[Doc] Update version info in doc (open-mmlab#2851)

csatsurnh · web-flow · commit 580887ad10fe · 2023-04-06T16:49:56.000+08:00
diff --git a/docs/en/get_started.md b/docs/en/get_started.md
@@ -43,7 +43,7 @@ We recommend that users follow our best practices to install MMSegmentation. How
 ```shell
 pip install -U openmim
 mim install mmengine
-mim install "mmcv>=2.0.0rc1"
+mim install "mmcv>=2.0.0"
 ```
 
 **Step 1.** Install MMSegmentation.
@@ -62,7 +62,7 @@ pip install -v -e .
 Case b: If you use mmsegmentation as a dependency or third-party package, install it with pip:
 
 ```shell
-pip install "mmsegmentation>=1.0.0rc0"
+pip install "mmsegmentation>=1.0.0"
 ```
 
 ### Verify the installation
@@ -136,15 +136,15 @@ MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex wa
 
 To install MMCV with pip instead of MIM, please follow [MMCV installation guides](https://mmcv.readthedocs.io/en/latest/get_started/installation.html). This requires manually specifying a find-url based on PyTorch version and its CUDA version.
 
-For example, the following command install mmcv==2.0.0rc1 built for PyTorch 1.10.x and CUDA 11.3.
+For example, the following command install mmcv==2.0.0 built for PyTorch 1.10.x and CUDA 11.3.
 
 ```shell
-pip install mmcv==2.0.0rc1 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
+pip install mmcv==2.0.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
 ```
 
 #### Install on CPU-only platforms
 
-MMSegmentation can be built for CPU only environment. In CPU mode you can train (requires MMCV-Lite version >= 2.0.0rc0), test or inference a model.
+MMSegmentation can be built for CPU only environment. In CPU mode you can train (requires MMCV version >= 2.0.0), test or inference a model.
 
 #### Install on Google Colab
 
@@ -156,7 +156,7 @@ thus we only need to install MMCV and MMSegmentation with the following commands
 ```shell
 !pip3 install openmim
 !mim install mmengine
-!mim install "mmcv>=2.0.0rc1"
+!mim install "mmcv>=2.0.0"
 ```
 
 **Step 2.** Install MMSegmentation from the source.
@@ -173,7 +173,7 @@ thus we only need to install MMCV and MMSegmentation with the following commands
 ```python
 import mmseg
 print(mmseg.__version__)
-# Example output: 1.0.0rc0
+# Example output: 1.0.0
 ```
 
 **Note:**
diff --git a/docs/en/migration/interface.md b/docs/en/migration/interface.md
@@ -12,11 +12,11 @@ Or install the below packages manually.
 
 1. [MMEngine](https://github.com/open-mmlab/mmengine): MMEngine is the core the OpenMMLab 2.0 architecture, and we splited many compentents unrelated to computer vision from MMCV to MMEngine.
 
-2. [MMCV](https://github.com/open-mmlab/mmcv): The computer vision package of OpenMMLab. This is not a new dependency, but you need to upgrade it to above **2.0.0rc1** version.
+2. [MMCV](https://github.com/open-mmlab/mmcv): The computer vision package of OpenMMLab. This is not a new dependency, but you need to upgrade it to **2.0.0** version or above.
 
-3. [MMClassification](https://github.com/open-mmlab/mmclassification)(Optional): The image classification toolbox and benchmark of OpenMMLab. This is not a new dependency, but you need to upgrade it to above **1.0.0rc0** version.
+3. [MMClassification](https://github.com/open-mmlab/mmclassification)(Optional): The image classification toolbox and benchmark of OpenMMLab. This is not a new dependency, but you need to upgrade it to **1.0.0rc6** version.
 
-4. [MMDetection](https://github.com/open-mmlab/mmdetection)(Optional): The object detection toolbox and benchmark of OpenMMLab. This is not a new dependency, but you need to upgrade it to above **3.0.0rc0** version.
+4. [MMDetection](https://github.com/open-mmlab/mmdetection)(Optional): The object detection toolbox and benchmark of OpenMMLab. This is not a new dependency, but you need to upgrade it to **3.0.0** version or above.
 
 ## Train launch
 
diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md
@@ -43,7 +43,7 @@ conda install pytorch torchvision cpuonly -c pytorch
 ```shell
 pip install -U openmim
 mim install mmengine
-mim install "mmcv>=2.0.0rc1"
+mim install "mmcv>=2.0.0"
 ```
 
 **步骤 1.** 安装 MMSegmentation
@@ -62,7 +62,7 @@ pip install -v -e .
 情况 b: 如果您把 mmsegmentation 作为依赖库或者第三方库，可以通过 pip 安装：
 
 ```shell
-pip install "mmsegmentation>=1.0.0rc0"
+pip install "mmsegmentation>=1.0.0"
 ```
 
 ### 验证是否安装成功
@@ -87,8 +87,7 @@ python demo/image_demo.py demo/demo.png configs/pspnet/pspnet_r50-d8_4xb2-40k_ci
 
 您将在当前文件夹中看到一个新图像 `result.jpg`，其中所有目标都覆盖了分割 mask
 
-选项 (b). 如果您通过 pip 安装 mmsegmentation, 打开您的 python
-解释器，复制粘贴以下代码：
+选项 (b). 如果您通过 pip 安装 mmsegmentation, 打开您的 python 解释器，复制粘贴以下代码：
 
 ```python
 from mmseg.apis import inference_model, init_model, show_result_pyplot
@@ -137,15 +136,15 @@ MMCV 包含 C++ 和 CUDA 扩展，因此与 PyTorch 的依赖方式比较复杂
 
 为了使用 pip 而不是 MIM 安装 MMCV, 请参考 [MMCV 安装指南](https://mmcv.readthedocs.io/en/latest/get_started/installation.html). 这需要手动指定一个基于 PyTorch 版本及其 CUDA 版本的 find-url.
 
-例如，以下命令可为 PyTorch 1.10.x and CUDA 11.3 安装 mmcv==2.0.0rc1
+例如，以下命令可为 PyTorch 1.10.x and CUDA 11.3 安装 mmcv==2.0.0
 
 ```shell
-pip install mmcv==2.0.0rc1 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
+pip install mmcv==2.0.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
 ```
 
 #### 在仅有 CPU 的平台安装
 
-MMSegmentation 可以在仅有 CPU 的版本上运行。在 CPU 模式，您可以训练（需要 MMCV-Lite 版本 >= 2.0.0rc0），测试和推理模型。
+MMSegmentation 可以在仅有 CPU 的版本上运行。在 CPU 模式，您可以训练（需要 MMCV 版本 >= 2.0.0），测试和推理模型。
 
 #### 在 Google Colab 上安装
 
@@ -156,7 +155,7 @@ MMSegmentation 可以在仅有 CPU 的版本上运行。在 CPU 模式，您可
 ```shell
 !pip3 install openmim
 !mim install mmengine
-!mim install "mmcv>=2.0.0rc1"
+!mim install "mmcv>=2.0.0"
 ```
 
 **Step 2.** 通过源码安装 MMSegmentation
@@ -173,7 +172,7 @@ MMSegmentation 可以在仅有 CPU 的版本上运行。在 CPU 模式，您可
 ```python
 import mmseg
 print(mmseg.__version__)
-# 示例输出: 1.0.0rc0
+# 示例输出: 1.0.0
 ```
 
 **注意:**
diff --git a/docs/zh_cn/migration/interface.md b/docs/zh_cn/migration/interface.md
@@ -12,11 +12,11 @@ MMSegmentation 1.x 依赖于一些新的软件包，您可以准备一个新的
 
 1. [MMEngine](https://github.com/open-mmlab/mmengine)：MMEngine 是 OpenMMLab 2.0 架构的核心，我们将许多与计算机视觉无关的内容从 MMCV 拆分到 MMEngine 中。
 
-2. [MMCV](https://github.com/open-mmlab/mmcv)：OpenMMLab 的计算机视觉包。这不是一个新的依赖，但您需要将其升级到 **2.0.0rc1** 以上的版本。
+2. [MMCV](https://github.com/open-mmlab/mmcv)：OpenMMLab 的计算机视觉包。这不是一个新的依赖，但您需要将其升级到 **2.0.0** 或以上的版本。
 
-3. [MMClassification](https://github.com/open-mmlab/mmclassification)（可选）：OpenMMLab 的图像分类工具箱和基准。这不是一个新的依赖，但您需要将其升级到 **1.0.0rc0** 以上的版本。
+3. [MMClassification](https://github.com/open-mmlab/mmclassification)（可选）：OpenMMLab 的图像分类工具箱和基准。这不是一个新的依赖，但您需要将其升级到 **1.0.0rc6** 版本。
 
-4. [MMDetection](https://github.com/open-mmlab/mmdetection)(可选): OpenMMLab 的目标检测工具箱和基准。这不是一个新的依赖，但您需要将其升级到 **3.0.0rc0** 以上的版本。
+4. [MMDetection](https://github.com/open-mmlab/mmdetection)(可选): OpenMMLab 的目标检测工具箱和基准。这不是一个新的依赖，但您需要将其升级到 **3.0.0** 或以上的版本。
 
 ## 启动训练
 
