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SUNet_AWGN_denoising
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# Training configuration
GPU: [0,1,2,3]

VERBOSE: False

SWINUNET:
  IMG_SIZE: 256
  PATCH_SIZE: 4
  WIN_SIZE: 8
  EMB_DIM: 96
  DEPTH_EN: [8, 8, 8, 8]
  HEAD_NUM: [8, 8, 8, 8]
  MLP_RATIO: 4.0
  QKV_BIAS: True
  QK_SCALE: 8
  DROP_RATE: 0.
  ATTN_DROP_RATE: 0.
  DROP_PATH_RATE: 0.1
  APE: False
  PATCH_NORM: True
  USE_CHECKPOINTS: False
  FINAL_UPSAMPLE: 'Dual up-sample'

MODEL:
  MODE: 'Denoising'

# Optimization arguments.
OPTIM:
  BATCH: 2
  EPOCHS: 200
  # EPOCH_DECAY: [10]
  LR_INITIAL: 2e-4
  LR_MIN: 1e-6
  # BETA1: 0.9

TRAINING:
  VAL_AFTER_EVERY: 1
  RESUME: True
  TRAIN_PS: 256
  VAL_PS: 256
  TRAIN_DIR: './datasets/Denoising_DIV2K/train'       # path to training data
  VAL_DIR: './datasets/Denoising_DIV2K/test' # path to validation data
  SAVE_DIR: './checkpoints'           # path to save models and images