Progressive-GAN-mammogram

Implementing Progressive GAN to generate mammogram images. Any deep learning model requires a signinficant amount of dataset. The publicly available dataset for mammogram images is not enough to train a deep learning model. This is a pytorch implementation of progressive GAN to generate mammogram images.

The dataset used is obtained from [here]

How to Use

[step 1.] Prepare dataset

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The training data folder should look like : 
<train_data_root>
                |--Your Folder
                        |--image 1
                        |--image 2
                        |--image 3 ...
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[step 2.] Prepare environment using virtualenv

• you can easily set PyTorch (v0.3) and TensorFlow environment using virtualenv.
• CAUTION: if you have trouble installing PyTorch, install it mansually using pip. [PyTorch Install]
• For install please take your time and install all dependencies of PyTorch and also install tensorflow

$ virtualenv --python=python2.7 venv
$ . venv/bin/activate
$ pip install -r requirements.txt
$ conda install pytorch torchvision -c pytorch

[step 3.] Run training

• edit config.py to change parameters. (don't forget to change path to training images)
• specify which gpu devices to be used, and change "n_gpu" option in config.py to support Multi-GPU training.
• run it.

  (example)
  If using Single-GPU (device_id = 0):
  $ vim config.py   -->   change "n_gpu=1"
  $ CUDA_VISIBLE_DEVICES=0 python trainer.py
  
  If using Multi-GPUs (device id = 1,3,7):
  $ vim config.py   -->   change "n_gpu=3"
  $ CUDA_VISIBLE_DEVICES=1,3,7 python trainer.py

[step 4.] Generate fake images using linear interpolation

CUDA_VISIBLE_DEVICES=0 python generate_interpolated.py

Experimental Results