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

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
d57daa6f · Patrick Labatut · Facebook GitHub Bot · eb512ffd · d57daa6f · d57daa6f
Commit d57daa6f authored Mar 29, 2020 by Patrick Labatut Committed by Facebook GitHub Bot Mar 29, 2020
20 changed files
--- a/tests/test_compositing.py
+++ b/tests/test_compositing.py
 # 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] += (
+                            output[b, c, j, i] += features[c, n_idx] * alpha * t_alpha
-                                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] += (
+                            output[b, c, j, i] += features[c, n_idx] * alpha / t_alpha
-                                features[c, n_idx] * alpha / t_alpha
-                            )
        return output
    def test_python(self):
        device = torch.device("cpu")
-        self._simple_alphacomposite(
+        self._simple_alphacomposite(self.accumulate_alphacomposite_python, device)
-            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._python_vs_cpu_vs_cuda(self.accumulate_weightedsum_python, weighted_sum)
-            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(
+            self.assertTrue(torch.allclose(grads1[i].cpu(), grads2[i].cpu(), atol=1e-6))
-                torch.allclose(grads1[i].cpu(), grads2[i].cpu(), atol=1e-6)
-            )
    def _simple_wsum(self, accum_func, device):
        # Initialise variables
-        features = torch.Tensor(
+        features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
-            [[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(
+        self.assertTrue(torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3))
-            torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3)
-        )
    def _simple_wsumnorm(self, accum_func, device):
        # Initialise variables
-        features = torch.Tensor(
+        features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
-            [[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(
+        self.assertTrue(torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3))
-            torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3)
-        )
    def _simple_alphacomposite(self, accum_func, device):
        # Initialise variables
-        features = torch.Tensor(
+        features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
-            [[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]
-        ).to(device)
        alphas = torch.Tensor(
            [

--- a/tests/test_cubify.py
+++ b/tests/test_cubify.py
 # 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(
+        self.assertTrue(torch.allclose(faces.max(), torch.tensor([verts.size(0) - 1])))
-            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(
+        self.assertTrue(torch.allclose(faces.max(), torch.tensor([verts.size(0) - 1])))
-            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(
+        voxels = torch.rand((batch_size, V, V, V), dtype=torch.float32, device=device)
-            (batch_size, V, V, V), dtype=torch.float32, device=device
-        )
        torch.cuda.synchronize()
        def convert():

--- a/tests/test_face_areas_normals.py
+++ b/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),
+                (num_verts, 3), dtype=torch.float32, device=device, requires_grad=True
-                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(
+        face_normals = torch.nn.functional.normalize(normals, p=2, dim=1, eps=1e-6)
-            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)

--- a/tests/test_graph_conv.py
+++ b/tests/test_graph_conv.py
 # 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 (
+from pytorch3d.ops.graph_conv import GraphConv, gather_scatter, gather_scatter_python
-    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]],
+            [[1 + 2 + 3 - 4 - 5 - 6 - 7 - 8 - 9], [4 + 5 + 6], [7 + 8 + 9]], dtype=dtype
-            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]],
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float32, device=device
-            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(
+        self.assertTrue("tensors must be on the same device." in str(err.exception))
-            "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(
+        verts_list = torch.tensor(num_verts * [[0.11, 0.22, 0.33]], device=device).view(
-            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
        )
+        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(
+        x = torch.randn(total_verts, gconv_dim, device=device, requires_grad=True)
-            total_verts, gconv_dim, device=device, requires_grad=True
-        )
        torch.cuda.synchronize()
        def run_graph_conv():

--- a/tests/test_knn.py
+++ b/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

--- a/tests/test_lighting.py
+++ b/tests/test_lighting.py
 # 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(
+        d_light = DirectionalLights(ambient_color=((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)))
-            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),
+                ambient_color=torch.randn(10, 3), diffuse_color=torch.randn(15, 3)
-                diffuse_color=torch.randn(15, 3),
            )
        with self.assertRaises(ValueError):
            PointLights(
-                ambient_color=torch.randn(10, 3),
+                ambient_color=torch.randn(10, 3), diffuse_color=torch.randn(15, 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)],
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
-            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)],
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
-            dtype=torch.float32,
        )
        expected_output = expected_output.view(-1, 1, 3)
-        light = PointLights(
+        light = PointLights(diffuse_color=color[None, :], location=location[None, :])
-            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(
+        light = PointLights(diffuse_color=color[None, :], location=location[None, :])
-            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)],
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
-            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)],
+            [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
-            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)],
+            [0, 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32, device=device
-            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(
+        lights = PointLights(specular_color=color[None, :], location=location[None, :])
-            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]],
+            [[-1 / np.sqrt(2), 1 / np.sqrt(2), 0], [-1, 1, 0]], dtype=torch.float32
-            dtype=torch.float32,
        )
        camera_position = torch.tensor(
            [

--- a/tests/test_materials.py
+++ b/tests/test_materials.py
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 import unittest
-import torch
-from pytorch3d.renderer.materials import Materials
+import torch
 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),
+                ambient_color=torch.randn(10, 3), diffuse_color=torch.randn(15, 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(
+        mat = Materials(ambient_color=torch.randn(1, 3), diffuse_color=((1, 1, 1),))
-            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(
+        mat = Materials(ambient_color=torch.randn(10, 3), diffuse_color=((1, 1, 1),))
-            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))

--- a/tests/test_mesh_edge_loss.py
+++ b/tests/test_mesh_edge_loss.py
 # 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(
+        self.assertClose(loss, torch.tensor([0.0], dtype=torch.float32, device=device))
-            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] += (
+                predlosses[b] += ((v0 - v1).norm(dim=0, p=2) - target_length) ** 2.0
-                    (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(
+    def mesh_edge_loss(num_meshes: int = 10, max_v: int = 100, max_f: int = 300):
-        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")
-    ):
-        meshes = TestSamplePoints.init_meshes(
-            num_meshes, max_v, max_f, device="cuda:0"
-        )
        torch.cuda.synchronize()
        def compute_loss():

--- a/tests/test_mesh_laplacian_smoothing.py
+++ b/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(
+        inv_areas = torch.zeros((V, 1), dtype=torch.float32, device=meshes.device)
-            (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 = (
+            face_area = (s * (s - A) * (s - B) * (s - C)).clamp_(min=1e-12).sqrt()
-                (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(
+    def init_meshes(num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000):
-        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)
+                torch.rand((num_verts, 3), dtype=torch.float32, device=device) * 2.0
-                * 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(
+        naive_out = TestLaplacianSmoothing.laplacian_smoothing_naive_uniform(meshes)
-            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(
+            verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
-                (num_verts, 3), dtype=torch.float32, device=device
-            )
            faces = torch.randint(
                num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
            )

--- a/tests/test_mesh_normal_consistency.py
+++ b/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(
+    def init_meshes(num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000):
-        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)
+                torch.rand((num_verts, 3), dtype=torch.float32, device=device) * 2.0
-                * 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[i].view(1, 3), -normals[j].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(
+        faces1 = torch.tensor([[0, 1, 2], [2, 1, 3]], dtype=torch.int64, device=device)
-            [[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(
+        meshes = Meshes(verts=[verts1, verts2, verts3], faces=[faces1, faces2, faces3])
-            verts=[verts1, verts2, verts3], faces=[faces1, faces2, faces3]
-        )
        # mesh1: normal consistency computation
        n0 = (verts1[1] - verts1[2]).cross(verts1[3] - verts1[2])

--- a/tests/test_mesh_rendering_utils.py
+++ b/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

--- a/tests/test_meshes.py
+++ b/tests/test_meshes.py
--- a/tests/test_nearest_neighbor_points.py
+++ b/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(
+                        idx2 = TestNearestNeighborPoints.nn_points_idx_naive(x, y)
-                            x, y
-                        )
                        self.assertTrue(idx1.size(1) == P1)
                        self.assertTrue(torch.all(idx1 == idx2))

--- a/tests/test_obj_io.py
+++ b/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(
+        expected_faces_normals_idx = torch.tensor([[1, 1, 1]], dtype=torch.int64)
-            [[1, 1, 1]], dtype=torch.int64
+        expected_faces_textures_idx = torch.tensor([[0, 0, 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(
+        expected_faces_normals_idx = torch.tensor([[0, 0, 1]], dtype=torch.int64)
-            [[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(
+        expected_faces_textures_idx = torch.tensor([[0, 0, 1]], dtype=torch.int64)
-            [[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(
+        self.assertTrue("Vertex properties are inconsistent" in str(err.exception))
-            "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(
+        self.assertTrue("Face vertices can ony have 3 properties" in str(err.exception))
-            "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 = (
+        DATA_DIR = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
-            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(
+            for k1, k2 in zip(materials[n1].keys(), expected_materials[n2].keys()):
-                materials[n1].keys(), expected_materials[n2].keys()
-            ):
                self.assertTrue(
-                    torch.allclose(
+                    torch.allclose(materials[n1][k1], expected_materials[n2][k2])
-                        materials[n1][k1], expected_materials[n2][k2]
-                    )
                )
    def test_load_mtl_noload(self):
-        DATA_DIR = (
+        DATA_DIR = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
-            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(),
+                    mesh.textures.verts_uvs_padded(), mesh3.textures.verts_uvs_padded()
-                    mesh3.textures.faces_uvs_padded(),
                )
                check_item(
-                    mesh.textures.verts_uvs_padded(),
+                    mesh.textures.verts_rgb_padded(), mesh3.textures.verts_rgb_padded()
-                    mesh3.textures.verts_uvs_padded(),
-                )
-                check_item(
-                    mesh.textures.verts_rgb_padded(),
-                    mesh3.textures.verts_rgb_padded(),
                )
-        DATA_DIR = (
+        DATA_DIR = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
-            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(
+        self.assertTupleEqual(mesh.textures.maps_padded().shape, (1, 1024, 1024, 3))
-            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(
+        mix_mesh = load_objs_as_meshes([obj_filename, teapot_obj], load_textures=False)
-            [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(
+    def _bm_save_obj(verts: torch.Tensor, faces: torch.Tensor, decimal_places: int):
-        verts: torch.Tensor, faces: torch.Tensor, decimal_places: int
-    ):
        return lambda: save_obj(StringIO(), verts, faces, decimal_places)
    @staticmethod
-    def _bm_load_obj(
+    def _bm_load_obj(verts: torch.Tensor, faces: torch.Tensor, decimal_places: int):
-        verts: torch.Tensor, faces: torch.Tensor, decimal_places: int
-    ):
        f = StringIO()
        save_obj(f, verts, faces, decimal_places)
        s = f.getvalue()

--- a/tests/test_packed_to_padded.py
+++ b/tests/test_packed_to_padded.py
 # 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(
+            verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
-                (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(
+            inputs_padded = torch.zeros((num_meshes, max_size, D), device=device)
-                (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(
+            values = torch.rand((faces.shape[0],), device=device, requires_grad=True)
-                (faces.shape[0],), device=device, requires_grad=True
-            )
        else:
-            values = torch.rand(
+            values = torch.rand((faces.shape[0], D), device=device, requires_grad=True)
-                (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,
+            grad_inputs, mesh_to_faces_packed_first_idx, values.size(0), device=device
-            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,
+            values, mesh_to_faces_packed_first_idx, num_faces_per_mesh.sum().item()
-            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(
+            grad_inputs = torch.rand((num_faces_per_mesh.sum().item()), device=device)
-                (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,
+            grad_inputs, mesh_to_faces_packed_first_idx, values.size(1), device=device
-            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(
+        with self.assertRaisesRegex(ValueError, "input can only be 2-dimensional."):
-            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(
+        with self.assertRaisesRegex(ValueError, "input can only be 3-dimensional."):
-            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(
+        with self.assertRaisesRegex(ValueError, "input can only be 3-dimensional."):
-            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_meshes: int, num_verts: int, num_faces: int, num_d: int, device: str = "cpu"
-        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_meshes: int, num_verts: int, num_faces: int, num_d: int, device: str = "cpu"
-        num_verts: int,
-        num_faces: int,
-        num_d: int,
-        device: str = "cpu",
    ):
        meshes = TestPackedToPadded.init_meshes(
            num_meshes, num_verts, num_faces, device

--- a/tests/test_ply_io.py
+++ b/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 0 " "0 0 1 " "0 1 1 " "0 1 0 " "1 0 0 " "1 0 1 " "1 1 1 " "1 1 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" + (
+            mixed_data = b"\00\00" b"\03\03" + (short_one + b"\00\01\01\01" b"\00\02")
-                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(
+        with self.assertRaisesRegex(ValueError, "Inconsistent data for vertex."):
-            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(
+        with self.assertRaisesRegex(ValueError, "Found an element with no properties."):
-            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(
+        with self.assertRaisesRegex(ValueError, "Too little data for an element."):
-            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(
+        with self.assertRaisesRegex(ValueError, "Too much data for an element."):
-            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(
+        with self.assertRaisesRegex(ValueError, "The ply file has no face element."):
-            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(
+        with self.assertRaisesRegex(ValueError, "Faces must have at least 3 vertices."):
-            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(
+    def _bm_save_ply(verts: torch.Tensor, faces: torch.Tensor, decimal_places: int):
-        verts: torch.Tensor, faces: torch.Tensor, decimal_places: int
+        return lambda: save_ply(StringIO(), verts, faces, decimal_places=decimal_places)
-    ):
-        return lambda: save_ply(
-            StringIO(), verts, faces, decimal_places=decimal_places
-        )
    @staticmethod
-    def _bm_load_ply(
+    def _bm_load_ply(verts: torch.Tensor, faces: torch.Tensor, decimal_places: int):
-        verts: torch.Tensor, faces: torch.Tensor, decimal_places: int
-    ):
        f = StringIO()
        save_ply(f, verts, faces, decimal_places)
        s = f.getvalue()

--- a/tests/test_pointclouds.py
+++ b/tests/test_pointclouds.py
--- a/tests/test_rasterize_meshes.py
+++ b/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(
+        self._simple_triangle_raster(rasterize_meshes_python, device, bin_size=-1)
-            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(
+        self._test_perspective_correct(rasterize_meshes_python, device, bin_size=-1)
-            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(
+        self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
-            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(
+        self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
-            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(
+        self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
-            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(
+    def _test_perspective_correct(self, rasterize_meshes_fn, device, bin_size=None):
-        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_diff = (zbuf_f_bary[mask] - zbuf_f_expected[mask]).abs().max()
-            (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_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, :] = (
+        expected_p2face_k1[0, :] = torch.ones_like(expected_p2face_k1[0, :]) * -1
-            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(
+            p2face, zbuf, bary, pix_dists = raster_fn(meshes, image_size, 0.0, 2)
-                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(
+            torch.ones((1, 2, 2, max_faces_per_bin), dtype=torch.int32, device=device)
-                (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,
+                meshes_batch, image_size, blur_radius, 8, bin_size, max_faces_per_bin
-                image_size,
-                blur_radius,
-                8,
-                bin_size,
-                max_faces_per_bin,
            )
            torch.cuda.synchronize()

--- a/tests/test_rasterize_points.py
+++ b/tests/test_rasterize_points.py
--- a/tests/test_rasterizer.py
+++ b/tests/test_rasterizer.py