DL-Art-School/codes/options/train/train_SRResNet.yml
XintaoWang 037933ba66 mmsr
2019-08-23 21:42:47 +08:00

71 lines
1.4 KiB
YAML

# Not exactly the same as SRResNet in <Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network>
# With 16 Residual blocks w/o BN
#### general settings
name: 001_MSRResNetx4_scratch_DIV2K
use_tb_logger: true
model: sr
distortion: sr
scale: 4
gpu_ids: [0]
#### datasets
datasets:
train:
name: DIV2K
mode: LQGT
dataroot_GT: ../datasets/DIV2K/DIV2K800_sub.lmdb
dataroot_LQ: ../datasets/DIV2K/DIV2K800_sub_bicLRx4.lmdb
use_shuffle: true
n_workers: 6 # per GPU
batch_size: 16
GT_size: 128
use_flip: true
use_rot: true
color: RGB
val:
name: val_set5
mode: LQGT
dataroot_GT: ../datasets/val_set5/Set5
dataroot_LQ: ../datasets/val_set5/Set5_bicLRx4
#### network structures
network_G:
which_model_G: MSRResNet
in_nc: 3
out_nc: 3
nf: 64
nb: 16
upscale: 4
#### path
path:
pretrain_model_G: ~
strict_load: true
resume_state: ~
#### training settings: learning rate scheme, loss
train:
lr_G: !!float 2e-4
lr_scheme: CosineAnnealingLR_Restart
beta1: 0.9
beta2: 0.99
niter: 1000000
warmup_iter: -1 # no warm up
T_period: [250000, 250000, 250000, 250000]
restarts: [250000, 500000, 750000]
restart_weights: [1, 1, 1]
eta_min: !!float 1e-7
pixel_criterion: l1
pixel_weight: 1.0
manual_seed: 10
val_freq: !!float 5e3
#### logger
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3