epochs: 200
output_dir: output_dir

model:
  name: CycleGANModel
  generator:
    name: ResnetGenerator
    output_nc: 3
    n_blocks: 9
    ngf: 64
    use_dropout: False
    norm_type: instance
    input_nc: 3
  discriminator:
    name: NLayerDiscriminator
    ndf: 64
    n_layers: 3
    norm_type: instance
    input_nc: 3
  cycle_criterion:
    name: L1Loss
  idt_criterion:
    name: L1Loss
    loss_weight: 0.5
  gan_criterion:
    name: GANLoss
    gan_mode: lsgan

dataset:
  train:
    name: UnpairedDataset
    dataroot_a: data/horse2zebra/trainA
    dataroot_b: data/horse2zebra/trainB
    num_workers: 0
    batch_size: 1
    is_train: True
    max_size: inf
    preprocess:
      - name: LoadImageFromFile
        key: A
      - name: LoadImageFromFile
        key: B
      - name: Transforms
        input_keys: [A, B]
        pipeline:
          - name: Resize
            size: [286, 286]
            interpolation: 'bicubic' #cv2.INTER_CUBIC
            keys: ['image', 'image']
          - name: RandomCrop
            size: [256, 256]
            keys: ['image', 'image']
          - name: RandomHorizontalFlip
            prob: 0.5
            keys: ['image', 'image']
          - name: Transpose
            keys: ['image', 'image']
          - name: Normalize
            mean: [127.5, 127.5, 127.5]
            std: [127.5, 127.5, 127.5]
            keys: ['image', 'image']
  test:
    name: UnpairedDataset
    dataroot_a: data/horse2zebra/testA
    dataroot_b: data/horse2zebra/testB
    num_workers: 0
    batch_size: 1
    max_size: inf
    is_train: False
    preprocess:
      - name: LoadImageFromFile
        key: A
      - name: LoadImageFromFile
        key: B
      - name: Transforms
        input_keys: [A, B]
        pipeline:
          - name: Resize
            size: [256, 256]
            interpolation: 'bicubic' #cv2.INTER_CUBIC
            keys: ['image', 'image']
          - name: Transpose
            keys: ['image', 'image']
          - name: Normalize
            mean: [127.5, 127.5, 127.5]
            std: [127.5, 127.5, 127.5]
            keys: ['image', 'image']

lr_scheduler:
  name: LinearDecay
  learning_rate: 0.0002
  start_epoch: 100
  decay_epochs: 100
  # will get from real dataset
  iters_per_epoch: 1

optimizer:
  optimG:
    name: Adam
    net_names:
      - netG_A
      - netG_B
    beta1: 0.5
  optimD:
    name: Adam
    net_names:
      - netD_A
      - netD_B
    beta1: 0.5

log_config:
  interval: 100
  visiual_interval: 500

snapshot_config:
  interval: 5