total_iters: 300000
output_dir: output_dir
find_unused_parameters: True
checkpoints_dir: checkpoints
use_dataset: True
# tensor range for function tensor2img
min_max:
  (0., 1.)

model:
  name: PReNetModel
  generator:
    name: PReNet
  pixel_criterion:
    name: SSIM

dataset:
  train:
    name: SRDataset
    gt_folder: data/RainH/RainTrainH/norain
    lq_folder: data/RainH/RainTrainH/rain
    num_workers: 4
    batch_size: 16
    scale: 1
    preprocess:
      - name: LoadImageFromFile
        key: lq
      - name: LoadImageFromFile
        key: gt
      - name: Transforms
        input_keys: [lq, gt]
        pipeline:
          - name: PairedRandomHorizontalFlip
            keys: [image, image]
          - name: PairedRandomVerticalFlip
            keys: [image, image]
          - name: PairedRandomTransposeHW
            keys: [image, image]
          - name: PairedRandomCrop
            size: [100, 100]
            keys: [image, image]
          - name: PairedToTensor
            keys: [image, image]
  test:
    name: SRDataset
    gt_folder: data/RainH/Rain100H/norain
    lq_folder: data/RainH/Rain100H/rain
    scale: 1
    preprocess:
      - name: LoadImageFromFile
        key: lq
      - name: LoadImageFromFile
        key: gt
      - name: Transforms
        input_keys: [lq, gt]
        pipeline:
          - name: PairedToTensor
            keys: [image, image]


lr_scheduler:
  name: MultiStepDecay
  learning_rate: 0.0013
  milestones: [36000,60000,96000]
  gamma: 0.2

optimizer:
  name: Adam
  # add parameters of net_name to optim
  # name should in self.nets
  net_names:
    - generator
  beta1: 0.9
  beta2: 0.99

validate:
  interval: 5000
  save_img: false

  metrics:
    psnr: # metric name, can be arbitrary
      name: PSNR
      crop_border: 0
      test_y_channel: True
    ssim:
      name: SSIM
      crop_border: 0
      test_y_channel: True

log_config:
  interval: 100
  visiual_interval: 500

snapshot_config:
  interval: 5000

export_model:
  - {name: 'generator', inputs_num: 1}