project_module

This is a project module for a new project, which inludes the file framework, DL pipline, RM.md example and some useful utils. To be updating now.

Any questions, please contact by [email protected]

paper_name

Paper | arXiv | Poster | Tweet

Official repo for the paper Paper Name.
Author name ICLR 2024 spotlight.

We propose a novel XXX.

Framework of paper:

Installation

1. Install dependencies.

conda create -n ENV_NAME python=3.x.x

Install dependencies:

pip install -r requirements.txt
pip install -e .

Replace the directory name standard_repo with your project name and the corresponding directory name in setup.py, .gitignore, .

file structure

• project_module
  • dataset # datasets ready for training or analysis
  • docs # documentation files
  • $project_name
    • data # data class and dataloader used in the project
      • data_demo.py # A demo code for data class
    • config # configuration files for training and inference
    • inference # scripts for model inference
    • model # model definitions
    • train # Scripts and configuration files for training models
      • train_demo.py # A demo code for training
    • utils # Utility scripts and helper functions
      • utils.py # A demo code for utility functions
    • tests # unit tests for the project
  • results # results and logs from training and inference
  • scripts # bash scripts for running training and inference
  • .gitignore # Specifies intentionally untracked files to ignore by git
  • filepath.py # Python script for file path handling
  • README.md # Markdown file with information about the project for users
  • reproducibility_statement.md # Markdown file with statements on reproducibility practices
  • requirements.txt # Text file with a list of dependencies to install

Dataset and checkpoint

All the dataset can be downloaded in this this link. Checkpoints are in this link. Both dataset.zip and checkpoint_path.zip should be decompressed to the root directory of this project.

Training

Below we provide example commands for training the diffusion model/forward model.

training model

python train_1d.py 

inference

Here we provide commands for inference using the trained model:

model 1

python inference.py

Related Projects

• NAME (ICLR 2023 spotlight): brief description of the project.

Numerous practices and standards were adopted from CinDM.

Citation

If you find our work and/or our code useful, please cite us via:

@inproceedings{
    ...
}