spelling

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

Reviewed By: gkioxari

Differential Revision: D26101323

fbshipit-source-id: 652f62bc9d71a4ff872efa21141225e43191353a

projects/nerf/nerf/nerf_renderer.py

@@ -266,7 +266,7 @@ class RadianceFieldRenderer(torch.nn.Module):
         image: torch.Tensor,
     ) -> Tuple[dict, dict]:
         """
-        Performs the coarse and fine rendering passees of the radiance field
+        Performs the coarse and fine rendering passes of the radiance field
         from the viewpoint of the input `camera`.
         Afterwards, both renders are compared to the input ground truth `image`
         by evaluating the peak signal-to-noise ratio and the mean-squared error.

projects/nerf/nerf/raymarcher.py

@@ -36,7 +36,7 @@ class EmissionAbsorptionNeRFRaymarcher(EmissionAbsorptionRaymarcher):
             rays_features: Per-ray feature values represented with a tensor
                 of shape `(..., n_points_per_ray, feature_dim)`.
             eps: A lower bound added to `rays_densities` before computing
-                the absorbtion function (cumprod of `1-rays_densities` along
+                the absorption function (cumprod of `1-rays_densities` along
                 each ray). This prevents the cumprod to yield exact 0
                 which would inhibit any gradient-based learning.
 
@@ -44,7 +44,7 @@ class EmissionAbsorptionNeRFRaymarcher(EmissionAbsorptionRaymarcher):
             features: A tensor of shape `(..., feature_dim)` containing
                 the rendered features for each ray.
             weights: A tensor of shape `(..., n_points_per_ray)` containing
-                the ray-specific emission-absorbtion distribution.
+                the ray-specific emission-absorption distribution.
                 Each ray distribution `(..., :)` is a valid probability
                 distribution, i.e. it contains non-negative values that integrate
                 to 1, such that `weights.sum(dim=-1)==1).all()` yields `True`.

projects/nerf/test_nerf.py

@@ -60,7 +60,7 @@ def main(cfg: DictConfig):
     print(f"Loading checkpoint {checkpoint_path}.")
     loaded_data = torch.load(checkpoint_path)
     # Do not load the cached xy grid.
-    # - this allows to set an arbitrary evaluation image size.
+    # - this allows setting an arbitrary evaluation image size.
     state_dict = {
         k: v
         for k, v in loaded_data["model"].items()
@@ -121,7 +121,7 @@ def main(cfg: DictConfig):
             test_image = test_image.to(device)
         test_camera = test_camera.to(device)
 
-        # Activate eval mode of the model (allows to do a full rendering pass).
+        # Activate eval mode of the model (lets us do a full rendering pass).
         model.eval()
         with torch.no_grad():
             test_nerf_out, test_metrics = model(

projects/nerf/tests/test_raysampler.py

@@ -70,7 +70,7 @@ class TestRaysampler(unittest.TestCase):
 
     def test_probabilistic_raysampler(self, batch_size=1, n_pts_per_ray=60):
         """
-        Check that the probabilisitc ray sampler does not crash for various
+        Check that the probabilistic ray sampler does not crash for various
         settings.
         """
 

projects/nerf/train_nerf.py

@@ -31,7 +31,7 @@ def main(cfg: DictConfig):
     else:
         warnings.warn(
             "Please note that although executing on CPU is supported,"
-            + "the training is unlikely to finish in resonable time."
+            + "the training is unlikely to finish in reasonable time."
         )
         device = "cpu"
 
@@ -109,7 +109,7 @@ def main(cfg: DictConfig):
         optimizer, lr_lambda, last_epoch=start_epoch - 1, verbose=False
     )
 
-    # Initialize the cache for storing variables needed for visulization.
+    # Initialize the cache for storing variables needed for visualization.
     visuals_cache = collections.deque(maxlen=cfg.visualization.history_size)
 
     # Init the visualization visdom env.
@@ -194,7 +194,7 @@ def main(cfg: DictConfig):
             if iteration % cfg.stats_print_interval == 0:
                 stats.print(stat_set="train")
 
-            # Update the visualisatioon cache.
+            # Update the visualization cache.
             visuals_cache.append(
                 {
                     "camera": camera.cpu(),
@@ -219,7 +219,7 @@ def main(cfg: DictConfig):
             val_image = val_image.to(device)
             val_camera = val_camera.to(device)
 
-            # Activate eval mode of the model (allows to do a full rendering pass).
+            # Activate eval mode of the model (lets us do a full rendering pass).
             model.eval()
             with torch.no_grad():
                 val_nerf_out, val_metrics = model(

pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu

@@ -105,7 +105,7 @@ __device__ bool CheckPointOutsideBoundingBox(
 // which contains Pixel structs with the indices of the faces which intersect
 // with this pixel sorted by closest z distance. If the point pxy lies in the
 // face, the list (q) is updated and re-orderered in place. In addition
-// the auxillary variables q_size, q_max_z and q_max_idx are also modified.
+// the auxiliary variables q_size, q_max_z and q_max_idx are also modified.
 // This code is shared between RasterizeMeshesNaiveCudaKernel and
 // RasterizeMeshesFineCudaKernel.
 template <typename FaceQ>
@@ -275,7 +275,7 @@ __global__ void RasterizeMeshesNaiveCudaKernel(
     const int yi = H - 1 - pix_idx / W;
     const int xi = W - 1 - pix_idx % W;
 
-    // screen coordinates to ndc coordiantes of pixel.
+    // screen coordinates to ndc coordinates of pixel.
     const float xf = PixToNonSquareNdc(xi, W, H);
     const float yf = PixToNonSquareNdc(yi, H, W);
     const float2 pxy = make_float2(xf, yf);

pytorch3d/csrc/rasterize_points/rasterize_points.cu

@@ -27,7 +27,7 @@ __device__ inline bool operator<(const Pix& a, const Pix& b) {
 // which contains Pixel structs with the indices of the points which intersect
 // with this pixel sorted by closest z distance. If the pixel pxy lies in the
 // point, the list (q) is updated and re-orderered in place. In addition
-// the auxillary variables q_size, q_max_z and q_max_idx are also modified.
+// the auxiliary variables q_size, q_max_z and q_max_idx are also modified.
 // This code is shared between RasterizePointsNaiveCudaKernel and
 // RasterizePointsFineCudaKernel.
 template <typename PointQ>
@@ -104,7 +104,7 @@ __global__ void RasterizePointsNaiveCudaKernel(
     const int yi = H - 1 - pix_idx / W;
     const int xi = W - 1 - pix_idx % W;
 
-    // screen coordinates to ndc coordiantes of pixel.
+    // screen coordinates to ndc coordinates of pixel.
     const float xf = PixToNonSquareNdc(xi, W, H);
     const float yf = PixToNonSquareNdc(yi, H, W);
 

pytorch3d/csrc/utils/geometry_utils.cuh

@@ -224,7 +224,7 @@ BarycentricPerspectiveCorrectionBackward(
 // Clip negative barycentric coordinates to 0.0 and renormalize so
 // the barycentric coordinates for a point sum to 1. When the blur_radius
 // is greater than 0, a face will still be recorded as overlapping a pixel
-// if the pixel is outisde the face. In this case at least one of the
+// if the pixel is outside the face. In this case at least one of the
 // barycentric coordinates for the pixel relative to the face will be negative.
 // Clipping will ensure that the texture and z buffer are interpolated
 // correctly.

pytorch3d/csrc/utils/geometry_utils.h

@@ -245,7 +245,7 @@ inline std::tuple<vec3<T>, T, T, T> BarycentricPerspectiveCorrectionBackward(
 // Clip negative barycentric coordinates to 0.0 and renormalize so
 // the barycentric coordinates for a point sum to 1. When the blur_radius
 // is greater than 0, a face will still be recorded as overlapping a pixel
-// if the pixel is outisde the face. In this case at least one of the
+// if the pixel is outside the face. In this case at least one of the
 // barycentric coordinates for the pixel relative to the face will be negative.
 // Clipping will ensure that the texture and z buffer are interpolated
 // correctly.

pytorch3d/datasets/r2n2/r2n2.py

@@ -99,7 +99,7 @@ class R2N2(ShapeNetBase):
             path.join(SYNSET_DICT_DIR, "r2n2_synset_dict.json"), "r"
         ) as read_dict:
             self.synset_dict = json.load(read_dict)
-        # Inverse dicitonary mapping synset labels to corresponding offsets.
+        # Inverse dictionary mapping synset labels to corresponding offsets.
         self.synset_inv = {label: offset for offset, label in self.synset_dict.items()}
 
         # Store synset and model ids of objects mentioned in the splits_file.
@@ -383,7 +383,7 @@ class R2N2(ShapeNetBase):
             view_idxs: each model will be rendered with the orientation(s) of the specified
                 views. Only render by view_idxs if no camera or args for BlenderCamera is
                 supplied.
-            Accepts any of the args of the render function in ShapnetBase:
+            Accepts any of the args of the render function in ShapeNetBase:
             model_ids: List[str] of model_ids of models intended to be rendered.
             categories: List[str] of categories intended to be rendered. categories
                 and sample_nums must be specified at the same time. categories can be given

pytorch3d/datasets/r2n2/utils.py

@@ -97,8 +97,8 @@ def compute_extrinsic_matrix(azimuth, elevation, distance):
     Copied from meshrcnn codebase:
     https://github.com/facebookresearch/meshrcnn/blob/master/shapenet/utils/coords.py#L96
 
-    Compute 4x4 extrinsic matrix that converts from homogenous world coordinates
-    to homogenous camera coordinates. We assume that the camera is looking at the
+    Compute 4x4 extrinsic matrix that converts from homogeneous world coordinates
+    to homogeneous camera coordinates. We assume that the camera is looking at the
     origin.
     Used in R2N2 Dataset when computing calibration matrices.
 
@@ -189,7 +189,7 @@ def _compute_idxs(vals, counts):
 
     Args:
         vals: tensor of binary values indicating voxel presence in a dense format.
-        counts: tensor of number of occurence of each value in vals.
+        counts: tensor of number of occurrence of each value in vals.
 
     Returns:
         idxs: A tensor of shape (N), where N is the number of nonzero voxels.
@@ -379,7 +379,7 @@ def project_verts(verts, P, eps=1e-1):
     Copied from meshrcnn codebase:
     https://github.com/facebookresearch/meshrcnn/blob/master/shapenet/utils/coords.py#L159
 
-    Project verticies using a 4x4 transformation matrix.
+    Project vertices using a 4x4 transformation matrix.
 
     Args:
         verts: FloatTensor of shape (N, V, 3) giving a batch of vertex positions or of
@@ -403,7 +403,7 @@ def project_verts(verts, P, eps=1e-1):
 
     # Add an extra row of ones to the world-space coordinates of verts before
     # multiplying by the projection matrix. We could avoid this allocation by
-    # instead multiplying by a 4x3 submatrix of the projectio matrix, then
+    # instead multiplying by a 4x3 submatrix of the projection matrix, then
     # adding the remaining 4x1 vector. Not sure whether there will be much
     # performance difference between the two.
     ones = torch.ones(N, V, 1, dtype=dtype, device=device)

pytorch3d/datasets/shapenet/shapenet_core.py

@@ -37,7 +37,7 @@ class ShapeNetCore(ShapeNetBase):
                 synset offsets or labels. A combination of both is also accepted.
                 When no category is specified, all categories in data_dir are loaded.
             version: (int) version of ShapeNetCore data in data_dir, 1 or 2.
-                Default is set to be 1. Version 1 has 57 categories and verions 2 has 55
+                Default is set to be 1. Version 1 has 57 categories and version 2 has 55
                 categories.
                 Note: version 1 has two categories 02858304(boat) and 02992529(cellphone)
                 that are hyponyms of categories 04530566(watercraft) and 04401088(telephone)
@@ -63,7 +63,7 @@ class ShapeNetCore(ShapeNetBase):
         dict_file = "shapenet_synset_dict_v%d.json" % version
         with open(path.join(SYNSET_DICT_DIR, dict_file), "r") as read_dict:
             self.synset_dict = json.load(read_dict)
-        # Inverse dicitonary mapping synset labels to corresponding offsets.
+        # Inverse dictionary mapping synset labels to corresponding offsets.
         self.synset_inv = {label: offset for offset, label in self.synset_dict.items()}
 
         # If categories are specified, check if each category is in the form of either

pytorch3d/datasets/shapenet_base.py

@@ -250,7 +250,7 @@ class ShapeNetBase(torch.utils.data.Dataset):
         Helper function for sampling a number of indices from the given category.
 
         Args:
-            sample_num: number of indicies to be sampled from the given category.
+            sample_num: number of indices to be sampled from the given category.
             category: category synset of the category to be sampled from. If not
                 specified, sample from all models in the loaded dataset.
         """

pytorch3d/io/mtl_io.py

@@ -28,7 +28,7 @@ def make_mesh_texture_atlas(
 
     Args:
         material_properties: dict of properties for each material. If a material
-                does not have any properties it will have an emtpy dict.
+                does not have any properties it will have an empty dict.
         texture_images: dict of material names and texture images
         face_material_names: numpy array of the material name corresponding to each
             face. Faces which don't have an associated material will be an empty string.
@@ -220,13 +220,13 @@ def make_material_atlas(
 
     For each grid cell we can now calculate the centroid `(c_y, c_x)`
     of the corresponding texture triangle:
-        - if `(x + y) < R`, then offsett the centroid of
+        - if `(x + y) < R`, then offset the centroid of
             triangle 0 by `(y, x) * (1/R)`
         - if `(x + y) > R`, then offset the centroid of
             triangle 8 by `((R-1-y), (R-1-x)) * (1/R)`.
 
     This is equivalent to updating the portion of Grid 1
-    above the diagnonal, replacing `(y, x)` with `((R-1-y), (R-1-x))`:
+    above the diagonal, replacing `(y, x)` with `((R-1-y), (R-1-x))`:
 
     ..code-block::python
 

pytorch3d/io/obj_io.py

@@ -109,13 +109,13 @@ def load_obj(
 
     Faces are interpreted as follows:
     ::
-        5/2/1 describes the first vertex of the first triange
+        5/2/1 describes the first vertex of the first triangle
         - 5: index of vertex [1.000000 1.000000 -1.000000]
         - 2: index of texture coordinate [0.749279 0.501284]
         - 1: index of normal [0.000000 0.000000 -1.000000]
 
     If there are faces with more than 3 vertices
-    they are subdivided into triangles. Polygonal faces are assummed to have
+    they are subdivided into triangles. Polygonal faces are assumed to have
     vertices ordered counter-clockwise so the (right-handed) normal points
     out of the screen e.g. a proper rectangular face would be specified like this:
     ::
@@ -368,7 +368,7 @@ def _parse_face(
                 face_normals.append(int(vert_props[2]))
             if len(vert_props) > 3:
                 raise ValueError(
-                    "Face vertices can ony have 3 properties. \
+                    "Face vertices can only have 3 properties. \
                                 Face vert %s, Line: %s"
                     % (str(vert_props), str(line))
                 )

pytorch3d/io/pluggable.py

@@ -33,7 +33,7 @@ and to save a point cloud you might do
 
 ```
 pcl = Pointclouds(...)
-IO().save_pointcloud(pcl, "output_poincloud.obj")
+IO().save_pointcloud(pcl, "output_pointcloud.obj")
 ```
 
 """
@@ -43,7 +43,7 @@ class IO:
     """
     This class is the interface to flexible loading and saving of meshes and point clouds.
 
-    In simple cases the user will just initialise an instance of this class as `IO()`
+    In simple cases the user will just initialize an instance of this class as `IO()`
     and then use its load and save functions. The arguments of the initializer are not
     usually needed.
 
@@ -53,7 +53,7 @@ class IO:
     Args:
         include_default_formats: If False, the built-in file formats will not be available.
             Then only user-registered formats can be used.
-        path_manager: Used to customise how paths given as strings are interpreted.
+        path_manager: Used to customize how paths given as strings are interpreted.
     """
 
     def __init__(

pytorch3d/io/ply_io.py

@@ -380,7 +380,7 @@ def _try_read_ply_constant_list_ascii(f, definition: _PlyElementType):
     return data[:, 1:]
 
 
-def _parse_heterogenous_property_ascii(datum, line_iter, property: _Property):
+def _parse_heterogeneous_property_ascii(datum, line_iter, property: _Property):
     """
     Read a general data property from an ascii .ply file.
 
@@ -431,7 +431,7 @@ def _read_ply_element_ascii(f, definition: _PlyElementType):
         In simple cases where every element has the same size, 2D numpy array
         corresponding to the data. The rows are the different values.
         Otherwise a list of lists of values, where the outer list is
-        each occurence of the element, and the inner lists have one value per
+        each occurrence of the element, and the inner lists have one value per
         property.
     """
     if not definition.count:
@@ -454,7 +454,7 @@ def _read_ply_element_ascii(f, definition: _PlyElementType):
         datum = []
         line_iter = iter(line_string.strip().split())
         for property in definition.properties:
-            _parse_heterogenous_property_ascii(datum, line_iter, property)
+            _parse_heterogeneous_property_ascii(datum, line_iter, property)
         data.append(datum)
         if next(line_iter, None) is not None:
             raise ValueError("Too much data for an element.")
@@ -669,7 +669,7 @@ def _read_ply_element_binary(f, definition: _PlyElementType, big_endian: bool) -
         In simple cases where every element has the same size, 2D numpy array
         corresponding to the data. The rows are the different values.
         Otherwise a list of lists/tuples of values, where the outer list is
-        each occurence of the element, and the inner lists have one value per
+        each occurrence of the element, and the inner lists have one value per
         property.
     """
     if not definition.count:
@@ -1027,7 +1027,7 @@ def _save_ply(
     Args:
         f: File object to which the 3D data should be written.
         verts: FloatTensor of shape (V, 3) giving vertex coordinates.
-        faces: LongTensor of shsape (F, 3) giving faces.
+        faces: LongTensor of shape (F, 3) giving faces.
         verts_normals: FloatTensor of shape (V, 3) giving vertex normals.
         ascii: (bool) whether to use the ascii ply format.
         decimal_places: Number of decimal places for saving if ascii=True.

pytorch3d/loss/mesh_laplacian_smoothing.py

@@ -9,7 +9,7 @@ def mesh_laplacian_smoothing(meshes, method: str = "uniform"):
     Computes the laplacian smoothing objective for a batch of meshes.
     This function supports three variants of Laplacian smoothing,
     namely with uniform weights("uniform"), with cotangent weights ("cot"),
-    and cotangent cuvature ("cotcurv").For more details read [1, 2].
+    and cotangent curvature ("cotcurv").For more details read [1, 2].
 
     Args:
         meshes: Meshes object with a batch of meshes.

pytorch3d/loss/point_mesh_distance.py

@@ -91,7 +91,7 @@ class _FacePointDistance(Function):
             max_tris: Scalar equal to maximum number of faces in the batch
         Returns:
             dists: FloatTensor of shape `(T,)`, where `dists[t]` is the squared
-                euclidean distance of `t`-th trianguar face to the closest point in the
+                euclidean distance of `t`-th triangular face to the closest point in the
                 corresponding example in the batch
             idxs: LongTensor of shape `(T,)` indicating the closest point in the
                 corresponding example in the batch.

pytorch3d/ops/interp_face_attrs.py

@@ -21,7 +21,7 @@ def interpolate_face_attributes(
             pixel in the image. A value < 0 indicates that the pixel does not
             overlap any face and should be skipped.
         barycentric_coords: FloatTensor of shape (N, H, W, K, 3) specifying
-            the barycentric coordianates of each pixel
+            the barycentric coordinates of each pixel
             relative to the faces (in the packed
             representation) which overlap the pixel.
         face_attributes: packed attributes of shape (total_faces, 3, D),