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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)) | |