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model: |
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swinir: |
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target: model.swinir.SwinIR |
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params: |
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img_size: 64 |
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patch_size: 1 |
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in_chans: 3 |
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embed_dim: 180 |
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depths: [6, 6, 6, 6, 6, 6, 6, 6] |
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num_heads: [6, 6, 6, 6, 6, 6, 6, 6] |
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window_size: 8 |
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mlp_ratio: 2 |
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sf: 8 |
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img_range: 1.0 |
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upsampler: "nearest+conv" |
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resi_connection: "1conv" |
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unshuffle: True |
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unshuffle_scale: 8 |
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dataset: |
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train: |
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target: dataset.codeformer.CodeformerDataset |
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params: |
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file_list: |
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file_backend_cfg: |
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target: dataset.file_backend.HardDiskBackend |
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out_size: 512 |
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crop_type: center |
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blur_kernel_size: 41 |
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kernel_list: ['iso', 'aniso'] |
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kernel_prob: [0.5, 0.5] |
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blur_sigma: [0.1, 12] |
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downsample_range: [1, 12] |
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noise_range: [0, 15] |
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jpeg_range: [30, 100] |
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val: |
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target: dataset.codeformer.CodeformerDataset |
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params: |
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file_list: |
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file_backend_cfg: |
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target: dataset.file_backend.HardDiskBackend |
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out_size: 512 |
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|
crop_type: center |
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|
blur_kernel_size: 41 |
|
|
kernel_list: ['iso', 'aniso'] |
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|
kernel_prob: [0.5, 0.5] |
|
|
blur_sigma: [0.1, 12] |
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|
downsample_range: [1, 12] |
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|
noise_range: [0, 15] |
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|
jpeg_range: [30, 100] |
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train: |
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|
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exp_dir: |
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learning_rate: 1e-4 |
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batch_size: 96 |
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|
num_workers: |
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|
train_steps: 150000 |
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|
log_every: 50 |
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|
ckpt_every: 10000 |
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|
image_every: 1000 |
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val_every: 1000 |
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resume: ~ |
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|
