forked from mrq/DL-Art-School
3b4e54c4c5
Add RRDBNetXL, which performs processing at multiple image sizes. Add DiscResnet_passthrough, which allows passthrough of image at different sizes for discrimination. Adjust the rest of the repo to allow generators that return more than just a single image.
88 lines
1.7 KiB
YAML
88 lines
1.7 KiB
YAML
#### general settings
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name: blacked_fix_and_upconv_xl
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use_tb_logger: true
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model: srgan
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distortion: sr
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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: vixcloseup
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mode: LQGT
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dataroot_GT: K:\4k6k\4k_closeup\hr
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dataroot_LQ: K:\4k6k\4k_closeup\lr_corrupted
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doCrop: false
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use_shuffle: true
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n_workers: 8 # per GPU
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batch_size: 6
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target_size: 256
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color: RGB
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val:
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name: adrianna_val
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mode: LQGT
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dataroot_GT: E:\4k6k\datasets\adrianna\val\hhq
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dataroot_LQ: E:\4k6k\datasets\adrianna\val\hr
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#### network structures
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network_G:
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which_model_G: RRDBNetXL
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in_nc: 3
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out_nc: 3
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nf: 64
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nblo: 18
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nbmed: 8
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nbhi: 6
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network_D:
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which_model_D: discriminator_resnet_passthrough
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in_nc: 3
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nf: 42
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#### path
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path:
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pretrain_model_G: ../experiments/blacked_fix_and_upconv_xl_part1/models/3000_G.pth
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pretrain_model_D: ~
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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 1e-4
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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-4
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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: [20000, 40000, 50000, 60000]
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lr_gamma: 0.5
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mega_batch_factor: 1
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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: 1
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feature_weight_decay: 1
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feature_weight_decay_steps: 500
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feature_weight_minimum: 1
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gan_type: gan # gan | ragan
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gan_weight: !!float 1e-2
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D_update_ratio: 1
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D_init_iters: -1
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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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