[Feat] Download dataset by using MIM&OpenDataLab (open-mmlab#1630)

* add dataset.index

* update preprocess shell

* update shell

* update docs

* update docs

MANIFEST.in

@@ -1,4 +1,5 @@
 include requirements/*.txt
 include mmpretrain/.mim/model-index.yml
+include mmpretrain/.mim/dataset-index.yml
 recursive-include mmpretrain/.mim/configs *.py *.yml
 recursive-include mmpretrain/.mim/tools *.py *.sh

dataset-index.yml

@@ -0,0 +1,11 @@
+imagenet1k:
+  dataset: ImageNet-1K
+  download_root: data
+  data_root: data/imagenet
+  script: tools/dataset_converters/odl_imagenet1k_preprocess.sh
+
+cub:
+  dataset: CUB-200-2011
+  download_root: data
+  data_root: data/CUB_200_2011
+  script: tools/dataset_converters/odl_cub_preprocess.sh

docs/en/user_guides/dataset_prepare.md

@@ -140,12 +140,37 @@ For a complete example about how to use the `CustomDataset`, please see [How to
 
 ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/). It can be accessed with the following steps.
 
+`````{tabs}
+
+````{group-tab} Download by MIM
+
+MIM supports downloading from [OpenDataLab](https://opendatalab.com/) and preprocessing ImageNet dataset with one command line.
+
+_You need to register an account at [OpenDataLab official website](https://opendatalab.com/) and login by CLI._
+
+```Bash
+# install OpenDataLab CLI tools
+pip install -U opendatalab
+# log in OpenDataLab, register if you don't have an account.
+odl login
+# download and preprocess by MIM, better to execute in $MMPreTrain directory.
+mim download mmpretrain --dataset imagenet1k
+```
+
+````
+
+````{group-tab} Download form Official Source
+
 1. Register an account and login to the [download page](http://www.image-net.org/download-images).
 2. Find download links for ILSVRC2012 and download the following two files
    - ILSVRC2012_img_train.tar (~138GB)
    - ILSVRC2012_img_val.tar (~6.3GB)
 3. Untar the downloaded files
 
+````
+
+`````
+
 ### The Directory Structrue of the ImageNet dataset
 
 We support two ways of organizing the ImageNet dataset: Subfolder Format and Text Annotation File Format.

docs/zh_CN/user_guides/dataset_prepare.md

@@ -138,12 +138,37 @@ train_dataloader = dict(
 
 ImageNet 有多个版本，但最常用的一个是 [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/)。 可以通过以下步骤使用它。
 
+`````{tabs}
+
+````{group-tab} MIM 下载
+
+MIM支持使用一条命令行从 [OpenDataLab](https://opendatalab.com/) 下载并预处理 ImageNet 数据集。
+
+_需要在 [OpenDataLab 官网](https://opendatalab.com/) 注册账号并命令行登录_。
+
+```Bash
+# 安装opendatalab库
+pip install -U opendatalab
+# 登录到 OpenDataLab, 如果还没有注册，请到官网注册一个
+odl login
+# 使用 MIM 下载数据集, 最好在 $MMPreTrain 目录执行
+mim download mmpretrain --dataset imagenet1k
+```
+
+````
+
+````{group-tab} 从官网下载
+
+
 1. 注册一个帐户并登录到[下载页面](http://www.image-net.org/download-images)。
 2. 找到 ILSVRC2012 的下载链接，下载以下两个文件：
    - ILSVRC2012_img_train.tar (~138GB)
    - ILSVRC2012_img_val.tar (~6.3GB)
 3. 解压已下载的图片。
 
+````
+`````
+
 ### ImageNet数据集目录结构
 
 我们支持两种方式组织ImageNet数据集，子目录格式和文本注释文件格式。

setup.py

@@ -117,7 +117,7 @@ def add_mim_extension():
     else:
         return
 
-    filenames = ['tools', 'configs', 'model-index.yml']
+    filenames = ['tools', 'configs', 'model-index.yml', 'dataset-index.yml']
     repo_path = osp.dirname(__file__)
     mim_path = osp.join(repo_path, 'mmpretrain', '.mim')
     os.makedirs(mim_path, exist_ok=True)

tools/dataset_converters/odl_cub_preprocess.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+set -x
+
+DOWNLOAD_DIR=$1
+DATA_ROOT=$2
+
+# unzip all of data
+cat $DOWNLOAD_DIR/CUB-200-2011/raw/*.tar.gz | tar -xvz -C $DOWNLOAD_DIR
+
+# move data into DATA_ROOT
+mv -f $DOWNLOAD_DIR/CUB-200-2011/CUB-200-2011/* $DATA_ROOT/
+
+# remove useless data file
+rm -R $DOWNLOAD_DIR/CUB-200-2011/

tools/dataset_converters/odl_imagenet1k_preprocess.sh

@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+set -x
+
+DOWNLOAD_DIR=$1
+DATA_ROOT=$2
+
+# unzip all of data
+cat $DOWNLOAD_DIR/ImageNet-1K/raw/*.tar.gz.* | tar -xvz -C $DOWNLOAD_DIR
+
+# move images into data/imagenet
+mv $DOWNLOAD_DIR/ImageNet-1K/{train,val,test} $DATA_ROOT
+
+# download the mate ann_files file
+wget -P $DATA_ROOT  https://download.openmmlab.com/mmclassification/datasets/imagenet/meta/caffe_ilsvrc12.tar.gz
+
+# unzip mate ann_files file and put it into 'meta' folder
+mkdir $DATA_ROOT/meta
+tar -xzvf $DATA_ROOT/caffe_ilsvrc12.tar.gz -C $DATA_ROOT/meta
+
+# remove useless data files
+rm -R $DOWNLOAD_DIR/ImageNet-1K