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[Docs] Refactor docs (#1102) 

* [Docs] Refactor documentation

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* set sphinx==3.1.2

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CONTRIBUTING.md
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# Contributing to OpenMMLab
## Contributing to OpenMMLab
All kinds of contributions are welcome, including but not limited to the following.
- Fixes (typo, bugs)
- New features and components
## Workflow
### Workflow
1. fork and pull the latest OpenMMLab repository
2. checkout a new branch (do not use master branch for PRs)
......@@ -14,9 +14,9 @@ All kinds of contributions are welcome, including but not limited to the followi
Note: If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
## Code style
### Code style
### Python
#### Python
We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
......@@ -64,6 +64,6 @@ After this on every commit check code linters and formatter will be enforced.
>Before you create a PR, make sure that your code lints and is formatted by yapf.
### C++ and CUDA
#### C++ and CUDA
We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
docs/api.rst
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API Documentation
API Reference
=================
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docs/community.rst 0 → 100644
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Community
===========
.. toctree::
:maxdepth: 2
community/contributing.md
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../../CONTRIBUTING.md
\ No newline at end of file
docs/deployment.rst
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Deployment
========
================
.. toctree::
:maxdepth: 2
onnx.md
onnxruntime_op.md
onnxruntime_custom_ops.md
tensorrt_plugin.md
tensorrt_custom_ops.md
deployment/onnx.md
deployment/onnxruntime_op.md
deployment/onnxruntime_custom_ops.md
deployment/tensorrt_plugin.md
deployment/tensorrt_custom_ops.md
docs/onnx.md → docs/deployment/onnx.md
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# Introduction of `onnx` module in MMCV (Experimental)
# Introduction of onnx module in MMCV (Experimental)
## register_extra_symbolics
......
docs/trouble_shooting.md → docs/faq.md
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## Trouble Shooting
## Frequently Asked Questions
We list some common troubles faced by many users and their corresponding solutions here.
Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them.
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docs/get_started.rst 0 → 100644
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Get started
===================
.. toctree::
:maxdepth: 2
get_started/introduction.md
get_started/installation.md
get_started/build.md
docs/get_started/installation.md 0 → 100644
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## Installation
There are two versions of MMCV:
- **mmcv-full**: comprehensive, with full features and various CUDA ops out of box. It takes longer time to build.
- **mmcv**: lite, without CUDA ops but all other features, similar to mmcv<1.0.0. It is useful when you do not need those CUDA ops.
**Note**: Do not install both versions in the same environment, otherwise you may encounter errors like `ModuleNotFound`. You need to uninstall one before installing the other. `Installing the full verion is highly recommended if CUDA is avaliable`.
a. Install the full version.
Before installing mmcv-full, make sure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/).
We provide pre-built mmcv packages (recommended) with different PyTorch and CUDA versions to simplify the building.
i. Install the latest version.
The rule for installing the latest ``mmcv-full`` is as follows:
```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
```
Please replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired one. For example,
to install the latest ``mmcv-full`` with ``CUDA 11`` and ``PyTorch 1.7.0``, use the following command:
```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html
```
For more details, please refer the the following tables and delete ``=={mmcv_version}``.
ii. Install a specified version.
The rule for installing a specified ``mmcv-full`` is as follows:
```shell
pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
```
First of all, please refer to the Releases and replace ``{mmcv_version}`` a specified one. e.g. ``1.2.2``.
Then replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired versions. For example,
to install ``mmcv-full==1.2.2`` with ``CUDA 11`` and ``PyTorch 1.7.0``, use the following command:
```shell
pip install mmcv-full==1.2.2 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html
```
For more details, please refer the the following tables.
<table class="docutils">
<tbody>
<tr>
<th width="80"> CUDA </th>
<th valign="bottom" align="left" width="100">torch 1.8</th>
<th valign="bottom" align="left" width="100">torch 1.7</th>
<th valign="bottom" align="left" width="100">torch 1.6</th>
<th valign="bottom" align="left" width="100">torch 1.5</th>
<th valign="bottom" align="left" width="100">torch 1.4</th>
<th valign="bottom" align="left" width="100">torch 1.3</th>
</tr>
<tr>
<td align="left">11.1</td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html</code></pre> </details> </td>
<td align="left"> </td>
<td align="left"> </td>
<td align="left"> </td>
<td align="left"> </td>
<td align="left"> </td>
</tr>
<tr>
<td align="left">11.0</td>
<td align="left"> </td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html</code></pre> </details> </td>
<td align="left"> </td>
<td align="left"> </td>
<td align="left"> </td>
<td align="left"> </td>
</tr>
<tr>
<td align="left">10.2</td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.7.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.6.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.5.0/index.html</code></pre> </details> </td>
<td align="left"> </td>
<td align="left"> </td>
</tr>
<tr>
<td align="left">10.1</td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.8.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.7.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.5.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.4.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.3.0/index.html</code></pre> </details> </td>
</tr>
<tr>
<td align="left">9.2</td>
<td align="left"> </td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu92/torch1.7.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu92/torch1.6.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu92/torch1.5.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu92/torch1.4.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu92/torch1.3.0/index.html</code></pre> </details> </td>
</tr>
<tr>
<td align="left">cpu</td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.7.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.6.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.5.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.4.0/index.html</code></pre> </details> </td>
<td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.3.0/index.html</code></pre> </details> </td>
</tr>
</tbody>
</table>
Another way is to compile locally by running
```python
pip install mmcv-full
```
Note that the local compiling may take up to 10 mins.
b. Install the lite version.
```python
pip install mmcv
```
c. Install full version with custom operators for onnxruntime
- Check [here](docs/onnxruntime_op.md) for detailed instruction.
If you would like to build MMCV from source, please refer to the [guide](build.md).
docs/get_started/introduction.md 0 → 100644
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## Introduction
<div align="center">
<img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/mmcv-logo.png" width="300"/>
</div>
[![PyPI](https://img.shields.io/pypi/v/mmcv)](https://pypi.org/project/mmcv) [![badge](https://github.com/open-mmlab/mmcv/workflows/build/badge.svg)](https://github.com/open-mmlab/mmcv/actions) [![codecov](https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmcv) [![license](https://img.shields.io/github/license/open-mmlab/mmcv.svg)](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
MMCV is a foundational library for computer vision research and supports many
research projects as below:
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition and understanding toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
It provides the following functionalities.
- Universal IO APIs
- Image/Video processing
- Image and annotation visualization
- Useful utilities (progress bar, timer, ...)
- PyTorch runner with hooking mechanism
- Various CNN architectures
- High-quality implementation of common CUDA ops
Note: MMCV requires Python 3.6+.
docs/index.rst
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.. mdinclude:: readme.md
Contents
========
Welcome to MMCV's documentation!
================================
.. toctree::
:maxdepth: 2
io.md
image.md
video.md
visualization.md
utils.md
runner.md
registry.md
cnn.md
ops.md
build.md
get_started.rst
deployment.rst
trouble_shooting.md
understand_mmcv.rst
api.rst
faq.md
community.rst
Indices and tables
......
docs/understand_mmcv.rst 0 → 100644
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Understand MMCV
=================
.. toctree::
:maxdepth: 2
understand_mmcv/config.md
understand_mmcv/registry.md
understand_mmcv/runner.md
understand_mmcv/io.md
understand_mmcv/data_process.md
understand_mmcv/visualization.md
understand_mmcv/cnn.md
understand_mmcv/ops.md
understand_mmcv/utils.md
docs/utils.md → docs/understand_mmcv/config.md
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## Utils
### Config
## Config
`Config` class is used for manipulating config and config files. It supports
loading configs from multiple file formats including **python**, **json** and **yaml**.
......@@ -69,7 +67,7 @@ a = 1
b = dict(b1=[0, 1, 2], b2=None)
```
#### Inherit from base config without overlapped keys
### Inherit from base config without overlapped keys
`config_b.py`
......@@ -90,7 +88,7 @@ d = 'string'
New fields in `config_b.py` are combined with old fields in `config_a.py`
#### Inherit from base config with overlapped keys
### Inherit from base config with overlapped keys
`config_c.py`
......@@ -110,7 +108,7 @@ c = (1, 2)
`b.b2=None` in `config_a` is replaced with `b.b2=1` in `config_c.py`.
#### Inherit from base config with ignored fields
### Inherit from base config with ignored fields
`config_d.py`
......@@ -130,7 +128,7 @@ c = (1, 2)
You may also set `_delete_=True` to ignore some fields in base configs. All old keys `b1, b2, b3` in `b` are replaced with new keys `b2, b3`.
#### Inherit from multiple base configs (the base configs should not contain the same keys)
### Inherit from multiple base configs (the base configs should not contain the same keys)
`config_e.py`
......@@ -154,7 +152,7 @@ _base_ = ['./config_a.py', './config_e.py']
... d='string')
```
#### Reference variables from base
### Reference variables from base
You can reference variables defined in base using the following grammar.
......@@ -179,75 +177,3 @@ item1 = 'a'
item2 = dict(item3='b')
item = dict(a='a', b='b')
```
### ProgressBar
If you want to apply a method to a list of items and track the progress, `track_progress`
is a good choice. It will display a progress bar to tell the progress and ETA.
```python
import mmcv
def func(item):
# do something
pass
tasks = [item_1, item_2, ..., item_n]
mmcv.track_progress(func, tasks)
```
The output is like the following.
![progress](_static/progress.gif)
There is another method `track_parallel_progress`, which wraps multiprocessing and
progress visualization.
```python
mmcv.track_parallel_progress(func, tasks, 8) # 8 workers
```
![progress](_static/parallel_progress.gif)
If you want to iterate or enumerate a list of items and track the progress, `track_iter_progress`
is a good choice. It will display a progress bar to tell the progress and ETA.
```python
import mmcv
tasks = [item_1, item_2, ..., item_n]
for task in mmcv.track_iter_progress(tasks):
# do something like print
print(task)
for i, task in enumerate(mmcv.track_iter_progress(tasks)):
# do something like print
print(i)
print(task)
```
### Timer
It is convenient to compute the runtime of a code block with `Timer`.
```python
import time
with mmcv.Timer():
# simulate some code block
time.sleep(1)
```
or try with `since_start()` and `since_last_check()`. This former can
return the runtime since the timer starts and the latter will return the time
since the last time checked.
```python
timer = mmcv.Timer()
# code block 1 here
print(timer.since_start())
# code block 2 here
print(timer.since_last_check())
print(timer.since_start())
```
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