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from tops.config import LazyCall as L
from dp2.discriminator import SG2Discriminator
import torch
from dp2.loss import StyleGAN2Loss
discriminator = L(SG2Discriminator)(
imsize="${data.imsize}",
im_channels="${data.im_channels}",
min_fmap_resolution=4,
max_cnum_mul=8,
cnum=80,
input_condition=True,
conv_clamp=256,
input_cse=False,
cse_nc="${data.cse_nc}",
fix_residual=False,
)
loss_fnc = L(StyleGAN2Loss)(
lazy_regularization=True,
lazy_reg_interval=16,
r1_opts=dict(lambd=5, mask_out=False, mask_out_scale=False),
EP_lambd=0.001,
pl_reg_opts=dict(weight=0, batch_shrink=2,start_nimg=int(1e6), pl_decay=0.01)
)
def build_D_optim(type, lr, betas, lazy_regularization, lazy_reg_interval, **kwargs):
if lazy_regularization:
# From Analyzing and improving the image quality of stylegan, CVPR 2020
c = lazy_reg_interval / (lazy_reg_interval + 1)
betas = [beta ** c for beta in betas]
lr *= c
print(f"Lazy regularization on. Setting lr to: {lr}, betas to: {betas}")
return type(lr=lr, betas=betas, **kwargs)
D_optim = L(build_D_optim)(
type=torch.optim.Adam, lr=0.001, betas=(0.0, 0.99),
lazy_regularization="${loss_fnc.lazy_regularization}",
lazy_reg_interval="${loss_fnc.lazy_reg_interval}")
G_optim = L(torch.optim.Adam)(lr=0.001, betas=(0.0, 0.99))
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