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| # python3.7 | |
| """Configuration for training StyleGAN on FF-HQ (256) dataset. | |
| All settings are particularly used for one replica (GPU), such as `batch_size` | |
| and `num_workers`. | |
| """ | |
| runner_type = 'StyleGANRunner' | |
| gan_type = 'stylegan' | |
| resolution = 256 | |
| batch_size = 4 | |
| val_batch_size = 64 | |
| total_img = 25000_000 | |
| # Training dataset is repeated at the beginning to avoid loading dataset | |
| # repeatedly at the end of each epoch. This can save some I/O time. | |
| data = dict( | |
| num_workers=4, | |
| repeat=500, | |
| # train=dict(root_dir='data/ffhq', resolution=resolution, mirror=0.5), | |
| # val=dict(root_dir='data/ffhq', resolution=resolution), | |
| train=dict(root_dir='data/ffhq.zip', data_format='zip', | |
| resolution=resolution, mirror=0.5), | |
| val=dict(root_dir='data/ffhq.zip', data_format='zip', | |
| resolution=resolution), | |
| ) | |
| controllers = dict( | |
| RunningLogger=dict(every_n_iters=10), | |
| ProgressScheduler=dict( | |
| every_n_iters=1, init_res=8, minibatch_repeats=4, | |
| lod_training_img=600_000, lod_transition_img=600_000, | |
| batch_size_schedule=dict(res4=64, res8=32, res16=16, res32=8), | |
| ), | |
| Snapshoter=dict(every_n_iters=500, first_iter=True, num=200), | |
| FIDEvaluator=dict(every_n_iters=5000, first_iter=True, num=50000), | |
| Checkpointer=dict(every_n_iters=5000, first_iter=True), | |
| ) | |
| modules = dict( | |
| discriminator=dict( | |
| model=dict(gan_type=gan_type, resolution=resolution), | |
| lr=dict(lr_type='FIXED'), | |
| opt=dict(opt_type='Adam', base_lr=1e-3, betas=(0.0, 0.99)), | |
| kwargs_train=dict(), | |
| kwargs_val=dict(), | |
| ), | |
| generator=dict( | |
| model=dict(gan_type=gan_type, resolution=resolution), | |
| lr=dict(lr_type='FIXED'), | |
| opt=dict(opt_type='Adam', base_lr=1e-3, betas=(0.0, 0.99)), | |
| kwargs_train=dict(w_moving_decay=0.995, style_mixing_prob=0.9, | |
| trunc_psi=1.0, trunc_layers=0, randomize_noise=True), | |
| kwargs_val=dict(trunc_psi=1.0, trunc_layers=0, randomize_noise=False), | |
| g_smooth_img=10_000, | |
| ) | |
| ) | |
| loss = dict( | |
| type='LogisticGANLoss', | |
| d_loss_kwargs=dict(r1_gamma=10.0), | |
| g_loss_kwargs=dict(), | |
| ) | |