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train_lmlt.toml
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name = "train_lmlt"
model_type = "image"
scale = 4
use_amp = true
bfloat16 = true
fast_matmul = true
#compile = true
#manual_seed = 1024
[datasets.train]
type = "paired"
dataroot_gt = 'C:\datasets\gt\'
dataroot_lq = 'C:\datasets\lq\'
patch_size = 64
batch_size = 8
#accumulate = 1
augmentation = [ "none", "mixup", "cutmix", "resizemix", "cutblur" ]
aug_prob = [ 0.5, 0.1, 0.1, 0.1, 0.5 ]
[datasets.val]
name = "val"
type = "paired"
dataroot_gt = 'C:\datasets\val\gt\'
dataroot_lq = 'C:\datasets\val\lq\'
[val]
val_freq = 1000
#tile = 200
#[val.metrics.psnr]
#type = "calculate_psnr"
#[val.metrics.ssim]
#type = "calculate_ssim"
#[val.metrics.dists]
#type = "calculate_dists"
#better = "lower"
[path]
#pretrain_network_g = 'experiments\pretrain_g.pth'
#pretrain_network_d = 'experiments\pretrain_d.pth'
[network_g]
type = "lmlt"
#type = "lmlt_tiny"
#type = "lmlt_large"
[network_d]
type = "metagan"
[train]
ema = 0.999
wavelet_guided = true
wavelet_init = 80000
#sam = "fsam"
#sam_init = 1000
#eco = true
#eco_init = 15000
#match_lq_colors = true
[train.optim_g]
type = "adan_sf"
lr = 1e-3
betas = [ 0.98, 0.92, 0.99 ]
weight_decay = 0.01
schedule_free = true
warmup_steps = 1600
[train.optim_d]
type = "adan_sf"
lr = 1e-4
betas = [ 0.98, 0.92, 0.99 ]
weight_decay = 0.01
schedule_free = true
warmup_steps = 600
# losses
[train.mssim_opt]
type = "mssim_loss"
loss_weight = 1.0
[train.consistency_opt]
type = "consistency_loss"
loss_weight = 1.0
[train.ldl_opt]
type = "ldl_loss"
loss_weight = 1.0
[train.fdl_opt]
type = "fdl_loss"
model = "vgg" # "resnet", "effnet", "inception"
loss_weight = 0.5
[train.gan_opt]
type = "gan_loss"
gan_type = "bce"
loss_weight = 0.3
#[train.msswd_opt]
#type = "msswd_loss"
#loss_weight = 1.0
#[train.perceptual_opt]
#type = "vgg_perceptual_loss"
#loss_weight = 0.5
#criterion = "huber"
##patchloss = true
##ipk = true
##patch_weight = 1.0
#[train.dists_opt]
#type = "dists_loss"
#loss_weight = 0.5
#[train.ff_opt]
#type = "ff_loss"
#loss_weight = 0.35
#[train.ncc_opt]
#type = "ncc_loss"
#loss_weight = 1.0
#[train.kl_opt]
#type = "kl_loss"
#loss_weight = 1.0
[logger]
total_iter = 1000000
save_checkpoint_freq = 1000
use_tb_logger = true
#save_tb_img = true
#print_freq = 100