option.yml

#
# @Author: wjm
# @Date: 2019-10-13 21:45:10
# @LastEditTime: 2019-10-13 21:46:06
# @Description: file content
# 

name: Net
algorithm: msddn #maunetv4_ns #ugcn_s4nba0  pan_unfolding_v4
nEpochs: 500
gpu_mode: True
save_best: True
gpus: [1]
threads: 4  #num_works
log_dir: /home/manman/xuanhua/newpan/log/


seed: 123 #123
checkpoint: /home/your_folder/your_folder/newpan/checkpoint
data_dir_train: /media/your_folder/data_disk/yaogan/WV3_data/train128
data_dir_eval: /media/your_folder/data_disk/yaogan/WV3_data/test128
#data_dir_train: /media/your_folder/data_disk/yaogan/GF2_data/train128
#data_dir_eval: /media/your_folder/data_disk/yaogan/GF2_data/test128
#data_dir_train: /media/your_folder/data_disk/yaogan/WV3_data/train128
#data_dir_eval: /media/your_folder/data_disk/yaogan/WV3_data/test128

source_ms: ms
source_pan: pan

pretrain:
  pretrained: False
  pre_sr: msddn_4_1670917477/bestSSIM.pth  #unet_pan_4_1669425995/best.pth
  pre_folder: /home/your_folder/your_folder/newpan/checkpoint/

test:  #用于指标测试的代码
  algorithm: msddn #pannetffft
  type: test #[test, eval]
  data_dir: /media/your_folder/data_disk/yaogan/WV3_data/test128
  source_ms: ms
  source_pan: pan
  model: you_folder/bestSSIM.pth #unet_pan_4_1669431831/best.pth #unet_pan_4_1669425995/best.pth #MAUNetV3_4_1638761339/best.pth  #ugcn_s4nb_4_1630230918/best.pth  latest
  #model: ugcn_s4nb_4_1630368984/best.pth
  save_dir: /home/your_folder/your_folder/result/net_WV3

data:
  upsacle:  4
  batch_size: 4 #4
  patch_size: 32
  data_augmentation: False
  n_colors: 4
  rgb_range: 255
  normalize : False
  
schedule:
  lr: 5e-4 #5e-6 #1e-2
  decay: 1000
  gamma: 0.1
  optimizer: ADAM #[ADAM, SGD, RMSprop]
  momentum: 0.9 #SGD momentum
  beta1: 0.9 #ADAM beta
  beta2: 0.999 #ADAM beta
  epsilon: 1e-8 #ADAM epsilon
  alpha: 0.9 #RMSprop alpha
  weight_dency: 0
  gclip: 40 #0.4   #0.4  0.04
  loss: L1 #[MSE, L1]
  use_YCbCr: False