Upload UltraSharp Config.yml

UltraSharp Config.yml

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+name: UltraSharp
+use_tb_logger: true
+model: sr
+scale: 4
+gpu_ids: [0]
+use_amp: true
+use_swa: false
+use_cem: false
+
+# Dataset options:
+datasets:
+  train:
+    name: UltraSharp
+    mode: aligned
+    dataroot_HR: [
+      '!!!! CHANGE THIS OR COMMENT OUT',
+    dataroot_LR: [
+      '!!!! CHANGE THIS OR COMMENT OUT'
+      ] # low resolution images
+    subset_file: null
+    use_shuffle: true
+    znorm: false
+    n_workers: 4
+    batch_size: 4
+    virtual_batch_size: 8
+    preprocess: crop
+    crop_size: 128
+    image_channels: 3
+
+    # Color space conversion
+    # color: 'y'
+    # color_LR: 'y'
+    # color_HR: 'y'
+    
+    # LR and HR modifiers.
+    # aug_downscale: 0.2
+    # shape_change: reshape_lr
+    
+    # Enable random downscaling of HR images (will fix LR pair to correct size)
+    # hr_downscale: true
+    # hr_downscale_amt: [2, 1.75, 1.5, 1]
+    # #pre_crop: true
+    
+    # Presets and on the fly (OTF) augmentations
+    #augs_strategy: combo
+    add_blur_preset: custom_blur
+    add_resize_preset: custom_resize
+    add_noise_preset: custom_noise
+    aug_downscale: 0.2
+    resize_strat: pre
+    
+    # On the fly generation of LR:
+    dataroot_kernels: 'KERNEL PATH !!!! CHANGE THIS OR COMMENT OUT'
+    lr_downscale: true
+    lr_downscale_types: ["linear", "bicubic", "nearest_aligned"]
+
+    # Rotations augmentations:
+    use_flip: true
+    use_rot: true
+    use_hrrot: false
+    
+    # Noise and blur augmentations:
+    lr_blur: false
+    lr_blur_types: {sinc: 0.2, iso: 0.2, ansio2: 0.4, sinc2: 0.2, clean: 3}
+    noise_data: 'NOISE PATH !!!! CHANGE THIS OR COMMENT OUT'
+    lr_noise: false
+    lr_noise_types: {camera: 0.1, jpeg: 0.8, clean: 3}
+    lr_noise2: false
+    lr_noise_types2: {jpeg: 1, webp: 0, clean: 2, camera: 2}
+    hr_noise: false
+    hr_noise_types:  {gaussian: 1, clean: 4}
+    
+    # Color augmentations
+    # lr_fringes: false
+    # lr_fringes_chance: 0.4
+    # auto_levels: HR
+    # rand_auto_levels: 0.7
+    #lr_unsharp_mask: true
+    #lr_rand_unsharp: 0.7
+    # hr_unsharp_mask: true
+    # hr_rand_unsharp: 1
+    
+    # Augmentations for classification or (maybe) inpainting networks:
+    # lr_cutout: false
+    # lr_erasing: false
+
+  #val: 
+    #name: val_set14_part
+    #mode: aligned
+    #dataroot_B: '../datasets/val/hr'
+    #dataroot_A: '../datasets/val/lr'
+    
+    #znorm: false
+    
+    # Color space conversion:
+    # color: 'y'
+    # color_LR: 'y'
+    # color_HR: 'y'
+    
+    lr_downscale: true
+    lr_downscale_types: ["linear", "bicubic", "nearest_aligned"]
+
+path:
+    root: '../'
+    pretrain_model_G: 'UniScale-Balanced was used originally !!!! CHANGE THIS OR COMMENT OUT'
+    #pretrain_model_G: '../experiments/pretrained_models//.pth'
+    # pretrain_model_D: '../experiments/pretrained_models/patchgan.pth'
+    #resume_state: '!!!! CHANGE THIS OR COMMENT OUT'
+
+# Generator options:
+network_G:  # configurations for the Generator network
+    which_model_G: RRDB_net  # check:  
+
+# Discriminator options:
+network_D: unet
+
+train:
+    # Optimizer options:
+    optim_G: AdamP
+    optim_D: AdamP
+    
+    # Schedulers options:
+    lr_scheme: MultiStepLR
+    lr_steps_rel: [0.1, 0.2, 0.4, 0.6]
+    lr_gamma: 0.4
+
+    # For SWA scheduler
+    swa_start_iter_rel: 0.05
+    swa_lr: 1e-4
+    swa_anneal_epochs: 10
+    swa_anneal_strategy: "cos"
+    
+    # Losses:
+    pixel_criterion: clipl1  # pixel (content) loss
+    pixel_weight: 0.12
+    feature_criterion: l1 # feature loss (VGG feature network)
+    feature_weight: 0.3
+    cx_type: contextual  # contextual loss
+    cx_weight: 0.25
+    cx_vgg_layers: {conv_3_2: 1, conv_4_2: 1}
+    #hfen_criterion: l1  # hfen
+    #hfen_weight: 1e-6 
+    #grad_type: grad-4d-l1  # image gradient loss
+    #grad_weight: 4e-1
+    # tv_type: normal  # total variation
+    # tv_weight: 1e-5
+    # tv_norm: 1
+    ssim_type: ssim  # structural similarity
+    ssim_weight: 0.05
+    lpips_weight: 0.25  # [.25] perceptual loss
+    lpips_type: net-lin
+    lpips_net: squeeze
+    
+    # Experimental losses
+    # spl_type: spl  # spatial profile loss
+    # spl_weight: 0.1
+    #of_type: overflow  # overflow loss
+    #of_weight: 0.1
+    # range_weight: 1  # range loss
+    fft_type: fft  # FFT loss
+    fft_weight: 0.3 #[.2]
+    # color_criterion: color-l1cosinesim  # color consistency loss
+    # color_weight: 1
+    # avg_criterion: avg-l1  # averaging downscale loss
+    # avg_weight: 5
+    # ms_criterion: multiscale-l1  # multi-scale pixel loss
+    # ms_weight: 1e-2
+    #fdpl_type: fdpl  # frequency domain-based perceptual loss
+    #fdpl_weight: 1e-3
+    
+    # Adversarial loss:
+    gan_type: vanilla
+    gan_weight: 4e-3
+    # freeze_loc: 4
+    # For wgan-gp:
+    # D_update_ratio: 1
+    # D_init_iters: 0
+    # gp_weigth: 10
+    # Feature matching (if using the discriminator_vgg_128_fea or discriminator_vgg_fea):
+    # gan_featmaps: true
+    # dis_feature_criterion: cb  # discriminator feature loss
+    # dis_feature_weight: 0.01
+    
+    # For PPON:
+    # p1_losses: [pix]
+    # p2_losses: [pix-multiscale, ms-ssim]
+    # p3_losses: [fea]
+    # ppon_stages: [1000, 2000]
+    
+    # Differentiable Augmentation for Data-Efficient GAN Training
+    # diffaug: true
+    # dapolicy: 'color,transl_zoom,flip,rotate,cutout'
+    
+    # Batch (Mixup) augmentations
+    mixup: true
+    mixopts: [blend, rgb, mixup, cutmix, cutmixup] # , "cutout", "cutblur"]
+    mixprob: [1, 1, 1.0, 1.0, 1.0] #, 1.0, 1.0]
+    #mixalpha: [0.6, 1.0, 1.2, 0.7, 0.7] #, 0.001, 0.7]
+    aux_mixprob: 1.0
+    #aux_mixalpha: 1.2
+    mix_p: 1.2
+    
+    # Frequency Separator
+    # fs: true
+    # lpf_type: average
+    # hpf_type: average
+    
+    # Other training options:
+    manual_seed: 0
+    niter: 5e5
+    # warmup_iter: -1
+    #val_freq: 5e3
+    # overwrite_val_imgs: true
+    # val_comparison: true
+    metrics: 'psnr,ssim,lpips'
+    grad_clip: norm
+    grad_clip_value: 0.1 # "auto"
+
+logger:
+    print_freq: 100
+    save_checkpoint_freq: 800
+    overwrite_chkp: false