Let us take test_SRFBN_example.json as an example.
Note: Before you run python test.py -opt options/test/*.json, please carefully check options: "scale", "degradation", "self_ensemble", "dataroot_HR", "dataroot_LR", "networks" and "pretrained_path".
{
"mode": "sr", // solver type (only "sr" is provided)
"use_cl": true, // whether use multiple losses (required by our SRFBN)
"gpu_ids": [0], // GPU ID to use
"scale": 4, // super resolution scale (*Please carefully check it*)
"degradation": "BI", // degradation model for SR: "BI" | "BD" | "DN" (*Please carefully check it*)
"is_train": false, // whether train the model
"use_chop": true, // whether enable memory-efficient test
"rgb_range": 255, // maximum value of images
"self_ensemble": false, // whether use self-ensemble strategy
// test dataset specifications (you can place more than one test dataset here) (*Please carefully check dateset mode/root*)
"datasets": {
"test_set1": {
"mode": "LRHR", // dataset mode: "LRHR" | "LR"
"dataroot_HR": "./results/HR/Set5/x4", // HR dataset root (required by "LRHR" dataset mode)
"dataroot_LR": "./results/LR/LRBI/Set5/x4", // LR dataset root (required by "LRHR"/"LR" dataset mode)
"data_type": "img" // data type: "img" (image files) | "npy" (binary files), "npy" is recommended during training
},
// "test_set2": {
// "mode": "LRHR",
// "dataroot_HR": "./results/HR/Set14/x4",
// "dataroot_LR": "./results/LR/LRBI/Set14/x4",
// "data_type": "img"
// },
"test_set3": {
"mode": "LR",
"dataroot_LR": "./results/LR/MyImage",
"data_type": "img"
}
},
// networks specifications
"networks": {
"which_model": "SRFBN", // network name
"num_features": 64, // number of base feature maps
"in_channels": 3, // number of input channels
"out_channels": 3, // number of output channels
"num_steps": 4, // number of time steps (T)
"num_groups": 6 // number of projection groups (G)
},
"solver": {
"pretrained_path": "./models/SRFBN_x4_BI.pth" // pre-trained model directory (for test)
}
}