epochs: 200
output_dir: output_dir
lambda_A: 10.0
lambda_B: 10.0
lambda_identity: 0.5

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
  gan_mode: lsgan

dataset:
  train:
    name: UnpairedDataset
    dataroot: data/cityscapes
    num_workers: 0
    batch_size: 1
    phase: train
    max_dataset_size: inf
    direction: AtoB
    input_nc: 3
    output_nc: 3
    serial_batches: False
    pool_size: 50
    transforms:
      - name: Resize
        size: [286, 286]
        interpolation: 'bicubic' #cv2.INTER_CUBIC
      - name: RandomCrop
        size: [256, 256]
      - name: RandomHorizontalFlip
        prob: 0.5
      - name: Transpose
      - name: Normalize
        mean: [127.5, 127.5, 127.5]
        std: [127.5, 127.5, 127.5]
  test:
    name: SingleDataset
    dataroot: data/cityscapes/testB
    max_dataset_size: inf
    direction: BtoA
    input_nc: 3
    output_nc: 3
    serial_batches: False
    pool_size: 50
    transforms:
      - name: Resize
        size: [256, 256]
        interpolation: 'bicubic' #cv2.INTER_CUBIC
      - name: Transpose
      - name: Normalize
        mean: [127.5, 127.5, 127.5]
        std: [127.5, 127.5, 127.5]

optimizer:
  name: Adam
  beta1: 0.5

lr_scheduler:
  name: linear
  learning_rate: 0.0002
  start_epoch: 100
  decay_epochs: 100

log_config:
  interval: 100
  visiual_interval: 500

snapshot_config:
  interval: 5