85 lines
1.6 KiB
YAML
85 lines
1.6 KiB
YAML
#### general settings
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name: ESRGAN_adrianna_corrupt_finetune
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use_tb_logger: true
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model: corruptgan
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distortion: downsample
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scale: 4
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gpu_ids: [0]
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amp_opt_level: O1
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#### datasets
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datasets:
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train:
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name: blacked
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mode: downsample
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dataroot_GT: ../datasets/blacked/train/hr
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dataroot_LQ: ../datasets/adrianna/train/lr
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mismatched_Data_OK: true
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use_shuffle: true
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n_workers: 4 # per GPU
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batch_size: 16
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target_size: 64
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use_flip: false
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use_rot: false
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color: RGB
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val:
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name: blacked_val
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mode: downsample
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target_size: 64
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dataroot_GT: ../datasets/blacked/val/hr
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dataroot_LQ: ../datasets/blacked/val/lr
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#### network structures
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network_G:
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which_model_G: HighToLowResNet
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in_nc: 3
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out_nc: 3
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nf: 128
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nb: 30
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network_D:
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which_model_D: discriminator_vgg_128
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in_nc: 3
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nf: 96
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#### path
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path:
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pretrain_model_G: ../experiments/blacked_lqprn_corrupt_G.pth
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pretrain_model_D: ../experiments/blacked_lqprn_corrupt_D.pth
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resume_state: ~
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strict_load: true
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#### training settings: learning rate scheme, loss
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train:
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lr_G: !!float 1e-5
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weight_decay_G: 0
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beta1_G: 0.9
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beta2_G: 0.99
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lr_D: !!float 1e-5
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weight_decay_D: 0
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beta1_D: 0.9
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beta2_D: 0.99
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lr_scheme: MultiStepLR
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niter: 400000
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warmup_iter: -1 # no warm up
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lr_steps: [1000, 2000, 3000]
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lr_gamma: 0.5
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pixel_criterion: l1
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pixel_weight: !!float 1e-2
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feature_criterion: l1
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feature_weight: 0
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gan_type: gan # gan | ragan
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gan_weight: !!float 1e-1
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D_update_ratio: 1
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D_init_iters: 0
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manual_seed: 10
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val_freq: !!float 5e2
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#### logger
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logger:
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print_freq: 50
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save_checkpoint_freq: !!float 5e2
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