Preparing ActivityNet¶
Introduction¶
@article{Heilbron2015ActivityNetAL,
title={ActivityNet: A large-scale video benchmark for human activity understanding},
author={Fabian Caba Heilbron and Victor Escorcia and Bernard Ghanem and Juan Carlos Niebles},
journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2015},
pages={961-970}
}
For basic dataset information, please refer to the official website.
For action detection, you can either use the ActivityNet rescaled feature provided in this repo or extract feature with mmaction2 (which has better performance).
We release both pipeline.
Before we start, please make sure that current working directory is $MMACTION2/tools/data/activitynet/.
Option 1: Use the ActivityNet rescaled feature provided in this repo¶
Step 1. Download Annotations¶
First of all, you can run the following script to download annotation files.
bash download_feature_annotations.sh
Step 2. Prepare Videos Features¶
Then, you can run the following script to download activitynet features.
bash download_features.sh
Step 3. Process Annotation Files¶
Next, you can run the following script to process the downloaded annotation files for training and testing.
It first merges the two annotation files together and then separates the annoations by train, val and test.
python process_annotations.py
Option 2: Extract ActivityNet feature using MMAction2 with all videos provided in official website¶
Step 1. Download Annotations¶
First of all, you can run the following script to download annotation files.
bash download_annotations.sh
Step 2. Prepare Videos¶
Then, you can run the following script to prepare videos. The codes are adapted from the official crawler. Note that this might take a long time.
bash download_videos.sh
Since some videos in the ActivityNet dataset might be no longer available on YouTube, official website has made the full dataset available on Google and Baidu drives. To accommodate missing data requests, you can fill in this request form provided in official download page to have a 7-day-access to download the videos from the drive folders.
We also provide download steps for annotations from BSN repo
bash download_bsn_videos.sh
For this case, the downloading scripts update the annotation file after downloading to make sure every video in it exists.
Step 3. Extract RGB and Flow¶
Before extracting, please refer to install.md for installing denseflow.
Use following scripts to extract both RGB and Flow.
bash extract_frames.sh
The command above can generate images with new short edge 256. If you want to generate images with short edge 320 (320p), or with fix size 340x256, you can change the args --new-short 256 to --new-short 320 or --new-width 340 --new-height 256.
More details can be found in prepare dataset
Step 4. Generate File List for ActivityNet Finetuning¶
With extracted frames, you can generate video-level or clip-level lists of rawframes, which can be used for ActivityNet Finetuning.
python generate_rawframes_filelist.py
Step 5. Finetune TSN models on ActivityNet¶
You can use ActivityNet configs in configs/recognition/tsn to finetune TSN models on ActivityNet.
You need to use Kinetics models for pretraining.
Both RGB models and Flow models are supported.
Step 6. Extract ActivityNet Feature with finetuned ckpts¶
After finetuning TSN on ActivityNet, you can use it to extract both RGB and Flow feature.
python ../../misc/clip_feature_extraction.py tsn_extract_rgb_feat_config.py \
/path/to/rgb_checkpoint.pth ../../../data/ActivityNet/rgb_tarin_feat.pkl \
--video-list ../../../data/ActivityNet/anet_train_video.txt \
--video-root ../../../data/ActivityNet/rawframes \
--dump-score
python ../../misc/clip_feature_extraction.py tsn_extract_rgb_feat_config.py \
path/to/rgb_checkpoint.pth ../../../data/ActivityNet/rgb_val_feat.pkl \
--video-list ../../../data/ActivityNet/anet_val_video.txt \
--video-root ../../../data/ActivityNet/rawframes \
--dump-score
python ../../misc/clip_feature_extraction.py tsn_extract_flow_feat_config.py \
/path/to/flow_checkpoint.pth ../../../data/ActivityNet/flow_tarin_feat.pkl \
--video-list ../../../data/ActivityNet/anet_train_video.txt \
--video-root ../../../data/ActivityNet/rawframes \
--dump-score
python ../../misc/clip_feature_extraction.py tsn_extract_flow_feat_config.py \
/path/to/flow_checkpoint.pth ../../../data/ActivityNet/flow_val_feat.pkl \
--video-list ../../../data/ActivityNet/anet_val_video.txt \
--video-root ../../../data/ActivityNet/rawframes \
--dump-score
After feature extraction, you can use our post processing scripts to concat RGB and Flow feature, generate the 100-t X 400-d feature for Action Detection.
python activitynet_feature_postprocessing.py --rgb ../../../data/ActivityNet/rgb_feat --flow ../../../data/ActivityNet/flow_feat --dest ../../../data/ActivityNet/mmaction_feat
Final Step. Check Directory Structure¶
After the whole data pipeline for ActivityNet preparation, you will get the features, videos, frames and annotation files.
In the context of the whole project (for ActivityNet only), the folder structure will look like:
mmaction2
├── mmaction
├── tools
├── configs
├── data
│ ├── ActivityNet
(if Option 1 used)
│ │ ├── anet_anno_{train,val,test,full}.json
│ │ ├── anet_anno_action.json
│ │ ├── video_info_new.csv
│ │ ├── activitynet_feature_cuhk
│ │ │ ├── csv_mean_100
│ │ │ │ ├── v___c8enCfzqw.csv
│ │ │ │ ├── v___dXUJsj3yo.csv
│ │ │ | ├── ..
(if Option 2 used)
│ │ ├── anet_train_video.txt
│ │ ├── anet_val_video.txt
│ │ ├── anet_train_clip.txt
│ │ ├── anet_val_clip.txt
│ │ ├── activity_net.v1-3.min.json
│ │ ├── mmaction_feat
│ │ │ ├── v___c8enCfzqw.csv
│ │ │ ├── v___dXUJsj3yo.csv
│ │ │ ├── ..
│ │ ├── rawframes
│ │ │ ├── v___c8enCfzqw
│ │ │ │ ├── img_00000.jpg
│ │ │ │ ├── flow_x_00000.jpg
│ │ │ │ ├── flow_y_00000.jpg
│ │ │ │ ├── ..
│ │ │ ├── ..
For training and evaluating on ActivityNet, please refer to Training and Test Tutorial.