[doc] Update docs for release/2.8 (#3107)

README_CN.md

@@ -43,7 +43,7 @@
 
 ## <img src="./docs/images/feature.png" width="20"/> 特性
 
-* **高精度**：跟踪学术界的前沿分割技术，结合高精度训练的骨干网络，提供40+主流分割网络、140+的高质量预训练模型，效果优于其他开源实现。
+* **高精度**：跟踪学术界的前沿分割技术，结合高精度训练的骨干网络，提供45+主流分割网络、150+的高质量预训练模型，效果优于其他开源实现。
 
 * **高性能**：使用多进程异步I/O、多卡并行训练、评估等加速策略，结合飞桨核心框架的显存优化功能，大幅度减少分割模型的训练开销，让开发者更低成本、更高效地完成图像分割训练。
 
@@ -139,6 +139,13 @@
             <li><a href="./configs/uhrnet">UHRNet</a></li>
             <li><a href="./configs/topformer">TopFormer</a></li>
             <li><a href="./configs/mscale_ocrnet">MscaleOCRNet-PSA</a></li>
+            <li><a href="./configs/cae">CAE</a></li>
+            <li><a href="./configs/maskformer">MaskFormer</a></li>
+            <li><a href="./configs/vit_adapter">ViT-Adapter</a></li>
+            <li><a href="./configs/hrformer">HRFormer</a></li>
+            <li><a href="./configs/lpsnet">LPSNet</a></li>
+            <li><a href="./configs/segnext">SegNeXt</a></li>
+            <li><a href="./configs/knet">K-Net</a></li>
           </ul>
         </details>
         <details><summary><b>交互式分割模型</b></summary>
@@ -155,11 +162,13 @@
               <li><a href="./Matting/configs/dim/dim-vgg16.yml">DIM</a></li>
               <li><a href="./Matting/configs/modnet/modnet-hrnet_w18.yml">MODNet</a></li>
               <li><a href="./Matting/configs/human_matting/human_matting-resnet34_vd.yml">PP-HumanMatting</a></li>
+              <li><a href="./Matting/configs/rvm">RVM</a></li>
           </ul>
         </details>
         <details><summary><b>全景分割</b></summary>
           <ul>
-            <li><a href="./contrib/PanopticDeepLab/README_CN.md">Panoptic-DeepLab</a></li>
+            <li><a href="./contrib/PanopticSeg/configs/mask2former">Mask2Former</a></li>
+            <li><a href="./contrib/PanopticSeg/configs/panoptic_deeplab">Panoptic-DeepLab</a></li>
           </ul>
         </details>
       </td>
@@ -178,6 +187,9 @@
             <li><a href="./paddleseg/models/backbones/vision_transformer.py">VIT</a></li>
             <li><a href="./paddleseg/models/backbones/mix_transformer.py">MixVIT</a></li>
             <li><a href="./paddleseg/models/backbones/swin_transformer.py">Swin Transformer</a></li>
+            <li><a href="./paddleseg/models/backbones/top_transformer.py">TopTransformer</a></li>
+            <li><a href="./paddleseg/models/backbones/hrformer.py">HRTransformer</a></li>
+            <li><a href="./paddleseg/models/backbones/mscan.py">MSCAN</a></li>
           </ul>
         </details>
         <details><summary><b>损失函数</b></summary>
@@ -394,6 +406,7 @@
     * [导出ONNX模型](./docs/model_export_onnx_cn.md)
 
 * 模型部署
+    * [FastDeploy部署](./deploy/fastdeploy)
     * [Paddle Inference部署(Python)](./docs/deployment/inference/python_inference_cn.md)
     * [Paddle Inference部署(C++)](./docs/deployment/inference/cpp_inference_cn.md)
     * [Paddle Lite部署](./docs/deployment/lite/lite_cn.md)

README_EN.md

@@ -47,7 +47,7 @@ PaddleSeg is an end-to-end high-efficent development toolkit for image segmentat
 
 ## <img src="./docs/images/feature.png" width="20"/> Features
 
-* **High-Performance Model**: Following the state of the art segmentation methods and using high-performance backbone networks, we provide 40+ models and 140+ high-quality pre-training models, which are better than other open-source implementations.
+* **High-Performance Model**: Following the state of the art segmentation methods and using high-performance backbone networks, we provide 45+ models and 150+ high-quality pre-training models, which are better than other open-source implementations.
 
 * **High Efficiency**: PaddleSeg provides multi-process asynchronous I/O, multi-card parallel training, evaluation, and other acceleration strategies, combined with the memory optimization function of the PaddlePaddle, which can greatly reduce the training overhead of the segmentation model, all these allowing developers to train image segmentation models more efficiently and at a lower cost.
 
@@ -139,6 +139,13 @@ PaddleSeg is an end-to-end high-efficent development toolkit for image segmentat
             <li><a href="./configs/uhrnet">UHRNet</a></li>
             <li><a href="./configs/topformer">TopFormer</a></li>
             <li><a href="./configs/mscale_ocrnet">MscaleOCRNet-PSA</a></li>
+            <li><a href="./configs/cae">CAE</a></li>
+            <li><a href="./configs/maskformer">MaskFormer</a></li>
+            <li><a href="./configs/vit_adapter">ViT-Adapter</a></li>
+            <li><a href="./configs/hrformer">HRFormer</a></li>
+            <li><a href="./configs/lpsnet">LPSNet</a></li>
+            <li><a href="./configs/segnext">SegNeXt</a></li>
+            <li><a href="./configs/knet">K-Net</a></li>
           </ul>
         </details>
         <details><summary><b>Interactive Segmentation</b></summary>
@@ -155,11 +162,13 @@ PaddleSeg is an end-to-end high-efficent development toolkit for image segmentat
               <li><a href="./Matting/configs/dim/dim-vgg16.yml">DIM</a></li>
               <li><a href="./Matting/configs/modnet/modnet-hrnet_w18.yml">MODNet</a></li>
               <li><a href="./Matting/configs/human_matting/human_matting-resnet34_vd.yml">PP-HumanMatting</a></li>
+              <li><a href="./Matting/configs/rvm">RVM</a></li>
           </ul>
         </details>
         <details><summary><b>Panoptic Segmentation</b></summary>
           <ul>
-            <li><a href="./contrib/PanopticDeepLab/README_CN.md">Panoptic-DeepLab</a></li>
+            <li><a href="./contrib/PanopticSeg/configs/mask2former">Mask2Former</a></li>
+            <li><a href="./contrib/PanopticSeg/configs/panoptic_deeplab">Panoptic-DeepLab</a></li>
           </ul>
         </details>
       </td>
@@ -178,6 +187,9 @@ PaddleSeg is an end-to-end high-efficent development toolkit for image segmentat
             <li><a href="./paddleseg/models/backbones/vision_transformer.py">VIT</a></li>
             <li><a href="./paddleseg/models/backbones/mix_transformer.py">MixVIT</a></li>
             <li><a href="./paddleseg/models/backbones/swin_transformer.py">Swin Transformer</a></li>
+            <li><a href="./paddleseg/models/backbones/top_transformer.py">TopTransformer</a></li>
+            <li><a href="./paddleseg/models/backbones/hrformer.py">HRTransformer</a></li>
+            <li><a href="./paddleseg/models/backbones/mscan.py">MSCAN</a></li>
           </ul>
         </details>
         <details><summary><b>Losses</b></summary>
@@ -395,6 +407,7 @@ Note that:
     * [Export ONNX Model](./docs/model_export_onnx.md)
 
 * Model Deployment
+    * [FastDeploy](./deploy/fastdeploy)
     * [Paddle Inference (Python)](./docs/deployment/inference/python_inference.md)
     * [Paddle Inference (C++)](./docs/deployment/inference/cpp_inference.md)
     * [Paddle Lite](./docs/deployment/lite/lite.md)

docs/config/pre_config.md

@@ -19,7 +19,7 @@ For dataset config module,  the supported classes in `PaddleSeg/paddleseg/datase
 
 For data transforms config module, the supported classes in `PaddleSeg/paddleseg/transforms/transforms.py` are registered by `@manager.TRANSFORMS.add_component`.
 
-For optimizer config module, it supports all optimizer provided by PaddlePaddle. Please refer to the [document](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html#api).
+For optimizer config module, the supported classes in `PaddleSeg/paddleseg/optimizers` are registered by `@manager.OPTIMIZERS.add_component`.
 
 For lr_scheduler config module, it supports all lr_scheduler provided by PaddlePaddle. Please refer to the [document](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html#about-lr).
 
@@ -64,7 +64,7 @@ val_dataset:
     - type: Normalize
 
 optimizer:
-  type: sgd
+  type: SGD
   momentum: 0.9
   weight_decay: 4.0e-5
 

docs/config/pre_config_cn.md

@@ -20,7 +20,7 @@ PaddleSeg中所有语义分割模型都针对公开数据集，提供了对应
 
 数据预处理方式transforms模块，支持的transform类在`PaddleSeg/paddleseg/transforms/transforms.py`[文件](../../paddleseg/transforms/transforms.py)中，使用`@manager.TRANSFORMS.add_component`进行注册。
 
-优化器optimizer模块，支持Paddle提供的所有优化器类，具体参考[文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html#api)。
+优化器optimizer模块，支持的optimizer类在`PaddleSeg/paddleseg/optimizers`[目录](../../paddleseg/optimizers/)下，使用`@manager.OPTIMIZERS.add_component`进行注册。
 
 学习率衰减lr_scheduler模块，支持Paddle提供的所有lr_scheduler类，具体参考[文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html#about-lr)。
 
@@ -66,7 +66,7 @@ val_dataset:  #验证数据设置
     - type: Normalize #对原始图像进行归一化，标注图像保持不变
 
 optimizer: #设定优化器的类型
-  type: sgd #采用SGD（Stochastic Gradient Descent）随机梯度下降方法为优化器
+  type: SGD #采用SGD（Stochastic Gradient Descent）随机梯度下降方法为优化器
   momentum: 0.9 #设置SGD的动量
   weight_decay: 4.0e-5 #权值衰减，使用的目的是防止过拟合
 

docs/install.md

@@ -3,25 +3,25 @@ English | [简体中文](install_cn.md)
 
 ## 1 Environment Requirements
 
-- PaddlePaddle (the version >= 2.3)
 - OS: 64-bit
-- Python 3(3.5.1+/3.6/3.7/3.8/3.9)，64-bit version
+- Python 3(3.6/3.7/3.8/3.9/3.10)，64-bit version
 - pip/pip3(9.0.1+)，64-bit version
-- CUDA >= 10.1
+- CUDA >= 10.2
 - cuDNN >= 7.6
+- PaddlePaddle (the version >= 2.4)
 
 ## 2 Installation
 
 ### 2.1 Install PaddlePaddle
 
-Please refer to the [installation doc](https://www.paddlepaddle.org.cn/documentation/docs/en/install/index_en.html) to install PaddlePaddle (the version >= 2.3).
+Please refer to the [installation doc](https://www.paddlepaddle.org.cn/documentation/docs/en/install/index_en.html) to install PaddlePaddle (the version >= 2.4).
 
 Highly recommend you install the GPU version of PaddlePaddle, due to the large overhead of segmentation models, otherwise, it could be out of memory while running the models.
 
-For example, run the following command to install Paddle with pip for Linux, CUDA 10.1.
+For example, run the following command to install Paddle with pip for Linux, CUDA 10.2.
 
 ```
-python -m pip install paddlepaddle-gpu==2.3.2.post101 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
+python -m pip install paddlepaddle-gpu==2.4.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 

docs/install_cn.md

@@ -4,23 +4,23 @@
 
 ## 1 环境要求
 
-- PaddlePaddle (版本不低于2.3)
 - OS 64位操作系统
-- Python 3(3.5.1+/3.6/3.7/3.8/3.9)，64位版本
+- Python 3(3.6/3.7/3.8/3.9/3.10)，64位版本
 - pip/pip3(9.0.1+)，64位版本
-- CUDA >= 10.1
+- CUDA >= 10.2
 - cuDNN >= 7.6
+- PaddlePaddle (版本>=2.4)
 
 ## 2 本地安装说明
 
 ### 2.1 安装PaddlePaddle
 
-请参考[快速安装文档](https://www.paddlepaddle.org.cn/install/quick)或者[详细安装文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/index_cn.html)，安装PaddlePaddle （要求不低于2.3版本，推荐安装最新版本）。
+请参考[快速安装文档](https://www.paddlepaddle.org.cn/install/quick)或者[详细安装文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/index_cn.html)，安装PaddlePaddle （要求不低于2.4版本，推荐安装最新版本）。
 
-比如Linux、CUDA 10.1，使用pip安装GPU版本，执行如下命令。
+比如Linux、CUDA 10.2，使用pip安装GPU版本，执行如下命令。
 
 ```
-python -m pip install paddlepaddle-gpu==2.3.2.post101 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
+python -m pip install paddlepaddle-gpu==2.4.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 使用如下命令验证PaddlePaddle是否安装成功，并且查看版本。