spelling

Summary: Collection of spelling things, mostly in docs / tutorials.

Reviewed By: gkioxari

Differential Revision: D26101323

fbshipit-source-id: 652f62bc9d71a4ff872efa21141225e43191353a

pytorch3d/structures/volumes.py

@@ -77,7 +77,7 @@ class Volumes(object):
         World coordinates:
             - These define the locations of the centers of the volume cells
               in the world coordinates.
-            - They are specifiied with the following mapping that converts
+            - They are specified with the following mapping that converts
               points `x_local` in the local coordinates to points `x_world`
               in the world coordinates:
                 ```

pytorch3d/transforms/rotation_conversions.py

@@ -511,7 +511,7 @@ def quaternion_to_axis_angle(quaternions):
 def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
     """
     Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
-    using Gram--Schmidt orthogonalisation per Section B of [1].
+    using Gram--Schmidt orthogonalization per Section B of [1].
     Args:
         d6: 6D rotation representation, of size (*, 6)
 

pytorch3d/transforms/transform3d.py

@@ -190,7 +190,7 @@ class Transform3d:
 
     def compose(self, *others):
         """
-        Return a new Transform3d with the tranforms to compose stored as
+        Return a new Transform3d with the transforms to compose stored as
         an internal list.
 
         Args:
@@ -254,7 +254,7 @@ class Transform3d:
                   independently without composing them.
 
         Returns:
-            A new Transform3D object contaning the inverse of the original
+            A new Transform3D object containing the inverse of the original
             transformation.
         """
 
@@ -302,7 +302,7 @@ class Transform3d:
         Args:
             points: Tensor of shape (P, 3) or (N, P, 3)
             eps: If eps!=None, the argument is used to clamp the
-                last coordinate before peforming the final division.
+                last coordinate before performing the final division.
                 The clamping corresponds to:
                 last_coord := (last_coord.sign() + (last_coord==0)) *
                 torch.clamp(last_coord.abs(), eps),
@@ -681,7 +681,7 @@ def _broadcast_bmm(a, b):
         b: torch tensor of shape (N, K, K)
 
     Returns:
-        a and b broadcast multipled. The output batch dimension is max(N, M).
+        a and b broadcast multiplied. The output batch dimension is max(N, M).
 
     To broadcast transforms across a batch dimension if M != N then
     expect that either M = 1 or N = 1. The tensor with batch dimension 1 is

pytorch3d/vis/plotly_vis.py

@@ -567,7 +567,7 @@ def _add_pointcloud_trace(
         pointclouds: Pointclouds object to render. It can be batched.
         trace_name: name to label the trace with.
         subplot_idx: identifies the subplot, with 0 being the top left.
-        ncols: the number of sublpots per row.
+        ncols: the number of subplots per row.
         max_points_per_pointcloud: the number of points to render, which are randomly sampled.
         marker_size: the size of the rendered points
     """
@@ -648,7 +648,7 @@ def _add_camera_trace(
         cameras: the Cameras object to render. It can be batched.
         trace_name: name to label the trace with.
         subplot_idx: identifies the subplot, with 0 being the top left.
-        ncols: the number of sublpots per row.
+        ncols: the number of subplots per row.
         camera_scale: the size of the wireframe used to render the Cameras object.
     """
     cam_wires = get_camera_wireframe(camera_scale).to(cameras.device)

tests/test_io_obj.py

@@ -328,7 +328,9 @@ 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 only have 3 properties" in str(err.exception)
+        )
 
     def test_load_obj_error_invalid_vertex_indices(self):
         obj_file = "\n".join(
@@ -631,7 +633,7 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
         self.assertTrue(aux.normals is None)
         self.assertTrue(aux.verts_uvs is None)
 
-    def test_load_obj_mlt_no_image(self):
+    def test_load_obj_mtl_no_image(self):
         obj_filename = "obj_mtl_no_image/model.obj"
         filename = os.path.join(DATA_DIR, obj_filename)
         R = 8

tests/test_io_ply.py

@@ -317,7 +317,7 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
                     file.close()
             self.assertLess(lengths[False], lengths[True], "ascii should be longer")
 
-    def test_heterogenous_property(self):
+    def test_heterogeneous_property(self):
         ply_file_ascii = "\n".join(
             [
                 "ply",
@@ -670,7 +670,7 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
         with self.assertRaisesRegex(ValueError, msg):
             _load_ply_raw(StringIO("\n".join(lines2)))
 
-        # Heterogenous cases
+        # Heterogeneous cases
         lines2 = lines.copy()
         lines2.insert(4, "property double y")
 

tests/test_points_alignment.py

@@ -155,7 +155,7 @@ class TestICP(TestCaseMixin, unittest.TestCase):
                     self.assertClose(s_init, s, atol=atol)
                     self.assertClose(Xt_init, Xt, atol=atol)
 
-    def test_heterogenous_inputs(self, batch_size=10):
+    def test_heterogeneous_inputs(self, batch_size=10):
         """
         Tests whether we get the same result when running ICP on
         a set of randomly-sized Pointclouds and on their padded versions.

tests/test_points_to_volumes.py

@@ -230,9 +230,9 @@ class TestPointsToVolumes(TestCaseMixin, unittest.TestCase):
     def test_from_point_cloud(self, interp_mode="trilinear"):
         """
         Generates a volume from a random point cloud sampled from faces
-        of a 3D cube. Since each side of the cube is homogenously colored with
+        of a 3D cube. Since each side of the cube is homogeneously colored with
         a different color, this should result in a volume with a
-        predefined homogenous color of the cells along its borders
+        predefined homogeneous color of the cells along its borders
         and black interior. The test is run for both cube and non-cube shaped
         volumes.
         """

tests/test_rasterize_points.py

@@ -511,7 +511,7 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
         )
         # Note that the order is only deterministic here for CUDA if all points
         # fit in one chunk. This will the the case for this small example, but
-        # to properly exercise coordianted writes among multiple chunks we need
+        # to properly exercise coordinated writes among multiple chunks we need
         # to use a bigger test case.
         bin_points_expected[0, 0, 1, :2] = torch.tensor([0, 3])
         bin_points_expected[0, 1, 0, 0] = torch.tensor([2])

tests/test_rasterize_rectangle_images.py

@@ -62,7 +62,7 @@ verts0 = torch.tensor(
 )
 faces0 = torch.tensor([[1, 0, 2], [4, 3, 5]], dtype=torch.int64)
 
-# Points for a simple pointcloud. Get the vertices from a
+# Points for a simple point cloud. Get the vertices from a
 # torus and apply rotations such that the points are no longer
 # symmerical in X/Y.
 torus_mesh = torus(r=0.25, R=1.0, sides=5, rings=2 * 5)
@@ -771,7 +771,7 @@ class TestRasterizeRectangleImagesPointclouds(TestCaseMixin, unittest.TestCase):
 
     def test_render_pointcloud(self):
         """
-        Test a textured poincloud is rendered correctly in a non square image.
+        Test a textured point cloud is rendered correctly in a non square image.
         """
         device = torch.device("cuda:0")
         pointclouds = Pointclouds(

tests/test_raymarching.py

@@ -16,7 +16,7 @@ class TestRaymarching(TestCaseMixin, unittest.TestCase):
         n_rays=10, n_pts_per_ray=9, device="cuda", dtype=torch.float32
     ):
         """
-        Generate a batch of ray points with features, densities, and z-coodinates
+        Generate a batch of ray points with features, densities, and z-coordinates
         such that their EmissionAbsorption renderring results in
         feature renders `features_gt`, depth renders `depths_gt`,
         and opacity renders `opacities_gt`.

tests/test_render_meshes.py

@@ -1052,7 +1052,7 @@ class TestRenderMeshes(TestCaseMixin, unittest.TestCase):
         images[0, ...].sum().backward()
 
         fragments = rasterizer(mesh, raster_settings=raster_settings)
-        # Some of the bary coordinates are outisde the
+        # Some of the bary coordinates are outside the
         # [0, 1] range as expected because the blur is > 0
         self.assertTrue(fragments.bary_coords.ge(1.0).any())
         self.assertIsNotNone(atlas.grad)

tests/test_render_volumes.py

@@ -531,7 +531,7 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
                 # get the EA raymarcher
                 raymarcher = EmissionAbsorptionRaymarcher()
 
-                # intialize the renderer
+                # initialize the renderer
                 renderer = VolumeRenderer(
                     raysampler=raysampler,
                     raymarcher=raymarcher,
@@ -574,8 +574,8 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
     def test_rotating_cube_volume_render(self):
         """
         Generates 4 renders of 4 sides of a volume representing a 3D cube.
-        Since each side of the cube is homogenously colored with
-        a different color, this should result in 4 images of homogenous color
+        Since each side of the cube is homogeneously colored with
+        a different color, this should result in 4 images of homogeneous color
         with the depth of each pixel equal to a constant.
         """
 

tests/test_so3.py

@@ -148,8 +148,8 @@ class TestSO3(TestCaseMixin, unittest.TestCase):
         `so3_exponential_map(so3_log_map(so3_exponential_map(log_rot)))
         == so3_exponential_map(log_rot)`
         for a randomly generated batch of rotation matrix logarithms `log_rot`.
-        Unlike `test_so3_log_to_exp_to_log`, this test allows to check the
-        correctness of converting `log_rot` which contains values > math.pi.
+        Unlike `test_so3_log_to_exp_to_log`, this test checks the
+        correctness of converting a `log_rot` which contains values > math.pi.
         """
         log_rot = 2.0 * TestSO3.init_log_rot(batch_size=batch_size)
         # check also the singular cases where rot. angle = {0, pi, 2pi, 3pi}

website/pages/tutorials/index.js

@@ -28,7 +28,7 @@ class TutorialHome extends React.Component {
             </header>
             <p>
               Here you can learn about the structure and applications of
-              Pytorch3D from examples which are in the form of ipython
+              PyTorch3D from examples which are in the form of ipython
               notebooks.
             </p>
             <h3> Run interactively </h3>