branch name change (#118)

Summary:
changes to prepare branch name change

Pull Request resolved: #118

Reviewed By: haooooooqi

Differential Revision: D31042735

Pulled By: kalyanvasudev

fbshipit-source-id: 572b35a3a12f455c1abf26abd81ace4d940c6625

.circleci/config.yml

@@ -202,4 +202,4 @@ workflows:
           filters:
             branches:
               only:
-                - master
+                - main

.github/CONTRIBUTING.md

@@ -16,7 +16,7 @@ We do not always accept new features, and we take the following factors into con
 
 When sending a PR, please ensure you complete the following steps:
 
-1. Fork the repo and create your branch from `master`. Follow the instructions
+1. Fork the repo and create your branch from `main`. Follow the instructions
    in [INSTALL.md](../INSTALL.md) to build the repo.
 2. If you've added code that should be tested, add tests.
 3. If you've changed any APIs, please update the documentation.

CONTRIBUTING.md

@@ -5,7 +5,7 @@ possible.
 ## Pull Requests
 We actively welcome your pull requests.
 
-1. Fork the repo and create your branch from `master`.
+1. Fork the repo and create your branch from `main`.
 2. If you've added code that should be tested, add tests.
 3. If you've changed APIs, update the documentation.
 4. Ensure the test suite passes.
@@ -14,12 +14,12 @@ We actively welcome your pull requests.
 
 ## Testing
 
-Please follow the instructions mentioned in [test-README](https://github.com/facebookresearch/pytorchvideo/blob/master/tests/README.md) to run the existing and your newly added tests.
+Please follow the instructions mentioned in [test-README](https://github.com/facebookresearch/pytorchvideo/blob/main/tests/README.md) to run the existing and your newly added tests.
 
 ## Linting
 
 We provide a linting script to correctly format your code changes.
-Please follow the instructions mentioned in [dev-README](https://github.com/facebookresearch/pytorchvideo/blob/master/dev/README.md) to run the linter.
+Please follow the instructions mentioned in [dev-README](https://github.com/facebookresearch/pytorchvideo/blob/main/dev/README.md) to run the linter.
 
 
 ## Contributor License Agreement ("CLA")

INSTALL.md

@@ -60,9 +60,9 @@ conda install -c pytorch pytorch=1.8.0 torchvision cudatoolkit=10.2
 
 ## Testing
 
-Please follow the instructions mentioned in [test-README](https://github.com/facebookresearch/pytorchvideo/blob/master/tests/README.md) to run the provided tests.
+Please follow the instructions mentioned in [test-README](https://github.com/facebookresearch/pytorchvideo/blob/main/tests/README.md) to run the provided tests.
 
 ## Linting
 
 We also provide a linting script to correctly format your code edits.
-Please follow the instructions mentioned in [dev-README](https://github.com/facebookresearch/pytorchvideo/blob/master/dev/README.md) to run the linter.
+Please follow the instructions mentioned in [dev-README](https://github.com/facebookresearch/pytorchvideo/blob/main/dev/README.md) to run the linter.

README.md

@@ -3,17 +3,17 @@
 </p>
 
 <p align="center">
-   <a href="https://github.com/facebookresearch/pytorchvideo/blob/master/LICENSE">
+   <a href="https://github.com/facebookresearch/pytorchvideo/blob/main/LICENSE">
     <img src="https://img.shields.io/pypi/l/pytorchvideo" alt="CircleCI" />
   </a>
    <a href="https://pypi.org/project/pytorchvideo/">
     <img src="https://img.shields.io/pypi/v/pytorchvideo?color=blue&label=release" alt="CircleCI" />
   </a>
-    <a href="https://circleci.com/gh/facebookresearch/pytorchvideo/tree/master">
-    <img src="https://img.shields.io/circleci/build/github/facebookresearch/pytorchvideo/master?token=efdf3ff5b6f6acf44f4af39b683dea31d40e5901" alt="Coverage" />
+    <a href="https://circleci.com/gh/facebookresearch/pytorchvideo/tree/main">
+    <img src="https://img.shields.io/circleci/build/github/facebookresearch/pytorchvideo/main?token=efdf3ff5b6f6acf44f4af39b683dea31d40e5901" alt="Coverage" />
   </a>
-    <a href="https://codecov.io/gh/facebookresearch/pytorchvideo/branch/master">
-    <img src="https://codecov.io/gh/facebookresearch/pytorchvideo/branch/master/graph/badge.svg?token=OSZSI6JU31"/>
+    <a href="https://codecov.io/gh/facebookresearch/pytorchvideo/branch/main">
+    <img src="https://codecov.io/gh/facebookresearch/pytorchvideo/branch/main/graph/badge.svg?token=OSZSI6JU31"/>
   </a>
   </a>
     <a href="https://join.slack.com/t/pytorchvideo/shared_invite/zt-qjrkknes-7bt0qjcmVNvXcceg9zlgOA">
@@ -44,7 +44,7 @@ Key features include:
 
 ## Updates
 
-- Aug 2021: [Multiscale Vision Transformers](https://arxiv.org/abs/2104.11227) has been released in PyTorchVideo, details can be found from [here](https://github.com/facebookresearch/pytorchvideo/blob/master/pytorchvideo/models/vision_transformers.py#L97).
+- Aug 2021: [Multiscale Vision Transformers](https://arxiv.org/abs/2104.11227) has been released in PyTorchVideo, details can be found from [here](https://github.com/facebookresearch/pytorchvideo/blob/main/pytorchvideo/models/vision_transformers.py#L97).
 
 ## Installation
 
@@ -65,7 +65,7 @@ Get started with PyTorchVideo by trying out one of our [tutorials](https://pytor
 
 
 ## Model Zoo and Baselines
-We provide a large set of baseline results and trained models available for download in the [PyTorchVideo Model Zoo](https://github.com/facebookresearch/pytorchvideo/blob/master/docs/source/model_zoo.md).
+We provide a large set of baseline results and trained models available for download in the [PyTorchVideo Model Zoo](https://github.com/facebookresearch/pytorchvideo/blob/main/docs/source/model_zoo.md).
 
 ## Contributors
 

dev/README.md

@@ -2,7 +2,7 @@
 
 
 Before running the linter, please ensure that you installed the necessary additional linter dependencies.
-If not installed, check the [install-README](https://github.com/facebookresearch/pytorchvideo/blob/master/INSTALL.md) on how to do it.
+If not installed, check the [install-README](https://github.com/facebookresearch/pytorchvideo/blob/main/INSTALL.md) on how to do it.
 
 Post that, you can run the linter from the project root using,
 

docs/source/conf.py

@@ -175,7 +175,7 @@
     )
 ]
 
-github_doc_root = "https://github.com/facebookresearch/pytorchvideo/tree/master"
+github_doc_root = "https://github.com/facebookresearch/pytorchvideo/tree/main"
 
 
 def setup(app):

docs/source/data_preparation.md

@@ -18,12 +18,12 @@ path_to_video_3 label_3
 path_to_video_N label_N
 ```
 
-All the Kinetics models in the Model Zoo are trained and tested with the same data as [Non-local Network](https://github.com/facebookresearch/video-nonlocal-net/blob/master/DATASET.md) and [PySlowFast](https://github.com/facebookresearch/SlowFast/blob/master/slowfast/datasets/DATASET.md). For dataset specific issues, please reach out to the [dataset provider](https://deepmind.com/research/open-source/kinetics).
+All the Kinetics models in the Model Zoo are trained and tested with the same data as [Non-local Network](https://github.com/facebookresearch/video-nonlocal-net/blob/main/DATASET.md) and [PySlowFast](https://github.com/facebookresearch/SlowFast/blob/main/slowfast/datasets/DATASET.md). For dataset specific issues, please reach out to the [dataset provider](https://deepmind.com/research/open-source/kinetics).
 
 
 ### Charades
 
-We follow [PySlowFast](https://github.com/facebookresearch/SlowFast/blob/master/slowfast/datasets/DATASET.md) to prepare the Charades dataset as follow:
+We follow [PySlowFast](https://github.com/facebookresearch/SlowFast/blob/main/slowfast/datasets/DATASET.md) to prepare the Charades dataset as follow:
 
 1. Download the Charades RGB frames from [official website](http://ai2-website.s3.amazonaws.com/data/Charades_v1_rgb.tar).
 
@@ -32,7 +32,7 @@ We follow [PySlowFast](https://github.com/facebookresearch/SlowFast/blob/master/
 
 ### Something-Something V2
 
-We follow [PySlowFast](https://github.com/facebookresearch/SlowFast/blob/master/slowfast/datasets/DATASET.md) to prepare the Something-Something V2 dataset as follow:
+We follow [PySlowFast](https://github.com/facebookresearch/SlowFast/blob/main/slowfast/datasets/DATASET.md) to prepare the Something-Something V2 dataset as follow:
 
 1. Download the dataset and annotations from [official website](https://20bn.com/datasets/something-something).
 
@@ -49,7 +49,7 @@ We follow [PySlowFast](https://github.com/facebookresearch/SlowFast/blob/master/
 
 The AVA Dataset could be downloaded from the [official site](https://research.google.com/ava/download.html#ava_actions_download)
 
-We followed the same [downloading and preprocessing procedure](https://github.com/facebookresearch/video-long-term-feature-banks/blob/master/DATASET.md) as the [Long-Term Feature Banks for Detailed Video Understanding](https://arxiv.org/abs/1812.05038) do.
+We followed the same [downloading and preprocessing procedure](https://github.com/facebookresearch/video-long-term-feature-banks/blob/main/DATASET.md) as the [Long-Term Feature Banks for Detailed Video Understanding](https://arxiv.org/abs/1812.05038) do.
 
 You could follow these steps to download and preprocess the data:
 

docs/source/model_zoo.md

@@ -78,4 +78,4 @@ All top1/top5 accuracies are measured with 10-clip evaluation. Latency is benchm
 
 
 ### TorchHub models
-We provide a large set of [TorchHub](https://pytorch.org/hub/) models for the above video models with pre-trained weights. So it's easy to construct the networks and load pre-trained weights. Please refer to [PytorchVideo TorchHub models](https://github.com/facebookresearch/pytorchvideo/blob/master/pytorchvideo/models/hub/README.md) for more details.
+We provide a large set of [TorchHub](https://pytorch.org/hub/) models for the above video models with pre-trained weights. So it's easy to construct the networks and load pre-trained weights. Please refer to [PytorchVideo TorchHub models](https://github.com/facebookresearch/pytorchvideo/blob/main/pytorchvideo/models/hub/README.md) for more details.

projects/video_nerf/README.md

@@ -1,6 +1,6 @@
 # Train a NeRF model with PyTorchVideo and PyTorch3D
 
-This project demonstrates how to use the video decoder from PyTorchVideo to load frames from a video of an object from the [Objectron dataset](https://github.com/google-research-datasets/Objectron), and use this to train a NeRF [1] model with [PyTorch3D](https://github.com/facebookresearch/pytorch3d). Instead of decoding and storing all the video frames as images, PyTorchVideo offers an easy alternative to load and access frames on the fly.  For this project we will be using the [NeRF implementation from PyTorch3D](https://github.com/facebookresearch/pytorch3d/tree/master/projects/nerf).
+This project demonstrates how to use the video decoder from PyTorchVideo to load frames from a video of an object from the [Objectron dataset](https://github.com/google-research-datasets/Objectron), and use this to train a NeRF [1] model with [PyTorch3D](https://github.com/facebookresearch/pytorch3d). Instead of decoding and storing all the video frames as images, PyTorchVideo offers an easy alternative to load and access frames on the fly.  For this project we will be using the [NeRF implementation from PyTorch3D](https://github.com/facebookresearch/pytorch3d/tree/main/projects/nerf).
 
 ### Set up
 
@@ -116,7 +116,7 @@ python test_nerf.py --config-name objectron test.mode='export_video' data.image_
 
 For a higher resolution video you can increase the image size to e.g. [192, 256] (note that this will slow down inference).
 
-You will need to specify the `scene_center` for the video in the `objectron.yaml` file. This is set for the demo video specified in `download_objectron_data.py`. For a different video you can calculate the scene center inside [`eval_video_utils.py`](https://github.com/facebookresearch/pytorch3d/blob/master/projects/nerf/nerf/eval_video_utils.py#L99). After line 99 you can add the following code to compute the center:
+You will need to specify the `scene_center` for the video in the `objectron.yaml` file. This is set for the demo video specified in `download_objectron_data.py`. For a different video you can calculate the scene center inside [`eval_video_utils.py`](https://github.com/facebookresearch/pytorch3d/blob/main/projects/nerf/nerf/eval_video_utils.py#L99). After line 99 you can add the following code to compute the center:
 
 ```python
 # traj is the circular camera trajectory on the camera mean plane.

pytorchvideo/models/hub/README.md

@@ -5,7 +5,7 @@ PyTorchVideo provides a large set of [TorchHub](https://pytorch.org/hub/) models
 
 ### Kinetics-400
 
-Models are trained on Kinetics-400. For more benchmarking and model details, please check the [PyTorchVideo Model Zoo](https://github.com/facebookresearch/pytorchvideo/blob/master/docs/source/model_zoo.md)
+Models are trained on Kinetics-400. For more benchmarking and model details, please check the [PyTorchVideo Model Zoo](https://github.com/facebookresearch/pytorchvideo/blob/main/docs/source/model_zoo.md)
 
 torchhub name            | arch     | depth | frame length x sample rate | top 1 | top 5 |
 ------------------------ | -------- | ----- | -------------------------- | ----- | ----- |
@@ -45,4 +45,4 @@ model = torch.hub.load("facebookresearch/pytorchvideo", model=model_name, pretra
 
 Notes:
 * Please check [torchhub inference tutorial](https://pytorchvideo.org/docs/tutorial_torchhub_inference) for more details about how to load models from TorchHub and perform inference.
-* Check [Model Zoo](https://github.com/facebookresearch/pytorchvideo/blob/master/docs/source/model_zoo.md) for the full set of supported PytorchVideo model zoo and more details about how the model zoo is prepared.
+* Check [Model Zoo](https://github.com/facebookresearch/pytorchvideo/blob/main/docs/source/model_zoo.md) for the full set of supported PytorchVideo model zoo and more details about how the model zoo is prepared.

tests/README.md

@@ -2,7 +2,7 @@
 
 
 Before running the tests, please ensure that you installed the necessary additional test dependencies. 
-If not installed, check the [install-README](https://github.com/facebookresearch/pytorchvideo/blob/master/INSTALL.md) on how to do it.
+If not installed, check the [install-README](https://github.com/facebookresearch/pytorchvideo/blob/main/INSTALL.md) on how to do it.
 
 Use the the following command to run the tests:
 ```

tutorials/torchhub_inference_tutorial.ipynb

@@ -115,7 +115,7 @@
    "source": [
     "### Load Model using Torch Hub API\n",
     "\n",
-    "PyTorchVideo provides several pretrained models through Torch Hub. Available models are described in [model zoo documentation](https://github.com/facebookresearch/pytorchvideo/blob/master/docs/source/model_zoo.md#kinetics-400). \n",
+    "PyTorchVideo provides several pretrained models through Torch Hub. Available models are described in [model zoo documentation](https://github.com/facebookresearch/pytorchvideo/blob/main/docs/source/model_zoo.md#kinetics-400). \n",
     "\n",
     "Here we are selecting the `slowfast_r50` model which was trained using a 8x8 setting on the Kinetics 400 dataset. \n",
     "\n",

tutorials/video_detection_example/video_detection_inference_tutorial.ipynb

@@ -101,7 +101,7 @@
    "metadata": {},
    "source": [
     "## Load Model using Torch Hub API\n",
-    "PyTorchVideo provides several pretrained models through Torch Hub. Available models are described in [model zoo documentation.](https://github.com/facebookresearch/pytorchvideo/blob/master/docs/source/model_zoo.md)\n",
+    "PyTorchVideo provides several pretrained models through Torch Hub. Available models are described in [model zoo documentation.](https://github.com/facebookresearch/pytorchvideo/blob/main/docs/source/model_zoo.md)\n",
     "\n",
     "Here we are selecting the slow_r50_detection model which was trained using a 4x16 setting on the Kinetics 400 dataset and \n",
     "fine tuned on AVA V2.2 actions dataset.\n",
@@ -132,7 +132,7 @@
     "These bounding boxes later feed into our video action detection model.\n",
     "For more details, please refer to the Detectron2's object detection tutorials.\n",
     "\n",
-    "To install Detectron2, please follow the instructions mentioned [here](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md)"
+    "To install Detectron2, please follow the instructions mentioned [here](https://github.com/facebookresearch/detectron2/blob/main/INSTALL.md)"
    ]
   },
   {

tutorials/video_detection_example/visualization.py

@@ -53,7 +53,7 @@ def __init__(
         self, img_rgb: torch.Tensor, meta: Optional[SimpleNamespace] = None, **kwargs
     ) -> None:
         """
-        See https://github.com/facebookresearch/detectron2/blob/master/detectron2/utils/visualizer.py
+        See https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/visualizer.py
         for more details.
         Args:
             img_rgb: a tensor or numpy array of shape (H, W, C), where H and W correspond to

website/docs/tutorial_torchhub_detection_inference.md

@@ -9,7 +9,7 @@ PyTorchVideo provides several pretrained models through [Torch Hub](https://pyto
 
 NOTE: Currently, this tutorial only works if ran on local clone from the directory `pytorchvideo/tutorials/video_detection_example`
 
-This tutorial assumes that you have installed [Detectron2]((https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md)) and [Opencv-python](https://pypi.org/project/opencv-python/) on your machine.
+This tutorial assumes that you have installed [Detectron2]((https://github.com/facebookresearch/detectron2/blob/main/INSTALL.md)) and [Opencv-python](https://pypi.org/project/opencv-python/) on your machine.
 
 # Imports
 ```python
@@ -38,7 +38,7 @@ from visualization import VideoVisualizer
 ```
 
 # Load Model using Torch Hub API
-PyTorchVideo provides several pretrained models through Torch Hub. Available models are described in [model zoo documentation.](https://github.com/facebookresearch/pytorchvideo/blob/master/docs/source/model_zoo.md)
+PyTorchVideo provides several pretrained models through Torch Hub. Available models are described in [model zoo documentation.](https://github.com/facebookresearch/pytorchvideo/blob/main/docs/source/model_zoo.md)
 
 Here we are selecting the slow_r50_detection model which was trained using a 4x16 setting on the Kinetics 400 dataset and fine tuned on AVA V2.2 actions dataset.
 
@@ -56,7 +56,7 @@ We use the object detector to detect bounding boxes for the people.
 These bounding boxes later feed into our video action detection model.
 For more details, please refer to the Detectron2's object detection tutorials.
 
-To install Detectron2, please follow the instructions mentioned [here](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md)
+To install Detectron2, please follow the instructions mentioned [here](https://github.com/facebookresearch/detectron2/blob/main/INSTALL.md)
 
 ```python
 cfg = get_cfg()

website/website/pages/en/index.js

@@ -153,7 +153,7 @@ pip install pytorchvideo
         <Container>
           <ol>
             <li>
-              <strong>Install pytorchvideo  </strong> (Confirm requirements following the instructions <a href="https://github.com/facebookresearch/pytorchvideo/blob/master/INSTALL.md">here</a>)
+              <strong>Install pytorchvideo  </strong> (Confirm requirements following the instructions <a href="https://github.com/facebookresearch/pytorchvideo/blob/main/INSTALL.md">here</a>)
               <MarkdownBlock>{install}</MarkdownBlock>
             </li>
             <li>