Awesome

This repository contains the implementation of MAGR, a novel approach designed for Continual Action Quality Assessment (CAQA). MAGR leverages Manifold Projector (MP) and Intra-Inter-Joint Graph Regularization (IIJ-GR) to address the challenges of feature deviation and regressor confusion across incremental sessions. The method aims to adapt to real-world complexities while safeguarding user privacy.

Requirement

• torch==1.12.0
• torchvision==0.13.0
• torchvideotransforms
• tqdm
• numpy
• scipy
• quadprog

Usage

Install dependencies:

pip install -r requirements.txt

Preparing datasets:

To get started with the experiments, follow the steps below to prepare the datasets:

MTL-AQA

1. Download the MTL-AQA dataset from the MTL-AQA repository.
2. Organize the dataset in the following structure:

$DATASET_ROOT
├── MTL-AQA/
    ├── new
        ├── 01
        ...
        └── 26
    ├── info
        ├── final_annotations_dict_with_dive_number
        ├── test_split_0.pkl
        └── train_split_0.pkl
    └── model_rgb.pth

UNLV-Dive

1. Download the AQA-7 dataset:

mkdir AQA-Seven & cd AQA-Seven
wget http://rtis.oit.unlv.edu/datasets/AQA-7.zip
unzip AQA-7.zip

2. Organize the dataset as follows:

$DATASET_ROOT
├── Seven/
    ├── diving-out
        ├── 001
            ├── img_00001.jpg
            ...
        ...
        └── 370
    ├── gym_vault-out
        ├── 001
            ├── img_00001.jpg
            ...
    ...

    └── Split_4
        ├── split_4_test_list.mat
        └── split_4_train_list.mat

JDM-MSA

Contact the corresponding author of the JDM-MSA paper to obtain access to the dataset. You may need to complete a form before using this dataset for academic research.

Pre-trained model:

Please download the pre-trained I3D model and then put it to weights/model_rgb.pth.

Training from scratch:

We provide for training our model in both distributed and dataparallel modes. Here's a breakdown of each command:

1. In distributed mode:

torchrun \
	--nproc_per_node 2 --master_port 29505 main.py \
	--config ./configs/{config file name}.yaml \
	--model {model name: joint/adam/fea_gr} \
	--dataset {dataset name: class-mtl/class-aqa/class-jdm} \
	--batch_size 5 --minibatch_size 3 --n_tasks 5 --n_epochs 50 \
    --fewshot True --buffer_size 50 \
    --gpus 0 1

2. In dataparallel mode

python main.py \
	--config ./configs/{config file name}.yaml \
	--model {model name: joint/adam/fea_gr} \
	--dataset {dataset name: class-mtl/class-aqa/class-jdm} \
	--batch_size 5 --minibatch_size 3 --n_tasks 5 --n_epochs 50 \
    --fewshot True --buffer_size 50 \
    --gpus 0 1

Choose the appropriate command based on your training setup (distributed or dataparallel) and adjust the configurations as needed.

Evaluation:

If you want to perform evaluation using the same configurations as training but with the addition of the --phase test option.

A pretrained example is provided in this repository. You can find the pretrained logs and weights in the directory outputs/zkl-fscl/class-mtl/fea_gr05-buffer50. This model is trained using the base session pretrained weights located at weights/class-mtl05.pth. In practice, training a robust base session model allows us to build on these weights for further experiments, saving on additional training time.

To evaluate the pretrained model, use the following command:

torchrun \
	--nproc_per_node 2 --master_port 29503 main.py \
	--config ./configs/mtl.yaml \
	--model fea_gr --dataset class-mtl \
	--batch_size 5 --minibatch_size 3 \
	--n_tasks 5 --n_epochs 50 --gpus 2 3 \
	--base_pretrain True --fewshot True \
	--buffer_size 50 --phase test \
    --exp_name fea_gr05-buffer50

Logs for this evaluation can be found in the outputs/zkl-fscl/class-mtl/fea_gr05-buffer50/logs directory.

Acknowledgements

This repository is based on mammoth, many thanks.

If you have any specific questions or if there's anything else you'd like assistance with regarding the code, feel free to let us know.