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finetune_realesrgan_x4plus_pairdata.yml
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finetune_realesrgan_x4plus_pairdata.yml
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# general settings
name: finetune_RealESRGANx4plus_400k_pairdata
model_type: RealESRGANModel
scale: 4
num_gpu: auto
manual_seed: 0
# USM the ground-truth
l1_gt_usm: True
percep_gt_usm: True
gan_gt_usm: False
high_order_degradation: False # do not use the high-order degradation generation process
# dataset and data loader settings
datasets:
train:
name: DIV2K
type: RealESRGANPairedDataset
dataroot_gt: datasets/DF2K
dataroot_lq: datasets/DF2K
meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt
io_backend:
type: disk
gt_size: 256
use_hflip: True
use_rot: False
# data loader
use_shuffle: true
num_worker_per_gpu: 5
batch_size_per_gpu: 12
dataset_enlarge_ratio: 1
prefetch_mode: ~
# Uncomment these for validation
# val:
# name: validation
# type: PairedImageDataset
# dataroot_gt: path_to_gt
# dataroot_lq: path_to_lq
# io_backend:
# type: disk
# network structures
network_g:
type: RRDBNet
num_in_ch: 3
num_out_ch: 3
num_feat: 64
num_block: 23
num_grow_ch: 32
network_d:
type: UNetDiscriminatorSN
num_in_ch: 3
num_feat: 64
skip_connection: True
# path
path:
# use the pre-trained Real-ESRNet model
pretrain_network_g: experiments/pretrained_models/RealESRNet_x4plus.pth
param_key_g: params_ema
strict_load_g: true
pretrain_network_d: experiments/pretrained_models/RealESRGAN_x4plus_netD.pth
param_key_d: params
strict_load_d: true
resume_state: ~
# training settings
train:
ema_decay: 0.999
optim_g:
type: Adam
lr: !!float 1e-4
weight_decay: 0
betas: [0.9, 0.99]
optim_d:
type: Adam
lr: !!float 1e-4
weight_decay: 0
betas: [0.9, 0.99]
scheduler:
type: MultiStepLR
milestones: [400000]
gamma: 0.5
total_iter: 400000
warmup_iter: -1 # no warm up
# losses
pixel_opt:
type: L1Loss
loss_weight: 1.0
reduction: mean
# perceptual loss (content and style losses)
perceptual_opt:
type: PerceptualLoss
layer_weights:
# before relu
'conv1_2': 0.1
'conv2_2': 0.1
'conv3_4': 1
'conv4_4': 1
'conv5_4': 1
vgg_type: vgg19
use_input_norm: true
perceptual_weight: !!float 1.0
style_weight: 0
range_norm: false
criterion: l1
# gan loss
gan_opt:
type: GANLoss
gan_type: vanilla
real_label_val: 1.0
fake_label_val: 0.0
loss_weight: !!float 1e-1
net_d_iters: 1
net_d_init_iters: 0
# Uncomment these for validation
# validation settings
# val:
# val_freq: !!float 5e3
# save_img: True
# metrics:
# psnr: # metric name, can be arbitrary
# type: calculate_psnr
# crop_border: 4
# test_y_channel: false
# logging settings
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500