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train_animesr_step2_lbo_1_gan.yml
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train_animesr_step2_lbo_1_gan.yml
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# general settings
name: train_animesr_step2_lbo_1_gan
model_type: DegradationGANModel
scale: 2
num_gpu: auto # set num_gpu: 0 for cpu mode
manual_seed: 0
# dataset and data loader settings
datasets:
train:
name: LBO_1
type: CustomPairedImageDataset
dataroot_gt: results/input_rescaling_strategy_lbo_1/frames # TO_MODIFY
dataroot_lq: datasets/lbo_training_data/real_world_video_to_train_lbo_1 # TO_MODIFY
io_backend:
type: disk
gt_size: 256
use_hflip: true
use_rot: true
# data loader
use_shuffle: true
num_worker_per_gpu: 12
batch_size_per_gpu: 16
dataset_enlarge_ratio: 200
prefetch_mode: ~
# network structures
network_g:
type: SimpleDegradationArch
num_in_ch: 3
num_out_ch: 3
num_feat: 64
downscale: 2
network_d:
type: MultiScaleDiscriminator
num_in_ch: 3
num_feat: 64
num_layers: [3]
max_nf_mult: 8
norm_type: none
use_sigmoid: False
use_sn: True
use_downscale: True
# path
path:
pretrain_network_g: experiments/train_animesr_step2_lbo_1_net/models/net_g_100000.pth
param_key_g: params
strict_load_g: true
resume_state: ~
# training settings
train:
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: [50000]
gamma: 0.5
total_iter: 100000
warmup_iter: -1 # no warm up
# losses
pixel_opt:
type: L1Loss
loss_weight: 1.0
reduction: mean
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
range_norm: false
perceptual_weight: 1.0
style_weight: 0
criterion: l1
gan_opt:
type: MultiScaleGANLoss
gan_type: lsgan
real_label_val: 1.0
fake_label_val: 0.0
loss_weight: !!float 1.0
# 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