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- scale: 4
- num_gpu: 1
- manual_seed: 0
- is_train: True
- dist: False
- # ----------------- options for synthesizing training data ----------------- #
- gt_usm: True # USM the ground-truth
- # the first degradation process
- resize_prob: [0.2, 0.7, 0.1] # up, down, keep
- resize_range: [0.15, 1.5]
- gaussian_noise_prob: 1
- noise_range: [1, 30]
- poisson_scale_range: [0.05, 3]
- gray_noise_prob: 1
- jpeg_range: [30, 95]
- # the second degradation process
- second_blur_prob: 1
- resize_prob2: [0.3, 0.4, 0.3] # up, down, keep
- resize_range2: [0.3, 1.2]
- gaussian_noise_prob2: 1
- noise_range2: [1, 25]
- poisson_scale_range2: [0.05, 2.5]
- gray_noise_prob2: 1
- jpeg_range2: [30, 95]
- gt_size: 32
- queue_size: 1
- # network structures
- network_g:
- type: RRDBNet
- num_in_ch: 3
- num_out_ch: 3
- num_feat: 4
- num_block: 1
- num_grow_ch: 2
- # path
- path:
- pretrain_network_g: ~
- param_key_g: params_ema
- strict_load_g: true
- resume_state: ~
- # training settings
- train:
- ema_decay: 0.999
- optim_g:
- type: Adam
- lr: !!float 2e-4
- weight_decay: 0
- betas: [0.9, 0.99]
- scheduler:
- type: MultiStepLR
- milestones: [1000000]
- gamma: 0.5
- total_iter: 1000000
- warmup_iter: -1 # no warm up
- # losses
- pixel_opt:
- type: L1Loss
- loss_weight: 1.0
- reduction: mean
- # validation settings
- val:
- val_freq: !!float 5e3
- save_img: False
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