Description

This repository serves as the primary codebase for implementing a Diffusion model, specifically designed for the generation of synthetic signals. The model is a pivotal component used in the research paper titled "BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis" (accessible here).

Setup

Before running the code, ensure that you have the following prerequisites installed:

• Python 3.x
• PyTorch
• Nvidia CUDA toolkit and cuDNN (for GPU acceleration)

bash pip install torch torchvision conda install cudatoolkit

Conda Virtual Environment

Create the Conda virtual environment using the environment file: ```bash conda env create -f environment.yml

dynamically set python path for the environment

conda activate BioDiffusion conda env config vars set PYTHONPATH=$(pwd):$(pwd)/src ```

Training

In order to use our model first you will need to retrain it:

Unconditional diffusion model for 3 channel data:

python ddpm.py

Conditional diffusion model for 3 channel data:

python ddpm_conditional.py

Classifier-free diffusion model for 1 channel data:

cd signal/ python load_dataset.py #KaggleKey #KaggleName python ddpm1d_cls_free.py

Signal conditional diffusion model for 1 channel data:

cd signal/ python load_dataset.py #KaggleKey #KaggleName python ddpm1d_sign_cond.py

Sampling

After training, you can sample from the trained model using the following steps:

Unconditional Diffusion Model for 3 Channel Data:

```python

Set device and load the pre-trained UNet model

device = "cuda:2" model = UNet().to(device) ckpt = torch.load("../../src/models/DDPMUnconditional/ckpt.pt") model.loadstate_dict(ckpt)

Create a Diffusion model instance and sample from it

diffusion = Diffusion(img_size=32, device=device) x = diffusion.sample(model, 10) ```

Conditional Diffusion Model for 3 Channel Data:

```python

Set the number of samples and device

n = 10 device = "cuda:3"

Create a Diffusion model instance and load the trained model checkpoint

diffusion = Diffusion(imgsize=32, device=device) diffusion.load("../../src/models/DDPMconditional")

Prepare labels and sample from the diffusion model

labels = torch.full((n,), 1).long().to(diffusion.device) sampledimages = diffusion.sample(useema=False, labels=labels) ```

Classifier-Free Diffusion Model for 1 Channel Data:

```python

Set the number of samples, device, and create the Unet1Dclsfree model

n = 10 device = "cuda:3" model = Unet1Dclsfree( dim=64, dimmults=(1, 2, 4, 8), numclasses=5, conddropprob=0.5, channels=1 )

Load the pre-trained model checkpoint

ckpt = torch.load("../../src/signal/checkpoint/DDPM1DclsfreeMITBIH/checkpoint.pt") model.loadstatedict(ckpt['modelstate_dict']) model = model.to(device)

Create the GaussianDiffusion1Dclsfree model and sample from it

diffusion = GaussianDiffusion1Dclsfree( model, seqlength=128, timesteps=1000 ).to(device) y = torch.Tensor([0] * n).long().to(device) x = diffusion.sample(classes=y, condscale=3.) ```

Self-Conditional Diffusion Model for 1 Channel Data:

```python

Set the device and create the Unet1D model with self-conditioning

device = "cuda:3" model = Unet1D( dim=64, selfcondition=True, dimmults=(1, 2, 4, 8), channels=1 )

Load the pre-trained model checkpoint

ckpt = torch.load("../../src/signal/checkpoint/DDPM1DSelfconditionalmaskedCond/checkpoint.pt") model.loadstatedict(ckpt['modelstatedict']) model = model.to(device)

Create the GaussianDiffusion1D model and sample from it

seqlength must be able to be divided by dimmults

diffusion = GaussianDiffusion1D( model, seqlength=128, timesteps=1000, objective='predv' ).to(device) ```

Make sure to adjust the file paths and model names as needed.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Citation

Feel free to cite our paper using this .bibtex or .cff formats in this repository.

bibtex @misc{li2024biodiffusion, title={BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis}, author={Xiaomin Li and Mykhailo Sakevych and Gentry Atkinson and Vangelis Metsis}, year={2024}, eprint={2401.10282}, archivePrefix={arXiv}, primaryClass={eess.SP} }