This is PyTorch implementation of Progressive Growing GANs. The network is trainable on custom image dataset.
Place your dataset folder inside data folder. The training stats are added to repo folder as the training progresses.
The network training parameters can be configured with following flags.
--train_data_root- set your data sirectory--random_seed- random seed to reproduce the experiments--n_gpu- multiple GPU training
--lr- learning rate--lr_decay- learning rate decay at every resolution transition--eps_drift- coefficient for the drift loss--smoothing- smoothing factor for smoothed generator--nc- number of input channel--nz- input dimension of noise--ngf- feature dimension of final layer of generator--ndf- feature dimension of first layer of discriminator--TICK- 1 tick = 1000 images = (1000/batch_size) iteration--max_resl- 10-->1024, 9-->512, 8-->256--trns_tick- transition tick--stab_tick- stabilization tick
--flag_wn- use of equalized-learning rate--flag_bn- use of batch-normalization (not recommended)--flag_pixelwise- use of pixelwise normalization for generator--flag_gdrop- use of generalized dropout layer for discriminator--flag_leaky- use of leaky relu instead of relu--flag_tanh- use of tanh at the end of the generator--flag_sigmoid- use of sigmoid at the end of the discriminator--flag_add_noise- add noise to the real image(x)--flag_norm_latent- pixelwise normalization of latent vector (z)--flag_add_drift- add drift loss
--optimizer- optimizer type--beta1- beta1 for adam--beta2- beta2 for adam
--use_tb- enable tensorboard visualization--save_img_every- save images every specified iteration--display_tb_every- display progress every specified iteration
Make sure your machine has CUDA enabled GPU(s) if you want to train on GPUs. Change the --n_gpu flag to positive integral value <= available number of GPUs.
- WGAN training methodology