spelling

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

Reviewed By: gkioxari

Differential Revision: D26101323

fbshipit-source-id: 652f62bc9d71a4ff872efa21141225e43191353a

projects/nerf/nerf/eval_video_utils.py

@@ -77,7 +77,7 @@ def generate_eval_video_cameras(
         cam_centers_c = cam_centers - plane_mean[None]
 
         if up is not None:
-            # us the up vector instad of the plane through the camera centers
+            # us the up vector instead of the plane through the camera centers
             plane_normal = torch.FloatTensor(up)
         else:
             cov = (cam_centers_c.t() @ cam_centers_c) / cam_centers_c.shape[0]
@@ -99,7 +99,7 @@ def generate_eval_video_cameras(
         traj = traj @ e_vec.t() + plane_mean[None]
 
     else:
-        raise ValueError(f"Uknown trajectory_type {trajectory_type}.")
+        raise ValueError(f"Unknown trajectory_type {trajectory_type}.")
 
     # point all cameras towards the center of the scene
     R, T = look_at_view_transform(

projects/nerf/nerf/harmonic_embedding.py

@@ -42,7 +42,7 @@ class HarmonicEmbedding(torch.nn.Module):
             )`
 
         Note that `x` is also premultiplied by the base frequency `omega0`
-        before evaluting the harmonic functions.
+        before evaluating the harmonic functions.
         """
         super().__init__()
 

projects/nerf/nerf/implicit_function.py

@@ -169,7 +169,7 @@ class NeuralRadianceField(torch.nn.Module):
 
         Returns:
             rays_densities: A tensor of shape `(minibatch, ..., num_points_per_ray, 1)`
-                denoting the opacitiy of each ray point.
+                denoting the opacity of each ray point.
             rays_colors: A tensor of shape `(minibatch, ..., num_points_per_ray, 3)`
                 denoting the color of each ray point.
         """

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`.