Data Preparation

We provide some tips for MMAction2 data preparation in this file.

Notes on Video Data Format

MMAction2 supports two types of data format: raw frames and video. The former is widely used in previous projects such as TSN. This is fast when SSD is available but fails to scale to the fast-growing datasets. (For example, the newest edition of Kinetics has 650K videos and the total frames will take up several TBs.) The latter saves much space but has to do the computation intensive video decoding at execution time. To make video decoding faster, we support several efficient video loading libraries, such as decord, PyAV, etc.

Getting Data

The following guide is helpful when you want to experiment with custom dataset. Similar to the datasets stated above, it is recommended organizing in $MMACTION2/data/$DATASET.

Prepare videos

Please refer to the official website and/or the official script to prepare the videos. Note that the videos should be arranged in either

(1). A two-level directory organized by ${CLASS_NAME}/${VIDEO_ID}, which is recommended to be used for action recognition datasets (such as UCF101 and Kinetics)

(2). A single-level directory, which is recommended to be used for action detection datasets or those with multiple annotations per video (such as THUMOS14).

Extract frames

To extract both frames and optical flow, you can use the tool denseflow we wrote. Since different frame extraction tools produce different number of frames, it is beneficial to use the same tool to do both frame extraction and the flow computation, to avoid mismatching of frame counts.

python ${SRC_FOLDER} ${OUT_FOLDER} [--task ${TASK}] [--level ${LEVEL}] \
    [--num-worker ${NUM_WORKER}] [--flow-type ${FLOW_TYPE}] [--out-format ${OUT_FORMAT}] \
    [--ext ${EXT}] [--new-width ${NEW_WIDTH}] [--new-height ${NEW_HEIGHT}] [--new-short ${NEW_SHORT}] \
    [--resume] [--use-opencv] [--mixed-ext]
  • SRC_FOLDER: Folder of the original video.

  • OUT_FOLDER: Root folder where the extracted frames and optical flow store.

  • TASK: Extraction task indicating which kind of frames to extract. Allowed choices are rgb, flow, both.

  • LEVEL: Directory level. 1 for the single-level directory or 2 for the two-level directory.

  • NUM_WORKER: Number of workers to build rawframes.

  • FLOW_TYPE: Flow type to extract, e.g., None, tvl1, warp_tvl1, farn, brox.

  • OUT_FORMAT: Output format for extracted frames, e.g., jpg, h5, png.

  • EXT: Video file extension, e.g., avi, mp4.

  • NEW_WIDTH: Resized image width of output.

  • NEW_HEIGHT: Resized image height of output.

  • NEW_SHORT: Resized image short side length keeping ratio.

  • --resume: Whether to resume optical flow extraction instead of overwriting.

  • --use-opencv: Whether to use OpenCV to extract rgb frames.

  • --mixed-ext: Indicate whether process video files with mixed extensions.

The recommended practice is

  1. set $OUT_FOLDER to be a folder located in SSD.

  2. symlink the link $OUT_FOLDER to $MMACTION2/data/$DATASET/rawframes.

  3. set new-short instead of using new-width and new-height.

ln -s ${YOUR_FOLDER} $MMACTION2/data/$DATASET/rawframes

Alternative to denseflow

In case your device doesn’t fulfill the installation requirement of denseflow(like Nvidia driver version), or you just want to see some quick demos about flow extraction, we provide a python script tools/misc/ as an alternative to denseflow. You can use it for rgb frames and optical flow extraction from one or several videos. Note that the speed of the script is much slower than denseflow, since it runs optical flow algorithms on CPU.

python tools/misc/ --input ${INPUT} [--prefix ${PREFIX}] [--dest ${DEST}] [--rgb-tmpl ${RGB_TMPL}] \
    [--flow-tmpl ${FLOW_TMPL}] [--start-idx ${START_IDX}] [--method ${METHOD}] [--bound ${BOUND}] [--save-rgb]
  • INPUT: Videos for frame extraction, can be single video or a video list, the video list should be a txt file and just consists of filenames without directories.

  • PREFIX: The prefix of input videos, used when input is a video list.

  • DEST: The destination to save extracted frames.

  • RGB_TMPL: The template filename of rgb frames.

  • FLOW_TMPL: The template filename of flow frames.

  • START_IDX: The start index of extracted frames.

  • METHOD: The method used to generate flow.

  • BOUND: The maximum of optical flow.

  • SAVE_RGB: Also save extracted rgb frames.

Generate file list

We provide a convenient script to generate annotation file list. You can use the following command to generate file lists given extracted frames / downloaded videos.

python tools/data/ ${DATASET} ${SRC_FOLDER} [--rgb-prefix ${RGB_PREFIX}] \
    [--flow-x-prefix ${FLOW_X_PREFIX}] [--flow-y-prefix ${FLOW_Y_PREFIX}] [--num-split ${NUM_SPLIT}] \
    [--subset ${SUBSET}] [--level ${LEVEL}] [--format ${FORMAT}] [--out-root-path ${OUT_ROOT_PATH}] \
    [--seed ${SEED}] [--shuffle]
  • DATASET: Dataset to be prepared, e.g., ucf101, kinetics400, thumos14, sthv1, sthv2, etc.

  • SRC_FOLDER: Folder of the corresponding data format:

    • “$MMACTION2/data/$DATASET/rawframes” if --format rawframes.

    • “$MMACTION2/data/$DATASET/videos” if --format videos.

  • RGB_PREFIX: Name prefix of rgb frames.

  • FLOW_X_PREFIX: Name prefix of x flow frames.

  • FLOW_Y_PREFIX: Name prefix of y flow frames.

  • NUM_SPLIT: Number of split to file list.

  • SUBSET: Subset to generate file list. Allowed choice are train, val, test.

  • LEVEL: Directory level. 1 for the single-level directory or 2 for the two-level directory.

  • FORMAT: Source data format to generate file list. Allowed choices are rawframes, videos.

  • OUT_ROOT_PATH: Root path for output

  • SEED: Random seed.

  • --shuffle: Whether to shuffle the file list.

Now, you can go to to train and test the model.

Prepare audio

We also provide a simple script for audio waveform extraction and mel-spectrogram generation.

python tools/data/ ${ROOT} ${DST_ROOT} [--ext ${EXT}] [--num-workers ${N_WORKERS}] \
    [--level ${LEVEL}]
  • ROOT: The root directory of the videos.

  • DST_ROOT: The destination root directory of the audios.

  • EXT: Extension of the video files. e.g., mp4.

  • N_WORKERS: Number of processes to be used.

After extracting audios, you are free to decode and generate the spectrogram on-the-fly such as this. As for the annotations, you can directly use those of the rawframes as long as you keep the relative position of audio files same as the rawframes directory. However, extracting spectrogram on-the-fly is slow and bad for prototype iteration. Therefore, we also provide a script (and many useful tools to play with) for you to generation spectrogram off-line.

python tools/data/ ${AUDIO_HOME_PATH} ${SPECTROGRAM_SAVE_PATH} [--level ${LEVEL}] \
    [--ext $EXT] [--num-workers $N_WORKERS] [--part $PART]
  • AUDIO_HOME_PATH: The root directory of the audio files.

  • SPECTROGRAM_SAVE_PATH: The destination root directory of the audio features.

  • EXT: Extension of the audio files. e.g., m4a.

  • N_WORKERS: Number of processes to be used.

  • PART: Determines how many parts to be splited and which part to run. e.g., 2/5 means splitting all files into 5-fold and executing the 2nd part. This is useful if you have several machines.

The annotations for audio spectrogram features are identical to those of rawframes. You can simply make a copy of dataset_[train/val]_list_rawframes.txt and rename it as dataset_[train/val]_list_audio_feature.txt

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