spelling

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

Reviewed By: gkioxari

Differential Revision: D26101323

fbshipit-source-id: 652f62bc9d71a4ff872efa21141225e43191353a

projects/nerf/README.md

@@ -50,7 +50,7 @@ python ./test_nerf.py --config-name lego
 ```
 Will load a trained model from the `./checkpoints` directory and evaluate it on the test split of the corresponding dataset (Lego in the case above).
 
-### Exporting multi-view video of the radience field
+### Exporting multi-view video of the radiance field
 Furthermore, the codebase supports generating videos of the neural radiance field.
 The following generates a turntable video of the Lego scene:
 ```

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`.

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(