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DATA_PREP.md

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Data Preparation

There are three main steps in setting up the dataset. See ./data/README.md for annotation file structure.

  1. Download the Annotation and Split Files

    export ROOT=$(PWD)
export VDS_LINK="https://ai2-prior-vidsitu.s3-us-west-2.amazonaws.com/vsitu_data/vidsitu_data.zip"
mkdir $ROOT/data
cd $ROOT/data
wget -c $VDS_LINK
unzip vidsitu_data.zip -d vidsitu_annotations/
rm vidsitu_data.zip

    The directory should look as follows:

    data
└── vidsitu_annotations
    ├── split_files
    │   ├── vseg_split_testevrel_lb.json
    │   ├── vseg_split_testsrl_lb.json
    │   ├── vseg_split_testvb_lb.json
    │   ├── vseg_split_train_lb.json
    │   └── vseg_split_valid_lb.json
    ├── vinfo_files
    │   ├── vinfo_train_lb.json
    │   └── vinfo_valid_lb.json
    └── vseg_ann_files
        ├── vsann_testevrel_noann_lb.json
        ├── vsann_testsrl_noann_lb.json
        ├── vsann_train_lb.json
        └── vsann_valid_lb.json

  2. Download the Videos from youtube. It should take around ~8-10 hours depending on network speed and is around 50GB in size. In case any video is not available, please contact Arka ([email protected]).

    1. Install required dependencies:

      • tqdm
      • yacs
      • yt-dlp

      NOTE: yt-dlp needs to be updated with the following command before downloading the videos using

      pip install yt-dlp

      We also provide a basic conda environment:

      MINICONDA_ROOT=[to your Miniconda/Anaconda root directory]
conda env create -f barebones_data_setup.yml --prefix $MINICONDA_ROOT/envs/vsitu_data_env
conda activate vsitu_data_env

      NOTE: You may have to install yt-dlp separately since the original environment used youtub-dl

      If you are using your own conda environment, you need to run:

      conda activate $ENV_NAME
conda install tqdm
pip install yacs
pip install yt-dlp --upgrade
conda install ffmpeg

    2. Download the videos: (Following works for ~28k / 29k videos)

      cd $ROOT
export PYTHONPATH=$(pwd)
python prep_data/dwn_yt.py --task_type='dwn_vids' --max_processes=30

      There are around 1k Age-restricted videos. To download these you need to add a cookies.txt file, which you can give using --cookies_file=/path/to/cookies.txt. So the command would be:

      cd $ROOT
export PYTHONPATH=$(pwd)
python prep_data/dwn_yt.py --task_type='dwn_vids' --max_processes=30 --cookies_file=/path/to/cookies.txt

      To generate cookies.txt, follow the steps below:

      • Download Get Cookies.txt extension Chrome
      • Login to youtube.com, and use the extension to export the cookies in txt form.

  3. Extract the frames from the video.

    cd $ROOT
export PYTHONPATH=$(pwd)
python prep_data/dwn_yt.py --task_type='extract_frames'

  4. Alternatively, you can skip the video download process and directly use the pre-extracted features from google drive link

    To download directly on the remote, you can use the following convenience function

    function gdrive_download () {
    CONFIRM=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate "https://docs.google.com/uc?export=download&id=$1" -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')
    wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$CONFIRM&id=$1" -O $2
    rm -rf /tmp/cookies.txt
}

cd $ROOT/data
export FEATURE_ZIP_DRIVE_ID="1rBrRmew7Soul51MjLN6F55oTEzUfzyXv" # to be filled after upload
gdrive_download "1rBrRmew7Soul51MjLN6F55oTEzUfzyXv" vsitu_vidfeats_drive.zip
unzip vsitu_vidfeats_drive.zip -d vsitu_vid_feats
rm vsitu_vidfeats_drive.zip

  5. Download the vocabulary files from here: https://drive.google.com/file/d/1TAreioObLGKqU7M9wmnuaXh4b5s_2YdK/view?usp=sharing and place them under data/vsitu_vocab

    function gdrive_download () {
    CONFIRM=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate "https://docs.google.com/uc?export=download&id=$1" -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')
    wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$CONFIRM&id=$1" -O $2
    rm -rf /tmp/cookies.txt
}

cd $ROOT/data
export VOCAB_ZIP_DRIVE_ID="1TAreioObLGKqU7M9wmnuaXh4b5s_2YdK" # to be filled after upload
gdrive_download $VOCAB_ZIP_DRIVE_ID vsitu_vocab.zip
unzip vsitu_vocab.zip -d vsitu_vocab
rm vsitu_vocab.zip

  6. Feature Extration: After training a verb model, one might be interested in re-extracting the features on their end. We provide vidsitu_code/feat_extractor.py.

    To run a particular saved model, use the following command:

    export PYTHONPATH=$(pwd)
python vidsitu_code/feat_extractor.py ----mdl_resume_path='/path/to/saved_model' --mdl_name_used='some_name_used_as_dir_for_feats' --ds.vsitu.vsu_frm_feats='/top/level/featuredir' --mdl.mdl_name='sf_base' --mdl.sf_mdl_name='i3d_r50_nl_8x8' --is_cu=False

    Note that sf_mdl_name needs to match the name in extended_config.py. If you want to use a checkpoint from Slowfast repository where some of the models are saved in caffe2, use --is_cu=True in the argument.

    Thus, for using I3D_NL model trained on vidsitu verbs, the command could be:

    export PYTHONPATH=$(pwd)
CUDA_VISIBLE_DEVICES=5 python vidsitu_code/feat_extractor.py --mdl_resume_path='./weights/i3d_nln_r50_vsitu.pth' --mdl_name_used='i3d_recheck' --ds.vsitu.vsitu_frm_feats='./data/vsitu_features' --mdl.mdl_name='sf_base' --mdl.sf_mdl_name='i3d_r50_nl_8x8' --is_cu=False

    To use I3D_NL model from Slowfast, it would be:

    export PYTHONPATH=$(pwd)
CUDA_VISIBLE_DEVICES=5 python vidsitu_code/feat_extractor.py --mdl_resume_path='./weights/I3D_NLN_8x8_R50.pkl' --mdl_name_used='i3d_recheck_kpret' --ds.vsitu.vsitu_frm_feats='./data/vsitu_features' --mdl.mdl_name='sf_base' --mdl.sf_mdl_name='i3d_r50_nl_8x8' --is_cu=True