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Action Recognition in CNN-LSTM

This repository is a tutorial on the CNN-LSTM model of Action recognition using the UCF101 dataset. This is a refactored repository of eriklindernoren/Action-Recognition. I'm glad if you can use it as a reference.

🎁 Dataset

Details of UCF101 can be found at the following link. UCF101 - Action Recognition Data Set.

🐳 Instalation

I run in the following environment. If you have a similar environment, you can prepare the environment immediately with pipenv.

  • Ubuntu 20.04.1 LTS
  • CUDA Version 11.0
  • Python 3.8.5
$ pip install pipenv
$ pipenv sync

If you do not have a cuda environment, please use Docker. Build docker with the following command.

$ docker-compose up -d dev

Run docker with the following command.

$ docker run --rm -it --runtime=nvidia \
      -v /mnt/:/mnt \
      -v /home/kuroyanagi/clones/Action-Recognition-CNN-LSTM/:/work/Action-Recognition-CNN-LSTM \
      -u (id -u):(id -g) \
      -e HOSTNAME=(hostname) \
      -e HOME=/home/docker \
      --workdir /work/Action-Recognition-CNN-LSTM \
      --ipc host \
      ubuntu20-cuda11-py38 bash

🎁 Prepare dataset

$ cd data/
$ bash download_ucf101.sh # Downloads the UCF-101 dataset (~7.2 GB)
$ python extract_frames.py # Extracts frames from the video (~26.2 GB)

✍️ CNN-LSTM

The original repository defined the model as ConvLSTM, but it was renamed because CNNLSTM is correct.

What is the difference between ConvLSTM and CNN LSTM? - Quora https://www.quora.com/What-is-the-difference-between-ConvLSTM-and-CNN-LSTM

🏃 Train

train.py performs training/validation according to the specified config. A checkpoint for each epoch is saved and evaluated for validation.

To execute the experiment of configs/experiments/train_exp01.yaml, execute as follows. Specify the output destination as hydra.run.dir=outputs/train/exp01.

$ pipenv run python train.py +experiments=train_exp01 hydra.run.dir=outputs/train/exp01

If you use Docker, execute the following command.

$ export TORCH_HOME=/home/docker
$ python train.py +experiments=train_exp01 hydra.run.dir=outputs/train/exp01

🧍 Test

test.py performs only inference for a checkpoint. The specifications of config and output are the same as train.

$ pipenv run python test.py +experiments=test_exp01 hydra.run.dir=outputs/test/exp01

🎥 Test on Video

test_on_video.py makes inferences for a video.

$ pipenv run python test_on_video.py +experiments=test_on_video_exp01 hydra.run.dir=outputs/test_on_video/exp01

📈 Results

The results of TensorBoard in split 1 are as follows.

tensorboard

🚥 Hydra color log

This repository also sets up a beautiful hydra color log.

📕 References

🚀 TODOs

  • fix bug of original repository
  • Docker and pipenv
  • check code format with black, isort, vulture
  • hydra and color logger
  • TensorBoard
  • [] PyTorch Lightning and wandb