Fix ResourceWarning in Wandb and Update README.md (#8103)

* add hint in wandb

* update readme

* update readme

* update hint of wandb

Author: hhaAndroid

README.md

@@ -2,17 +2,17 @@
   <img src="resources/mmdet-logo.png" width="600"/>
   <div>&nbsp;</div>
   <div align="center">
-    <b>OpenMMLab website</b>
+    <b><font size="5">OpenMMLab website</font></b>
     <sup>
       <a href="https://openmmlab.com">
-        <i>HOT</i>
+        <i><font size="4">HOT</font></i>
       </a>
     </sup>
     &nbsp;&nbsp;&nbsp;&nbsp;
-    <b>OpenMMLab platform</b>
+    <b><font size="5">OpenMMLab platform</font></b>
     <sup>
       <a href="https://platform.openmmlab.com">
-        <i>TRY IT OUT</i>
+        <i><font size="4">TRY IT OUT</font></i>
       </a>
     </sup>
   </div>
@@ -24,27 +24,32 @@
 [![codecov](https://codecov.io/gh/open-mmlab/mmdetection/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection)
 [![license](https://img.shields.io/github/license/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/blob/master/LICENSE)
 [![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
 
-<img src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png"/>
-
-[📘Documentation](https://mmdetection.readthedocs.io/en/v2.21.0/) |
-[🛠️Installation](https://mmdetection.readthedocs.io/en/v2.21.0/get_started.html) |
-[👀Model Zoo](https://mmdetection.readthedocs.io/en/v2.21.0/model_zoo.html) |
-[🆕Update News](https://mmdetection.readthedocs.io/en/v2.21.0/changelog.html) |
+[📘Documentation](https://mmdetection.readthedocs.io/en/stable/) |
+[🛠️Installation](https://mmdetection.readthedocs.io/en/stable/get_started.html) |
+[👀Model Zoo](https://mmdetection.readthedocs.io/en/stable/model_zoo.html) |
+[🆕Update News](https://mmdetection.readthedocs.io/en/stable/changelog.html) |
 [🚀Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) |
 [🤔Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose)
 
 </div>
 
-## Introduction
+<div align="center">
 
 English | [简体中文](README_zh-CN.md)
 
+</div>
+
+## Introduction
+
 MMDetection is an open source object detection toolbox based on PyTorch. It is
 a part of the [OpenMMLab](https://openmmlab.com/) project.
 
 The master branch works with **PyTorch 1.5+**.
 
+<img src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png"/>
+
 <details open>
 <summary>Major features</summary>
 
@@ -68,22 +73,42 @@ The master branch works with **PyTorch 1.5+**.
 
 Apart from MMDetection, we also released a library [mmcv](https://github.com/open-mmlab/mmcv) for computer vision research, which is heavily depended on by this toolbox.
 
-## License
+## What's New
 
-This project is released under the [Apache 2.0 license](LICENSE).
+**2.25.0** was released in 1/6/2022:
 
-## Changelog
-
-**2.24.1** was released in 30/4/2022:
-
-- Support [Simple Copy Paste](configs/simple_copy_paste)
-- Support automatically scaling LR according to GPU number and samples per GPU
-- Support Class Aware Sampler that improves performance on OpenImages Dataset
+- Support dedicated `MMDetWandbHook` hook
+- Support [ConvNeXt](configs/convnext), [DDOD](configs/ddod), [SOLOv2](configs/solov2)
+- Support [Mask2Former](configs/mask2former) for instance segmentation
+- Rename [config files of Mask2Former](configs/mask2former)
 
 Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
 For compatibility changes between different versions of MMDetection, please refer to [compatibility.md](docs/en/compatibility.md).
 
+## Installation
+
+Please refer to [Installation](docs/en/get_started.md/#Installation) for installation instructions.
+
+## Getting Started
+
+Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMDetection. We provide [colab tutorial](demo/MMDet_Tutorial.ipynb) and [instance segmentation colab tutorial](demo/MMDet_InstanceSeg_Tutorial.ipynb), and other tutorials for:
+
+- [with existing dataset](docs/en/1_exist_data_model.md)
+- [with new dataset](docs/en/2_new_data_model.md)
+- [with existing dataset_new_model](docs/en/3_exist_data_new_model.md)
+- [learn about configs](docs/en/tutorials/config.md)
+- [customize_datasets](docs/en/tutorials/customize_dataset.md)
+- [customize data pipelines](docs/en/tutorials/data_pipeline.md)
+- [customize_models](docs/en/tutorials/customize_models.md)
+- [customize runtime settings](docs/en/tutorials/customize_runtime.md)
+- [customize_losses](docs/en/tutorials/customize_losses.md)
+- [finetuning models](docs/en/tutorials/finetune.md)
+- [export a model to ONNX](docs/en/tutorials/pytorch2onnx.md)
+- [export ONNX to TRT](docs/en/tutorials/onnx2tensorrt.md)
+- [weight initialization](docs/en/tutorials/init_cfg.md)
+- [how to xxx](docs/en/tutorials/how_to.md)
+
 ## Overview of Benchmark and Model Zoo
 
 Results and models are available in the [model zoo](docs/en/model_zoo.md).
@@ -144,6 +169,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
             <li><a href="configs/yolox">YOLOX (CVPR'2021)</a></li>
             <li><a href="configs/deformable_detr">Deformable DETR (ICLR'2021)</a></li>
             <li><a href="configs/tood">TOOD (ICCV'2021)</a></li>
+            <li><a href="configs/ddod">DDOD (ACM MM'2021)</a></li>
       </ul>
       </td>
       <td>
@@ -160,6 +186,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
           <li><a href="configs/solov2">SOLOv2 (NeurIPS'2020)</a></li>
           <li><a href="configs/scnet">SCNet (AAAI'2021)</a></li>
           <li><a href="configs/queryinst">QueryInst (ICCV'2021)</a></li>
+          <li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
         </ul>
       </td>
       <td>
@@ -272,15 +299,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 
 Some other methods are also supported in [projects using MMDetection](./docs/en/projects.md).
 
-## Installation
-
-Please refer to [get_started.md](docs/en/get_started.md) for installation.
-
-## Getting Started
-
-Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMDetection.
-We provide [detection colab tutorial](demo/MMDet_Tutorial.ipynb) and [instance segmentation colab tutorial](demo/MMDet_InstanceSeg_Tutorial.ipynb), and full guidance for quick run [with existing dataset](docs/en/1_exist_data_model.md) and [with new dataset](docs/en/2_new_data_model.md) for beginners.
-There are also tutorials for [finetuning models](docs/en/tutorials/finetune.md), [adding new dataset](docs/en/tutorials/customize_dataset.md), [designing data pipeline](docs/en/tutorials/data_pipeline.md), [customizing models](docs/en/tutorials/customize_models.md), [customizing runtime settings](docs/en/tutorials/customize_runtime.md) and [useful tools](docs/en/useful_tools.md).
+## FAQ
 
 Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.
 
@@ -311,6 +330,10 @@ If you use this toolbox or benchmark in your research, please cite this project.
 }
 ```
 
+## License
+
+This project is released under the [Apache 2.0 license](LICENSE).
+
 ## Projects in OpenMMLab
 
 - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.

README_zh-CN.md

@@ -24,26 +24,31 @@
 [![codecov](https://codecov.io/gh/open-mmlab/mmdetection/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection)
 [![license](https://img.shields.io/github/license/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/blob/master/LICENSE)
 [![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
 
-<img src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png"/>
-
-[📘使用文档](https://mmdetection.readthedocs.io/zh_CN/v2.21.0/) |
-[🛠️安装教程](https://mmdetection.readthedocs.io/zh_CN/v2.21.0/get_started.html) |
-[👀模型库](https://mmdetection.readthedocs.io/zh_CN/v2.21.0/model_zoo.html) |
-[🆕更新日志](https://mmdetection.readthedocs.io/en/v2.21.0/changelog.html) |
+[📘使用文档](https://mmdetection.readthedocs.io/zh_CN/stable/) |
+[🛠️安装教程](https://mmdetection.readthedocs.io/zh_CN/stable/get_started.html) |
+[👀模型库](https://mmdetection.readthedocs.io/zh_CN/stable/model_zoo.html) |
+[🆕更新日志](https://mmdetection.readthedocs.io/en/stable/changelog.html) |
 [🚀进行中的项目](https://github.com/open-mmlab/mmdetection/projects) |
 [🤔报告问题](https://github.com/open-mmlab/mmdetection/issues/new/choose)
 
 </div>
 
-## 简介
+<div align="center">
 
 [English](README.md) | 简体中文
 
+</div>
+
+## 简介
+
 MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
 
 主分支代码目前支持 PyTorch 1.5 以上的版本。
 
+<img src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png"/>
+
 <details open>
 <summary>主要特性</summary>
 
@@ -67,22 +72,45 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 
 除了 MMDetection 之外，我们还开源了计算机视觉基础库 [MMCV](https://github.com/open-mmlab/mmcv)，MMCV 是 MMDetection 的主要依赖。
 
-## 开源许可证
-
-该项目采用 [Apache 2.0 开源许可证](LICENSE)。
-
-## 更新日志
+## 最新进展
 
-最新的 **2.24.1** 版本已经在 2022.04.30 发布:
+最新的 **2.25.0** 版本已经在 2022.06.01 发布:
 
-- 支持算法 [Simple Copy Paste](configs/simple_copy_paste)
-- 支持训练时根据总 batch 数自动缩放学习率
-- 支持类别可知的采样器来提高算法在 OpenImages 数据集上的性能
+- 支持功能更丰富的 `MMDetWandbHook`
+- 支持算法 [ConvNeXt](configs/convnext), [DDOD](configs/ddod) 和 [SOLOv2](configs/solov2)
+- [Mask2Former](configs/mask2former) 支持实例分割
+- 为了加入 Mask2Former 实例分割的模型，对 Mask2Former 原有的全景分割的配置文件进行了重命名
 
 如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/changelog.md)。
 
 如果想了解 MMDetection 不同版本之间的兼容性, 请参考[兼容性说明文档](docs/zh_cn/compatibility.md)。
 
+## 安装
+
+请参考[安装指令](docs/zh_cn/get_started.md/#Installation)进行安装。
+
+## 教程
+
+请参考[快速入门文档](docs/zh_cn/get_started.md)学习 MMDetection 的基本使用。
+我们提供了 [检测的 colab 教程](demo/MMDet_Tutorial.ipynb) 和 [实例分割的 colab 教程](demo/MMDet_InstanceSeg_Tutorial.ipynb)，也为新手提供了完整的运行教程，其他教程如下
+
+- [使用已有模型在标准数据集上进行推理](docs/zh_cn/1_exist_data_model.md)
+- [在自定义数据集上进行训练](docs/zh_cn/2_new_data_model.md)
+- [在标准数据集上训练自定义模型](docs/zh_cn/3_exist_data_new_model.md)
+- [学习配置文件](docs/zh_cn/tutorials/config.md)
+- [自定义数据集](docs/zh_cn/tutorials/customize_dataset.md)
+- [自定义数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md)
+- [自定义模型](docs/zh_cn/tutorials/customize_models.md)
+- [自定义训练配置](docs/zh_cn/tutorials/customize_runtime.md)
+- [自定义损失函数](docs/zh_cn/tutorials/customize_losses.md)
+- [模型微调](docs/zh_cn/tutorials/finetune.md)
+- [Pytorch 到 ONNX 的模型转换](docs/zh_cn/tutorials/pytorch2onnx.md)
+- [ONNX 到 TensorRT 的模型转换](docs/zh_cn/tutorials/onnx2tensorrt.md)
+- [权重初始化](docs/zh_cn/tutorials/init_cfg.md)
+- [how to xxx](docs/zh_cn/tutorials/how_to.md)
+
+同时，我们还提供了 [MMDetection 中文解读文案汇总](docs/zh_cn/article.md)
+
 ## 基准测试和模型库
 
 测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
@@ -143,6 +171,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
             <li><a href="configs/yolox">YOLOX (CVPR'2021)</a></li>
             <li><a href="configs/deformable_detr">Deformable DETR (ICLR'2021)</a></li>
             <li><a href="configs/tood">TOOD (ICCV'2021)</a></li>
+            <li><a href="configs/ddod">DDOD (ACM MM'2021)</a></li>
       </ul>
       </td>
       <td>
@@ -159,6 +188,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
           <li><a href="configs/solov2">SOLOv2 (NeurIPS'2020)</a></li>
           <li><a href="configs/scnet">SCNet (AAAI'2021)</a></li>
           <li><a href="configs/queryinst">QueryInst (ICCV'2021)</a></li>
+          <li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
         </ul>
       </td>
       <td>
@@ -271,22 +301,9 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 
 我们在[基于 MMDetection 的项目](./docs/zh_cn/projects.md)中列举了一些其他的支持的算法。
 
-## 安装
-
-请参考[快速入门文档](docs/zh_cn/get_started.md)进行安装。
-
-## 快速入门
-
-请参考[快速入门文档](docs/zh_cn/get_started.md)学习 MMDetection 的基本使用。
-我们提供了 [检测的 colab 教程](demo/MMDet_Tutorial.ipynb) 和 [实例分割的 colab 教程](demo/MMDet_InstanceSeg_Tutorial.ipynb)，也为新手提供了完整的运行教程，分别针对[已有数据集](docs/zh_cn/1_exist_data_model.md)和[新数据集](docs/zh_cn/2_new_data_model.md) 完整的使用指南
-
-我们也提供了一些进阶教程，内容覆盖了 [finetune 模型](docs/zh_cn/tutorials/finetune.md)，[增加新数据集支持](docs/zh_cn/tutorials/customize_dataset.md)，[设计新的数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md)，[增加自定义模型](docs/zh_cn/tutorials/customize_models.md)，[增加自定义的运行时配置](docs/zh_cn/tutorials/customize_runtime.md)，[常用工具和脚本](docs/zh_cn/useful_tools.md)。
-
-如果遇到问题，请参考 [常见问题解答](docs/zh_cn/faq.md)。
-
-## 相关教程
+## 常见问题
 
-请参考 [MMDetection 中文解读文案汇总](docs/zh_cn/article.md)
+请参考 [FAQ](docs/zh_cn/faq.md) 了解其他用户的常见问题。
 
 ## 贡献指南
 
@@ -314,6 +331,10 @@ MMDetection 是一款由来自不同高校和企业的研发人员共同参与
 }
 ```
 
+## 开源许可证
+
+该项目采用 [Apache 2.0 开源许可证](LICENSE)。
+
 ## OpenMMLab 的其他项目
 
 - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库

mmdet/core/hook/wandblogger_hook.py

@@ -2,6 +2,7 @@
 import importlib
 import os.path as osp
 import sys
+import warnings
 
 import mmcv
 import numpy as np
@@ -10,6 +11,7 @@
 from mmcv.runner.dist_utils import master_only
 from mmcv.runner.hooks.checkpoint import CheckpointHook
 from mmcv.runner.hooks.logger.wandb import WandbLoggerHook
+from mmcv.utils import digit_version
 
 from mmdet.core import DistEvalHook, EvalHook
 from mmdet.core.mask.structures import polygon_to_bitmap
@@ -109,6 +111,25 @@ def __init__(self,
         self.ckpt_hook: CheckpointHook = None
         self.eval_hook: EvalHook = None
 
+    def import_wandb(self):
+        try:
+            import wandb
+            from wandb import init  # noqa
+
+            # Fix ResourceWarning when calling wandb.log in wandb v0.12.10.
+            # https://github.com/wandb/client/issues/2837
+            if digit_version(wandb.__version__) < digit_version('0.12.10'):
+                warnings.warn(
+                    f'The current wandb {wandb.__version__} is '
+                    f'lower than v0.12.10 will cause ResourceWarning '
+                    f'when calling wandb.log, Please run '
+                    f'"pip install --upgrade wandb"')
+
+        except ImportError:
+            raise ImportError(
+                'Please run "pip install "wandb>=0.12.10"" to install wandb')
+        self.wandb = wandb
+
     @master_only
     def before_run(self, runner):
         super(MMDetWandbHook, self).before_run(runner)
@@ -198,9 +219,9 @@ def after_train_epoch(self, runner):
                 }
             else:
                 metadata = None
-            aliases = [f'epoch_{runner.epoch+1}', 'latest']
+            aliases = [f'epoch_{runner.epoch + 1}', 'latest']
             model_path = osp.join(self.ckpt_hook.out_dir,
-                                  f'epoch_{runner.epoch+1}.pth')
+                                  f'epoch_{runner.epoch + 1}.pth')
             self._log_ckpt_as_artifact(model_path, aliases, metadata)
 
         # Save prediction table
@@ -238,9 +259,9 @@ def after_train_iter(self, runner):
                 }
             else:
                 metadata = None
-            aliases = [f'iter_{runner.iter+1}', 'latest']
+            aliases = [f'iter_{runner.iter + 1}', 'latest']
             model_path = osp.join(self.ckpt_hook.out_dir,
-                                  f'iter_{runner.iter+1}.pth')
+                                  f'iter_{runner.iter + 1}.pth')
             self._log_ckpt_as_artifact(model_path, aliases, metadata)
 
         # Save prediction table