UNetKAN

This is a warehouse for UNetKAN-Pytorch-model, can be used to train your medical-image-datasets for segmentation tasks.

The code mainly come from official source code

KAN: Kolmogorov–Arnold Networks

U-KANMakesStrong Backbone for Medical Image Segmentation and Generation

Preparation

Create conda virtual-environment

conda env create -f environment.yml

Download Datasets

CVC-ClinicDB Dataset
Kvasir Dataset

Project Structure

├── datasets: Load datasets
    ├── CVC.py: CVC-ClinicDB & Kvasir Datasets
    ├── transforms.py: image data aug methods
├── models: UNetKAN Model
    ├── build_models.py: Construct UNetKAN & UNetMLP models
    ├── kan.py: Define KAN model replaces MLP layers
├── scheduler:
    ├──scheduler_main.py: Fundamental Scheduler module
    ├──scheduler_factory.py: Create lr_scheduler methods according to parameters what you set
    ├──other_files: Construct lr_schedulers (cosine_lr, poly_lr, multistep_lr, etc)
├── util:
    ├── losses.py: DiceLoss
    ├── metrics.py: Define Metrics (pixel_acc, f1score, miou)
    ├── utils.py: Record various indicator information and output and distributed environment
├── engine.py: Function code for a training/validation process
├── estimate_model.py: Visualized performance of pretrained model in validset
└── train_gpu.py: Training model startup file (including infer process)

Precautions

Before you use the code to train your own data set, please first enter the train_gpu.py file and modify the Kvasir_path, ClinicDB_path, batch_size, num_workers and nb_classes parameters.

Train this model

Parameters Meaning:

1. nproc_per_node: <The number of GPUs you want to use on each node (machine/server)>
2. CUDA_VISIBLE_DEVICES: <Specify the index of the GPU corresponding to a single node (machine/server) (starting from 0)>
3. nnodes: <number of nodes (machine/server)>
4. node_rank: <node (machine/server) serial number>
5. master_addr: <master node (machine/server) IP address>
6. master_port: <master node (machine/server) port number>

Transfer Learning:

Step 1: Write the pre-training weight path into the args.fintune in string format.
Step 2: Modify the args.freeze_layers according to your own GPU memory. If you don't have enough memory, you can set this to True to freeze the weights of the remaining layers except the last layer of classification-head without updating the parameters. If you have enough memory, you can set this to False and not freeze the model weights.

Here is an example for setting parameters:

Note:

If you want to use multiple GPU for training, whether it is a single machine with multiple GPUs or multiple machines with multiple GPUs, each GPU will divide the batch_size equally. For example, batch_size=4 in my train_gpu.py. If I want to use 2 GPUs for training, it means that the batch_size on each GPU is 4. Do not let batch_size=1 on each GPU, otherwise BN layer maybe report an error.

train model with single-machine single-GPU：

python train_gpu.py

train model with single-machine multi-GPU：

python -m torch.distributed.run --nproc_per_node=8 train_gpu.py

train model with single-machine multi-GPU:

Using a specified part of the GPUs: for example, I want to use the second and fourth GPUs:

CUDA_VISIBLE_DEVICES=1,3 python -m torch.distributed.run --nproc_per_node=2 train_gpu.py

train model with multi-machine multi-GPU:

For the specific number of GPUs on each machine, modify the value of --nproc_per_node.
If you want to specify a certain GPU, just add CUDA_VISIBLE_DEVICES= to specify the index number of the GPU before each command.
The principle is the same as single-machine multi-GPU training:

On the first machine: python -m torch.distributed.run --nproc_per_node=1 --nnodes=2 --node_rank=0 --master_addr=<Master node IP address> --master_port=<Master node port number> train_gpu.py

On the second machine: python -m torch.distributed.run --nproc_per_node=1 --nnodes=2 --node_rank=1 --master_addr=<Master node IP address> --master_port=<Master node port number> train_gpu.py

ONNX deployment

step 1: ONNX export

python onnx_export.py

step2: ONNX optimise

python onnx_optimise.py

step3: ONNX validate

python onnx_validate.py

Citation

@article{li2024ukan,
  author    = {Chenxin Li and Xinyu Liu and Wuyang Li and Cheng Wang and Hengyu Liu and Yixuan Yuan},
  title     = {U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation},
  journal   = {arXiv preprint arXiv:2406.02918},
  year      = {2024}
}

@article{liu2024kan,
  title={Kan: Kolmogorov-arnold networks},
  author={Liu, Ziming and Wang, Yixuan and Vaidya, Sachin and Ruehle, Fabian and Halverson, James and Solja{\v{c}}i{\'c}, Marin and Hou, Thomas Y and Tegmark, Max},
  journal={arXiv preprint arXiv:2404.19756},
  year={2024}
}