stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py

_base_ = [
    '../_base_/models/base_styleganv3.py',
    '../_base_/datasets/ffhq_flip.py',
    '../_base_/gen_default_runtime.py',
]

synthesis_cfg = {
    'type': 'SynthesisNetwork',
    'channel_base': 65536,
    'channel_max': 1024,
    'magnitude_ema_beta': 0.999,
    'conv_kernel': 1,
    'use_radial_filters': True
}
r1_gamma = 3.3  # set by user
d_reg_interval = 16
g_reg_interval = 4

g_reg_ratio = g_reg_interval / (g_reg_interval + 1)
d_reg_ratio = d_reg_interval / (d_reg_interval + 1)

load_from = 'https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth'  # noqa

# ada settings
aug_kwargs = {
    'xflip': 1,
    'rotate90': 1,
    'xint': 1,
    'scale': 1,
    'rotate': 1,
    'aniso': 1,
    'xfrac': 1,
    'brightness': 1,
    'contrast': 1,
    'lumaflip': 1,
    'hue': 1,
    'saturation': 1
}

ema_half_life = 10.  # G_smoothing_kimg
ema_kimg = 10
ema_nimg = ema_kimg * 1000
ema_beta = 0.5**(32 / max(ema_nimg, 1e-8))

ema_config = dict(
    type='ExponentialMovingAverage',
    interval=1,
    momentum=ema_beta,
    start_iter=0)

model = dict(
    generator=dict(
        out_size=1024,
        img_channels=3,
        rgb2bgr=True,
        synthesis_cfg=synthesis_cfg),
    discriminator=dict(
        type='ADAStyleGAN2Discriminator',
        in_size=1024,
        input_bgr2rgb=True,
        data_aug=dict(type='ADAAug', aug_pipeline=aug_kwargs, ada_kimg=100)),
    loss_config=dict(
        r1_loss_weight=r1_gamma / 2.0 * d_reg_interval,
        r1_interval=d_reg_interval,
        norm_mode='HWC'),
    ema_config=ema_config)

optim_wrapper = dict(
    generator=dict(
        optimizer=dict(
            type='Adam', lr=0.0025 * g_reg_ratio, betas=(0,
                                                         0.99**g_reg_ratio))),
    discriminator=dict(
        optimizer=dict(
            type='Adam', lr=0.002 * d_reg_ratio, betas=(0,
                                                        0.99**d_reg_ratio))))

batch_size = 4
data_root = 'data/metfaces/images/'

train_dataloader = dict(
    batch_size=batch_size, dataset=dict(data_root=data_root))

val_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))

test_dataloader = dict(
    batch_size=batch_size, dataset=dict(data_root=data_root))

train_cfg = dict(max_iters=160000)

# VIS_HOOK
custom_hooks = [
    dict(
        type='VisualizationHook',
        interval=5000,
        fixed_input=True,
        vis_kwargs_list=dict(type='GAN', name='fake_img'))
]

# METRICS
metrics = [
    dict(
        type='FrechetInceptionDistance',
        prefix='FID-Full-50k',
        fake_nums=50000,
        inception_style='StyleGAN',
        sample_model='ema')
]
# NOTE: config for save multi best checkpoints
# default_hooks = dict(
#     checkpoint=dict(
#         save_best=['FID-Full-50k/fid', 'IS-50k/is'],
#         rule=['less', 'greater']))
default_hooks = dict(checkpoint=dict(save_best='FID-Full-50k/fid'))

val_evaluator = dict(metrics=metrics)
test_evaluator = dict(metrics=metrics)