Real-ESRGAN / tests /data /test_realesrnet_model.yml
AK391
updates
810c8ea
scale: 4
num_gpu: 1
manual_seed: 0
is_train: True
dist: False
# ----------------- options for synthesizing training data ----------------- #
gt_usm: True # USM the ground-truth
# the first degradation process
resize_prob: [0.2, 0.7, 0.1] # up, down, keep
resize_range: [0.15, 1.5]
gaussian_noise_prob: 1
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 1
jpeg_range: [30, 95]
# the second degradation process
second_blur_prob: 1
resize_prob2: [0.3, 0.4, 0.3] # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 1
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 1
jpeg_range2: [30, 95]
gt_size: 32
queue_size: 1
# network structures
network_g:
type: RRDBNet
num_in_ch: 3
num_out_ch: 3
num_feat: 4
num_block: 1
num_grow_ch: 2
# path
path:
pretrain_network_g: ~
param_key_g: params_ema
strict_load_g: true
resume_state: ~
# training settings
train:
ema_decay: 0.999
optim_g:
type: Adam
lr: !!float 2e-4
weight_decay: 0
betas: [0.9, 0.99]
scheduler:
type: MultiStepLR
milestones: [1000000]
gamma: 0.5
total_iter: 1000000
warmup_iter: -1 # no warm up
# losses
pixel_opt:
type: L1Loss
loss_weight: 1.0
reduction: mean
# validation settings
val:
val_freq: !!float 5e3
save_img: False