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| { | |
| "task": "swinir_sr_classical_patch48_x2" // classical image sr for x2/x3/x4/x8. root/task/images-models-options | |
| , "model": "plain" // "plain" | "plain2" if two inputs | |
| , "gpu_ids": [0,1,2,3,4,5,6,7] | |
| , "dist": true | |
| , "scale": 2 // 2 | 3 | 4 | 8 | |
| , "n_channels": 3 // broadcast to "datasets", 1 for grayscale, 3 for color | |
| , "path": { | |
| "root": "superresolution" // "denoising" | "superresolution" | "dejpeg" | |
| , "pretrained_netG": null // path of pretrained model. We fine-tune X3/X4/X8 models from X2 model, so that `G_optimizer_lr` and `G_scheduler_milestones` can be halved to save time. | |
| , "pretrained_netE": null // path of pretrained model | |
| } | |
| , "datasets": { | |
| "train": { | |
| "name": "train_dataset" // just name | |
| , "dataset_type": "sr" // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg" | |
| , "dataroot_H": "/home/cll/datasets/REDS/train/train_sharp"// path of H training dataset. DIV2K (800 training images) | |
| , "dataroot_L": null // path of L training dataset | |
| , "H_size": 96 // 96/144|192/384 | 128/192/256/512. LR patch size is set to 48 or 64 when compared with RCAN or RRDB. | |
| , "dataloader_shuffle": true | |
| , "dataloader_num_workers": 16 | |
| , "dataloader_batch_size": 32 // batch size 1 | 16 | 32 | 48 | 64 | 128. Total batch size =4x8=32 in SwinIR | |
| } | |
| , "test": { | |
| "name": "test_dataset" // just name | |
| , "dataset_type": "sr" // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg" | |
| , "dataroot_H": "/home/cll/datasets/REDS/val/val_sharp" // path of H testing dataset | |
| , "dataroot_L": null // path of L testing dataset | |
| } | |
| } | |
| , "netG": { | |
| "net_type": "swinir" | |
| , "upscale": 2 // 2 | 3 | 4 | 8 | |
| , "in_chans": 3 | |
| , "img_size": 48 // For fair comparison, LR patch size is set to 48 or 64 when compared with RCAN or RRDB. | |
| , "window_size": 8 | |
| , "img_range": 1.0 | |
| , "depths": [6, 6, 6, 6, 6, 6] | |
| , "embed_dim": 180 | |
| , "num_heads": [6, 6, 6, 6, 6, 6] | |
| , "mlp_ratio": 2 | |
| , "upsampler": "pixelshuffle" // "pixelshuffle" | "pixelshuffledirect" | "nearest+conv" | null | |
| , "resi_connection": "1conv" // "1conv" | "3conv" | |
| , "init_type": "default" | |
| } | |
| , "train": { | |
| "G_lossfn_type": "l1" // "l1" preferred | "l2sum" | "l2" | "ssim" | "charbonnier" | |
| , "G_lossfn_weight": 1.0 // default | |
| , "E_decay": 0.999 // Exponential Moving Average for netG: set 0 to disable; default setting 0.999 | |
| , "G_optimizer_type": "adam" // fixed, adam is enough | |
| , "G_optimizer_lr": 2e-4 // learning rate | |
| , "G_optimizer_wd": 0 // weight decay, default 0 | |
| , "G_optimizer_clipgrad": null // unused | |
| , "G_optimizer_reuse": true // | |
| , "G_scheduler_type": "MultiStepLR" // "MultiStepLR" is enough | |
| , "G_scheduler_milestones": [250000, 400000, 450000, 475000, 500000] | |
| , "G_scheduler_gamma": 0.5 | |
| , "G_regularizer_orthstep": null // unused | |
| , "G_regularizer_clipstep": null // unused | |
| , "G_param_strict": true | |
| , "E_param_strict": true | |
| , "checkpoint_test": 5000 // for testing | |
| , "checkpoint_save": 5000 // for saving model | |
| , "checkpoint_print": 200 // for print | |
| } | |
| } | |