FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment

This repository contains the PyTorch implementation for FineCausal.

Requirements

Make sure the following dependencies installed (python):

• pytorch >= 0.4.0
• matplotlib=3.1.0
• einops
• timm
• torch_videovision

pip install git+https://github.com/hassony2/torch_videovision

Dataset & Annotations

FineDiving-HM Download

To download the FineDiving-HM dataset and annotations, please follow FineParser.

Data Structure

$DATASET_ROOT
├── FineDiving
|  ├── FINADivingWorldCup2021_Men3m_final_r1
|     ├── 0
|        ├── 00489.jpg
|        ...
|        └── 00592.jpg
|     ...
|     └── 11
|        ├── 14425.jpg
|        ...
|        └── 14542.jpg
|  ...
|  └── FullMenSynchronised10mPlatform_Tokyo2020Replays_2
|     ├── 0
|     ...
|     └── 16 
└──FineDiving_HM
|  ├── FINADivingWorldCup2021_Men3m_final_r1
|     ├── 0
|        ├── 00489.jpg
|        ...
|        └── 00592.jpg
|     ...
|     └── 11
|        ├── 14425.jpg
|        ...
|        └── 14542.jpg
|  ...
|  └── FullMenSynchronised10mPlatform_Tokyo2020Replays_2
|     ├── 0
|     ...
|     └── 16 

$ANNOTATIONS_ROOT
|  ├── FineDiving_coarse_annotation.pkl
|  ├── FineDiving_fine-grained_annotation.pkl
|  ├── Sub_action_Types_Table.pkl
|  ├── fine-grained_annotation_aqa.pkl
|  ├── train_split.pkl
|  ├── test_split.pkl

Training

To download pretrained_i3d_wight, please follow kinetics_i3d_pytorch, and put model_rgb.pth in models folder.

To train the model, please run:

python launch.py

Test

To test the trained model, please set test: True in config and run:

python launch.py