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pytorch-BEAL

Code for paper 'Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation' early accepted by MICCAI 2019.

Introduction

This is a PyTorch(1.0.1.post2) implementation of BEAL. The code was tested with Anaconda and Python 3.7.1.

    conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

Installation

After installing the dependency:

    pip install pyyaml
    pip install pytz
    pip install tensorboardX==1.4 matplotlib pillow 
    pip install tqdm
    conda install scipy==1.1.0
    conda install -c conda-forge opencv

  1. Clone the repo:

    git clone https://github.com/EmmaW8/BEAL.git
cd BEAL

  2. Install dependencies:

    For PyTorch dependency, see pytorch.org for more details.

    For custom dependencies:

    

    3. 

  3. 

    Configure your dataset path in train.py with parameter '--data-dir'.
    

    4. 

  4. 

    You can train deeplab v3+ using mobilenetv2 or others as backbone.
    

    To train it, please do:
    

    python train.py -g 0 --data-dir /data/ssd/public/sjwang/fundus_data/domain_adaptation --batch-size 8 --datasetT RIM-ONE_r3
    

    To test it, please do:
Download the weights can put them into the log folder from link.
    

    python test.py --model-file ./logs/DGS_weights.tar --dataset Drishti-GS
    

    5.