epochs: 200 output_dir: output_dir model: name: Pix2PixModel generator: name: UnetGenerator norm_type: batch input_nc: 3 output_nc: 3 num_downs: 8 #unet256 ngf: 64 use_dropout: False discriminator: name: NLayerDiscriminator ndf: 64 n_layers: 3 input_nc: 6 norm_type: batch direction: b2a pixel_criterion: name: L1Loss loss_weight: 100 gan_criterion: name: GANLoss gan_mode: vanilla dataset: train: name: PairedDataset dataroot: data/facades/train num_workers: 4 batch_size: 1 preprocess: - name: LoadImageFromFile key: pair - name: SplitPairedImage key: pair paired_keys: [A, B] - name: Transforms input_keys: [A, B] pipeline: - name: Resize size: [286, 286] interpolation: 'bicubic' #cv2.INTER_CUBIC keys: [image, image] - name: PairedRandomCrop size: [256, 256] keys: [image, image] - name: PairedRandomHorizontalFlip 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: PairedDataset dataroot: data/facades/test num_workers: 4 batch_size: 1 preprocess: - name: LoadImageFromFile key: pair - name: SplitPairedImage key: pair paired_keys: [A, 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 beta1: 0.5 optimD: name: Adam net_names: - netD beta1: 0.5 log_config: interval: 100 visiual_interval: 500 snapshot_config: interval: 5 validate: interval: 500 save_img: false metrics: fid: # metric name, can be arbitrary name: FID batch_size: 8