more readthedocs

Summary: Quote formats, spelling

Reviewed By: shapovalov

Differential Revision: D40913734

fbshipit-source-id: d6dea65d5204b3c463c656a07ef9b447b7be6a0a

docs/generate_implicitron_stubs.py

@@ -49,7 +49,7 @@ def iterate_directory(directory_path, dest):
     toc = []
     if not dest.exists():
         dest.mkdir()
-    for file in directory_path.glob("*.py"):
+    for file in sorted(directory_path.glob("*.py")):
         if file.stem.startswith("_"):
             continue
         module = paths_to_modules([file])
@@ -121,7 +121,7 @@ basic_dataset = [
 ]
 basic_dataset_modules = [f"pytorch3d.implicitron.dataset.{i}" for i in basic_dataset]
 create_one_file(
-    "pytorch3d.implicitron.dataset",
+    "pytorch3d.implicitron.dataset in general",
     "Basics of data for implicitron",
     basic_dataset_modules,
     DEST_DIR / "data_basics.rst",
@@ -131,7 +131,7 @@ specific_dataset_files = [
     i for i in dataset_files if i.stem.find("_dataset_map_provider") != -1
 ]
 create_one_file(
-    "pytorch3d.impliciton.dataset",
+    "pytorch3d.implicitron.dataset specific datasets",
     "specific datasets",
     paths_to_modules(specific_dataset_files),
     DEST_DIR / "datasets.rst",
@@ -139,7 +139,7 @@ create_one_file(
 
 evaluation_files = sorted(ROOT_DIR.glob("pytorch3d/implicitron/evaluation/*.py"))
 create_one_file(
-    "pytorch3d.impliciton.evaluation",
+    "pytorch3d.implicitron.evaluation",
     "evaluation",
     paths_to_modules(evaluation_files),
     DEST_DIR / "evaluation.rst",

docs/modules/implicitron/data_basics.rst

-pytorch3d.implicitron.dataset
-=============================
+pytorch3d.implicitron.dataset in general
+========================================
 
 Basics of data for implicitron
 

docs/modules/implicitron/datasets.rst

-pytorch3d.impliciton.dataset
-============================
+pytorch3d.implicitron.dataset specific datasets
+===============================================
 
 specific datasets
 

docs/modules/implicitron/evaluation.rst

-pytorch3d.impliciton.evaluation
-===============================
+pytorch3d.implicitron.evaluation
+================================
 
 evaluation
 

docs/modules/implicitron/models/implicit_function/index.rst

@@ -4,10 +4,10 @@ pytorch3d.implicitron.models.implicit_function
 .. toctree::
 
     base
+    decoding_functions
     idr_feature_field
     neural_radiance_field
     scene_representation_networks
     utils
-    decoding_functions
     voxel_grid
     voxel_grid_implicit_function

pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py

@@ -57,8 +57,8 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
     Generates the training, validation, and testing dataset objects for
     a dataset laid out on disk like CO3Dv2, with annotations in gzipped json files.
 
-    The dataset is organized in the filesystem as follows:
-        ```
+    The dataset is organized in the filesystem as follows::
+
         self.dataset_root
             ├── <category_0>
             │   ├── <sequence_name_0>
@@ -90,7 +90,6 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
             ├── <category_1>
             ├── ...
             ├── <category_K>
-        ```
 
     The dataset contains sequences named `<sequence_name_i>` from `K` categories with
     names `<category_j>`. Each category comprises sequence folders
@@ -106,8 +105,8 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
     the list of all frames and sequences of the given category stored as lists of
     `FrameAnnotation` and `SequenceAnnotation` objects respectivelly.
 
-    Each `set_lists_<subset_name_l>.json` file contains the following dictionary:
-        ```
+    Each `set_lists_<subset_name_l>.json` file contains the following dictionary::
+
         {
             "train": [
                 (sequence_name: str, frame_number: int, image_path: str),
@@ -122,7 +121,7 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
                 ...
             ],
         ]
-        ```
+
     defining the list of frames (identified with their `sequence_name` and `frame_number`)
     in the "train", "val", and "test" subsets of the dataset.
     Note that `frame_number` can be obtained only from `frame_annotations.jgz` and
@@ -131,8 +130,8 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
     have its frame number set to `20`, not 5).
 
     Each `eval_batches_<subset_name_l>.json` file contains a list of evaluation examples
-    in the following form:
-        ```
+    in the following form::
+
         [
             [  # batch 1
                 (sequence_name: str, frame_number: int, image_path: str),
@@ -143,7 +142,7 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
                 ...
             ],
         ]
-        ```
+
     Note that the evaluation examples always come from the `"test"` subset of the dataset.
     (test frames can repeat across batches).
 
@@ -341,14 +340,13 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
 
         Returns:
             category_to_subset_name_list: A dictionary containing subset names available
-                per category of the following form:
-                    ```
+                per category of the following form::
+
                     {
                         category_0: [category_0_subset_name_0, category_0_subset_name_1, ...],
                         category_1: [category_1_subset_name_0, category_1_subset_name_1, ...],
                         ...
                     }
-                    ```
 
         """
         category_to_subset_name_list_json = "category_to_subset_name_list.json"

pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py

@@ -554,8 +554,8 @@ def _get_flat_nvs_metric_key(result, metric_name) -> str:
 
 def flatten_nvs_results(results):
     """
-    Takes input `results` list of dicts of the form:
-    ```
+    Takes input `results` list of dicts of the form::
+
         [
             {
                 'subset':'train/test/...',
@@ -564,12 +564,14 @@ def flatten_nvs_results(results):
             },
             ...
         ]
-    ```
-    And converts to a flat dict as follows:
-    {
-        'subset=train/test/...|subsubset=src=1/src=2/...': nvs_eval_metrics,
-        ...
-    }
+
+    And converts to a flat dict as follows::
+
+        {
+            'subset=train/test/...|subsubset=src=1/src=2/...': nvs_eval_metrics,
+            ...
+        }
+
     """
     results_flat = {}
     for result in results:

pytorch3d/implicitron/models/renderer/multipass_ea.py

@@ -29,21 +29,21 @@ class MultiPassEmissionAbsorptionRenderer(  # pyre-ignore: 13
     During each ray marching pass, features, depth map, and masks
     are integrated: Let o_i be the opacity estimated by the implicit function,
     and d_i be the offset between points `i` and `i+1` along the respective ray.
-    Ray marching is performed using the following equations:
-    ```
-    ray_opacity_n = cap_fn(sum_i=1^n cap_fn(d_i * o_i)),
-    weight_n = weight_fn(cap_fn(d_i * o_i), 1 - ray_opacity_{n-1}),
-    ```
+    Ray marching is performed using the following equations::
+
+        ray_opacity_n = cap_fn(sum_i=1^n cap_fn(d_i * o_i)),
+        weight_n = weight_fn(cap_fn(d_i * o_i), 1 - ray_opacity_{n-1}),
+
     and the final rendered quantities are computed by a dot-product of ray values
     with the weights, e.g. `features = sum_n(weight_n * ray_features_n)`.
 
     By default, for the EA raymarcher from [1] (
         activated with `self.raymarcher_class_type="EmissionAbsorptionRaymarcher"`
-    ):
-        ```
+    )::
+
         cap_fn(x) = 1 - exp(-x),
         weight_fn(x) = w * x.
-        ```
+
     Note that the latter can altered by changing `self.raymarcher_class_type`,
     e.g. to "CumsumRaymarcher" which implements the cumulative-sum raymarcher
     from NeuralVolumes [2].

pytorch3d/implicitron/models/view_pooler/feature_aggregator.py

@@ -250,12 +250,11 @@ class AngleWeightedReductionFeatureAggregator(torch.nn.Module, FeatureAggregator
     Performs a weighted aggregation using a set of predefined `reduction_functions`
     and concatenates the results of each aggregation function along the
     channel dimension. The weights are proportional to the cosine of the
-    angle between the target ray and the source ray:
-    ```
-    weight = (
-        dot(target_ray, source_ray) * 0.5 + 0.5 + self.min_ray_angle_weight
-    )**self.weight_by_ray_angle_gamma
-    ```
+    angle between the target ray and the source ray::
+
+        weight = (
+            dot(target_ray, source_ray) * 0.5 + 0.5 + self.min_ray_angle_weight
+        )**self.weight_by_ray_angle_gamma
 
     The reduction functions singularize the second dimension
     of the sampled features which stacks the source views.
@@ -359,12 +358,11 @@ class AngleWeightedIdentityFeatureAggregator(torch.nn.Module, FeatureAggregatorB
     """
     This aggregator does not perform any feature aggregation. It only weights
     the features by the weights proportional to the cosine of the
-    angle between the target ray and the source ray:
-    ```
-    weight = (
-        dot(target_ray, source_ray) * 0.5 + 0.5 + self.min_ray_angle_weight
-    )**self.weight_by_ray_angle_gamma
-    ```
+    angle between the target ray and the source ray::
+
+        weight = (
+            dot(target_ray, source_ray) * 0.5 + 0.5 + self.min_ray_angle_weight
+        )**self.weight_by_ray_angle_gamma
 
     Settings:
         min_ray_angle_weight: The minimum possible aggregation weight

pytorch3d/implicitron/models/view_pooler/view_sampler.py

@@ -218,8 +218,8 @@ def cameras_points_cartesian_product(
 ) -> Tuple[CamerasBase, torch.Tensor]:
     """
     Generates all pairs of pairs of elements from 'camera' and 'pts' and returns
-    `camera_rep` and `pts_rep` such that:
-        ```
+    `camera_rep` and `pts_rep` such that::
+
         camera_rep = [                 pts_rep = [
             camera[0]                      pts[0],
             camera[0]                      pts[1],
@@ -235,15 +235,14 @@ def cameras_points_cartesian_product(
             camera[n_cameras-1]            ...,
             ...                            pts[batch_pts-1],
         ]                              ]
-        ```
 
     Args:
         camera: A batch of `n_cameras` cameras.
         pts: A batch of `batch_pts` points of shape `(batch_pts, ..., dim)`
 
     Returns:
-        camera_rep: A batch of batch_pts*n_cameras cameras such that:
-            ```
+        camera_rep: A batch of batch_pts*n_cameras cameras such that::
+
             camera_rep = [
                 camera[0]
                 camera[0]
@@ -258,11 +257,11 @@ def cameras_points_cartesian_product(
                 camera[n_cameras-1]
                 camera[n_cameras-1]
             ]
-            ```
+
 
         pts_rep: Repeated `pts` of shape `(batch_pts*n_cameras, ..., dim)`,
-            such that:
-            ```
+            such that::
+
             pts_rep = [
                 pts[0],
                 pts[1],
@@ -278,7 +277,7 @@ def cameras_points_cartesian_product(
                 ...,
                 pts[batch_pts-1],
             ]
-            ```
+
     """
     n_cameras = camera.R.shape[0]
     batch_pts = pts.shape[0]

pytorch3d/implicitron/tools/stats.py

@@ -73,29 +73,29 @@ class Stats(object):
     # TODO: update this with context manager
     """
     stats logging object useful for gathering statistics of training a deep net in pytorch
-    Example:
-    ```
-    # init stats structure that logs statistics 'objective' and 'top1e'
-    stats = Stats( ('objective','top1e') )
-    network = init_net() # init a pytorch module (=nueral network)
-    dataloader = init_dataloader() # init a dataloader
-    for epoch in range(10):
-        # start of epoch -> call new_epoch
-        stats.new_epoch()
-
-        # iterate over batches
-        for batch in dataloader:
-
-            output = network(batch) # run and save into a dict of output variables "output"
-
-            # stats.update() automatically parses the 'objective' and 'top1e' from
-            # the "output" dict and stores this into the db
-            stats.update(output)
-            stats.print() # prints the averages over given epoch
-        # stores the training plots into '/tmp/epoch_stats.pdf'
-        # and plots into a visdom server running at localhost (if running)
-        stats.plot_stats(plot_file='/tmp/epoch_stats.pdf')
-    ```
+    Example::
+
+        # init stats structure that logs statistics 'objective' and 'top1e'
+        stats = Stats( ('objective','top1e') )
+        network = init_net() # init a pytorch module (=nueral network)
+        dataloader = init_dataloader() # init a dataloader
+        for epoch in range(10):
+            # start of epoch -> call new_epoch
+            stats.new_epoch()
+
+            # iterate over batches
+            for batch in dataloader:
+
+                output = network(batch) # run and save into a dict of output variables
+
+                # stats.update() automatically parses the 'objective' and 'top1e' from
+                # the "output" dict and stores this into the db
+                stats.update(output)
+                stats.print() # prints the averages over given epoch
+            # stores the training plots into '/tmp/epoch_stats.pdf'
+            # and plots into a visdom server running at localhost (if running)
+            stats.plot_stats(plot_file='/tmp/epoch_stats.pdf')
+
     """
 
     def __init__(

pytorch3d/implicitron/tools/utils.py

@@ -181,11 +181,11 @@ class Timer:
     """
     A simple class for timing execution.
 
-    Example:
-        ```
+    Example::
+
         with Timer():
             print("This print statement is timed.")
-        ```
+
     """
 
     def __init__(self, name="timer", quiet=False):