train_valar_v1.yml

name: 4x_Valar_v1
use_tb_logger: false
model: sr
scale: 4
gpu_ids: [0]
use_amp: false
use_swa: false
use_cem: false

# Dataset options:
datasets:
  train:
    name: AdobeMIT5k
    mode: aligned
    dataroot_HR: [
      '../mit5k/hr',
      ] # high resolution / ground truth images
    dataroot_LR: [
      '../mit5k/lr',
      ] # low resolution images
    subset_file: null
    use_shuffle: true
    znorm: false
    n_workers: 4
    batch_size: 1
    virtual_batch_size: 1
    preprocess: crop
    crop_size: 112
    image_channels: 3

    # AdaTarget
    use_atg: true
    atg_start_iter_rel: 0.83

    # Color space conversion
    # color: 'y'
    # color_LR: 'y'
    # color_HR: 'y'

    # Rotations augmentations:
    use_flip: true
    use_rot: true
    use_hrrot: false

    # Presets and on the fly (OTF) augmentations

    # Resize Options
    lr_downscale: true
    lr_downscale_types: [linear, bicubic, realistic]

    aug_downscale: 0.5
    resize_strat: pre 

    # Blur degradations
    #lr_blur: true
    #lr_blur_types: {sinc: 0.05, iso: 0.1, aniso: 0.1}
    #iso:
    #  p: 0.4
    #  min_kernel_size: 1
    #  kernel_size: 5
    #  sigmaX: [0.1, 1.0]
    #  noise: null

    #aniso:
    #  p: 0.3
    #  min_kernel_size: 1
    #  kernel_size: 3
    #  sigmaX: [0.1, 1.0]
    #  sigmaY: [0.1, 1.0]
    #  angle: [0, 180]
    #  noise: null

    #sinc:
    #  p: 0.2
    #  min_kernel_size: 1
    #  kernel_size: 3
    #  min_cutoff: null

    lr_noise: true
    lr_noise_types: {JPEG: 3, camera: 1.6, patches: 2.5, clean: 1.5}
    hr_unsharp_mask: true
    hr_rand_unsharp: 1

    camera:
      p: 0.25
      demosaic_fn: malvar
      xyz_arr: D50
      rg_range: [0.7, 3.0]
      bg_range: [0.7, 3.0]

    jpeg:
      p: 0.75
      min_quality: 30
      max_quality: 95

    unsharp:
      p: 0.12
      blur_algo: median
      kernel_size: 1
      strength: 0.10
      unsharp_algo: laplacian
     
    dataroot_kernels: '../mit5k/kernelgan_hr/'
    noise_data: '../mit5k/noise_patches_path/'

    # pre_crop: true
    # hr_downscale: true
    # hr_downscale_amt: [2, 1.75, 1.5, 1]
    # shape_change: reshape_lr

path:
    root: './'
    #pretrain_model_G: '../models/4x_RRDB_ESRGAN.pth'
    #pretrain_model_Loc: '../models/locnet.pth'
    #resume_state: './experiments/4x_Valar_v1/training_state/latest.state'

# Generator options:
network_G:
    which_model_G: esrgan
    plus: true
    gaussian_noise: true

# 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: 0.5

    # For SWA scheduler
    swa_start_iter_rel: 0.75
    swa_lr: 1e-4
    swa_anneal_epochs: 10
    swa_anneal_strategy: "cos"
    
    # Losses:
    pixel_criterion: clipl1  # pixel (content) loss
    pixel_weight: 0.25
    perceptual_opt:
      perceptual_layers: {"conv1_2": 0.1, "conv2_2": 0.1, "conv3_4": 1.0, "conv4_4": 1.0, "conv5_4": 1.0}
    use_input_norm: true
    perceptual_weight: 1.05
    style_weight: 0
    feature_criterion: l1 # feature loss (VGG feature network)
    feature_weight: 1
    cx_type: contextual  # contextual loss
    cx_weight: 0.3
    cx_vgg_layers: {conv_3_2: 1.0, conv_4_2: 1.0}
    # 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: ms-ssim  # structural similarity
    #ssim_weight: 1
    #lpips_weight: 0.6 # 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: 0.2
    # range_weight: 1  # range loss
    # fft_type: fft  # FFT loss
    # fft_weight: 0.1
    color_criterion: color-l1cosinesim  # color consistency loss
    color_weight: 1.0
    # 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: 1e-1
    # 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
    
    # 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: [0.5, 0.5, 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: 4e5
    warmup_iter: -1
    # overwrite_val_imgs: true

logger:
    print_freq: 100
    save_checkpoint_freq: 5e3
    overwrite_chkp: false