name: VoxCeleb2Anime engine: UGATIT result_dir: ./result max_pairs: 1000000 distributed: model: # broadcast_buffers: False misc: random_seed: 324 checkpoint: epoch_interval: 1 # one checkpoint every 1 epoch n_saved: 2 interval: print_per_iteration: 10 # print once per 10 iteration tensorboard: scalar: 10 image: 500 model: generator: _type: UGATIT-Generator in_channels: 3 out_channels: 3 base_channels: 64 num_blocks: 4 img_size: 128 light: True local_discriminator: _type: UGATIT-Discriminator in_channels: 3 base_channels: 64 num_blocks: 5 global_discriminator: _type: UGATIT-Discriminator in_channels: 3 base_channels: 64 num_blocks: 7 loss: gan: loss_type: lsgan weight: 1.0 real_label_val: 1.0 fake_label_val: 0.0 cycle: level: 1 weight: 10.0 id: level: 1 weight: 10.0 cam: weight: 1000 optimizers: generator: _type: Adam lr: 0.0001 betas: [ 0.5, 0.999 ] weight_decay: 0.0001 discriminator: _type: Adam lr: 1e-4 betas: [ 0.5, 0.999 ] weight_decay: 0.0001 data: train: scheduler: start_proportion: 0.5 target_lr: 0 buffer_size: 50 dataloader: batch_size: 20 shuffle: True num_workers: 2 pin_memory: True drop_last: True dataset: _type: GenerationUnpairedDataset root_a: "/data/i2i/VoxCeleb2Anime/trainA" root_b: "/data/i2i/VoxCeleb2Anime/trainB" random_pair: True pipeline: - Load - Resize: size: [ 135, 135 ] - RandomCrop: size: [ 128, 128 ] - RandomHorizontalFlip - ToTensor - Normalize: mean: [ 0.5, 0.5, 0.5 ] std: [ 0.5, 0.5, 0.5 ] test: dataloader: batch_size: 8 shuffle: False num_workers: 1 pin_memory: False drop_last: False dataset: _type: GenerationUnpairedDataset root_a: "/data/i2i/VoxCeleb2Anime/testA" root_b: "/data/i2i/VoxCeleb2Anime/testB" random_pair: False pipeline: - Load - Resize: size: [ 128, 128 ] - ToTensor - Normalize: mean: [ 0.5, 0.5, 0.5 ] std: [ 0.5, 0.5, 0.5 ] video_dataset: _type: SingleFolderDataset root: "/data/i2i/VoxCeleb2Anime/test_video_frames/" with_path: True pipeline: - Load - Resize: size: [ 128, 128 ] - ToTensor - Normalize: mean: [ 0.5, 0.5, 0.5 ] std: [ 0.5, 0.5, 0.5 ]