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AIC2022-Video-Event-Retrieval

This repo contains the code and data for our project, which was accepted at CVPRW 2022. Our project is a new approach to a natural language-based vehicle retrieval task. paper

For reproducibility, we also provide a colab notebook notebook that contains the code for reproducing the results.

Development environment

Before using this repo, please use the environment setup as below.

Pre-installation

Install conda according to the instructions on the homepage Before installing the repo, we need to install the CUDA driver version >=10.2.

$ conda env create -f environment.yml
$ conda activate hcmus
$ pip install -r requirements.txt
$ pip install -e .

Prepare data

Create a symbolic link to the data directory in the data directory of the project.

$ cd /Users/your_short_username/path/to/where/you/want/to/put/the/symlink
$ ln -s /Volumes/HDD_name/path/to/where/you/are/storing/the/moved/files    symbolic_link_name_you_want_to_use

Ensure your data folder structure as same as our data_sample before running the code.

$ ./tools/extract_vdo2frms_AIC.sh ./data/AIC22_Track2_NL_Retrieval/ ./data/meta/extracted_frames/
$ cp ./data/AIC22_Track2_NL_Retrieval/*.json ./data/meta/
$ ./tools/preproc_motion.sh ./data/meta
$ ./tools/preproc_srl.sh ./data/meta

For detail, please take a look at extract data notebook

For testing purpose, you can use the command above with data_dir is ./data_sample/meta

Reading detail document of preprocessing part can be found in the srl part and basic part (adapted from hcmus team and alibaba team source code).

Inference

We provide a simple inference script for inference purpose. With artifacts/ is the directory where you store the trained classification model.

$ ./tools/infer.sh ./data/meta/

For detail, please take a look at Predictor class in src/predictor.py or inference notebook

Training

Updating

Deployment (not working yet)

For deployment/training purpose, docker is an ready-to-use solution.

To build docker image:

$ cd <this-repo>
$ DOCKER_BUILDKIT=1 docker build -t aic22:latest .

To start docker container:

$ docker run --rm --name aic-t2 --gpus device=0 --shm-size 16G -it -v $(pwd)/:/home/workspace/src/ aic22:latest /bin/bash

With device is the GPU device number, and shm-size is the shared memory size (should be larger than the size of the model).

To attach to the container:

$ docker attach aic-t2

Contribution guide

If you want to contribute to this repo, please follow steps below:

  1. Fork your own version from this repository
  2. Checkout to another branch, e.g. fix-loss, add-feat.
  3. Make changes/Add features/Fix bugs
  4. Add test cases in the test folder and run them to make sure they are all passed (see below)
  5. Create and describe feature/bugfix in the PR description (or create new document)
  6. Push the commit(s) to your own repository
  7. Create a pull request on this repository
pip install pytest
python -m pytest tests/

Expected result:

============================== test session starts ===============================
platform darwin -- Python 3.7.12, pytest-7.1.1, pluggy-1.0.0
rootdir: /Users/nhtlong/workspace/aic/aic2022
collected 10 items

tests/test_args.py ...                                                     [ 30%]
tests/test_utils.py .                                                      [ 40%]
tests/uts/test_dataset.py .                                                [ 50%]
tests/uts/test_eval.py .                                                   [ 60%]
tests/uts/test_extractor.py ...                                            [ 90%]
tests/uts/test_model.py .                                                  [100%]