python3 train.py --opt [path_to_training_config]
- An directory with all the high resolution images for training
- Should be specified in config file, such as
options/train/train_example.json
...
,datasets": {
"train": {
"name": "VISHR"
, "mode": "LRHR"
, "resize": 0.9
, "dataroot_HR": "/mnt/WXRG0235/jnli/datasets/VIS/VISHR/VISHR_train"
//, "image_lists": ["/mnt/ficuszambia/jnli/facesr/datasets/zhengjian/train.list", "/mnt/WXRG0235/jnli/datasets/VIS/VISHR/VISHR_train.list"]
, "subset_file": null
, "use_shuffle": true
, "n_workers": 8
, "batch_size": 16
, "HR_size": 128
, "use_flip": true
, "use_rot": true
, "downsample": "numpy"
}
...
subset_file the list to HR imagesname the name of the training datasetmode LRHR: user can only provide HR, or provide both HR & LRdataroot_HR path to the directoryHR_size size of patches random cropped for traininguse_flip use_rot flip or rotatecrop to crop or just resize the whole faceresize resize the init HR face (to find the best performance of LR size), 1 for not resizedownsample downsample method for generating LR image. numpy | cubic | linearimage_lists the path to all the HR images when dataroot_HR is not given.
"path": {
"root": "/mnt/ficuszambia/jnli/facesr"
// , "resume_state": "../experiments/debug_002_RRDB_ESRGAN_x4_DIV2K/training_state/16.state"
// , "pretrain_model_G": "../experiments/pretrained_models/RRDB_PSNR_x4.pth"
}
root the root path of this project for saving checkpoint (saving at root/Experiments)
, "train": {
"lr_G": 1e-4
, "weight_decay_G": 0
, "beta1_G": 0.9
, "lr_D": 1e-4
, "weight_decay_D": 0
, "beta1_D": 0.9
, "lr_scheme": "MultiStepLR"
, "lr_steps": [50000, 100000, 200000, 300000]
, "lr_gamma": 0.5
, "pixel_criterion": "l1"
, "pixel_weight": 1e-2
, "feature_criterion": "l1"
, "feature_weight": 1
, "gan_type": "vanilla"
, "gan_weight": 5e-3
, "recloss": {
"name":"lightcnn",
"path": "/mnt/ficuszambia/jnli/facesr/recognition/pre-trained/LightCNN_29Layers_V2_checkpoint.pth",
"weight": 1
}
//for wgan-gp
// , "D_update_ratio": 1
// , "D_init_iters": 0
// , "gp_weigth": 10
, "manual_seed": 0
, "niter": 1e6
, "val_freq": 1000
}
recloss-name the name of recognition loss to be used. currently onlt lightcnn is supported. Leaving blank '' for not using recognition lossrecloss-path path to the pre-trained modelrecloss-weight weight of recloss, when set to 1:
19-04-03 17:00:12.447 - INFO: <epoch: 0, iter: 200, lr:1.000e-04> l_g_pix: 4.4571e-03 l_g_fea: 2.6769e+00 l_g_rec: 6.0616e-01 l_g_gan: 1.3311e-01 l_d_real: 0.0000e+00 l_d_fake: 1.6391e-06 D_real: 2.3803e+01 D_fake: -2.8195e+00
...
19-04-03 20:50:43.945 - INFO: <epoch: 1, iter: 19,600, lr:1.000e-04> l_g_pix: 2.0350e-04 l_g_fea: 8.0707e-01 l_g_rec: 4.8295e-02 l_g_gan: 5.6367e-02 l_d_real: 7.4582e-04 l_d_fake: 7.7790e-05 D_real: -3.0652e+00 D_fake: -1.4338e+01
niter how many batches to train?val_freq val image will be saved in /valid. Need manually deleted for each experiment
"name": "lightcnn_VISHR_numpy_128crop" // please remove "debug_" during training
, "use_tb_logger": true
, "model":"srragan"
,"scale": 4
, "gpu_ids": [0,1,2,3,4,5,6]
name checkpoints will be saved in root/Experiments/name/models. Attention: new experiment with the same name will overwrite the old one
python3 test.py --opt [path_to_test_config]
"test 1": {
"name": "FaceTest_Crop_1",
"resize": 1,
"mode": "LRHR",
"downsample": "numpy",
"dataroot_HR": "/mnt/WXRG0235/jnli/05_RegWorkspace/image/A40P/__backups_rgbfull__collected_crop"
},
resize resize the HR image before downsampling
ESRGAN https://github.com/xinntao/BasicSR