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InvNet: Encoding Geometric and Statistical Invariance in Deep Generative Models

Pytorch implementation of WGAN-GP with a projection operator.

Acknowledgements

Prerequisites

  • Python >= 3.6
  • Pytorch >= v1.0.0
  • Numpy
  • SciPy
  • tensorboardX (installation here). It is very convenient to see costs and results during training with TensorboardX for Pytorch
  • TensorFlow for tensorboardX

Model

  • gan_train.py: This model is mainly based on GoodGenerator and GoodDiscriminator from Improved Training of Wasserstein GANs. Generator and discriminator are modified for 128x128 (width, height) dataset.

  • Usage example, run: python gan_train.py --dataset 'circle' --output 'output_dir'. Then, the output file will be written to ./output_dir folder.

Dataset

Testing

During the implementation of this model, we built a test module to compare the result between original model (Tensorflow) and our model (Pytorch) for every layer we implemented. It is available at compare-tensorflow-pytorch

TensorboardX

Results such as costs, generated images (every 200 iters) for tensorboard will be written to ./runs folder.

To display the results to tensorboard, run: tensorboard --logdir runs

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