Delete x1_ITF_SkinDiffDetail_Lite_v1.yml

x1_ITF_SkinDiffDetail_Lite_v1.yml

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-# python train.py -opt options/sr/x1_ITF_SkinDiffDetail_Lite_v1.yml
-name: x1_ITF_SkinDiffDetail_Lite_v1
-# the name that defines the experiment and the directory that will be created in the experiments directory.
-# name: debug_001_template  # use the "debug" or "debug_nochkp" prefix in the name to run a test session and check everything is working. Does validation and state saving every 8 iterations. Remove "debug" to run the real training session.
-use_tb_logger: false
-# wheter to enable Tensorboard logging or not. Output will be saved in: traiNNer/tb_logger/
-model: sr 
-# the model training strategy to be used. Depends on the type of model, from: https://github.com/victorca25/traiNNer/tree/master/codes/models
-scale: 1 # the scale factor that will be used for training for super-resolution cases. Default is "1".
-gpu_ids: [0] # the list of `CUDA_VISIBLE_DEVICES` that will be used during training, ie. for two GPUs, use [0, 1]. The batch size should be a multiple of the number of 'gpu_ids', since images will be distributed from the batch to each GPU.
-use_amp: true # select to use PyTorch's Automatic Mixed Precision package to train in low-precision FP16 mode (lowers VRAM requirements).
-use_swa: false # select to use Stochastic Weight Averaging
-use_cem: false # select to use CEM during training. https://github.com/victorca25/traiNNer/tree/master/codes/models/modules/architectures/CEM
-
-# Dataset options:
-datasets: # configure the datasets
-  train: # the stage the dataset will be used for (training)
-    name: x1_ITF_SkinDiffDetail_Lite_v1 # the name of your dataset (only informative)
-    mode: aligned 
-    # dataset mode: https://github.com/victorca25/traiNNer/tree/master/codes/data
-    dataroot_HR: [
-      #'K:/TRAINING/data/Skin_Diff2Nrml/hr_clean_tiles/'
-      '../datasets/Skin_DiffDetail/hr/'
-      ]
-    dataroot_LR: [
-      #'K:/TRAINING/data/Skin_Diff2Nrml/lr_clean_tiles/'
-      '../datasets/Skin_DiffDetail/lr_soft/'
-      ] # low resolution images
-    subset_file: null
-    use_shuffle: true
-    znorm: false
-    n_workers: 8
-    batch_size: 12
-    virtual_batch_size: 12
-    preprocess: crop
-    crop_size: 64
-    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_types: [0, 3]
-    hr_downscale_amount: [1, 2, 4]
-    # #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: false
-    #lr_downscale_types: ["linear", "bicubic", "nearest_aligned"]
-
-    # Rotations augmentations:
-    use_flip: true
-    use_rot: true
-    use_hrrot: true
-    
-    # Noise and blur augmentations:
-    #lr_blur: true
-    #lr_blur_types: {sinc: 0.2, iso: 0.2, ansio2: 0.4, sinc2: 0.2, clean: 3}
-    #noise_data: 'K:/TRAINING/traiNNer/noise_patches/'
-    #lr_noise: true
-    #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'
-    
-
-path:
-    root: '../'
-    pretrain_model_G: '../experiments/pretrained_models/1x_DIV2K-Lite_SpongeBC1-Lite_interp.pth'
-    # pretrain_model_D: 'K:/TRAINING/data/models/x1_ITF_SkinDiff2Nrm_Lite_v3_208500_D.pth'
-    resume_state: '../experiments/x1_ITF_SkinDiffDetail_Lite_v1/training_state/latest.state'
-
-# Generator options:
-network_G: esrgan-lite # configurations for the Generator network
-
-
-# Discriminator options:
-network_D:
-    # ESRGAN (default)| PPON:
-    which_model_D: multiscale # discriminator_vgg_128 | discriminator_vgg | discriminator_vgg_128_fea (feature extraction) | patchgan | multiscale
-    norm_type: batch
-    act_type: leakyrelu
-    mode: CNA # CNA | NAC
-    nf: 32
-    in_nc: 3
-    nlayer: 3 # only for patchgan and multiscale
-    num_D: 3 # only for multiscale
-
-train:
-    # Optimizer options:
-    optim_G: adamp
-    optim_D: adamp
-    
-    # Schedulers options:
-    lr_scheme: MultiStepLR
-    lr_steps_rel: [50000, 100000, 200000, 300000]
-    lr_gamma: 0.5
-
-    # For SWA scheduler
-    swa_start_iter_rel: 0.05
-    swa_lr: 1e-4
-    swa_anneal_epochs: 10
-    swa_anneal_strategy: "cos"
-    
-    # Losses:
-    pixel_criterion: l1  # pixel (content) loss
-    pixel_weight: 0.05
-    feature_criterion: l1 # feature loss (VGG feature network)
-    feature_weight: 0.3
-    cx_type: contextual  # contextual loss
-    cx_weight: 1
-    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: 0.2 #[.2]
-    color_criterion: color-l1cosinesim  # color consistency loss
-    color_weight: 0.1
-    # 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: false
-    #mixopts: [blend, rgb, mixup, cutmix, cutmixup] # , "cutout", "cutblur"]
-    #mixprob: [1.0, 1.0, 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: 250000
-    # warmup_iter: -1
-    #val_freq: 5e3
-    # overwrite_val_imgs: true
-    # val_comparison: true
-    # metrics: 'psnr,ssim,lpips'
-    #grad_clip: auto
-    #grad_clip_value: 0.1 # "auto"
-
-logger:
-    print_freq: 50
-    save_checkpoint_freq: 500
-    overwrite_chkp: false