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Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment

[Paper] [Supplementary Material] [Jittor Code] [Pytorch Code(coming soon)]

This repository contains a Jittor implementation of the paper "Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment" (CVPR 2021)

pipeline

Contents

Requirements

Jittor environment requirements:

  • System: Linux(e.g. Ubuntu/CentOS/Arch), macOS, or Windows Subsystem of Linux (WSL)
  • Python version >= 3.7
  • CPU compiler (require at least one of the following)
    • g++ (>=5.4.0)
    • clang (>=8.0)
  • GPU compiler (optional)
    • nvcc (>=10.0 for g++ or >=10.2 for clang)
  • GPU library: cudnn-dev (recommend tar file installation, reference link)

Install

  • Clone repo

    git clone https://github.com/shedy-pub/hlagcn-jittor
cd hlagcn-jittor

  • Install dependencies ( jittor, imageio, scikit-learn, opencv-python, pandas. Recommend to use Anaconda.)

    # Create a new conda environment
conda create -n menv python=3.8
conda activate menv

# Install other packages
pip install -r requirements.txt

Dataset

  • AVA dataset

    path_to_AVAdataset
├──aesthetics_image_list
├──images
├──AVA.txt
├──trian.txt
└──val.txt

  • AADB dataset

    • Download the AADB dataset into path_to_AADB/. The directory structure should be like:
    path_to_AADBdataset
├──AADB_imgListFiles_label
├──datasetImages_originalSize
└──AADB_AllinAll.csv

Training

Traning scripts for two datasets can be found in scripts/. The dataroot argument should be modified to path_to_<dataset_name>. Run the follwing command for training:

# Training on AVA
sh script/train_jittor_aadb.sh

# Training on AABD
sh script/train_jittor_aadb.sh

Our code will process the dataset information and save file in preprocess/, which needs few minutes for the first time.

Testing

Testing model by runing the scripts or the follwing command:

python -m utils_jittor.eval \
    --dataset <dataset_name> \
    --dataroot path_to_<dataset_name> \
    --eval_model path_to_model

Citation

If you found this code useful please cite our work as:

@InProceedings{She_2021_CVPR,
    author    = {She, Dongyu and Lai, Yu-Kun and Yi, Gaoxiong and Xu, Kun},
    title     = {Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8475-8484}
}