[Doc] improve Chinese documents

README_zh-CN.md

@@ -29,7 +29,7 @@
 [📘使用文档](https://mmediting.readthedocs.io/zh_CN/1.x/) |
 [🛠️安装教程](https://mmediting.readthedocs.io/zh_CN/1.x/2_get_started.htmll) |
 [👀模型库](https://mmediting.readthedocs.io/zh_CN/1.x/3_model_zoo.html) |
-[🆕更新记录](docs/zh_cn/community/changelog.md) |
+[🆕更新记录](docs/zh_cn/changelog.md) |
 [🚀进行中的项目](https://github.com/open-mmlab/mmediting/projects) |
 [🤔提出问题](https://github.com/open-mmlab/mmediting/issues)
 
@@ -124,7 +124,7 @@ MMEditing 缜密地设计新的框架并将其精心实现，希望能够为您
 - 修复 FLAVR 的注册问题。
 - 修正 RDB 模型中的通道数。
 
-如果像了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/community/changelog.md)。
+如果像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)。
 
 ## 安装
 
@@ -151,11 +151,11 @@ cd mmediting
 pip3 install -e .
 ```
 
-更详细的安装指南请参考 [get_started.md](docs/zh_cn/2_get_started.md) 。
+更详细的安装指南请参考 [installation](docs/zh_cn/get_started/install.md) 。
 
 ## 开始使用
 
-请参考[使用教程](docs/zh_cn/2_get_started.md)和[功能演示](docs/zh_cn/user_guides/3_inference.md)获取MMEditing的基本用法。
+请参考[使用教程](docs/zh_cn/get_started/install.md)和[功能演示](docs/zh_cn/get_started/quick_run.md)获取MMEditing的基本用法。
 
 ## 模型库
 

docs/en/.dev_scripts/update_dataset_zoo.py

@@ -25,7 +25,7 @@ def update_dataset_zoo():
 """
 
     subfolders = os.listdir(source_dir)
-    for subf in tqdm(subfolders):
+    for subf in tqdm(subfolders, desc='update dataset zoo'):
 
         target_subf = subf.replace('-', '_').lower()
         target_readme = os.path.join(target_dir, target_subf + '.md')

docs/en/.dev_scripts/update_model_zoo.py

@@ -21,16 +21,19 @@ def anchor(name):
                          name.strip().lower())).strip('-')
 
 
-def summarize(stats, name, task='all'):
+def summarize(stats, name):
     allpapers = func.reduce(lambda a, b: a.union(b),
-                            [p for p, _, _, _, _, _ in stats])
+                            [p for p, _, _, _, _, _, _ in stats])
     allconfigs = func.reduce(lambda a, b: a.union(b),
-                             [c for _, c, _, _, _, _ in stats])
+                             [c for _, c, _, _, _, _, _ in stats])
     allckpts = func.reduce(lambda a, b: a.union(b),
-                           [c for _, _, c, _, _, _ in stats])
+                           [c for _, _, c, _, _, _, _ in stats])
     alltasks = func.reduce(lambda a, b: a.union(b),
-                           [t for _, _, _, t, _, _ in stats])
-    task_desc = '\n    - '.join(list(alltasks))
+                           [t for _, _, _, t, _, _, _ in stats])
+    task_desc = '\n'.join([
+        f"    - [{task}]({task.replace('-', '_').replace(' ', '_').lower()}.md)"  # noqa
+        for task in list(alltasks)
+    ])
 
     # Overview
     papertypes, papercounts = np.unique([t for t, _ in allpapers],
@@ -55,9 +58,9 @@ def summarize(stats, name, task='all'):
     if name == 'Overview':
         summary += f"""
 * Tasks:
-    - {task_desc}
+{task_desc}
 
-    """
+"""
 
     return summary
 
@@ -138,15 +141,16 @@ def update_model_zoo():
 
 """
         # * We should have: {len(glob.glob(osp.join(dirname(f), '*.py')))}
-        stats.append((papers, configs, ckpts, tasks, year, statsmsg))
+        content = content.replace('# ', '## ')
+        stats.append((papers, configs, ckpts, tasks, year, statsmsg, content))
 
     # overview
     overview = summarize(stats, 'Overview')
     with open(osp.join(target_dir, 'overview.md'), 'w') as f:
         f.write(overview)
 
     alltasks = func.reduce(lambda a, b: a.union(b),
-                           [t for _, _, _, t, _, _ in stats])
+                           [t for _, _, _, t, _, _, _ in stats])
 
     # index.rst
     indexmsg = """
@@ -166,13 +170,15 @@ def update_model_zoo():
 
     #  task-specific
     for task in alltasks:
-        filtered_model = [(paper, config, ckpt, tasks, year, x)
-                          for paper, config, ckpt, tasks, year, x in stats
-                          if task in tasks]
-        filtered_model = sorted(filtered_model, key=lambda x: x[-2])[::-1]
+        filtered_model = [
+            (paper, config, ckpt, tasks, year, x, content)
+            for paper, config, ckpt, tasks, year, x, content in stats
+            if task in tasks
+        ]
+        filtered_model = sorted(filtered_model, key=lambda x: x[-3])[::-1]
         overview = summarize(filtered_model, task)
-        msglist = '\n'.join(x for _, _, _, _, _, x in filtered_model)
 
+        msglist = '\n'.join(x for _, _, _, _, _, _, x in filtered_model)
         task = task.replace(' ', '_').replace('-', '_').lower()
         with open(osp.join(target_dir, f'{task}.md'), 'w') as f:
             f.write(overview + '\n' + msglist)

docs/en/get_started/install.md

@@ -7,7 +7,6 @@ In this section, you will know about:
   - [Best practices](#best-practices)
   - [Customize installation](#customize-installation)
   - [Developing with multiple MMEditing versions](#developing-with-multiple-mmediting-versions)
-- [Quick run](#quick-run)
 
 ## Installation
 

docs/en/index.rst

@@ -63,7 +63,6 @@ Documentation
    user_guides/visualization.md
    user_guides/useful_tools.md
    user_guides/deploy.md
-   user_guides/faq.md
 
 
 .. toctree::

docs/en/user_guides/deploy.md

@@ -1,4 +1,4 @@
-# Tutorial 7: Deploy models in MMEditing
+# Tutorial 8: Deploy models in MMEditing
 
 [MMDeploy](https://github.com/open-mmlab/mmdeploy) is an open-source deep learning model deployment toolset.
 MMDeploy supports deploying models in MMEditing. Please refer to [MMDeploy](https://github.com/open-mmlab/mmdeploy) for more information.

docs/en/user_guides/metrics.md

@@ -1,4 +1,4 @@
-# Tutorial 8: Using metrics in MMEditing
+# Tutorial 5: Using metrics in MMEditing
 
 MMEditing supports **17 metrics** to assess the quality of models.
 

docs/en/user_guides/useful_tools.md

@@ -1,4 +1,4 @@
-# Tutorial 6: Useful tools
+# Tutorial 7: Useful tools
 
 We provide lots of useful tools under `tools/` directory.
 

docs/en/user_guides/visualization.md

@@ -1,4 +1,4 @@
-# Tutorial 5: Visualization
+# Tutorial 6: Visualization
 
 The visualization of images is an important way to measure the quality of image processing, editing and synthesis.
 Using `visualizer` in config file can save visual results when training or testing. You can follow [MMEngine Documents](https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/visualization.md) to learn the usage of visualization. MMEditing provides a rich set of visualization functions.

docs/zh_cn/.dev_scripts/update_dataset_zoo.py

@@ -0,0 +1,51 @@
+import os
+
+from tqdm import tqdm
+
+
+def update_dataset_zoo():
+
+    target_dir = 'dataset_zoo'
+    source_dir = '../../tools/dataset_converters'
+    os.makedirs(target_dir, exist_ok=True)
+
+    # generate overview
+    overviewmsg = """
+# 概览
+
+"""
+
+    # generate index.rst
+    rstmsg = """
+.. toctree::
+   :maxdepth: 1
+   :caption: Dataset Zoo
+
+   overview.md
+"""
+
+    subfolders = os.listdir(source_dir)
+    for subf in tqdm(subfolders, desc='update dataset zoo'):
+
+        target_subf = subf.replace('-', '_').lower()
+        target_readme = os.path.join(target_dir, target_subf + '.md')
+        source_readme = os.path.join(source_dir, subf, 'README_zh-CN.md')
+        if not os.path.exists(source_readme):
+            continue
+
+        overviewmsg += f'\n- [{subf}]({target_subf}.md)'
+        rstmsg += f'\n   {target_subf}.md'
+
+        # generate all tasks dataset_zoo
+        command = f'cat {source_readme} > {target_readme}'
+        os.popen(command)
+
+    with open(os.path.join(target_dir, 'overview.md'), 'w') as f:
+        f.write(overviewmsg)
+
+    with open(os.path.join(target_dir, 'index.rst'), 'w') as f:
+        f.write(rstmsg)
+
+
+if __name__ == '__main__':
+    update_dataset_zoo()