[Docs] add index number for tutorial and merge config.md into it. (#491)

* add index * Update README.md
open-mmlab · Dec 25, 2020 · 264c3ad · 264c3ad
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diff --git a/README.md b/README.md
@@ -118,7 +118,7 @@ The supported datasets are listed in [supported_datasets.md](docs/supported_data
 ## Get Started
 
 Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMAction2.
-There are also tutorials for [finetuning models](docs/tutorials/finetune.md), [adding new dataset](docs/tutorials/new_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [exporting model to onnx](docs/tutorials/export_model.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [adding new modules](docs/tutorials/new_modules.md).
+There are also tutorials for [learn about configs](docs/tutorials/1_config.md), [finetuning models](docs/tutorials/2_finetune.md), [adding new dataset](docs/tutorials/3_new_dataset.md), [designing data pipeline](docs/tutorials/4_data_pipeline.md), [adding new modules](docs/tutorials/5_new_modules.md), [exporting model to onnx](docs/tutorials/6_export_model.md) and [customizing runtime settings](docs/tutorials/7_customize_runtime.md).
 
 A Colab tutorial is also provided. You may preview the notebook [here](demo/mmaction2_tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmaction2/blob/master/demo/mmaction2_tutorial.ipynb) on Colab.
 

diff --git a/docs/faq.md b/docs/faq.md
@@ -33,7 +33,7 @@ If the contents here do not cover your issue, please create an issue using the [
 
 - **How to just use trained recognizer models for backbone pre-training ?**
 
-    Refer to [Use Pre-Trained Model](https://github.com/open-mmlab/mmaction2/blob/master/docs/tutorials/finetune.md#use-pre-trained-model),
+    Refer to [Use Pre-Trained Model](https://github.com/open-mmlab/mmaction2/blob/master/docs/tutorials/2_finetune.md#use-pre-trained-model),
     in order to use the pre-trained model for the whole network, the new config adds the link of pre-trained models in the `load_from`.
 
     And to use backbone for pre-training, you can change `pretrained` value in the backbone dict of config files to the checkpoint path / url.
@@ -47,7 +47,7 @@ If the contents here do not cover your issue, please create an issue using the [
     log_config=dict(interval=20, hooks=[dict(type='TensorboardLoggerHook')])
     ```
 
-    You can refer to [tutorials/customize_runtime.md](tutorials/customize_runtime.md#log-config), [config.md](config.md) and [this](https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsm/tsm_r50_1x1x8_50e_kinetics400_rgb.py#L118).
+    You can refer to [tutorials/1_config.md](tutorials/1_config.md), [tutorials/7_customize_runtime.md](tutorials/7_customize_runtime.md#log-config), and [this](https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsm/tsm_r50_1x1x8_50e_kinetics400_rgb.py#L118).
 
 ## Testing
 

diff --git a/docs/getting_started.md b/docs/getting_started.md
@@ -49,7 +49,7 @@ mmaction2
 
 For more information on data preparation, please see [data_preparation.md](data_preparation.md)
 
-For using custom datasets, please refer to [Tutorial 2: Adding New Dataset](tutorials/new_dataset.md)
+For using custom datasets, please refer to [Tutorial 2: Adding New Dataset](tutorials/3_new_dataset.md)
 
 ## Inference with Pre-Trained Models
 
@@ -417,6 +417,6 @@ CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NA
 
 ## Tutorials
 
-Currently, we provide some tutorials for users to [finetune model](tutorials/finetune.md),
-[add new dataset](tutorials/new_dataset.md), [customize data pipelines](tutorials/data_pipeline.md),
-[add new modules](tutorials/new_modules.md), [export a model to ONNX](tutorials/export_model.md) and [customize runtime settings](tutorials/customize_runtime.md).
+Currently, we provide some tutorials for users to [learn about configs](tutorials/1_config.md), [finetune model](tutorials/2_finetune.md),
+[add new dataset](tutorials/3_new_dataset.md), [customize data pipelines](tutorials/4_data_pipeline.md),
+[add new modules](tutorials/5_new_modules.md), [export a model to ONNX](tutorials/6_export_model.md) and [customize runtime settings](tutorials/7_customize_runtime.md).
diff --git a/docs/index.rst b/docs/index.rst
@@ -8,7 +8,6 @@ Welcome to MMAction2's documentation!
    getting_started.md
    demo.md
    benchmark.md
-   config.md
 
 .. toctree::
    :maxdepth: 2
@@ -30,12 +29,13 @@ Welcome to MMAction2's documentation!
    :maxdepth: 2
    :caption: Tutorials
 
-   tutorials/finetune.md
-   tutorials/new_dataset.md
-   tutorials/data_pipeline.md
-   tutorials/new_modules.md
-   tutorials/export_model.md
-   tutorials/customize_runtime.md
+   tutorials/1_config.md
+   tutorials/2_finetune.md
+   tutorials/3_new_dataset.md
+   tutorials/4_data_pipeline.md
+   tutorials/5_new_modules.md
+   tutorials/6_export_model.md
+   tutorials/7_customize_runtime.md
 
 .. toctree::
    :maxdepth: 2

diff --git a/docs/merge_docs.sh b/docs/merge_docs.sh
@@ -51,7 +51,6 @@ sed -i 's=](/=](https://github.com/open-mmlab/mmaction2/tree/master/=g' detectio
 sed -i 's=](/=](https://github.com/open-mmlab/mmaction2/tree/master/=g' benchmark.md
 sed -i 's=](/=](https://github.com/open-mmlab/mmaction2/tree/master/=g' getting_started.md
 sed -i 's=](/=](https://github.com/open-mmlab/mmaction2/tree/master/=g' install.md
-sed -i 's=](/=](https://github.com/open-mmlab/mmaction2/tree/master/=g' config.md
 sed -i 's=](/=](https://github.com/open-mmlab/mmaction2/tree/master/=g' changelog.md
 sed -i 's/](\/docs\//](/g' ./tutorials/*.md
 sed -i 's=](/=](https://github.com/open-mmlab/mmaction2/tree/master/=g' ./tutorials/*.md

diff --git a/docs/config.md → docs/tutorials/1_config.md b/docs/config.md → docs/tutorials/1_config.md
@@ -1,4 +1,4 @@
-# Config System
+# Tutorial 1: Learn about Configs
 
 We use python files as configs. You can find all the provided configs under `$MMAction2/configs`.
 
@@ -149,7 +149,7 @@ which is convenient to conduct various experiments.
     optimizer = dict(
         # Config used to build optimizer, support (1). All the optimizers in PyTorch
         # whose arguments are also the same as those in PyTorch. (2). Custom optimizers
-        # which are builed on `constructor`, referring to "tutorials/new_modules.md"
+        # which are builed on `constructor`, referring to "tutorials/5_new_modules.md"
         # for implementation.
         type='Adam',  # Type of optimizer, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details
         lr=0.001,  # Learning rate, see detail usages of the parameters in the documentaion of PyTorch
@@ -360,7 +360,7 @@ which is convenient to conduct various experiments.
     optimizer = dict(
         # Config used to build optimizer, support (1). All the optimizers in PyTorch
         # whose arguments are also the same as those in PyTorch. (2). Custom optimizers
-        # which are builed on `constructor`, referring to "tutorials/new_modules.md"
+        # which are builed on `constructor`, referring to "tutorials/5_new_modules.md"
         # for implementation.
         type='SGD',  # Type of optimizer, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details
         lr=0.01,  # Learning rate, see detail usages of the parameters in the documentaion of PyTorch
@@ -584,7 +584,7 @@ data['test'] = data['val']    # Set test_dataset as val_dataset
 optimizer = dict(
     # Config used to build optimizer, support (1). All the optimizers in PyTorch
     # whose arguments are also the same as those in PyTorch. (2). Custom optimizers
-    # which are builed on `constructor`, referring to "tutorials/new_modules.md"
+    # which are builed on `constructor`, referring to "tutorials/5_new_modules.md"
     # for implementation.
     type='SGD',  # Type of optimizer, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details
     lr=0.2,  # Learning rate, see detail usages of the parameters in the documentaion of PyTorch (for 8gpu)

diff --git a/docs/tutorials/finetune.md → docs/tutorials/2_finetune.md b/docs/tutorials/finetune.md → docs/tutorials/2_finetune.md
@@ -1,4 +1,4 @@
-# Tutorial 1: Finetuning Models
+# Tutorial 2: Finetuning Models
 
 This tutorial provides instructions for users to use the pre-trained models
 to finetune them on other datasets, so that better performance can be get.
@@ -17,11 +17,11 @@ to finetune them on other datasets, so that better performance can be get.
 
 There are two steps to finetune a model on a new dataset.
 
-1. Add support for the new dataset. See [Tutorial 2: Adding New Dataset](new_dataset.md).
+1. Add support for the new dataset. See [Tutorial 2: Adding New Dataset](3_new_dataset.md).
 1. Modify the configs. This will be discussed in this tutorial.
 
 For example, if the user want to finetune models pre-trained on Kinetics-400 Dataset to another dataset, say UCF101,
-then four parts in the config (see [here](../config.md)) needs attention.
+then four parts in the config (see [here](1_config.md)) needs attention.
 
 ## Modify Head
 

diff --git a/docs/tutorials/new_dataset.md → docs/tutorials/3_new_dataset.md b/docs/tutorials/new_dataset.md → docs/tutorials/3_new_dataset.md
@@ -1,4 +1,4 @@
-# Tutorial 2: Adding New Dataset
+# Tutorial 3: Adding New Dataset
 
 In this tutorial, we will introduce some methods about how to customize your own dataset by reorganizing data and mixing dataset for the project.
 

diff --git a/docs/tutorials/data_pipeline.md → docs/tutorials/4_data_pipeline.md b/docs/tutorials/data_pipeline.md → docs/tutorials/4_data_pipeline.md
@@ -1,4 +1,4 @@
-# Tutorial 3: Customize Data Pipelines
+# Tutorial 4: Customize Data Pipelines
 
 In this tutorial, we will introduce some methods about the design of data pipelines, and how to customize and extend your own data pipelines for the project.
 

diff --git a/docs/tutorials/new_modules.md → docs/tutorials/5_new_modules.md b/docs/tutorials/new_modules.md → docs/tutorials/5_new_modules.md
@@ -1,4 +1,4 @@
-# Tutorial 4: Adding New Modules
+# Tutorial 5: Adding New Modules
 
 In this tutorial, we will introduce some methods about how to customize optimizer, develop new components and new a learning rate scheduler for this project.
 

diff --git a/docs/tutorials/export_model.md → docs/tutorials/6_export_model.md b/docs/tutorials/export_model.md → docs/tutorials/6_export_model.md
@@ -1,4 +1,4 @@
-# Tutorial 5: Exporting a model to ONNX
+# Tutorial 6: Exporting a model to ONNX
 
 Open Neural Network Exchange [(ONNX)](https://onnx.ai/) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves.
 

diff --git a/docs/tutorials/customize_runtime.md → docs/tutorials/7_customize_runtime.md b/docs/tutorials/customize_runtime.md → docs/tutorials/7_customize_runtime.md
@@ -1,4 +1,4 @@
-# Tutorial 6: Customize Runtime Settings
+# Tutorial 7: Customize Runtime Settings
 
 In this tutorial, we will introduce some methods about how to customize optimization methods, training schedules, workflow and hooks when running your own settings for the project.