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input_80_90.json
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{
"task": "input_80_90" // real-world image sr. root/task/images-models-options
, "model": "plain" // "plain" | "plain2" if two inputs
, "gpu_ids": [0,1,2]
, "dist": false
, "scale": 1 // broadcast to "datasets"
, "n_channels": 3 // broadcast to "datasets", 1 for grayscale, 3 for color
, "path": {
"root": "masked_denoising" // "denoising" | "superresolution" | "masked_denoising"
, "pretrained_netG": null // path of pretrained model
, "pretrained_netE": null // path of pretrained model
}
, "datasets": {
"train": {
"name": "train_dataset" // just name
, "dataset_type": "masked_denoising" // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg" | "masked_denoising"
, "dataroot_H": "/home/haoyu/data/train/" // path of H training dataset. DIV2K + Flickr2K + BSD500 + WED
, "dataroot_L": null // path of L training dataset
, "H_size": 64
, "lq_patchsize": 64
, "dataloader_shuffle": true
, "dataloader_num_workers": 16
, "dataloader_batch_size": 64 // batch size, bigger is better
, "noise_level": 15 // training noise level
, "if_mask": true // if use input mask
, "mask1": 80 // input mask ratio,
, "mask2": 90 // randomly sampling from [mask1, mask2]
}
, "test": {
"name": "test_dataset" // just name
, "dataset_type": "plain" // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg"
, "dataroot_H": "testset/McM/HR" // path of H testing dataset
, "dataroot_L": "testset/McM/McM_poisson_20" // path of L testing dataset
}
}
, "netG": {
"net_type": "swinir"
, "upscale": 1
, "in_chans": 3
, "img_size": 64
, "window_size": 8
, "img_range": 1.0
, "depths": [6, 6, 6, 6]
, "embed_dim": 60
, "num_heads": [6, 6, 6, 6]
, "mlp_ratio": 2
, "upsampler": null // "pixelshuffle" | "pixelshuffledirect" | "nearest+conv" | null
, "resi_connection": "3conv" // "1conv" | "3conv"
, "init_type": "default"
, "talking_heads": false
, "attn_fn": "softmax"
, "head_scale": false
, "on_attn": false
, "use_mask": true // if use attention mask
, "mask_ratio1": 75 // attention mask ratio,
, "mask_ratio2": 75 // randomly sampling from [mask_ratio1, mask_ratio2]
, "mask_is_diff": false
, "type": "stand"
}
, "train": {
"manual_seed": 1
, "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": 1e-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": 100 // for print
, "save_image": ["img_043_x1", "img_021_x1", "img_024_x1", "img_031_x1", "img_041_x1", "img_032_x1"] // image names to be saved (tensorboard) during testing
}
}