189 lines
3.9 KiB
YAML
189 lines
3.9 KiB
YAML
# general settings
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name: finetune_RealESRGANx4plus_400k
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model_type: RealESRGANModel
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scale: 4
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num_gpu: auto
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manual_seed: 0
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# ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
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# USM the ground-truth
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l1_gt_usm: True
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percep_gt_usm: True
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gan_gt_usm: False
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# the first degradation process
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resize_prob: [0.2, 0.7, 0.1] # up, down, keep
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resize_range: [0.15, 1.5]
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gaussian_noise_prob: 0.5
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noise_range: [1, 30]
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poisson_scale_range: [0.05, 3]
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gray_noise_prob: 0.4
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jpeg_range: [30, 95]
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# the second degradation process
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second_blur_prob: 0.8
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resize_prob2: [0.3, 0.4, 0.3] # up, down, keep
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resize_range2: [0.3, 1.2]
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gaussian_noise_prob2: 0.5
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noise_range2: [1, 25]
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poisson_scale_range2: [0.05, 2.5]
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gray_noise_prob2: 0.4
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jpeg_range2: [30, 95]
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gt_size: 256
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queue_size: 180
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# dataset and data loader settings
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datasets:
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train:
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name: DF2K+OST
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type: RealESRGANDataset
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dataroot_gt: datasets/DF2K
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meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
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io_backend:
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type: disk
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blur_kernel_size: 21
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kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
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kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
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sinc_prob: 0.1
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blur_sigma: [0.2, 3]
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betag_range: [0.5, 4]
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betap_range: [1, 2]
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blur_kernel_size2: 21
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kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
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kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
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sinc_prob2: 0.1
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blur_sigma2: [0.2, 1.5]
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betag_range2: [0.5, 4]
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betap_range2: [1, 2]
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final_sinc_prob: 0.8
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gt_size: 256
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use_hflip: True
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use_rot: False
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# data loader
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use_shuffle: true
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num_worker_per_gpu: 5
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batch_size_per_gpu: 12
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dataset_enlarge_ratio: 1
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prefetch_mode: ~
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# Uncomment these for validation
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# val:
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# name: validation
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# type: PairedImageDataset
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# dataroot_gt: path_to_gt
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# dataroot_lq: path_to_lq
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# io_backend:
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# type: disk
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# network structures
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network_g:
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type: RRDBNet
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num_in_ch: 3
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num_out_ch: 3
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num_feat: 64
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num_block: 23
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num_grow_ch: 32
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network_d:
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type: UNetDiscriminatorSN
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num_in_ch: 3
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num_feat: 64
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skip_connection: True
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# path
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path:
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# use the pre-trained Real-ESRNet model
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pretrain_network_g: experiments/pretrained_models/RealESRNet_x4plus.pth
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param_key_g: params_ema
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strict_load_g: true
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pretrain_network_d: experiments/pretrained_models/RealESRGAN_x4plus_netD.pth
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param_key_d: params
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strict_load_d: true
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resume_state: ~
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# training settings
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train:
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ema_decay: 0.999
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optim_g:
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type: Adam
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lr: !!float 1e-4
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weight_decay: 0
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betas: [0.9, 0.99]
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optim_d:
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type: Adam
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lr: !!float 1e-4
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weight_decay: 0
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betas: [0.9, 0.99]
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scheduler:
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type: MultiStepLR
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milestones: [400000]
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gamma: 0.5
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total_iter: 400000
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warmup_iter: -1 # no warm up
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# losses
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pixel_opt:
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type: L1Loss
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loss_weight: 1.0
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reduction: mean
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# perceptual loss (content and style losses)
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perceptual_opt:
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type: PerceptualLoss
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layer_weights:
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# before relu
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'conv1_2': 0.1
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'conv2_2': 0.1
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'conv3_4': 1
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'conv4_4': 1
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'conv5_4': 1
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vgg_type: vgg19
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use_input_norm: true
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perceptual_weight: !!float 1.0
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style_weight: 0
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range_norm: false
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criterion: l1
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# gan loss
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gan_opt:
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type: GANLoss
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gan_type: vanilla
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real_label_val: 1.0
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fake_label_val: 0.0
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loss_weight: !!float 1e-1
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net_d_iters: 1
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net_d_init_iters: 0
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# Uncomment these for validation
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# validation settings
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# val:
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# val_freq: !!float 5e3
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# save_img: True
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# metrics:
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# psnr: # metric name
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# type: calculate_psnr
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# crop_border: 4
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# test_y_channel: false
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# logging settings
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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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use_tb_logger: true
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wandb:
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project: ~
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resume_id: ~
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# dist training settings
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dist_params:
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backend: nccl
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port: 29500
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