STGAN (CVPR 2019)

An unofficial PyTorch implementation of STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing.

Requirements

• Python 3.6+
• PyTorch 1.0+
• tensorboardX 1.6+
• torchsummary
• tqdm
• Pillow

Preparation

Please download the CelebA dataset from this project page. Then organize the directory as:

├── data_root
│   └── image
│       ├── 000001.jpg
│       ├── 000002.jpg
│       ├── 000003.jpg
│       └── ...
│   └── anno
│       ├── list_attr_celeba.txt
│       └── ...

Training

• For quickly start, you can simply use the following command to train:

  CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config ./configs/train_stgan.yaml

• Or simply run the shell script train_stgan.sh:

  sh train_stgan.sh

• If you want to modify some hyper-parameters, please edit them in the configuration file ./configs/train_stgan.yaml following the explanations below:

  • exp_name: the name of current experiment.
  • mode: 'train' or 'test'.
  • cuda: use CUDA or not.
  • ngpu: how many gpu cards to use. Notice: this number should be no more than the length of CUDA_VISIBLE_DEVICES list.
  • data_root: the root of dataset.
  • crop_size: the crop size of images.
  • image_size: the size of images after being resized.
  • g_conv_dim: the base filter numbers of convolutional layers in G.
  • d_conv_dim: the base filter numbers of convolutional layers in D.
  • d_fc_dim: the dimmension of fully-connected layers in D.
  • g_layers: the number of convolutional layers in G. Notice: same for both encoder and decoder.
  • d_layers: the number of convolutional layers in D.
  • attrs: the list of all selected atrributes. Notice: please refer to list_attr_celeba.txt for all avaliable attributes.
  • checkpoint: the iteration step number of the checkpoint to be resumed. Notice: please set this to ~ if it's first time to train.
  • batch_size: batch size of data loader.
  • beta1: beta1 value of Adam optimizer.
  • beta2: beta2 value of Adam optimizer.
  • g_lr: the base learning rate of G.
  • d_lr: the base learning rate of D.
  • n_critic: number of D updates per each G update.
  • lambda_gp: tradeoff coefficient of D_loss_gp.
  • lambda1: tradeoff coefficient of D_loss_att.
  • lambda2: tradeoff coefficient of G_loss_att.
  • lambda3: tradeoff coefficient of G_loss_rec.
  • max_iters: maximum iteration steps.
  • lr_decay_iters: iteration steps per learning rate decay.
  • summary_step: iteration steps per summary operation with tensorboardX.
  • sample_step: iteration steps per sampling operation.
  • checkpoint_step: iteration steps per checkpoint saving operation.

Acknowledgements

This code refers to the following two projects:

[1] TensorFlow implementation of STGAN

[2] PyTorch implementation of StarGAN