LynnHo / GAN-Techniques-Tensorflow

GANs: Losses, Regularizations and Normalizations

GANs: Losses, Regularizations and Normalizations

Tensorflow implementation of some common techniques of GANs, including losses, regularizations and normalizations.

Techniques

• Losses:
  • GAN
  • LSGAN
  • WGAN
  • Hinge
• Gradient Regularizations:
  • WGAN-GP
  • DRAGAN
• Weights Normalizations/Regularizations:
  • SpectralNorm
  • Weight Clipping
  • WeightNorm
  • Orthonormal Regularization
• Generator Regularizations:
  • VGAN
• Normalizations:
  • BatchNorm, InstanceNorm, LayerNorm

Usage

• Prerequisites

  • Tensorflow 1.8
  • Python 2.7 or 3.6

• Training

  • Important Arguments (See the others in train.py)
    • --n_d: # of d steps in each iteration (default: 1)
    • --n_g: # of g steps in each iteration (default: 1)
    • --loss_mode: gan loss (choices: [gan, lsgan, wgan, hinge], default: gan)
    • --gp_mode: type of gradient penalty (choices: [none, dragan, wgan-gp], default: none)
    • --norm: normalization (choices: [batch_norm, instance_norm, layer_norm, none], default: batch_norm)
    • --weights_norm: weights normalization (choices: [none, spectral_norm, weight_clip], default: none)
    • --vgan: vgan regularization (store_true)
    • --model: model (choices: [conv_mnist, conv_64], default: conv_mnist)
    • --dataset: dataset (choices: [mnist, celeba], default: mnist)
    • --experiment_name: name for current experiment (default: default)
  • Examples (See more in examples.md)

    # vgan
    CUDA_VISIBLE_DEVICES=0 python train.py --n_d 1 --n_g 1 --loss_mode gan --gp_mode none --norm batch_norm --vgan --model conv_64 --dataset celeba --experiment_name conv64_celeba_loss{gan}_gp{none}_norm{batch_norm}_wnorm{none}_vgan
    # gan + dragan
    CUDA_VISIBLE_DEVICES=0 python train.py --n_d 1 --n_g 1 --loss_mode gan --gp_mode dragan --norm layer_norm --model conv_mnist --dataset mnist --experiment_name conv_mnist_loss{gan}_gp{dragan}_norm{layer_norm}_wnorm{none}
    # hinge + spectral_norm
    CUDA_VISIBLE_DEVICES=0 python train.py --n_d 1 --n_g 1 --loss_mode hinge --gp_mode none --norm none --weights_norm spectral_norm --model conv_64 --dataset celeba --experiment_name conv64_celeba_loss{hinge}_gp{none}_norm{none}_wnorm{spectral_norm}
    # hinge + dragan + instance_norm + spectral_norm
    CUDA_VISIBLE_DEVICES=0 python train.py --n_d 1 --n_g 1 --loss_mode hinge --gp_mode dragan --norm instance_norm --weights_norm spectral_norm --model conv_64 --dataset celeba --experiment_name conv64_celeba_loss{hinge}_gp{dragan}_norm{instance_norm}_wnorm{spectral_norm}
    # wgan + wgan-gp
    CUDA_VISIBLE_DEVICES=0 python train.py --n_d 5 --n_g 1 --loss_mode wgan --gp_mode wgan-gp --norm instance_norm --model conv_64 --dataset celeba --experiment_name conv64_celeba_loss{wgan}_gp{wgan-gp}_norm{instance_norm}_wnorm{none}

Datasets

1. CelebA should be prepared by yourself in ./data/celeba/img_align_celeba/.jpg*
   • Download the dataset: https://www.dropbox.com/sh/8oqt9vytwxb3s4r/AAB06FXaQRUNtjW9ntaoPGvCa?dl=0
   • the above links might be inaccessible, the alternatives are
     • img_align_celeba.zip
       • https://pan.baidu.com/s/1eSNpdRG#list/path=%2FCelebA%2FImg or
       • https://drive.google.com/drive/folders/0B7EVK8r0v71pTUZsaXdaSnZBZzg
2. Mnist will be automatically downloaded

Exemplar Results

1. Hinge + DRAGAN + InstanceNorm + SpectralNorm