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# python3.7
"""Configuration for StyleGAN training demo.
All settings are particularly used for one replica (GPU), such as `batch_size`
and `num_workers`.
"""
runner_type = 'StyleGANRunner'
gan_type = 'stylegan'
resolution = 64
batch_size = 4
val_batch_size = 32
total_img = 100_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/demo.zip', data_format='zip',
resolution=resolution, mirror=0.5),
val=dict(root_dir='data/demo.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=5_000, lod_transition_img=5_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=10000,
)
)
loss = dict(
type='LogisticGANLoss',
d_loss_kwargs=dict(r1_gamma=10.0),
g_loss_kwargs=dict(),
)
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