DL-Art-School/codes/options/train/train_ESRGAN.yml

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#### general settings
name: 003_RRDB_ESRGANx4_DIV2K
use_tb_logger: true
model: srgan
distortion: sr
scale: 4
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gpu_ids: [0]
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#### datasets
datasets:
train:
name: DIV2K
mode: LQGT
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dataroot_GT: ../datasets/div2k/DIV2K800_sub
dataroot_LQ: ../datasets/div2k/DIV2K800_sub_bicLRx4
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use_shuffle: true
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n_workers: 16 # per GPU
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batch_size: 16
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target_size: 128
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use_flip: true
use_rot: true
color: RGB
val:
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name: div2kval
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mode: LQGT
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dataroot_GT: ../datasets/div2k/div2k_valid_hr
dataroot_LQ: ../datasets/div2k/div2k_valid_lr_bicubic
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#### network structures
network_G:
which_model_G: RRDBNet
in_nc: 3
out_nc: 3
nf: 64
nb: 23
network_D:
which_model_D: discriminator_vgg_128
in_nc: 3
nf: 64
#### path
path:
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pretrain_model_G: ../experiments/RRDB_PSNR_x4.pth
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strict_load: true
resume_state: ~
#### training settings: learning rate scheme, loss
train:
lr_G: !!float 1e-4
weight_decay_G: 0
beta1_G: 0.9
beta2_G: 0.99
lr_D: !!float 1e-4
weight_decay_D: 0
beta1_D: 0.9
beta2_D: 0.99
lr_scheme: MultiStepLR
niter: 400000
warmup_iter: -1 # no warm up
lr_steps: [50000, 100000, 200000, 300000]
lr_gamma: 0.5
pixel_criterion: l1
pixel_weight: !!float 1e-2
feature_criterion: l1
feature_weight: 1
gan_type: ragan # gan | ragan
gan_weight: !!float 5e-3
D_update_ratio: 1
D_init_iters: 0
manual_seed: 10
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val_freq: !!float 5e2
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#### logger
logger:
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print_freq: 50
save_checkpoint_freq: !!float 5e2