mirrors
/
mmcv
mirror of https://github.com/open-mmlab/mmcv.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

[Docs] Replace markdownlint with mdformat for avoiding installing ruby (#1936) 

* Use mdformat pre-commit hook

* allows consecutive numbering

* improve .mdformat.toml

* test mdformat

* format markdown

* minor fix

* fix codespecll

* fix circleci

* add linkify-it-py dependency for cicleci

* add comments

* replace flake8 url

* add mdformat-myst dependency

* remove mdformat-myst dependency

* update contributing.md
pull/1937/head^2
Zaida Zhou 2022-05-16 20:47:56 +08:00 committed by GitHub
parent 8708851eca
commit b326a219a7
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
41 changed files with 1325 additions and 1316 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
.circleci/config.yml
View File

@ -5,12 +5,6 @@ jobs:
- image: cimg/python:3.7.4
steps:
- checkout
- run:
name: Install dependencies
command: |
sudo apt-add-repository ppa:brightbox/ruby-ng -y
sudo apt-get update
sudo apt-get install -y ruby2.7
- run:
name: Install pre-commit hook
command: |

5
.github/ISSUE_TEMPLATE/feature_request.md vendored
View File

@ -4,15 +4,14 @@ about: Suggest an idea for this project
title: ''
labels: ''
assignees: ''
---
**Describe the feature**
**Motivation**
A clear and concise description of the motivation of the feature.
Ex1. It is inconvenient when [....].
Ex2. There is a recent paper [....], which is very helpful for [....].
Ex1. It is inconvenient when \[....\].
Ex2. There is a recent paper \[....\], which is very helpful for \[....\].
**Related resources**
If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful.

1
.github/ISSUE_TEMPLATE/general_questions.md vendored
View File

@ -4,7 +4,6 @@ about: Ask general questions to get help
title: ''
labels: ''
assignees: ''
---
**Checklist**

3
.github/ISSUE_TEMPLATE/unexpected_report.md vendored
View File

@ -4,7 +4,6 @@ about: Create a report to help us improve
title: ''
labels: ''
assignees: ''
---
Thanks for reporting the unexpected results and we appreciate it a lot.
@ -32,7 +31,7 @@ A placeholder for the command.
1. Please run `python -c "from mmcv.utils import collect_env; print(collect_env())"` to collect necessary environment information and paste it here.
2. You may add addition that may be helpful for locating the problem, such as
- How you installed PyTorch [e.g., pip, conda, source]
- How you installed PyTorch \[e.g., pip, conda, source\]
- Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
**Error traceback**

17
.pre-commit-config.yaml
View File

@ -1,6 +1,6 @@
exclude: ^tests/data/
repos:
- repo: https://gitlab.com/pycqa/flake8.git
- repo: https://github.com/PyCQA/flake8
rev: 3.8.3
hooks:
- id: flake8
@ -25,16 +25,19 @@ repos:
args: ["--remove"]
- id: mixed-line-ending
args: ["--fix=lf"]
- repo: https://github.com/markdownlint/markdownlint
rev: v0.11.0
hooks:
- id: markdownlint
args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034",
"-t", "allow_different_nesting"]
- repo: https://github.com/codespell-project/codespell
rev: v2.1.0
hooks:
- id: codespell
- repo: https://github.com/executablebooks/mdformat
rev: 0.7.14
hooks:
- id: mdformat
args: ["--number"]
additional_dependencies:
- mdformat-gfm
- mdformat_frontmatter
- linkify-it-py
- repo: https://github.com/myint/docformatter
rev: v1.3.1
hooks:

22
CONTRIBUTING.md
View File

@ -16,6 +16,7 @@ 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
#### Python
@ -24,10 +25,11 @@ We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code
We use the following tools for linting and formatting:
- [flake8](http://flake8.pycqa.org/en/latest/): A wrapper around some linter tools.
- [yapf](https://github.com/google/yapf): A formatter for Python files.
- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
- [markdownlint](https://github.com/markdownlint/markdownlint): A linter to check markdown files and flag style issues.
- [yapf](https://github.com/google/yapf): A formatter for Python files.
- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
Style configurations of yapf and isort can be found in [setup.cfg](./setup.cfg).
@ -48,20 +50,6 @@ From the repository folder
pre-commit install
```
Try the following steps to install ruby when you encounter an issue on installing markdownlint
```shell
# install rvm
curl -L https://get.rvm.io | bash -s -- --autolibs=read-fail
[[ -s "$HOME/.rvm/scripts/rvm" ]] && source "$HOME/.rvm/scripts/rvm"
rvm autolibs disable
# install ruby
rvm install 2.7.1
```
Or refer to [this repo](https://github.com/innerlee/setup) and take [`zzruby.sh`](https://github.com/innerlee/setup/blob/master/zzruby.sh) according its instruction.
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.

2
LICENSES.md
View File

@ -3,6 +3,6 @@
In this file, we list the operations with other licenses instead of Apache 2.0. Users should be careful about adopting these operations in any commercial matters.
| Operation | Files | License |
| :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------: | :------------: |
| :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------: |
| upfirdn2d | [mmcv/ops/csrc/pytorch/cuda/upfirdn2d_kernel.cu](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/csrc/pytorch/cuda/upfirdn2d_kernel.cu) | NVIDIA License |
| fused_leaky_relu | [mmcv/ops/csrc/pytorch/cuda/fused_bias_leakyrelu_cuda.cu](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/csrc/pytorch/cuda/fused_bias_leakyrelu_cuda.cu) | NVIDIA License |

18
README.md
View File

@ -77,7 +77,7 @@ Note: MMCV requires Python 3.6+.
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.
- **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 version is highly recommended if CUDA is available`.
@ -89,14 +89,14 @@ We provide pre-built mmcv packages (recommended) with different PyTorch and CUDA
i. Install the latest version.
The rule for installing the latest ``mmcv-full`` is as follows:
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 11.1`` and ``PyTorch 1.9.0``, use the following command:
Please replace `{cu_version}` and `{torch_version}` in the url to your desired one. For example,
to install the latest `mmcv-full` with `CUDA 11.1` and `PyTorch 1.9.0`, use the following command:
```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
@ -108,19 +108,19 @@ pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html
```
For more details, please refer the the following tables and delete ``=={mmcv_version}``.
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:
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. ``1.3.9``.
Then replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired versions. For example,
to install ``mmcv-full==1.3.9`` with ``CUDA 11.1`` and ``PyTorch 1.9.0``, use the following command:
First of all, please refer to the Releases and replace `{mmcv_version}` a specified one. e.g. `1.3.9`.
Then replace `{cu_version}` and `{torch_version}` in the url to your desired versions. For example,
to install `mmcv-full==1.3.9` with `CUDA 11.1` and `PyTorch 1.9.0`, use the following command:
```shell
pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html

13
README_zh-CN.md
View File

@ -88,13 +88,13 @@ a. 安装完整版
i. 安装最新版本
如下是安装最新版 ``mmcv-full`` 的命令
如下是安装最新版 `mmcv-full` 的命令
```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
```
请将链接中的 ``{cu_version}`` 和 ``{torch_version}`` 根据自身需求替换成实际的版本号，例如想安装和 ``CUDA 11.1``、``PyTorch 1.9.0`` 兼容的最新版 ``mmcv-full``，使用如下替换过的命令
请将链接中的 `{cu_version}` 和 `{torch_version}` 根据自身需求替换成实际的版本号，例如想安装和 `CUDA 11.1`、`PyTorch 1.9.0` 兼容的最新版 `mmcv-full`，使用如下替换过的命令
```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
@ -106,18 +106,18 @@ pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html
```
如果想知道更多 CUDA 和 PyTorch 版本的命令，可以参考下面的表格，将链接中的 ``=={mmcv_version}`` 删去即可。
如果想知道更多 CUDA 和 PyTorch 版本的命令，可以参考下面的表格，将链接中的 `=={mmcv_version}` 删去即可。
ii. 安装特定的版本
如下是安装特定版本 ``mmcv-full`` 的命令
如下是安装特定版本 `mmcv-full` 的命令
```shell
pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
```
首先请参考版本发布信息找到想要安装的版本号，将 ``{mmcv_version}`` 替换成该版本号，例如 ``1.3.9``。
然后将链接中的 ``{cu_version}`` 和 ``{torch_version}`` 根据自身需求替换成实际的版本号，例如想安装和 ``CUDA 11.1``、``PyTorch 1.9.0`` 兼容的 ``mmcv-full`` 1.3.9 版本，使用如下替换过的命令
首先请参考版本发布信息找到想要安装的版本号，将 `{mmcv_version}` 替换成该版本号，例如 `1.3.9`。
然后将链接中的 `{cu_version}` 和 `{torch_version}` 根据自身需求替换成实际的版本号，例如想安装和 `CUDA 11.1`、`PyTorch 1.9.0` 兼容的 `mmcv-full` 1.3.9 版本，使用如下替换过的命令
```shell
pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
@ -255,6 +255,7 @@ c. 安装完整版并且编译 onnxruntime 的自定义算子
## 许可证
`MMCV` 目前以 Apache 2.0 的许可证发布，但是其中有一部分功能并不是使用的 Apache2.0 许可证，我们在 [许可证](LICENSES.md) 中详细地列出了这些功能以及他们对应的许可证，如果您正在从事盈利性活动，请谨慎参考此文档。
## 欢迎加入 OpenMMLab 社区
扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=3ijNTqfg)，或添加微信小助手”OpenMMLabwx“加入官方交流微信群。

2
TERMINOLOGY.md
View File

@ -5,7 +5,7 @@ This document is used as a reference for English-Chinese terminology translation
该文档用作中英文翻译对照参考。
| English | 中文 |
| :-----: | :---:|
| :---------------: | :----: |
| annotation | 标注 |
| backbone | 主干网络 |
| benchmark | 基准测试 |

28
docs/en/community/pr.md
View File

@ -21,7 +21,7 @@ Pull requests let you tell others about changes you have pushed to a branch in a
#### 1. Get the most recent codebase
+ When you work on your first PR
- When you work on your first PR
Fork the OpenMMLab repository: click the **fork** button at the top right corner of Github page
![avatar](../_static/community/1.png)
@ -38,7 +38,7 @@ Pull requests let you tell others about changes you have pushed to a branch in a
git remote add upstream git@github.com:open-mmlab/mmcv
```
+ After your first PR
- After your first PR
Checkout master branch of the local repository and pull the latest master branch of the source repository
@ -67,23 +67,23 @@ git commit -m 'messages'
#### 4. Push your changes to the forked repository and create a PR
+ Push the branch to your forked remote repository
- Push the branch to your forked remote repository
```bash
git push origin branchname
```
+ Create a PR
- Create a PR
![avatar](../_static/community/2.png)
+ Revise PR message template to describe your motivation and modifications made in this PR. You can also link the related issue to the PR manually in the PR message (For more information, checkout the [official guidance](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue)).
- Revise PR message template to describe your motivation and modifications made in this PR. You can also link the related issue to the PR manually in the PR message (For more information, checkout the [official guidance](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue)).
#### 5. Discuss and review your code
+ After creating a pull request, you can ask a specific person to review the changes you've proposed
- After creating a pull request, you can ask a specific person to review the changes you've proposed
![avatar](../_static/community/3.png)
+ Modify your codes according to reviewers' suggestions and then push your changes
- Modify your codes according to reviewers' suggestions and then push your changes
#### 6. Merge your branch to the master branch and delete the branch
@ -100,15 +100,15 @@ git push origin --delete branchname # delete remote branch
3. Accomplish a detailed change in one PR. Avoid large PR
+ Bad: Support Faster R-CNN
+ Acceptable: Add a box head to Faster R-CNN
+ Good: Add a parameter to box head to support custom conv-layer number
- Bad: Support Faster R-CNN
- Acceptable: Add a box head to Faster R-CNN
- Good: Add a parameter to box head to support custom conv-layer number
4. Provide clear and significant commit message
5. Provide clear and meaningful PR description
+ Task name should be clarified in title. The general format is: [Prefix] Short description of the PR (Suffix)
+ Prefix: add new feature [Feature], fix bug [Fix], related to documents [Docs], in developing [WIP] (which will not be reviewed temporarily)
+ Introduce main changes, results and influences on other modules in short description
+ Associate related issues and pull requests with a milestone
- Task name should be clarified in title. The general format is: \[Prefix\] Short description of the PR (Suffix)
- Prefix: add new feature \[Feature\], fix bug \[Fix\], related to documents \[Docs\], in developing \[WIP\] (which will not be reviewed temporarily)
- Introduce main changes, results and influences on other modules in short description
- Associate related issues and pull requests with a milestone

47
docs/en/deployment/mmcv_ops_definition.md
View File

@ -3,6 +3,7 @@
To make custom operators in MMCV more standard, precise definitions of each operator are listed in this document.
<!-- TOC -->
- [MMCV Operators](#mmcv-operators)
- [MMCVBorderAlign](#mmcvborderalign)
- [Description](#description)
@ -82,25 +83,26 @@ To make custom operators in MMCV more standard, precise definitions of each oper
- [Inputs](#inputs-12)
- [Outputs](#outputs-12)
- [Type Constraints](#type-constraints-12)
- [grid_sampler*](#grid_sampler)
- [grid_sampler\*](#grid_sampler)
- [Description](#description-13)
- [Parameters](#parameters-13)
- [Inputs](#inputs-13)
- [Outputs](#outputs-13)
- [Type Constraints](#type-constraints-13)
- [cummax*](#cummax)
- [cummax\*](#cummax)
- [Description](#description-14)
- [Parameters](#parameters-14)
- [Inputs](#inputs-14)
- [Outputs](#outputs-14)
- [Type Constraints](#type-constraints-14)
- [cummin*](#cummin)
- [cummin\*](#cummin)
- [Description](#description-15)
- [Parameters](#parameters-15)
- [Inputs](#inputs-15)
- [Outputs](#outputs-15)
- [Type Constraints](#type-constraints-15)
- [Reminders](#reminders)
<!-- TOC -->
## MMCVBorderAlign
@ -121,7 +123,7 @@ Read [BorderDet: Border Feature for Dense Object Detection](ttps://arxiv.org/abs
### Parameters
| Type | Parameter | Description |
|-------|-------------|-------------------------------------------------------------------------------------|
| ----- | ----------- | ----------------------------------------------------------------------------------- |
| `int` | `pool_size` | number of positions sampled over the boxes' borders(e.g. top, bottom, left, right). |
### Inputs
@ -155,7 +157,7 @@ Read [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.0
### Parameters
| Type | Parameter | Description |
|---------|----------------|-----------------------------------------------|
| ------- | -------------- | --------------------------------------------- |
| `int` | `kernel_size` | reassemble kernel size, should be odd integer |
| `int` | `group_size` | reassemble group size |
| `float` | `scale_factor` | upsample ratio(>=1) |
@ -242,7 +244,6 @@ None
- T:tensor(float32)
## MMCVCornerPool
### Description
@ -252,7 +253,7 @@ Perform CornerPool on `input` features. Read [CornerNet -- Detecting Objects as
### Parameters
| Type | Parameter | Description |
|-------|-----------|------------------------------------------------------------------|
| ----- | --------- | ---------------------------------------------------------------- |
| `int` | `mode` | corner pool mode, (0: `top`, 1: `bottom`, 2: `left`, 3: `right`) |
### Inputs
@ -284,7 +285,7 @@ Read [Deformable Convolutional Networks](https://arxiv.org/pdf/1703.06211.pdf) f
### Parameters
| Type | Parameter | Description |
|----------------|---------------------|-------------------------------------------------------------------------------------------------------------------|
| -------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------- |
| `list of ints` | `stride` | The stride of the convolving kernel, (sH, sW). Defaults to `(1, 1)`. |
| `list of ints` | `padding` | Paddings on both sides of the input, (padH, padW). Defaults to `(0, 0)`. |
| `list of ints` | `dilation` | The spacing between kernel elements (dH, dW). Defaults to `(1, 1)`. |
@ -324,7 +325,7 @@ Perform Modulated Deformable Convolution on input feature, read [Deformable Conv
### Parameters
| Type | Parameter | Description |
|----------------|---------------------|---------------------------------------------------------------------------------------|
| -------------- | ------------------- | ------------------------------------------------------------------------------------- |
| `list of ints` | `stride` | The stride of the convolving kernel. (sH, sW) |
| `list of ints` | `padding` | Paddings on both sides of the input. (padH, padW) |
| `list of ints` | `dilation` | The spacing between kernel elements. (dH, dW) |
@ -366,7 +367,7 @@ Deformable roi pooling layer
### Parameters
| Type | Parameter | Description |
|---------|------------------|---------------------------------------------------------------------------------------------------------------|
| ------- | ---------------- | ------------------------------------------------------------------------------------------------------------- |
| `int` | `output_height` | height of output roi |
| `int` | `output_width` | width of output roi |
| `float` | `spatial_scale` | used to scale the input boxes |
@ -405,7 +406,7 @@ Read [Pixel Recurrent Neural Networks](https://arxiv.org/abs/1601.06759) for mor
### Parameters
| Type | Parameter | Description |
|----------------|-----------|----------------------------------------------------------------------------------|
| -------------- | --------- | -------------------------------------------------------------------------------- |
| `list of ints` | `stride` | The stride of the convolving kernel. (sH, sW). **Only support stride=1 in mmcv** |
| `list of ints` | `padding` | Paddings on both sides of the input. (padH, padW). Defaults to `(0, 0)`. |
@ -444,7 +445,7 @@ Read [PSANet: Point-wise Spatial Attention Network for Scene Parsing](https://hs
### Parameters
| Type | Parameter | Description |
|----------------|-------------|----------------------------------------------|
| -------------- | ----------- | -------------------------------------------- |
| `int` | `psa_type` | `0` means collect and `1` means `distribute` |
| `list of ints` | `mask_size` | The size of mask |
@ -477,10 +478,10 @@ Note this definition is slightly different with [onnx: NonMaxSuppression](https:
### Parameters
| Type | Parameter | Description |
|---------|------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|
| `int` | `center_point_box` | 0 - the box data is supplied as [y1, x1, y2, x2], 1-the box data is supplied as [x_center, y_center, width, height]. |
| ------- | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| `int` | `center_point_box` | 0 - the box data is supplied as \[y1, x1, y2, x2\], 1-the box data is supplied as \[x_center, y_center, width, height\]. |
| `int` | `max_output_boxes_per_class` | The maximum number of boxes to be selected per batch per class. Default to 0, number of output boxes equal to number of input boxes. |
| `float` | `iou_threshold` | The threshold for deciding whether boxes overlap too much with respect to IoU. Value range [0, 1]. Default to 0. |
| `float` | `iou_threshold` | The threshold for deciding whether boxes overlap too much with respect to IoU. Value range \[0, 1\]. Default to 0. |
| `float` | `score_threshold` | The threshold for deciding when to remove boxes based on score. |
| `int` | `offset` | 0 or 1, boxes' width or height is (x2 - x1 + offset). |
@ -515,7 +516,7 @@ Perform RoIAlign on output feature, used in bbox_head of most two-stage detector
### Parameters
| Type | Parameter | Description |
|---------|------------------|---------------------------------------------------------------------------------------------------------------|
| ------- | ---------------- | ------------------------------------------------------------------------------------------------------------- |
| `int` | `output_height` | height of output roi |
| `int` | `output_width` | width of output roi |
| `float` | `spatial_scale` | used to scale the input boxes |
@ -552,7 +553,7 @@ Perform RoI align pooling for rotated proposals
### Parameters
| Type | Parameter | Description |
|---------|------------------|---------------------------------------------------------------------------------------------------------------|
| ------- | ---------------- | ------------------------------------------------------------------------------------------------------------- |
| `int` | `output_height` | height of output roi |
| `int` | `output_width` | width of output roi |
| `float` | `spatial_scale` | used to scale the input boxes |
@ -580,7 +581,7 @@ Perform RoI align pooling for rotated proposals
- T:tensor(float32)
## grid_sampler*
## grid_sampler\*
### Description
@ -591,7 +592,7 @@ Check [torch.nn.functional.grid_sample](https://pytorch.org/docs/stable/generate
### Parameters
| Type | Parameter | Description |
|-------|----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ----- | -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `int` | `interpolation_mode` | Interpolation mode to calculate output values. (0: `bilinear` , 1: `nearest`) |
| `int` | `padding_mode` | Padding mode for outside grid values. (0: `zeros`, 1: `border`, 2: `reflection`) |
| `int` | `align_corners` | If `align_corners=1`, the extrema (`-1` and `1`) are considered as referring to the center points of the input's corner pixels. If `align_corners=0`, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic. |
@ -616,7 +617,7 @@ Check [torch.nn.functional.grid_sample](https://pytorch.org/docs/stable/generate
- T:tensor(float32, Linear)
## cummax*
## cummax\*
### Description
@ -625,7 +626,7 @@ Returns a tuple (`values`, `indices`) where `values` is the cumulative maximum e
### Parameters
| Type | Parameter | Description |
|-------|-----------|----------------------------------------|
| ----- | --------- | -------------------------------------- |
| `int` | `dim` | the dimension to do the operation over |
### Inputs
@ -648,7 +649,7 @@ Returns a tuple (`values`, `indices`) where `values` is the cumulative maximum e
- T:tensor(float32)
## cummin*
## cummin\*
### Description
@ -657,7 +658,7 @@ Returns a tuple (`values`, `indices`) where `values` is the cumulative minimum e
### Parameters
| Type | Parameter | Description |
|-------|-----------|----------------------------------------|
| ----- | --------- | -------------------------------------- |
| `int` | `dim` | the dimension to do the operation over |
### Inputs

20
docs/en/deployment/onnxruntime_custom_ops.md
View File

@ -69,7 +69,7 @@ Perform soft NMS on `boxes` with `scores`. Read [Soft-NMS -- Improving Object De
#### Parameters
| Type | Parameter | Description |
|---------|-----------------|----------------------------------------------------------------|
| ------- | --------------- | -------------------------------------------------------------- |
| `float` | `iou_threshold` | IoU threshold for NMS |
| `float` | `sigma` | hyperparameter for gaussian method |
| `float` | `min_score` | score filter threshold |
@ -107,7 +107,7 @@ Perform RoIAlign on output feature, used in bbox_head of most two-stage detector
#### Parameters
| Type | Parameter | Description |
|---------|------------------|---------------------------------------------------------------------------------------------------------------|
| ------- | ---------------- | ------------------------------------------------------------------------------------------------------------- |
| `int` | `output_height` | height of output roi |
| `int` | `output_width` | width of output roi |
| `float` | `spatial_scale` | used to scale the input boxes |
@ -144,8 +144,8 @@ Filter out boxes has high IoU overlap with previously selected boxes.
#### Parameters
| Type | Parameter | Description |
|---------|-----------------|------------------------------------------------------------------------------------------------------------------|
| `float` | `iou_threshold` | The threshold for deciding whether boxes overlap too much with respect to IoU. Value range [0, 1]. Default to 0. |
| ------- | --------------- | ------------------------------------------------------------------------------------------------------------------ |
| `float` | `iou_threshold` | The threshold for deciding whether boxes overlap too much with respect to IoU. Value range \[0, 1\]. Default to 0. |
| `int` | `offset` | 0 or 1, boxes' width or height is (x2 - x1 + offset). |
#### Inputs
@ -177,7 +177,7 @@ Perform sample from `input` with pixel locations from `grid`.
#### Parameters
| Type | Parameter | Description |
|-------|----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ----- | -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `int` | `interpolation_mode` | Interpolation mode to calculate output values. (0: `bilinear` , 1: `nearest`) |
| `int` | `padding_mode` | Padding mode for outside grid values. (0: `zeros`, 1: `border`, 2: `reflection`) |
| `int` | `align_corners` | If `align_corners=1`, the extrema (`-1` and `1`) are considered as referring to the center points of the input's corner pixels. If `align_corners=0`, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic. |
@ -211,7 +211,7 @@ Perform CornerPool on `input` features. Read [CornerNet -- Detecting Objects as
#### Parameters
| Type | Parameter | Description |
|-------|-----------|------------------------------------------------------------------|
| ----- | --------- | ---------------------------------------------------------------- |
| `int` | `mode` | corner pool mode, (0: `top`, 1: `bottom`, 2: `left`, 3: `right`) |
#### Inputs
@ -241,7 +241,7 @@ Returns a tuple (`values`, `indices`) where `values` is the cumulative maximum e
#### Parameters
| Type | Parameter | Description |
|-------|-----------|----------------------------------------|
| ----- | --------- | -------------------------------------- |
| `int` | `dim` | the dimension to do the operation over |
#### Inputs
@ -273,7 +273,7 @@ Returns a tuple (`values`, `indices`) where `values` is the cumulative minimum e
#### Parameters
| Type | Parameter | Description |
|-------|-----------|----------------------------------------|
| ----- | --------- | -------------------------------------- |
| `int` | `dim` | the dimension to do the operation over |
#### Inputs
@ -305,7 +305,7 @@ Perform Modulated Deformable Convolution on input feature, read [Deformable Conv
#### Parameters
| Type | Parameter | Description |
|----------------|---------------------|---------------------------------------------------------------------------------------|
| -------------- | ------------------- | ------------------------------------------------------------------------------------- |
| `list of ints` | `stride` | The stride of the convolving kernel. (sH, sW) |
| `list of ints` | `padding` | Paddings on both sides of the input. (padH, padW) |
| `list of ints` | `dilation` | The spacing between kernel elements. (dH, dW) |
@ -347,7 +347,7 @@ Perform Deformable Convolution on input feature, read [Deformable Convolutional
#### Parameters
| Type | Parameter | Description |
|----------------|--------------------|-----------------------------------------------------------------------------------------------------------------------------------|
| -------------- | ------------------ | --------------------------------------------------------------------------------------------------------------------------------- |
| `list of ints` | `stride` | The stride of the convolving kernel. (sH, sW) |
| `list of ints` | `padding` | Paddings on both sides of the input. (padH, padW) |
| `list of ints` | `dilation` | The spacing between kernel elements. (dH, dW) |

5
docs/en/deployment/onnxruntime_op.md
View File

@ -21,7 +21,7 @@ Welcome to use the unified model deployment toolbox MMDeploy: https://github.com
### List of operators for ONNX Runtime supported in MMCV
| Operator | CPU | GPU | MMCV Releases |
|:-------------------------------------------------------|:---:|:---:|:-------------:|
| :----------------------------------------------------- | :-: | :-: | :-----------: |
| [SoftNMS](onnxruntime_custom_ops.md#softnms) | Y | N | 1.2.3 |
| [RoIAlign](onnxruntime_custom_ops.md#roialign) | Y | N | 1.2.5 |
| [NMS](onnxruntime_custom_ops.md#nms) | Y | N | 1.2.7 |
@ -96,6 +96,7 @@ onnx_results = sess.run(None, {'input' : input_data})
- *Please note that this feature is experimental and may change in the future. Strongly suggest users always try with the latest master branch.*
- The custom operator is not included in [supported operator list](https://github.com/microsoft/onnxruntime/blob/master/docs/OperatorKernels.md) in ONNX Runtime.
- The custom operator should be able to be exported to ONNX.
#### Main procedures
@ -103,7 +104,9 @@ onnx_results = sess.run(None, {'input' : input_data})
Take custom operator `soft_nms` for example.
1. Add header `soft_nms.h` to ONNX Runtime include directory `mmcv/ops/csrc/onnxruntime/`
2. Add source `soft_nms.cpp` to ONNX Runtime source directory `mmcv/ops/csrc/onnxruntime/cpu/`
3. Register `soft_nms` operator in [onnxruntime_register.cpp](../../mmcv/ops/csrc/onnxruntime/cpu/onnxruntime_register.cpp)
```c++

6
docs/en/deployment/tensorrt_custom_ops.md
View File

@ -102,7 +102,7 @@ detectors.
#### Description
ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by updates at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. Note that `indices` should not have duplicate entries. That is, two or more updates for the same index-location is not supported.
ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape\[-1\] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by updates at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. Note that `indices` should not have duplicate entries. That is, two or more updates for the same index-location is not supported.
The `output` is calculated via the following equation:
@ -151,9 +151,9 @@ Filter out boxes has high IoU overlap with previously selected boxes or low scor
| Type | Parameter | Description |
| ------- | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| `int` | `center_point_box` | 0 - the box data is supplied as [y1, x1, y2, x2], 1-the box data is supplied as [x_center, y_center, width, height]. |
| `int` | `center_point_box` | 0 - the box data is supplied as \[y1, x1, y2, x2\], 1-the box data is supplied as \[x_center, y_center, width, height\]. |
| `int` | `max_output_boxes_per_class` | The maximum number of boxes to be selected per batch per class. Default to 0, number of output boxes equal to number of input boxes. |
| `float` | `iou_threshold` | The threshold for deciding whether boxes overlap too much with respect to IoU. Value range [0, 1]. Default to 0. |
| `float` | `iou_threshold` | The threshold for deciding whether boxes overlap too much with respect to IoU. Value range \[0, 1\]. Default to 0. |
| `float` | `score_threshold` | The threshold for deciding when to remove boxes based on score. |
| `int` | `offset` | 0 or 1, boxes' width or height is (x2 - x1 + offset). |

6
docs/en/deployment/tensorrt_plugin.md
View File

@ -4,6 +4,7 @@
TensorRT support will be deprecated in the future.
Welcome to use the unified model deployment toolbox MMDeploy: https://github.com/open-mmlab/mmdeploy
<!-- TOC -->
- [TensorRT Deployment](#tensorrt-deployment)
@ -30,7 +31,7 @@ To ease the deployment of trained models with custom operators from `mmcv.ops` u
### List of TensorRT plugins supported in MMCV
| ONNX Operator | TensorRT Plugin | MMCV Releases |
|:--------------------------|:--------------------------------------------------------------------------------|:-------------:|
| :------------------------ | :------------------------------------------------------------------------------ | :-----------: |
| MMCVRoiAlign | [MMCVRoiAlign](./tensorrt_custom_ops.md#mmcvroialign) | 1.2.6 |
| ScatterND | [ScatterND](./tensorrt_custom_ops.md#scatternd) | 1.2.6 |
| NonMaxSuppression | [NonMaxSuppression](./tensorrt_custom_ops.md#nonmaxsuppression) | 1.3.0 |
@ -151,8 +152,11 @@ Below are the main steps:
**Take RoIAlign plugin `roi_align` for example.**
1. Add header `trt_roi_align.hpp` to TensorRT include directory `mmcv/ops/csrc/tensorrt/`
2. Add source `trt_roi_align.cpp` to TensorRT source directory `mmcv/ops/csrc/tensorrt/plugins/`
3. Add cuda kernel `trt_roi_align_kernel.cu` to TensorRT source directory `mmcv/ops/csrc/tensorrt/plugins/`
4. Register `roi_align` plugin in [trt_plugin.cpp](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/csrc/tensorrt/plugins/trt_plugin.cpp)
```c++

4
docs/en/faq.md
View File

@ -10,7 +10,7 @@ Feel free to enrich the list if you find any frequent issues and have ways to he
The registry mechanism will be triggered only when the file of the module is imported.
So you need to import that file somewhere. More details can be found at https://github.com/open-mmlab/mmdetection/issues/5974.
- "No module named 'mmcv.ops'"; "No module named 'mmcv._ext'"
- "No module named 'mmcv.ops'"; "No module named 'mmcv.\_ext'"
1. Uninstall existing mmcv in the environment using `pip uninstall mmcv`
2. Install mmcv-full following the [installation instruction](https://mmcv.readthedocs.io/en/latest/get_started/installation.html) or [Build MMCV from source](https://mmcv.readthedocs.io/en/latest/get_started/build.html)
@ -40,7 +40,7 @@ Feel free to enrich the list if you find any frequent issues and have ways to he
- "Segmentation fault"
1. Check your GCC version and use GCC >= 5.4. This usually caused by the incompatibility between PyTorch and the environment (e.g., GCC < 4.9 for PyTorch). We also recommend the users to avoid using GCC 5.5 because many feedbacks report that GCC 5.5 will cause "segmentation fault" and simply changing it to GCC 5.4 could solve the problem
1. Check your GCC version and use GCC >= 5.4. This usually caused by the incompatibility between PyTorch and the environment (e.g., GCC \< 4.9 for PyTorch). We also recommend the users to avoid using GCC 5.5 because many feedbacks report that GCC 5.5 will cause "segmentation fault" and simply changing it to GCC 5.4 could solve the problem
2. Check whether PyTorch is correctly installed and could use CUDA op, e.g. type the following command in your terminal and see whether they could correctly output results
```shell
python -c 'import torch; print(torch.cuda.is_available())'

6
docs/en/get_started/build.md
View File

@ -46,6 +46,7 @@ If you would like to use `opencv-python-headless` instead of `opencv-python`,
e.g., in a minimum container environment or servers without GUI,
you can first install it before installing MMCV to skip the installation of `opencv-python`.
```
### Build on Windows
Building MMCV on Windows is a bit more complicated than that on Linux.
@ -162,6 +163,7 @@ pip list
##### Option 2: Build MMCV (full version with CPU)
1. Finish above common steps
2. Set up environment variables
```shell
@ -187,6 +189,7 @@ pip list
##### Option 3: Build MMCV (full version with CUDA)
1. Finish above common steps
2. Make sure `CUDA_PATH` or `CUDA_HOME` is already set in `envs` via `ls env:`, desired output is shown as below:
```none
@ -245,13 +248,14 @@ If you are compiling against PyTorch 1.6.0, you might meet some errors from PyTo
If you meet issues when running or compiling mmcv, we list some common issues in [Frequently Asked Question](../faq.html).
## [Optional] Build MMCV on IPU machine
## \[Optional\] Build MMCV on IPU machine
Firstly, you need to apply for an IPU cloud machine, see [here](https://www.graphcore.ai/ipus-in-the-cloud).
### Option 1: Docker
1. Pull docker
```shell
docker pull graphcore/pytorch
```

18
docs/en/get_started/installation.md
View File

@ -3,7 +3,7 @@
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.
- **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.
```{warning}
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 version is highly recommended if CUDA is avaliable`.
@ -17,32 +17,32 @@ We provide pre-built mmcv packages (recommended) with different PyTorch and CUDA
i. Install the latest version.
The rule for installing the latest ``mmcv-full`` is as follows:
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 11.1`` and ``PyTorch 1.9.0``, use the following command:
Please replace `{cu_version}` and `{torch_version}` in the url to your desired one. For example,
to install the latest `mmcv-full` with `CUDA 11.1` and `PyTorch 1.9.0`, use the following command:
```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
```
For more details, please refer the the following tables and delete ``=={mmcv_version}``.
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:
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. ``1.3.9``.
Then replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired versions. For example,
to install ``mmcv-full==1.3.9`` with ``CUDA 11.1`` and ``PyTorch 1.9.0``, use the following command:
First of all, please refer to the Releases and replace `{mmcv_version}` a specified one. e.g. `1.3.9`.
Then replace `{cu_version}` and `{torch_version}` in the url to your desired versions. For example,
to install `mmcv-full==1.3.9` with `CUDA 11.1` and `PyTorch 1.9.0`, use the following command:
```shell
pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html

4
docs/en/get_started/previous_versions.md
View File

@ -4,7 +4,7 @@ We no longer provide `mmcv-full` packages compiled under lower versions of `PyTo
### PyTorch 1.4
| 1.0.0 <= mmcv_version <= 1.2.1
| 1.0.0 \<= mmcv_version \<= 1.2.1
#### CUDA 10.1
@ -26,7 +26,7 @@ pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dis
### PyTorch v1.3
| 1.0.0 <= mmcv_version <= 1.3.16
| 1.0.0 \<= mmcv_version \<= 1.3.16
#### CUDA 10.1

4
docs/en/understand_mmcv/cnn.md
View File

@ -376,7 +376,7 @@ Let us introduce the usage of `initialize` in detail.
`ModuleDict` is inherited from `BaseModule` and `torch.nn.ModuleDict`.
`````python
```python
import torch.nn as nn
from mmcv.runner import BaseModule, Sequential, ModuleList, ModuleDict
@ -539,7 +539,7 @@ Let us introduce the usage of `initialize` in detail.
# [[2., 2., 2.],
# [2., 2., 2.],
# [2., 2., 2.]]]], requires_grad=True)
`````
```
### Model Zoo

3
docs/en/understand_mmcv/registry.md
View File

@ -51,6 +51,7 @@ class Converter1(object):
self.a = a
self.b = b
```
```python
# converter2.py
from .builder import CONVERTERS
@ -61,6 +62,7 @@ from .converter1 import Converter1
def converter2(a, b)
return Converter1(a, b)
```
The key step to use registry for managing the modules is to register the implemented module into the registry `CONVERTERS` through
`@CONVERTERS.register_module()` when you are creating the module. By this way, a mapping between a string and the class (function) is built and maintained by `CONVERTERS` as below
@ -68,6 +70,7 @@ The key step to use registry for managing the modules is to register the impleme
'Converter1' -> <class 'Converter1'>
'converter2' -> <function 'converter2'>
```
```{note}
The registry mechanism will be triggered only when the file where the module is located is imported.
So you need to import that file somewhere. More details can be found at https://github.com/open-mmlab/mmdetection/issues/5974.

10
docs/en/understand_mmcv/runner.md
View File

@ -8,7 +8,7 @@ The runner class is designed to manage the training. It eases the training proce
### EpochBasedRunner
As its name indicates, workflow in `EpochBasedRunner` should be set based on epochs. For example, [('train', 2), ('val', 1)] means running 2 epochs for training and 1 epoch for validation, iteratively. And each epoch may contain multiple iterations. Currently, MMDetection uses `EpochBasedRunner` by default.
As its name indicates, workflow in `EpochBasedRunner` should be set based on epochs. For example, \[('train', 2), ('val', 1)\] means running 2 epochs for training and 1 epoch for validation, iteratively. And each epoch may contain multiple iterations. Currently, MMDetection uses `EpochBasedRunner` by default.
Let's take a look at its core logic:
@ -44,7 +44,7 @@ def train(self, data_loader, **kwargs):
### IterBasedRunner
Different from `EpochBasedRunner`, workflow in `IterBasedRunner` should be set based on iterations. For example, [('train', 2), ('val', 1)] means running 2 iters for training and 1 iter for validation, iteratively. Currently, MMSegmentation uses `IterBasedRunner` by default.
Different from `EpochBasedRunner`, workflow in `IterBasedRunner` should be set based on iterations. For example, \[('train', 2), ('val', 1)\] means running 2 iters for training and 1 iter for validation, iteratively. Currently, MMSegmentation uses `IterBasedRunner` by default.
Let's take a look at its core logic:
@ -156,8 +156,8 @@ runner.run(data_loaders, cfg.workflow)
Let's take `EpochBasedRunner` for example and go a little bit into details about setting workflow:
- Say we only want to put train in the workflow, then we can set: workflow = [('train', 1)]. The runner will only execute train iteratively in this case.
- Say we want to put both train and val in the workflow, then we can set: workflow = [('train', 3), ('val',1)]. The runner will first execute train for 3 epochs and then switch to val mode and execute val for 1 epoch. The workflow will be repeated until the current epoch hit the max_epochs.
- Workflow is highly flexible. Therefore, you can set workflow = [('val', 1), ('train',1)] if you would like the runner to validate first and train after.
- Say we only want to put train in the workflow, then we can set: workflow = \[('train', 1)\]. The runner will only execute train iteratively in this case.
- Say we want to put both train and val in the workflow, then we can set: workflow = \[('train', 3), ('val',1)\]. The runner will first execute train for 3 epochs and then switch to val mode and execute val for 1 epoch. The workflow will be repeated until the current epoch hit the max_epochs.
- Workflow is highly flexible. Therefore, you can set workflow = \[('val', 1), ('train',1)\] if you would like the runner to validate first and train after.
The code we demonstrated above is already in `train.py` in MM repositories. Simply modify the corresponding keys in the configuration files and the script will execute the expected workflow automatically.

26
docs/zh_cn/community/contributing.md
View File

@ -7,7 +7,9 @@
- 添加新功能和新组件
### 工作流
| 详细工作流见 [拉取请求](pr.md)
1. 复刻并拉取最新的 OpenMMLab 算法库
2. 创建新的分支（不建议使用主分支提拉取请求）
3. 提交你的修改
@ -16,16 +18,18 @@
```{note}
如果你计划添加新功能并且该功能包含比较大的改动，建议先开 issue 讨论
```
### 代码风格
#### Python
[PEP8](https://www.python.org/dev/peps/pep-0008/) 作为 OpenMMLab 算法库首选的代码规范，我们使用以下工具检查和格式化代码
- [flake8](http://flake8.pycqa.org/en/latest/): Python 官方发布的代码规范检查工具，是多个检查工具的封装
- [yapf](https://github.com/google/yapf): Google 发布的代码规范检查工具
- [flake8](https://github.com/PyCQA/flake8): Python 官方发布的代码规范检查工具，是多个检查工具的封装
- [isort](https://github.com/timothycrosley/isort): 自动调整模块导入顺序的工具
- [markdownlint](https://github.com/markdownlint/markdownlint): 检查 markdown 文件的工具
- [yapf](https://github.com/google/yapf): Google 发布的代码规范检查工具
- [codespell](https://github.com/codespell-project/codespell): 检查单词拼写是否有误
- [mdformat](https://github.com/executablebooks/mdformat): 检查 markdown 文件的工具
- [docformatter](https://github.com/myint/docformatter): 格式化 docstring 的工具
yapf 和 isort 的配置可以在 [setup.cfg](./setup.cfg) 找到
@ -46,22 +50,6 @@ pip install -U pre-commit
pre-commit install
```
如果安装 markdownlint 遇到了问题，可以尝试使用以下的步骤安装 ruby
```shell
# install rvm
curl -L https://get.rvm.io | bash -s -- --autolibs=read-fail
[[ -s "$HOME/.rvm/scripts/rvm" ]] && source "$HOME/.rvm/scripts/rvm"
rvm autolibs disable
# install ruby
rvm install 2.7.1
```
或者参考 [这个代码库](https://github.com/innerlee/setup) 和 [`zzruby.sh`](https://github.com/innerlee/setup/blob/master/zzruby.sh)。
至此，每一次 commit 修改都会触发 pre-commit 检查代码格式。
> 提交拉取请求前，请确保你的代码符合 yapf 的格式
#### C++ and CUDA

29
docs/zh_cn/community/pr.md
View File

@ -21,7 +21,7 @@
#### 1. 获取最新的代码库
+ 当你第一次提 PR 时
- 当你第一次提 PR 时
复刻 OpenMMLab 原代码库，点击 GitHub 页面右上角的 **Fork** 按钮即可
![avatar](../../en/_static/community/1.png)
@ -38,7 +38,7 @@
git remote add upstream git@github.com:open-mmlab/mmcv
```
+ 从第二个 PR 起
- 从第二个 PR 起
检出本地代码库的主分支，然后从最新的原代码库的主分支拉取更新
@ -56,6 +56,7 @@ git checkout -b branchname
```{tip}
为了保证提交历史清晰可读，我们强烈推荐您先检出主分支 (master)，再创建新的分支。
```
#### 3. 提交你的修改
```bash
@ -66,23 +67,23 @@ git commit -m 'messages'
#### 4. 推送你的修改到复刻的代码库，并创建一个`拉取请求`
+ 推送当前分支到远端复刻的代码库
- 推送当前分支到远端复刻的代码库
```bash
git push origin branchname
```
+ 创建一个`拉取请求`
- 创建一个`拉取请求`
![avatar](../../en/_static/community/2.png)
+ 修改`拉取请求`信息模板，描述修改原因和修改内容。还可以在 PR 描述中，手动关联到相关的`议题` (issue),（更多细节，请参考[官方文档](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue)）。
- 修改`拉取请求`信息模板，描述修改原因和修改内容。还可以在 PR 描述中，手动关联到相关的`议题` (issue),（更多细节，请参考[官方文档](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue)）。
#### 5. 讨论并评审你的代码
+ 创建`拉取请求`时，可以关联给相关人员进行评审
- 创建`拉取请求`时，可以关联给相关人员进行评审
![avatar](../../en/_static/community/3.png)
+ 根据评审人员的意见修改代码，并推送修改
- 根据评审人员的意见修改代码，并推送修改
#### 6. `拉取请求`合并之后删除该分支
@ -99,15 +100,15 @@ git push origin --delete branchname # delete remote branch
3. 粒度要细，一个PR只做一件事情，避免超大的PR
+ Bad：实现 Faster R-CNN
+ Acceptable：给 Faster R-CNN 添加一个 box head
+ Good：给 box head 增加一个参数来支持自定义的 conv 层数
- Bad：实现 Faster R-CNN
- Acceptable：给 Faster R-CNN 添加一个 box head
- Good：给 box head 增加一个参数来支持自定义的 conv 层数
4. 每次 Commit 时需要提供清晰且有意义 commit 信息
5. 提供清晰且有意义的`拉取请求`描述
+ 标题写明白任务名称，一般格式:[Prefix] Short description of the pull request (Suffix)
+ prefix: 新增功能 [Feature], 修 bug [Fix], 文档相关 [Docs], 开发中 [WIP] (暂时不会被review)
+ 描述里介绍`拉取请求`的主要修改内容，结果，以及对其他部分的影响, 参考`拉取请求`模板
+ 关联相关的`议题` (issue) 和其他`拉取请求`
- 标题写明白任务名称，一般格式:\[Prefix\] Short description of the pull request (Suffix)
- prefix: 新增功能 \[Feature\], 修 bug \[Fix\], 文档相关 \[Docs\], 开发中 \[WIP\] (暂时不会被review)
- 描述里介绍`拉取请求`的主要修改内容，结果，以及对其他部分的影响, 参考`拉取请求`模板
- 关联相关的`议题` (issue) 和其他`拉取请求`

102
docs/zh_cn/deployment/onnxruntime_custom_ops.md
View File

@ -2,55 +2,55 @@
<!-- TOC -->
- [ONNX Runtime自定义算子](#onnx-runtime自定义算子)
- [ONNX Runtime自定义算子](#onnx-runtime%E8%87%AA%E5%AE%9A%E4%B9%89%E7%AE%97%E5%AD%90)
- [SoftNMS](#softnms)
- [描述](#描述)
- [模型参数](#模型参数)
- [输入](#输入)
- [输出](#输出)
- [类型约束](#类型约束)
- [描述](#%E6%8F%8F%E8%BF%B0)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0)
- [输入](#%E8%BE%93%E5%85%A5)
- [输出](#%E8%BE%93%E5%87%BA)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F)
- [RoIAlign](#roialign)
- [描述](#描述-1)
- [模型参数](#模型参数-1)
- [输入](#输入-1)
- [输出](#输出-1)
- [类型约束](#类型约束-1)
- [描述](#%E6%8F%8F%E8%BF%B0-1)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-1)
- [输入](#%E8%BE%93%E5%85%A5-1)
- [输出](#%E8%BE%93%E5%87%BA-1)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-1)
- [NMS](#nms)
- [描述](#描述-2)
- [模型参数](#模型参数-2)
- [输入](#输入-2)
- [输出](#输出-2)
- [类型约束](#类型约束-2)
- [描述](#%E6%8F%8F%E8%BF%B0-2)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-2)
- [输入](#%E8%BE%93%E5%85%A5-2)
- [输出](#%E8%BE%93%E5%87%BA-2)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-2)
- [grid_sampler](#grid_sampler)
- [描述](#描述-3)
- [模型参数](#模型参数-3)
- [输入](#输入-3)
- [输出](#输出-3)
- [类型约束](#类型约束-3)
- [描述](#%E6%8F%8F%E8%BF%B0-3)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-3)
- [输入](#%E8%BE%93%E5%85%A5-3)
- [输出](#%E8%BE%93%E5%87%BA-3)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-3)
- [CornerPool](#cornerpool)
- [描述](#描述-4)
- [模型参数](#模型参数-4)
- [输入](#输入-4)
- [输出](#输出-4)
- [类型约束](#类型约束-4)
- [描述](#%E6%8F%8F%E8%BF%B0-4)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-4)
- [输入](#%E8%BE%93%E5%85%A5-4)
- [输出](#%E8%BE%93%E5%87%BA-4)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-4)
- [cummax](#cummax)
- [描述](#描述-5)
- [模型参数](#模型参数-5)
- [输入](#输入-5)
- [输出](#输出-5)
- [类型约束](#类型约束-5)
- [描述](#%E6%8F%8F%E8%BF%B0-5)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-5)
- [输入](#%E8%BE%93%E5%85%A5-5)
- [输出](#%E8%BE%93%E5%87%BA-5)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-5)
- [cummin](#cummin)
- [描述](#描述-6)
- [模型参数](#模型参数-6)
- [输入](#输入-6)
- [输出](#输出-6)
- [类型约束](#类型约束-6)
- [描述](#%E6%8F%8F%E8%BF%B0-6)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-6)
- [输入](#%E8%BE%93%E5%85%A5-6)
- [输出](#%E8%BE%93%E5%87%BA-6)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-6)
- [MMCVModulatedDeformConv2d](#mmcvmodulateddeformconv2d)
- [描述](#描述-7)
- [模型参数](#模型参数-7)
- [输入](#输入-7)
- [输出](#输出-7)
- [类型约束](#类型约束-7)
- [描述](#%E6%8F%8F%E8%BF%B0-7)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-7)
- [输入](#%E8%BE%93%E5%85%A5-7)
- [输出](#%E8%BE%93%E5%87%BA-7)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-7)
<!-- TOC -->
@ -63,8 +63,8 @@
#### 模型参数
| 类型 | 参数名 | 描述 |
| ------- | --------------- | ------------------------------------------------------- |
| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围[0, 1]。默认值为0 |
| ------- | --------------- | -------------------------------------------------- |
| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围\[0, 1\]。默认值为0 |
| `float` | `sigma` | 高斯方法的超参数 |
| `float` | `min_score` | NMS的score阈值 |
| `int` | `method` | NMS的计算方式, (0: `naive`, 1: `linear`, 2: `gaussian`) |
@ -101,7 +101,7 @@
#### 模型参数
| 类型 | 参数名 | 描述 |
| ------- | ---------------- | ------------------------------------------------------- |
| ------- | ---------------- | ----------------------------------- |
| `int` | `output_height` | roi特征的输出高度 |
| `int` | `output_width` | roi特征的输出宽度 |
| `float` | `spatial_scale` | 输入检测框的缩放系数 |
@ -138,8 +138,8 @@
#### 模型参数
| 类型 | 参数名 | 描述 |
| ------- | --------------- | ----------------------------------------------------- |
| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围[0, 1]。默认值为0 |
| ------- | --------------- | ----------------------------------- |
| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围\[0, 1\]。默认值为0 |
| `int` | `offset` | 用来计算候选框的宽高(x2 - x1 + offset)。可选值0或1 |
#### 输入
@ -171,7 +171,7 @@
#### 模型参数
| 类型 | 参数名 | 描述 |
| ----- | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
| ----- | -------------------- | ------------------------------------------------------------------------------------------------ |
| `int` | `interpolation_mode` | 计算输出使用的插值模式。(0: `bilinear` , 1: `nearest`) |
| `int` | `padding_mode` | 边缘填充模式。(0: `zeros`, 1: `border`, 2: `reflection`) |
| `int` | `align_corners` | 如果`align_corners=1`，则极值(`-1`和`1`)会被当做输入边缘像素的中心点。如果`align_corners=0`，则它们会被看做是边缘像素的边缘点,减小分辨率对采样的影响 |
@ -205,7 +205,7 @@
#### 模型参数
| 类型 | 参数名 | 描述 |
| ----- | ------ | -------------------------------------------------------- |
| ----- | ------ | --------------------------------------------------- |
| `int` | `mode` | 池化模式。(0: `top`, 1: `bottom`, 2: `left`, 3: `right`) |
#### 输入
@ -235,7 +235,7 @@
#### 模型参数
| 类型 | 参数名 | 描述 |
| ----- | ------ | ------------------ |
| ----- | ----- | --------- |
| `int` | `dim` | 进行累计计算的维度 |
#### 输入
@ -267,7 +267,7 @@
#### 模型参数
| 类型 | 参数名 | 描述 |
| ----- | ------ | ------------------ |
| ----- | ----- | --------- |
| `int` | `dim` | 进行累计计算的维度 |
#### 输入
@ -299,7 +299,7 @@
#### 模型参数
| 类型 | 参数名 | 描述 |
| -------------- | ------------------- | ------------------------------------------------------------- |
| -------------- | ------------------- | -------------------------------------- |
| `list of ints` | `stride` | 卷积的步长 (sH, sW) |
| `list of ints` | `padding` | 输入特征填充大小 (padH, padW) |
| `list of ints` | `dilation` | 卷积核各元素间隔 (dH, dW) |

4
docs/zh_cn/deployment/onnxruntime_op.md
View File

@ -16,7 +16,7 @@
### MMCV已支持的算子
| 算子 | CPU | GPU | MMCV版本 |
| :------------------------------------------------------------------------------: | :---: | :---: | :------: |
| :------------------------------------------------------------------------------: | :-: | :-: | :----: |
| [SoftNMS](onnxruntime_custom_ops.md#softnms) | Y | N | 1.2.3 |
| [RoIAlign](onnxruntime_custom_ops.md#roialign) | Y | N | 1.2.5 |
| [NMS](onnxruntime_custom_ops.md#nms) | Y | N | 1.2.7 |
@ -97,7 +97,9 @@ onnx_results = sess.run(None, {'input' : input_data})
以`soft_nms`为例：
1. 在ONNX Runtime头文件目录`mmcv/ops/csrc/onnxruntime/`下添加头文件`soft_nms.h`
2. 在ONNX Runtime源码目录`mmcv/ops/csrc/onnxruntime/cpu/`下添加算子实现`soft_nms.cpp`
3. 在[onnxruntime_register.cpp](../../mmcv/ops/csrc/onnxruntime/cpu/onnxruntime_register.cpp)中注册实现的算子`soft_nms`
```c++

114
docs/zh_cn/deployment/tensorrt_custom_ops.md
View File

@ -2,61 +2,61 @@
<!-- TOC -->
- [TensorRT自定义算子](#tensorrt自定义算子)
- [TensorRT自定义算子](#tensorrt%E8%87%AA%E5%AE%9A%E4%B9%89%E7%AE%97%E5%AD%90)
- [MMCVRoIAlign](#mmcvroialign)
- [描述](#描述)
- [模型参数](#模型参数)
- [输入](#输入)
- [输出](#输出)
- [类型约束](#类型约束)
- [描述](#%E6%8F%8F%E8%BF%B0)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0)
- [输入](#%E8%BE%93%E5%85%A5)
- [输出](#%E8%BE%93%E5%87%BA)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F)
- [ScatterND](#scatternd)
- [描述](#描述-1)
- [模型参数](#模型参数-1)
- [输入](#输入-1)
- [输出](#输出-1)
- [类型约束](#类型约束-1)
- [描述](#%E6%8F%8F%E8%BF%B0-1)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-1)
- [输入](#%E8%BE%93%E5%85%A5-1)
- [输出](#%E8%BE%93%E5%87%BA-1)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-1)
- [NonMaxSuppression](#nonmaxsuppression)
- [描述](#描述-2)
- [模型参数](#模型参数-2)
- [输入](#输入-2)
- [输出](#输出-2)
- [类型约束](#类型约束-2)
- [描述](#%E6%8F%8F%E8%BF%B0-2)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-2)
- [输入](#%E8%BE%93%E5%85%A5-2)
- [输出](#%E8%BE%93%E5%87%BA-2)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-2)
- [MMCVDeformConv2d](#mmcvdeformconv2d)
- [描述](#描述-3)
- [模型参数](#模型参数-3)
- [输入](#输入-3)
- [输出](#输出-3)
- [类型约束](#类型约束-3)
- [描述](#%E6%8F%8F%E8%BF%B0-3)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-3)
- [输入](#%E8%BE%93%E5%85%A5-3)
- [输出](#%E8%BE%93%E5%87%BA-3)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-3)
- [grid_sampler](#grid_sampler)
- [描述](#描述-4)
- [模型参数](#模型参数-4)
- [输入](#输入-4)
- [输出](#输出-4)
- [类型约束](#类型约束-4)
- [描述](#%E6%8F%8F%E8%BF%B0-4)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-4)
- [输入](#%E8%BE%93%E5%85%A5-4)
- [输出](#%E8%BE%93%E5%87%BA-4)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-4)
- [cummax](#cummax)
- [描述](#描述-5)
- [模型参数](#模型参数-5)
- [输入](#输入-5)
- [输出](#输出-5)
- [类型约束](#类型约束-5)
- [描述](#%E6%8F%8F%E8%BF%B0-5)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-5)
- [输入](#%E8%BE%93%E5%85%A5-5)
- [输出](#%E8%BE%93%E5%87%BA-5)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-5)
- [cummin](#cummin)
- [描述](#描述-6)
- [模型参数](#模型参数-6)
- [输入](#输入-6)
- [输出](#输出-6)
- [类型约束](#类型约束-6)
- [描述](#%E6%8F%8F%E8%BF%B0-6)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-6)
- [输入](#%E8%BE%93%E5%85%A5-6)
- [输出](#%E8%BE%93%E5%87%BA-6)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-6)
- [MMCVInstanceNormalization](#mmcvinstancenormalization)
- [描述](#描述-7)
- [模型参数](#模型参数-7)
- [输入](#输入-7)
- [输出](#输出-7)
- [类型约束](#类型约束-7)
- [描述](#%E6%8F%8F%E8%BF%B0-7)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-7)
- [输入](#%E8%BE%93%E5%85%A5-7)
- [输出](#%E8%BE%93%E5%87%BA-7)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-7)
- [MMCVModulatedDeformConv2d](#mmcvmodulateddeformconv2d)
- [描述](#描述-8)
- [模型参数](#模型参数-8)
- [输入](#输入-8)
- [输出](#输出-8)
- [类型约束](#类型约束-8)
- [描述](#%E6%8F%8F%E8%BF%B0-8)
- [模型参数](#%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0-8)
- [输入](#%E8%BE%93%E5%85%A5-8)
- [输出](#%E8%BE%93%E5%87%BA-8)
- [类型约束](#%E7%B1%BB%E5%9E%8B%E7%BA%A6%E6%9D%9F-8)
<!-- TOC -->
@ -69,7 +69,7 @@
#### 模型参数
| 类型 | 参数名 | 描述 |
| ------- | ---------------- | ------------------------------------------------------- |
| ------- | ---------------- | ----------------------------------- |
| `int` | `output_height` | roi特征的输出高度 |
| `int` | `output_width` | roi特征的输出宽度 |
| `float` | `spatial_scale` | 输入检测框的缩放系数 |
@ -100,7 +100,7 @@
#### 描述
ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`indices`以及秩为 q + r - indices.shape[-1] -1 的`update`。输出的计算方式为：首先创建一个`data`的拷贝，然后根据`indces`的值使用`update`对拷贝的`data`进行更新。注意`indices`中不应该存在相同的条目，也就是说对同一个位置进行一次以上的更新是不允许的。
ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`indices`以及秩为 q + r - indices.shape\[-1\] -1 的`update`。输出的计算方式为：首先创建一个`data`的拷贝，然后根据`indces`的值使用`update`对拷贝的`data`进行更新。注意`indices`中不应该存在相同的条目，也就是说对同一个位置进行一次以上的更新是不允许的。
输出的计算方式可以参考如下代码：
@ -148,10 +148,10 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
#### 模型参数
| 类型 | 参数名 | 描述 |
| ------- | ---------------------------- | ---------------------------------------------------------------------------------------- |
| `int` | `center_point_box` | 0 - 候选框的格式为[y1, x1, y2, x2]， 1-候选框的格式为[x_center, y_center, width, height] |
| ------- | ---------------------------- | ----------------------------------------------------------------------------- |
| `int` | `center_point_box` | 0 - 候选框的格式为\[y1, x1, y2, x2\]， 1-候选框的格式为\[x_center, y_center, width, height\] |
| `int` | `max_output_boxes_per_class` | 每一类最大的输出检测框个数。默认为0，输出检测框个数等于输入候选框数 |
| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围[0, 1]。默认值为0 |
| `float` | `iou_threshold` | 用来判断候选框重合度的阈值，取值范围\[0, 1\]。默认值为0 |
| `float` | `score_threshold` | 用来判断候选框是否合法的阈值 |
| `int` | `offset` | 检测框长宽计算方式为(x2 - x1 + offset)，可选值0或1 |
@ -186,7 +186,7 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
#### 模型参数
| 类型 | 参数名 | 描述 |
| -------------- | ------------------ | --------------------------------------------------------------------------------------------- |
| -------------- | ------------------ | ------------------------------------------------------- |
| `list of ints` | `stride` | 卷积的步长 (sH, sW) |
| `list of ints` | `padding` | 输入特征填充大小 (padH, padW) |
| `list of ints` | `dilation` | 卷积核各元素间隔 (dH, dW) |
@ -225,7 +225,7 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
#### 模型参数
| 类型 | 参数名 | 描述 |
| ----- | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
| ----- | -------------------- | ------------------------------------------------------------------------------------------------ |
| `int` | `interpolation_mode` | 计算输出使用的插值模式。(0: `bilinear` , 1: `nearest`) |
| `int` | `padding_mode` | 边缘填充模式。(0: `zeros`, 1: `border`, 2: `reflection`) |
| `int` | `align_corners` | 如果`align_corners=1`，则极值(`-1`和`1`)会被当做输入边缘像素的中心点。如果`align_corners=0`，则它们会被看做是边缘像素的边缘点,减小分辨率对采样的影响 |
@ -259,7 +259,7 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
#### 模型参数
| 类型 | 参数名 | 描述 |
| ----- | ------ | ------------------ |
| ----- | ----- | --------- |
| `int` | `dim` | 进行累计计算的维度 |
#### 输入
@ -291,7 +291,7 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
#### 模型参数
| 类型 | 参数名 | 描述 |
| ----- | ------ | ------------------ |
| ----- | ----- | --------- |
| `int` | `dim` | 进行累计计算的维度 |
#### 输入
@ -323,7 +323,7 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
#### 模型参数
| 类型 | 参数名 | 描述 |
| ------- | --------- | ---------------------------- |
| ------- | --------- | ----------------- |
| `float` | `epsilon` | 用来避免除0错误。默认为1e-05 |
#### 输入
@ -357,7 +357,7 @@ ScatterND接收三个输入，分别为秩为r >= 1的`data`，秩为q >= 1的`i
#### 模型参数
| 类型 | 参数名 | 描述 |
| -------------- | ------------------- | ------------------------------------------------------------- |
| -------------- | ------------------- | -------------------------------------- |
| `list of ints` | `stride` | 卷积的步长 (sH, sW) |
| `list of ints` | `padding` | 输入特征填充大小 (padH, padW) |
| `list of ints` | `dilation` | 卷积核各元素间隔 (dH, dW) |

29
docs/zh_cn/deployment/tensorrt_plugin.md
View File

@ -2,18 +2,18 @@
<!-- TOC -->
- [MMCV中的TensorRT自定义算子 (实验性)](#mmcv中的tensorrt自定义算子-实验性)
- [介绍](#介绍)
- [MMCV中的TensorRT插件列表](#mmcv中的tensorrt插件列表)
- [如何编译MMCV中的TensorRT插件](#如何编译mmcv中的tensorrt插件)
- [准备](#准备)
- [在Linux上编译](#在linux上编译)
- [创建TensorRT推理引擎并在python下进行推理](#创建tensorrt推理引擎并在python下进行推理)
- [如何在MMCV中添加新的TensorRT自定义算子](#如何在mmcv中添加新的tensorrt自定义算子)
- [主要流程](#主要流程)
- [注意](#注意)
- [已知问题](#已知问题)
- [引用](#引用)
- [MMCV中的TensorRT自定义算子 (实验性)](#mmcv%E4%B8%AD%E7%9A%84tensorrt%E8%87%AA%E5%AE%9A%E4%B9%89%E7%AE%97%E5%AD%90-%E5%AE%9E%E9%AA%8C%E6%80%A7)
- [介绍](#%E4%BB%8B%E7%BB%8D)
- [MMCV中的TensorRT插件列表](#mmcv%E4%B8%AD%E7%9A%84tensorrt%E6%8F%92%E4%BB%B6%E5%88%97%E8%A1%A8)
- [如何编译MMCV中的TensorRT插件](#%E5%A6%82%E4%BD%95%E7%BC%96%E8%AF%91mmcv%E4%B8%AD%E7%9A%84tensorrt%E6%8F%92%E4%BB%B6)
- [准备](#%E5%87%86%E5%A4%87)
- [在Linux上编译](#%E5%9C%A8linux%E4%B8%8A%E7%BC%96%E8%AF%91)
- [创建TensorRT推理引擎并在python下进行推理](#%E5%88%9B%E5%BB%BAtensorrt%E6%8E%A8%E7%90%86%E5%BC%95%E6%93%8E%E5%B9%B6%E5%9C%A8python%E4%B8%8B%E8%BF%9B%E8%A1%8C%E6%8E%A8%E7%90%86)
- [如何在MMCV中添加新的TensorRT自定义算子](#%E5%A6%82%E4%BD%95%E5%9C%A8mmcv%E4%B8%AD%E6%B7%BB%E5%8A%A0%E6%96%B0%E7%9A%84tensorrt%E8%87%AA%E5%AE%9A%E4%B9%89%E7%AE%97%E5%AD%90)
- [主要流程](#%E4%B8%BB%E8%A6%81%E6%B5%81%E7%A8%8B)
- [注意](#%E6%B3%A8%E6%84%8F)
- [已知问题](#%E5%B7%B2%E7%9F%A5%E9%97%AE%E9%A2%98)
- [引用](#%E5%BC%95%E7%94%A8)
<!-- TOC -->
@ -25,7 +25,7 @@
### MMCV中的TensorRT插件列表
| ONNX算子 | TensorRT插件 | MMCV版本 |
| :-----------------------: | :-----------------------------------------------------------------------------: | :------: |
| :-----------------------: | :-----------------------------------------------------------------------------: | :----: |
| MMCVRoiAlign | [MMCVRoiAlign](./tensorrt_custom_ops.md#mmcvroialign) | 1.2.6 |
| ScatterND | [ScatterND](./tensorrt_custom_ops.md#scatternd) | 1.2.6 |
| NonMaxSuppression | [NonMaxSuppression](./tensorrt_custom_ops.md#nonmaxsuppression) | 1.3.0 |
@ -146,8 +146,11 @@ with torch.no_grad():
**以RoIAlign算子插件`roi_align`举例。**
1. 在TensorRT包含目录`mmcv/ops/csrc/tensorrt/`中添加头文件`trt_roi_align.hpp`
2. 在TensorRT源码目录`mmcv/ops/csrc/tensorrt/plugins/`中添加头文件`trt_roi_align.cpp`
3. 在TensorRT源码目录`mmcv/ops/csrc/tensorrt/plugins/`中添加cuda kernel文件`trt_roi_align_kernel.cu`
4. 在[trt_plugin.cpp](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/csrc/tensorrt/plugins/trt_plugin.cpp)中注册`roi_align`插件
```c++

16
docs/zh_cn/faq.md
View File

@ -7,9 +7,9 @@
- KeyError: "xxx: 'yyy is not in the zzz registry'"
只有模块所在的文件被导入时，注册机制才会被触发，所以您需要在某处导入该文件，更多详情请查看 https://github.com/open-mmlab/mmdetection/issues/5974。
只有模块所在的文件被导入时，注册机制才会被触发，所以您需要在某处导入该文件，更多详情请查看 https://github.com/open-mmlab/mmdetection/issues/5974%E3%80%82
- "No module named 'mmcv.ops'"; "No module named 'mmcv._ext'"
- "No module named 'mmcv.ops'"; "No module named 'mmcv.\_ext'"
1. 使用 `pip uninstall mmcv` 卸载您环境中的 mmcv
2. 参考 [installation instruction](https://mmcv.readthedocs.io/en/latest/get_started/installation.html) 或者 [Build MMCV from source](https://mmcv.readthedocs.io/en/latest/get_started/build.html) 安装 mmcv-full
@ -38,7 +38,7 @@
- "Segmentation fault"
1. 检查 GCC 的版本，通常是因为 PyTorch 版本与 GCC 版本不匹配 （例如 GCC < 4.9 )，我们推荐用户使用 GCC 5.4，我们也不推荐使用 GCC 5.5， 因为有反馈 GCC 5.5 会导致 "segmentation fault" 并且切换到 GCC 5.4 就可以解决问题
1. 检查 GCC 的版本，通常是因为 PyTorch 版本与 GCC 版本不匹配 （例如 GCC \< 4.9 )，我们推荐用户使用 GCC 5.4，我们也不推荐使用 GCC 5.5， 因为有反馈 GCC 5.5 会导致 "segmentation fault" 并且切换到 GCC 5.4 就可以解决问题
2. 检查是否正确安装 CUDA 版本的 PyTorc。输入以下命令并检查是否返回 True
```shell
python -c 'import torch; print(torch.cuda.is_available())'
@ -59,11 +59,11 @@
- "error: member "torch::jit::detail::ModulePolicy::all_slots" may not be initialized"
如果您在 Windows 上编译 mmcv-full 并且 PyTorch 的版本是 1.5.0，您很可能会遇到这个问题 `- torch/csrc/jit/api/module.h(474): error: member "torch::jit::detail::ModulePolicy::all_slots" may not be initialized`。解决这个问题的方法是将 `torch/csrc/jit/api/module.h` 文件中所有 `static constexpr bool all_slots = false;` 替换为 `static bool all_slots = false;`。更多细节可以查看 https://github.com/pytorch/pytorch/issues/39394。
如果您在 Windows 上编译 mmcv-full 并且 PyTorch 的版本是 1.5.0，您很可能会遇到这个问题 `- torch/csrc/jit/api/module.h(474): error: member "torch::jit::detail::ModulePolicy::all_slots" may not be initialized`。解决这个问题的方法是将 `torch/csrc/jit/api/module.h` 文件中所有 `static constexpr bool all_slots = false;` 替换为 `static bool all_slots = false;`。更多细节可以查看 https://github.com/pytorch/pytorch/issues/39394%E3%80%82
- "error: a member with an in-class initializer must be const"
如果您在 Windows 上编译 mmcv-full 并且 PyTorch 的版本是 1.6.0，您很可能会遇到这个问题 `"- torch/include\torch/csrc/jit/api/module.h(483): error: a member with an in-class initializer must be const"`. 解决这个问题的方法是将 `torch/include\torch/csrc/jit/api/module.h` 文件中的所有 `CONSTEXPR_EXCEPT_WIN_CUDA ` 替换为 `const`。更多细节可以查看 https://github.com/open-mmlab/mmcv/issues/575。
如果您在 Windows 上编译 mmcv-full 并且 PyTorch 的版本是 1.6.0，您很可能会遇到这个问题 `"- torch/include\torch/csrc/jit/api/module.h(483): error: a member with an in-class initializer must be const"`. 解决这个问题的方法是将 `torch/include\torch/csrc/jit/api/module.h` 文件中的所有 `CONSTEXPR_EXCEPT_WIN_CUDA ` 替换为 `const`。更多细节可以查看 https://github.com/open-mmlab/mmcv/issues/575%E3%80%82
- "error: member "torch::jit::ProfileOptionalOp::Kind" may not be initialized"
@ -73,7 +73,7 @@
- 将 `torch\include\pybind11\cast.h` 文件中的 `explicit operator type&() { return *(this->value); }` 替换为 `explicit operator type&() { return *((type*)this->value); }`
- 将 `torch/include\torch/csrc/jit/api/module.h` 文件中的 所有 `CONSTEXPR_EXCEPT_WIN_CUDA` 替换为 `const`
更多细节可以查看 https://github.com/pytorch/pytorch/pull/45956。
更多细节可以查看 https://github.com/pytorch/pytorch/pull/45956%E3%80%82
- MMCV 和 MMDetection 的兼容性问题；"ConvWS is already registered in conv layer"
@ -83,9 +83,9 @@
- "RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one"
1. 这个错误是因为有些参数没有参与 loss 的计算，可能是代码中存在多个分支，导致有些分支没有参与 loss 的计算。更多细节见 https://github.com/pytorch/pytorch/issues/55582。
1. 这个错误是因为有些参数没有参与 loss 的计算，可能是代码中存在多个分支，导致有些分支没有参与 loss 的计算。更多细节见 https://github.com/pytorch/pytorch/issues/55582%E3%80%82
2. 你可以设置 DDP 中的 `find_unused_parameters` 为 `True`，或者手动查找哪些参数没有用到。
- "RuntimeError: Trying to backward through the graph a second time"
不能同时设置 `GradientCumulativeOptimizerHook` 和 `OptimizerHook`，这会导致 `loss.backward()` 被调用两次，于是程序抛出 `RuntimeError`。我们只需设置其中的一个。更多细节见 https://github.com/open-mmlab/mmcv/issues/1379。
不能同时设置 `GradientCumulativeOptimizerHook` 和 `OptimizerHook`，这会导致 `loss.backward()` 被调用两次，于是程序抛出 `RuntimeError`。我们只需设置其中的一个。更多细节见 https://github.com/open-mmlab/mmcv/issues/1379%E3%80%82

1
docs/zh_cn/get_started/build.md
View File

@ -42,6 +42,7 @@ CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' MMCV_WITH_OPS=1 pip install -e .
```{note}
如果你打算使用 `opencv-python-headless` 而不是 `opencv-python`，例如在一个很小的容器环境或者没有图形用户界面的服务器中，你可以先安装 `opencv-python-headless`，这样在安装 mmcv 依赖的过程中会跳过 `opencv-python`
```
### 在 Windows 上编译 MMCV
在 Windows 上编译 MMCV 比 Linux 复杂，本节将一步步介绍如何在 Windows 上编译 MMCV。

12
docs/zh_cn/get_started/installation.md
View File

@ -17,13 +17,13 @@ a. 安装完整版
i. 安装最新版本
如下是安装最新版 ``mmcv-full`` 的命令
如下是安装最新版 `mmcv-full` 的命令
```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
```
请将链接中的 ``{cu_version}`` 和 ``{torch_version}`` 根据自身需求替换成实际的版本号，例如想安装和 ``CUDA 11.1``、``PyTorch 1.9.0`` 兼容的最新版 ``mmcv-full``，使用如下替换过的命令
请将链接中的 `{cu_version}` 和 `{torch_version}` 根据自身需求替换成实际的版本号，例如想安装和 `CUDA 11.1`、`PyTorch 1.9.0` 兼容的最新版 `mmcv-full`，使用如下替换过的命令
```shell
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
@ -37,18 +37,18 @@ PyTorch 版本是 1.8.1、CUDA 版本是 11.1，你可以使用以下命令安
`pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html`
```
如果想知道更多 CUDA 和 PyTorch 版本的命令，可以参考下面的表格，将链接中的 ``=={mmcv_version}`` 删去即可。
如果想知道更多 CUDA 和 PyTorch 版本的命令，可以参考下面的表格，将链接中的 `=={mmcv_version}` 删去即可。
ii. 安装特定的版本
如下是安装特定版本 ``mmcv-full`` 的命令
如下是安装特定版本 `mmcv-full` 的命令
```shell
pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
```
首先请参考版本发布信息找到想要安装的版本号，将 ``{mmcv_version}`` 替换成该版本号，例如 ``1.3.9``。
然后将链接中的 ``{cu_version}`` 和 ``{torch_version}`` 根据自身需求替换成实际的版本号，例如想安装和 ``CUDA 11.1``、``PyTorch 1.9.0`` 兼容的 ``mmcv-full`` 1.3.9 版本，使用如下替换过的命令
首先请参考版本发布信息找到想要安装的版本号，将 `{mmcv_version}` 替换成该版本号，例如 `1.3.9`。
然后将链接中的 `{cu_version}` 和 `{torch_version}` 根据自身需求替换成实际的版本号，例如想安装和 `CUDA 11.1`、`PyTorch 1.9.0` 兼容的 `mmcv-full` 1.3.9 版本，使用如下替换过的命令
```shell
pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html

5
docs/zh_cn/get_started/previous_versions.md
View File

@ -1,11 +1,10 @@
## 其他版本的 PyTorch
我们不再提供在较低的 `PyTorch` 版本下编译的 `mmcv-full` 包，但为了您的方便，您可以在下面找到它们。
### PyTorch 1.4
| 1.0.0 <= mmcv_version <= 1.2.1
| 1.0.0 \<= mmcv_version \<= 1.2.1
#### CUDA 10.1
@ -27,7 +26,7 @@ pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dis
### PyTorch v1.3
| 1.0.0 <= mmcv_version <= 1.3.16
| 1.0.0 \<= mmcv_version \<= 1.3.16
#### CUDA 10.1

4
docs/zh_cn/understand_mmcv/cnn.md
View File

@ -365,7 +365,7 @@ conv = ConvModule(
`ModuleDict` 继承自 `BaseModule` 和 `torch.nn.ModuleDict`
`````python
```python
import torch.nn as nn
from mmcv.runner import BaseModule, Sequential, ModuleList, ModuleDict
@ -528,7 +528,7 @@ conv = ConvModule(
# [[2., 2., 2.],
# [2., 2., 2.],
# [2., 2., 2.]]]], requires_grad=True)
`````
```
### Model Zoo

3
docs/zh_cn/understand_mmcv/config.md
View File

@ -40,6 +40,7 @@ d = 'string'
这里是一个带有预定义变量的配置文件的例子。
`config_a.py`
```python
a = 1
b = './work_dir/{{ fileBasenameNoExtension }}'
@ -65,6 +66,7 @@ c = '{{ fileExtname }}'
a = 1
b = dict(b1=[0, 1, 2], b2=None)
```
### 不含重复键值对从基类配置文件继承
`config_b.py`
@ -83,6 +85,7 @@ d = 'string'
... c=(1, 2),
... d='string')
```
在`config_b.py`里的新字段与在`config_a.py`里的旧字段拼接
### 含重复键值对从基类配置文件继承

1
docs/zh_cn/understand_mmcv/io.md
View File

@ -107,6 +107,7 @@ c
d
e
```
#### 从硬盘读取
使用 `list_from_file` 读取 `a.txt`

6
docs/zh_cn/understand_mmcv/registry.md
View File

@ -1,4 +1,5 @@
## 注册器
MMCV 使用 [注册器](https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/registry.py) 来管理具有相似功能的不同模块, 例如, 检测器中的主干网络、头部、和模型颈部。
在 OpenMMLab 家族中的绝大部分开源项目使用注册器去管理数据集和模型的模块，例如 [MMDetection](https://github.com/open-mmlab/mmdetection), [MMDetection3D](https://github.com/open-mmlab/mmdetection3d), [MMClassification](https://github.com/open-mmlab/mmclassification), [MMEditing](https://github.com/open-mmlab/mmediting) 等。
@ -7,6 +8,7 @@ MMCV 使用 [注册器](https://github.com/open-mmlab/mmcv/blob/master/mmcv/util
```
### 什么是注册器
在MMCV中，注册器可以看作类或函数到字符串的映射。
一个注册器中的类或函数通常有相似的接口，但是可以实现不同的算法或支持不同的数据集。
借助注册器，用户可以通过使用相应的字符串查找类或函数，并根据他们的需要实例化对应模块或调用函数获取结果。
@ -46,6 +48,7 @@ class Converter1(object):
self.a = a
self.b = b
```
```python
# converter2.py
from .builder import CONVERTERS
@ -56,6 +59,7 @@ from .converter1 import Converter1
def converter2(a, b)
return Converter1(a, b)
```
使用注册器管理模块的关键步骤是，将实现的模块注册到注册表 `CONVERTERS` 中。通过 `@CONVERTERS.register_module()` 装饰所实现的模块，字符串到类或函数之间的映射就可以由 `CONVERTERS` 构建和维护，如下所示：
通过这种方式，就可以通过 `CONVERTERS` 建立字符串与类或函数之间的映射，如下所示：
@ -64,9 +68,11 @@ def converter2(a, b)
'Converter1' -> <class 'Converter1'>
'converter2' -> <function 'converter2'>
```
```{note}
只有模块所在的文件被导入时，注册机制才会被触发，所以您需要在某处导入该文件。更多详情请查看 https://github.com/open-mmlab/mmdetection/issues/5974。
```
如果模块被成功注册了，你可以通过配置文件使用这个转换器（converter），如下所示：
```python

14
docs/zh_cn/understand_mmcv/runner.md
View File

@ -8,7 +8,7 @@
### EpochBasedRunner
顾名思义，`EpochBasedRunner` 是指以 epoch 为周期的工作流，例如设置 workflow = [('train', 2), ('val', 1)] 表示循环迭代地训练 2 个 epoch，然后验证 1 个 epoch。MMDetection 目标检测框架默认采用的是 `EpochBasedRunner`。
顾名思义，`EpochBasedRunner` 是指以 epoch 为周期的工作流，例如设置 workflow = \[('train', 2), ('val', 1)\] 表示循环迭代地训练 2 个 epoch，然后验证 1 个 epoch。MMDetection 目标检测框架默认采用的是 `EpochBasedRunner`。
其抽象逻辑如下所示：
@ -25,6 +25,7 @@ while curr_epoch < max_epochs:
for _ in range(epochs):
epoch_runner(data_loaders[i], **kwargs)
```
目前支持训练和验证两个工作流，以训练函数为例，其抽象逻辑是：
```python
@ -40,7 +41,8 @@ def train(self, data_loader, **kwargs):
```
### IterBasedRunner
不同于 `EpochBasedRunner`，`IterBasedRunner` 是指以 iter 为周期的工作流，例如设置 workflow = [('train', 2)， ('val', 1)] 表示循环迭代的训练 2 个 iter，然后验证 1 个 iter，MMSegmentation 语义分割框架默认采用的是 `IterBasedRunner`。
不同于 `EpochBasedRunner`，`IterBasedRunner` 是指以 iter 为周期的工作流，例如设置 workflow = \[('train', 2)， ('val', 1)\] 表示循环迭代的训练 2 个 iter，然后验证 1 个 iter，MMSegmentation 语义分割框架默认采用的是 `IterBasedRunner`。
其抽象逻辑如下所示：
@ -59,6 +61,7 @@ while curr_iter < max_iters:
for _ in range(iters):
iter_runner(iter_loaders[i], **kwargs)
```
目前支持训练和验证两个工作流，以验证函数为例，其抽象逻辑是：
```python
@ -75,6 +78,7 @@ def val(self, data_loader, **kwargs):
除了上述基础功能外，`EpochBasedRunner` 和 `IterBasedRunner` 还提供了 resume 、 save_checkpoint 和注册 hook 功能。
### 一个简单例子
以最常用的分类任务为例详细说明 `runner` 的使用方法。 开启任何一个训练任务，都需要包括如下步骤：
**(1) dataloader、model 和优化器等类初始化**
@ -148,8 +152,8 @@ runner.run(data_loaders, cfg.workflow)
关于 workflow 设置，以 `EpochBasedRunner` 为例，详情如下：
- 假设只想运行训练工作流，则可以设置 workflow = [('train', 1)]，表示只进行迭代训练
- 假设想运行训练和验证工作流，则可以设置 workflow = [('train', 3), ('val', 1)]，表示先训练 3 个 epoch ，然后切换到 val 工作流，运行 1 个 epoch，然后循环，直到训练 epoch 次数达到指定值
- 工作流设置还自由定制，例如你可以先验证再训练 workflow = [('val', 1), ('train', 1)]
- 假设只想运行训练工作流，则可以设置 workflow = \[('train', 1)\]，表示只进行迭代训练
- 假设想运行训练和验证工作流，则可以设置 workflow = \[('train', 3), ('val', 1)\]，表示先训练 3 个 epoch ，然后切换到 val 工作流，运行 1 个 epoch，然后循环，直到训练 epoch 次数达到指定值
- 工作流设置还自由定制，例如你可以先验证再训练 workflow = \[('val', 1), ('train', 1)\]
上述代码都已经封装到了各个代码库的 train.py 中，用户只需要设置相应的配置即可，上述流程会自动运行。

1
docs/zh_cn/understand_mmcv/utils.md
View File

@ -58,7 +58,6 @@ with mmcv.Timer():
你也可以使用 `since_start()` 和 `since_last_check()` 。前者返回计时器启动后的运行时长，后者返回最近一次查看计时器后的运行时长。
```python
timer = mmcv.Timer()
# code block 1 here

6
setup.cfg
View File

@ -18,5 +18,9 @@ known_third_party = addict,cv2,matplotlib,numpy,onnx,onnxruntime,packaging,pytes
no_lines_before = STDLIB,LOCALFOLDER
default_section = THIRDPARTY
# ignore-words-list needs to be lowercase format. For example, if we want to
# ignore word "BA", then we need to append "ba" to ignore-words-list rather
# than "BA"
[codespell]
ignore-words-list = inout,hist
quiet-level = 3
ignore-words-list = inout,hist,ba