epochs: 2
output_dir: output_dir

model:
  name: AnimeGANV2PreTrainModel
  generator:
    name: AnimeGenerator
  discriminator:
    name: AnimeDiscriminator
  gan_criterion:
    name: GANLoss
    gan_mode: lsgan
  con_weight: 1
  pretrain_ckpt: null

dataset:
  train:
    name: AnimeGANV2Dataset
    num_workers: 4
    batch_size: 4
    dataroot: data/animedataset
    style: Hayao
    transform_real:
      - name: Transpose
      - name: Normalize
        mean: [127.5, 127.5, 127.5]
        std: [127.5, 127.5, 127.5]
    transform_anime:
      - name: Add
        value: [-4.4346957, -8.665916, 13.100612]
      - name: Transpose
      - name: Normalize
        mean: [127.5, 127.5, 127.5]
        std: [127.5, 127.5, 127.5]
    transform_gray:
      - name: Grayscale
        num_output_channels: 3
      - name: Transpose
      - name: Normalize
        mean: [127.5, 127.5, 127.5]
        std: [127.5, 127.5, 127.5]
  test:
    name: SingleDataset
    dataroot: data/animedataset/test/test_photo
    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]

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

optimizer:
  optimizer_G:
    name: Adam
    net_names:
      - netG
    beta1: 0.5
  optimizer_D:
    name: Adam
    net_names:
      - netD
    beta1: 0.5

log_config:
  interval: 100
  visiual_interval: 100

snapshot_config:
  interval: 5