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GaussianGrayDenoising_Restormer.yml
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# general settings
name: GaussianGrayDenoising_Restormer
model_type: ImageCleanModel
scale: 1
num_gpu: 8 # set num_gpu: 0 for cpu mode
manual_seed: 100
# dataset and data loader settings
datasets:
train:
name: TrainSet
type: Dataset_GaussianDenoising
sigma_type: random
sigma_range: [0,50]
in_ch: 1 ## Grayscale image
dataroot_gt: ./Denoising/Datasets/train/DFWB
dataroot_lq: none
geometric_augs: true
filename_tmpl: '{}'
io_backend:
type: disk
# data loader
use_shuffle: true
num_worker_per_gpu: 8
batch_size_per_gpu: 8
### -------------Progressive training--------------------------
mini_batch_sizes: [8,5,4,2,1,1] # Batch size per gpu
iters: [92000,64000,48000,36000,36000,24000]
gt_size: 384 # Max patch size for progressive training
gt_sizes: [128,160,192,256,320,384] # Patch sizes for progressive training.
### ------------------------------------------------------------
### ------- Training on single fixed-patch size 128x128---------
# mini_batch_sizes: [8]
# iters: [300000]
# gt_size: 128
# gt_sizes: [128]
### ------------------------------------------------------------
dataset_enlarge_ratio: 1
prefetch_mode: ~
val:
name: ValSet
type: Dataset_GaussianDenoising
sigma_test: 25
in_ch: 1 ## Grayscale image
dataroot_gt: ./Denoising/Datasets/test/BSD68
dataroot_lq: none
io_backend:
type: disk
# network structures
network_g:
type: Restormer
inp_channels: 1
out_channels: 1
dim: 48
num_blocks: [4,6,6,8]
num_refinement_blocks: 4
heads: [1,2,4,8]
ffn_expansion_factor: 2.66
bias: False
LayerNorm_type: BiasFree
dual_pixel_task: False
# path
path:
pretrain_network_g: ~
strict_load_g: true
resume_state: ~
# training settings
train:
total_iter: 300000
warmup_iter: -1 # no warm up
use_grad_clip: true
# Split 300k iterations into two cycles.
# 1st cycle: fixed 3e-4 LR for 92k iters.
# 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
scheduler:
type: CosineAnnealingRestartCyclicLR
periods: [92000, 208000]
restart_weights: [1,1]
eta_mins: [0.0003,0.000001]
mixing_augs:
mixup: true
mixup_beta: 1.2
use_identity: true
optim_g:
type: AdamW
lr: !!float 3e-4
weight_decay: !!float 1e-4
betas: [0.9, 0.999]
# losses
pixel_opt:
type: L1Loss
loss_weight: 1
reduction: mean
# validation settings
val:
window_size: 8
val_freq: !!float 4e3
save_img: false
rgb2bgr: true
use_image: false
max_minibatch: 8
metrics:
psnr: # metric name, can be arbitrary
type: calculate_psnr
crop_border: 0
test_y_channel: false
# logging settings
logger:
print_freq: 1000
save_checkpoint_freq: !!float 4e3
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500