[i] == verts_uvs_padded[i, ...].squeeze()).all().item()
             )
 
     def test_clone(self):
@@ -344,9 +324,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
             verts_uvs=torch.randn((B, V, 2)),
         )
         tex_mesh = Meshes(
-            verts=mesh.verts_padded(),
-            faces=mesh.faces_padded(),
-            textures=tex_uv,
+            verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tex_uv
         )
         N = 20
         new_mesh = tex_mesh.extend(N)
@@ -359,12 +337,10 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
         for i in range(len(tex_mesh)):
             for n in range(N):
                 self.assertClose(
-                    tex_init.faces_uvs_list()[i],
-                    new_tex.faces_uvs_list()[i * N + n],
+                    tex_init.faces_uvs_list()[i], new_tex.faces_uvs_list()[i * N + n]
                 )
                 self.assertClose(
-                    tex_init.verts_uvs_list()[i],
-                    new_tex.verts_uvs_list()[i * N + n],
+                    tex_init.verts_uvs_list()[i], new_tex.verts_uvs_list()[i * N + n]
                 )
         self.assertAllSeparate(
             [
@@ -384,9 +360,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
         # 2. Texture vertex RGB
         tex_rgb = Textures(verts_rgb=torch.randn((B, V, 3)))
         tex_mesh_rgb = Meshes(
-            verts=mesh.verts_padded(),
-            faces=mesh.faces_padded(),
-            textures=tex_rgb,
+            verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tex_rgb
         )
         N = 20
         new_mesh_rgb = tex_mesh_rgb.extend(N)
@@ -399,8 +373,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
         for i in range(len(tex_mesh_rgb)):
             for n in range(N):
                 self.assertClose(
-                    tex_init.verts_rgb_list()[i],
-                    new_tex.verts_rgb_list()[i * N + n],
+                    tex_init.verts_rgb_list()[i], new_tex.verts_rgb_list()[i * N + n]
                 )
         self.assertAllSeparate(
             [tex_init.verts_rgb_padded(), new_tex.verts_rgb_padded()]

tests/test_transforms.py

@@ -3,8 +3,8 @@
 
 import math
 import unittest
-import torch
 
+import torch
 from pytorch3d.transforms.so3 import so3_exponential_map
 from pytorch3d.transforms.transform3d import (
     Rotate,
@@ -18,9 +18,7 @@ from pytorch3d.transforms.transform3d import (
 class TestTransform(unittest.TestCase):
     def test_to(self):
         tr = Translate(torch.FloatTensor([[1.0, 2.0, 3.0]]))
-        R = torch.FloatTensor(
-            [[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]]
-        )
+        R = torch.FloatTensor([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
         R = Rotate(R)
         t = Transform3d().compose(R, tr)
         for _ in range(3):
@@ -36,9 +34,7 @@ class TestTransform(unittest.TestCase):
         the same as composition of clones of translation and rotation.
         """
         tr = Translate(torch.FloatTensor([[1.0, 2.0, 3.0]]))
-        R = torch.FloatTensor(
-            [[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]]
-        )
+        R = torch.FloatTensor([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
         R = Rotate(R)
 
         # check that the _matrix property of clones of
@@ -63,9 +59,9 @@ class TestTransform(unittest.TestCase):
 
     def test_translate(self):
         t = Transform3d().translate(1, 2, 3)
-        points = torch.tensor(
-            [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]
-        ).view(1, 3, 3)
+        points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(
+            1, 3, 3
+        )
         normals = torch.tensor(
             [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0]]
         ).view(1, 3, 3)
@@ -82,9 +78,9 @@ class TestTransform(unittest.TestCase):
 
     def test_scale(self):
         t = Transform3d().scale(2.0).scale(0.5, 0.25, 1.0)
-        points = torch.tensor(
-            [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]
-        ).view(1, 3, 3)
+        points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(
+            1, 3, 3
+        )
         normals = torch.tensor(
             [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0]]
         ).view(1, 3, 3)
@@ -101,9 +97,9 @@ class TestTransform(unittest.TestCase):
 
     def test_scale_translate(self):
         t = Transform3d().scale(2, 1, 3).translate(1, 2, 3)
-        points = torch.tensor(
-            [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]
-        ).view(1, 3, 3)
+        points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(
+            1, 3, 3
+        )
         normals = torch.tensor(
             [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0]]
         ).view(1, 3, 3)
@@ -120,9 +116,9 @@ class TestTransform(unittest.TestCase):
 
     def test_rotate_axis_angle(self):
         t = Transform3d().rotate_axis_angle(90.0, axis="Z")
-        points = torch.tensor(
-            [[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 1.0]]
-        ).view(1, 3, 3)
+        points = torch.tensor([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 1.0]]).view(
+            1, 3, 3
+        )
         normals = torch.tensor(
             [[1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [1.0, 0.0, 0.0]]
         ).view(1, 3, 3)
@@ -194,9 +190,7 @@ class TestTransform(unittest.TestCase):
                     t_ = Rotate(
                         so3_exponential_map(
                             torch.randn(
-                                (batch_size, 3),
-                                dtype=torch.float32,
-                                device=device,
+                                (batch_size, 3), dtype=torch.float32, device=device
                             )
                         ),
                         device=device,
@@ -717,9 +711,7 @@ class TestRotate(unittest.TestCase):
 
     def test_inverse(self, batch_size=5):
         device = torch.device("cuda:0")
-        log_rot = torch.randn(
-            (batch_size, 3), dtype=torch.float32, device=device
-        )
+        log_rot = torch.randn((batch_size, 3), dtype=torch.float32, device=device)
         R = so3_exponential_map(log_rot)
         t = Rotate(R)
         im = t.inverse()._matrix
@@ -749,9 +741,7 @@ class TestRotateAxisAngle(unittest.TestCase):
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 0.0, 1.0])
         self.assertTrue(
-            torch.allclose(
-                transformed_points.squeeze(), expected_points, atol=1e-7
-            )
+            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
         )
         self.assertTrue(torch.allclose(t._matrix, matrix))
 
@@ -775,9 +765,7 @@ class TestRotateAxisAngle(unittest.TestCase):
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 0.0, 1.0])
         self.assertTrue(
-            torch.allclose(
-                transformed_points.squeeze(), expected_points, atol=1e-7
-            )
+            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
         )
         self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
 
@@ -835,9 +823,7 @@ class TestRotateAxisAngle(unittest.TestCase):
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 0.0, -1.0])
         self.assertTrue(
-            torch.allclose(
-                transformed_points.squeeze(), expected_points, atol=1e-7
-            )
+            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
         )
         self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
 
@@ -866,9 +852,7 @@ class TestRotateAxisAngle(unittest.TestCase):
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 0.0, -1.0])
         self.assertTrue(
-            torch.allclose(
-                transformed_points.squeeze(), expected_points, atol=1e-7
-            )
+            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
         )
         self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
 
@@ -923,9 +907,7 @@ class TestRotateAxisAngle(unittest.TestCase):
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 1.0, 0.0])
         self.assertTrue(
-            torch.allclose(
-                transformed_points.squeeze(), expected_points, atol=1e-7
-            )
+            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
         )
         self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
 
@@ -949,9 +931,7 @@ class TestRotateAxisAngle(unittest.TestCase):
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 1.0, 0.0])
         self.assertTrue(
-            torch.allclose(
-                transformed_points.squeeze(), expected_points, atol=1e-7
-            )
+            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
         )
         self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
 

tests/test_vert_align.py

@@ -2,22 +2,18 @@
 
 
 import unittest
+
 import torch
 import torch.nn.functional as F
-
+from common_testing import TestCaseMixin
 from pytorch3d.ops.vert_align import vert_align
 from pytorch3d.structures.meshes import Meshes
 
-from common_testing import TestCaseMixin
-
 
 class TestVertAlign(TestCaseMixin, unittest.TestCase):
     @staticmethod
     def vert_align_naive(
-        feats,
-        verts_or_meshes,
-        return_packed: bool = False,
-        align_corners: bool = True,
+        feats, verts_or_meshes, return_packed: bool = False, align_corners: bool = True
     ):
         """
         Naive implementation of vert_align.
@@ -60,16 +56,13 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
         return out_feats
 
     @staticmethod
-    def init_meshes(
-        num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000
-    ):
+    def init_meshes(num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000):
         device = torch.device("cuda:0")
         verts_list = []
         faces_list = []
         for _ in range(num_meshes):
             verts = (
-                torch.rand((num_verts, 3), dtype=torch.float32, device=device)
-                * 2.0
+                torch.rand((num_verts, 3), dtype=torch.float32, device=device) * 2.0
                 - 1.0
             )  # verts in the space of [-1, 1]
             faces = torch.randint(
@@ -82,15 +75,11 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
         return meshes
 
     @staticmethod
-    def init_feats(
-        batch_size: int = 10, num_channels: int = 256, device: str = "cuda"
-    ):
+    def init_feats(batch_size: int = 10, num_channels: int = 256, device: str = "cuda"):
         H, W = [14, 28], [14, 28]
         feats = []
         for (h, w) in zip(H, W):
-            feats.append(
-                torch.rand((batch_size, num_channels, h, w), device=device)
-            )
+            feats.append(torch.rand((batch_size, num_channels, h, w), device=device))
         return feats
 
     def test_vert_align_with_meshes(self):
@@ -102,16 +91,12 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
 
         # feats in list
         out = vert_align(feats, meshes, return_packed=True)
-        naive_out = TestVertAlign.vert_align_naive(
-            feats, meshes, return_packed=True
-        )
+        naive_out = TestVertAlign.vert_align_naive(feats, meshes, return_packed=True)
         self.assertClose(out, naive_out)
 
         # feats as tensor
         out = vert_align(feats[0], meshes, return_packed=True)
-        naive_out = TestVertAlign.vert_align_naive(
-            feats[0], meshes, return_packed=True
-        )
+        naive_out = TestVertAlign.vert_align_naive(feats[0], meshes, return_packed=True)
         self.assertClose(out, naive_out)
 
     def test_vert_align_with_verts(self):
@@ -120,30 +105,21 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
         """
         feats = TestVertAlign.init_feats(10, 256)
         verts = (
-            torch.rand(
-                (10, 100, 3), dtype=torch.float32, device=feats[0].device
-            )
-            * 2.0
+            torch.rand((10, 100, 3), dtype=torch.float32, device=feats[0].device) * 2.0
             - 1.0
         )
 
         # feats in list
         out = vert_align(feats, verts, return_packed=True)
-        naive_out = TestVertAlign.vert_align_naive(
-            feats, verts, return_packed=True
-        )
+        naive_out = TestVertAlign.vert_align_naive(feats, verts, return_packed=True)
         self.assertClose(out, naive_out)
 
         # feats as tensor
         out = vert_align(feats[0], verts, return_packed=True)
-        naive_out = TestVertAlign.vert_align_naive(
-            feats[0], verts, return_packed=True
-        )
+        naive_out = TestVertAlign.vert_align_naive(feats[0], verts, return_packed=True)
         self.assertClose(out, naive_out)
 
-        out2 = vert_align(
-            feats[0], verts, return_packed=True, align_corners=False
-        )
+        out2 = vert_align(feats[0], verts, return_packed=True, align_corners=False)
         naive_out2 = TestVertAlign.vert_align_naive(
             feats[0], verts, return_packed=True, align_corners=False
         )
@@ -158,9 +134,7 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
         verts_list = []
         faces_list = []
         for _ in range(num_meshes):
-            verts = torch.rand(
-                (num_verts, 3), dtype=torch.float32, device=device
-            )
+            verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
             faces = torch.randint(
                 num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
             )