Source code for stylegan3_t_gamma20_8xb4_fp16_noaug_ffhq_256x256

# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base

with read_base():
    from .._base_.datasets.unconditional_imgs_flip_lanczos_resize_256x256 \
        import *
    from .._base_.gen_default_runtime import *
    from .._base_.models.base_styleganv3 import *

from torch.optim import Adam

from mmagic.engine.hooks.visualization_hook import VisualizationHook
from mmagic.evaluation.metrics.equivariance import Equivariance
from mmagic.evaluation.metrics.fid import FrechetInceptionDistance
from mmagic.models.base_models.average_model import RampUpEMA
from mmagic.models.base_models.base_gan import BaseGAN
from mmagic.models.editors.stylegan3.stylegan3_modules import SynthesisNetwork

[docs]synthesis_cfg = {
    'type': SynthesisNetwork,
    'channel_base': 16384,
    'channel_max': 512,
    'magnitude_ema_beta': 0.999
}
[docs]r1_gamma = 2.  # set by user
[docs]d_reg_interval = 16

[docs]ema_config = dict(
    type=RampUpEMA,
    interval=1,
    ema_kimg=10,
    ema_rampup=0.05,
    batch_size=32,
    eps=1e-8,
    start_iter=0)

model.update(
    generator=dict(out_size=256, img_channels=3, synthesis_cfg=synthesis_cfg),
    discriminator=dict(in_size=256, channel_multiplier=1),
    loss_config=dict(r1_loss_weight=r1_gamma / 2.0 * d_reg_interval),
    ema_config=ema_config)

[docs]g_reg_interval = 4

[docs]g_reg_ratio = g_reg_interval / (g_reg_interval + 1)
[docs]d_reg_ratio = d_reg_interval / (d_reg_interval + 1)

optim_wrapper.update(
    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))))

[docs]batch_size = 4
[docs]data_root = 'data/ffhq/images'

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

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

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

train_cfg.update(max_iters=800002)

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

# METRICS
[docs]metrics = [
    dict(
        type=FrechetInceptionDistance,
        prefix='FID-Full-50k',
        fake_nums=50000,
        inception_style='StyleGAN',
        sample_model='ema'),
    dict(
        type=Equivariance,
        fake_nums=50000,
        sample_mode='ema',
        prefix='EQ',
        eq_cfg=dict(
            compute_eqt_int=True, compute_eqt_frac=True, compute_eqr=True))
]
# 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.update(checkpoint=dict(save_best='FID-Full-50k/fid'))
val_evaluator.update(metrics=metrics)
test_evaluator.update(metrics=metrics)