Symmtrical-GAN

This is the backup code for "Fast Transformation of Discriminators into Encoders using Pre-Trained GANs".

Usage

1. Training DCGAN

python train_DCGAN.py

Tips: please refer to the below parameters to implement our ablation study (change ZoutDim 1 to 128 that equal with Zdim, and Zdim is G input dim).

• case 1 -- G_in: (128,2048) | D_out: (2048, 1):

ep100-Celeba_HQ-Gscale8-GDscale8-Dscale1-Zdim128-ZoutDim1-Hidden_Scale2-img_size256-batch_size30-BNFalse-GDstdFalse-GreluTrue

• case 2 -- G_in: (128,2048) | D_out: (2048, 2):

ep100-Celeba_HQ-Gscale8-GDscale8-Dscale1-Zdim128-ZoutDim2-Hidden_Scale2-img_size256-batch_size30-BNFalse-GDstdFalse-GreluTrue

• case 3 -- G_in: (128,2048) | D_out: (2048, 4):

ep100-Celeba_HQ-Gscale8-GDscale8-Dscale1-Zdim128-ZoutDim4-Hidden_Scale2-img_size256-batch_size30-BNFalse-GDstdFalse-GreluTrue

• case 4--7 ...

• case 8 -- G_in: (128,2048) | D_out: (2048, 128):

ep100-Celeba_HQ-Gscale8-GDscale8-Dscale1-Zdim128-ZoutDim16-Hidden_Scale2-img_size256-batch_size30-BNFalse-GDstdFalse-GreluTrue

2. Training pre-trained PGGAN (D to E)

python train_PGGAN.py

• Tips:

In this case, we can resue the weights of pre-trained D, and transform D to E.

before training, please download pre-trained models (D and G) to ./checkpoint

We also implement other type of PGGAN-FC in train_PGGAN_FC.py.

Metric and Pre-trained Models

• FID

  We use official code of FID and its default setting to evluate our results.

  The FID code is here: https://github.com/mseitzer/pytorch-fid.git

• DataSet

  We can directly download CelebA-HQ in here: https://github.com/switchablenorms/CelebAMask-HQ

  There 30,000 real aligned-face images with 1024x1024 (10,000 for PGGAN evaluation), and we resize image to 256x256 for DCGAN training and evaluation.

• Pre-trained Models

  We offered Pre-trianed model for PGGAN reusing model weights and training here: google drive.

  This link also include DCGAN pre-trained model (Doutput=128).

  If you need more pre-trained models (e.g. PGGAN-FC), please find blow.

Baseline (Acknowledgements)

• PGGAN in Pytorch: https://github.com/akanimax/pro_gan_pytorch
• PGGAN_FC: https://github.com/genforce/genforce.git
• DCGAN: https://github.com/LynnHo/DCGAN-LSGAN-WGAN-GP-DRAGAN-Pytorch