71 lines
1.4 KiB
YAML
71 lines
1.4 KiB
YAML
# Not exactly the same as SRResNet in <Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network>
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# With 16 Residual blocks w/o BN
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#### general settings
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name: 001_MSRResNetx4_scratch_DIV2K
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use_tb_logger: true
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model: sr
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distortion: sr
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scale: 4
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gpu_ids: [0]
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#### datasets
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datasets:
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train:
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name: DIV2K
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mode: LQGT
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dataroot_GT: ../datasets/DIV2K/DIV2K800_sub.lmdb
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dataroot_LQ: ../datasets/DIV2K/DIV2K800_sub_bicLRx4.lmdb
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use_shuffle: true
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n_workers: 6 # per GPU
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batch_size: 16
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target_size: 128
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use_flip: true
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use_rot: true
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color: RGB
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val:
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name: val_set5
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mode: LQGT
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dataroot_GT: ../datasets/val_set5/Set5
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dataroot_LQ: ../datasets/val_set5/Set5_bicLRx4
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#### network structures
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network_G:
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which_model_G: MSRResNet
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in_nc: 3
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out_nc: 3
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nf: 64
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nb: 16
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upscale: 4
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#### path
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path:
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pretrain_model_G: ~
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strict_load: true
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resume_state: ~
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#### training settings: learning rate scheme, loss
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train:
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lr_G: !!float 2e-4
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lr_scheme: CosineAnnealingLR_Restart
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beta1: 0.9
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beta2: 0.99
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niter: 1000000
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warmup_iter: -1 # no warm up
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T_period: [250000, 250000, 250000, 250000]
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restarts: [250000, 500000, 750000]
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restart_weights: [1, 1, 1]
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eta_min: !!float 1e-7
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pixel_criterion: l1
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pixel_weight: 1.0
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manual_seed: 10
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val_freq: !!float 5e3
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#### logger
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logger:
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print_freq: 100
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save_checkpoint_freq: !!float 5e3
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