Comments (3)
{
"task": "swinir_sr_classical_patch64_x4_l1" // classical image sr for x2/x3/x4/x8. root/task/images-models-options
, "model": "plain" // "plain" | "plain2" if two inputs
, "gpu_ids": [6,7]
, "dist": true
, "scale": 4 // 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": "../HR"// path of H training dataset. DIV2K (800 training images)
, "dataroot_L": "../LR" // path of L training dataset
, "H_size": 256 // 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": 8 // 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": "testsets/Set5/HR" // path of H testing dataset
, "dataroot_L": "testsets/Set5/LR_bicubic/X2" // path of L testing dataset
}
}
, "netG": {
"net_type": "swinir"
, "upscale": 4 // 2 | 3 | 4 | 8
, "in_chans": 3
, "img_size": 64 // 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
}
}
from swinir.
Same problem! How to address it? Thx!
from swinir.
The model is "upscale": 4
, but the LR image input is "dataroot_L": "testsets/Set5/LR_bicubic/X2"
from swinir.
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