/
MSDINet-Train.yml
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/
MSDINet-Train.yml
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name: GoPro-MSDINet
model_type: ImageRestorationModel
scale: 1
num_gpu: 8
manual_seed: 10
datasets:
train:
name: gopro-train
type: PairedImageDataset
dataroot_gt: ./datasets/GoPro/train/sharp_crops.lmdb
dataroot_lq: ./datasets/GoPro/train/blur_crops.lmdb
filename_tmpl: '{}'
io_backend:
type: lmdb
gt_size: 256
use_flip: true
use_rot: true
# data loader
use_shuffle: true
num_worker_per_gpu: 8
batch_size_per_gpu: 8
dataset_enlarge_ratio: 1
prefetch_mode: ~
val:
name: gopro-test
type: PairedImageDataset
dataroot_gt: ./datasets/GoPro/test/target.lmdb
dataroot_lq: ./datasets/GoPro/test/input.lmdb
io_backend:
type: lmdb
# network structures
network_g:
type: msdi_net
hin_position_left: 0
hin_position_right: 4
# path
path:
pretrain_network_g: ~
strict_load_g: true
resume_state: ~
train:
optim_g:
type: Adam
lr: !!float 3e-4
weight_decay: 0
betas: [0.9, 0.99]
scheduler:
type: TrueCosineAnnealingLR
T_max: 400000
eta_min: !!float 1e-7
total_iter: 400000
warmup_iter: -1 # no warm up
# losses
pixel_opt:
type: PSNRLoss
loss_weight: 0.5
reduction: mean
# validation settings
val:
val_freq: !!float 5e7
save_img: false
grids: true
crop_size: 256
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: 500
save_checkpoint_freq: !!float 5e4
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500