Awesome

Project Name

Released code for ECCV 2024 paper: Attention Beats Linear for Fast Implicit Neural Representation Generation. Arxiv paper link: https://arxiv.org/abs/2407.15355

Table of Contents

• Project Name
  • Table of Contents
  • Installation
    • Prerequisites
    • Steps
  • Usage
    • Configuration Parameters
    • Example Run
  • License
  • Contact

Installation

Prerequisites

• Python version 3.8+
• Operating System: Windows/Linux/macOS

Steps

1. Clone or download the project to your local machine.
2. Navigate to the project directory:

cd path/to/your/project

3. Install the dependencies:

pip install -r requirements.txt

4. download datasets

Download any image dataset or nerf dataset you want, for example:

• CelebA: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
• ShapeNet: https://paperswithcode.com/paper/shapenet-an-information-rich-3d-model

If you want to use nerf dataset, we expect the folder structure is like:

|--dataset_root
|  |--subset_1.json
|  |--subset_1
|  |  |--id_of_item1
|  |  |  |--transforms.json
|  |  |  |--r_0.png
|  |  |  |--r_1.png
|  |  |  |--   ...
|  |  |  |--r_n1.png
|  |  |--id_of_item2
|  |     |--transforms.json
|  |     |--r_0.png
|  |     |--r_1.png
|  |     |--   ...
|  |     |--r_n2.png
|  |--subset_2.json
|  |--subset_2
|     |--id_of_item1
|     |  |--transforms.json
|     |  |--r_0.png
|     |  |--   ...
|     |  |--r_m1.png
|     |--id_of_item2
|     ...
|

Usage

Configuration Parameters

We give the example configs in ./configs, and here's some important parameters:

• milestone: The path to an trained model, set to empty if it's a new experiment.
• model_comment: The training outputs will be saved at ./output/{model_comment}.
• image_shape: An int or list of int. The size of target reconstruction resolution, all target images will be reshape to image_shape.
• hypo_hiddim: Neural representation's hidden dim size.
• hypo_depth: Depth of MLP in Representation.
• train_target: Must be one of image or nerf, for different dataset setting.
• hyper_network,model_framework: Model parameter setting.
• dataset_kwargs: Dataset setting.

Example Run

1. make sure that your installation is correct

2. modify the configuration file

You can choose those in ./configs/test to test whether your setup is correct.

3. run script

The scripts for training/testing/evaling an model are present in ./script. If you want to train a model, just modify the first few rows in the main function to choose the correspoding config file.

for example:

if __name__ == "__main__":
    global config_file
    config_file = "./configs/Celeba128_anr_d5.yaml"

and then, run the following command in terminal:

python ./script/train_inr.py 

License

@article{zhang2024attention,
  title={Attention Beats Linear for Fast Implicit Neural Representation Generation},
  author={Zhang, Shuyi and Liu, Ke and Gu, Jingjun and Cai, Xiaoxu and Wang, Zhihua and Bu, Jiajun and Wang, Haishuai},
  journal={arXiv preprint arXiv:2407.15355},
  year={2024}
}

Contact

If you have any questions, you can reach me via the following:

Email: keliu99@zju.edu.cn