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