版权声明： https://blog.csdn.net/gwplovekimi/article/details/85260137
本博文是本人实现超分和去噪网络联合训练与fine tuning的实验笔记
先基于之前博文《基于pytorch的噪声估计网络》处理的噪声图片，进行bicubic downsample的操作（应该先bicubic再加噪）
进入对应子目录下，运行
$ matlab -nodesktop -nosplash -r matlabfile
python train.py -opt options/train/train_sub_sr.json
首先生成subnetwork板块（在networks.py）
#########################################################################################################################
def define_sub(opt):
gpu_ids = opt['gpu_ids']
opt_net = opt['network_sub']
which_model = opt_net['which_model_sub']
if which_model == 'noise_estimation':
subnet = arch.NENET(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'], nb=opt_net['nb'],
norm_type=opt_net['norm_type'], mode=opt_net['mode'])
else:
raise NotImplementedError('subnet model [{:s}] not recognized'.format(which_model))
if gpu_ids:
assert torch.cuda.is_available()
subnet = nn.DataParallel(subnet)
return subnet
############################################################################################################################对于setting，也就是.jason文件，如下
{
"name": "debug_finetune_srresnet_dncnn_DIVIK800" // please remove "debug_" during training
, "tb_logger_dir": "sr_c16s06"
, "use_tb_logger": true
, "model":"sr_sub"///////this is important
, "scale": 4
, "crop_scale": 4
, "gpu_ids": [3,5]
// , "init_type": "kaiming"
//
, "finetune_type": "sft" //sft | basic
// , "init_norm_type": "zero"
, "datasets": {
"train": {
"name": "DIV2K800"
// , "mode": "LQHQ"
// , "dataroot_HR": "/media/sdc/jwhe/BasicSR_v2/data/DIV2K/DIV2K800_sub"
// , "dataroot_HQ": "/media/sdc/jwhe/BasicSR_v2/data/DIV2K/DIV2K800_sub_Gaussian15"
// , "dataroot_LQ": "/media/sdc/jwhe/BasicSR_v2/data/DIV2K/DIV2K800_sub_Gaussian50"
, "mode": "LRHR"
, "dataroot_HR": "/home/guanwp/BasicSR_datasets/DIV2K800_sub"
, "dataroot_LR": "/home/guanwp/BasicSR_datasets/DIV2K800_sub_bicLRx4_noiseALL"
, "subset_file": null
, "use_shuffle": true
, "n_workers": 8
, "batch_size": 24 // 16
, "HR_size": 128 // 128 | 192 | 96
// , "noise_gt": true
, "use_flip": true
, "use_rot": true
}
//
// , "val": {
// "name": "val_CBSD68_Gaussian50",
// "mode": "LRHR",
// "dataroot_HR": "/home/jwhe/workspace/BasicSR_v3/data/CBSD68/mod2/CBSD68_mod",
// "dataroot_LR": "/home/jwhe/workspace/BasicSR_v3/data/CBSD68/mod2/CBSD68_Gaussian50"
//// , "noise_gt": true
// }
// , "val": {
// "name": "val_CBSD68_s08_c03",
// "mode": "LRHR",
// "dataroot_HR": "/home/jwhe/workspace/BasicSR_v3/data/CBSD68/mod2/CBSD68_mod",
// "dataroot_LR": "/home/jwhe/workspace/BasicSR_v3/data/CBSD68/mod2/CBSD68_s08_c03"
// , "noise_gt": true
// }
// , "val": {
// "name": "val_CBSD68_clean",
// "mode": "LRHR",
// "dataroot_HR": "/media/sdc/jwhe/BasicSR_v2/data/CBSD68/mod2/CBSD68_mod",
// "dataroot_LR": "/media/sdc/jwhe/BasicSR_v2/data/CBSD68/mod2/CBSD68_mod"
// }
// , "val": {
// "name": "val_LIVE1_gray_JEPG10",
// "mode": "LRHR",
// "dataroot_HR": "/media/hjw/jwhe/BasicSR_v2/data/val/LIVE1_val/LIVE1_gray_mod",
// "dataroot_LR": "/media/hjw/jwhe/BasicSR_v2/data/val/LIVE1_val/LIVE1_gray_jpg10"
// }
// , "val": {
// "name": "val_LIVE1_JEPG80",
// "mode": "LRHR",
// "dataroot_HR": "/media/hjw/jwhe/BasicSR_v2/data/val/LIVE1_val/LIVE1_mod",
// "dataroot_LR": "/media/hjw/jwhe/BasicSR_v2/data/val/LIVE1_val/LIVE1_jpg80"
// }
// , "val_2": {
// "name": "val_Classic5_gray_JEPG30",
// "mode": "LRHR",
// "dataroot_HR": "/media/sdc/jwhe/BasicSR_v2/data/val/Classic5_val/classic5_mod",
// "dataroot_LR": "/media/sdc/jwhe/BasicSR_v2/data/val/Classic5_val/classic5_jpg30"
// }
// , "val": {
// "name": "val_BSD68_gray_Gaussian50",
// "mode": "LRHR",
// "dataroot_HR": "/media/sdc/jwhe/BasicSR_v2/data/BSD68/mod2/BSD68_mod",
// "dataroot_LR": "/media/sdc/jwhe/BasicSR_v2/data/BSD68/mod2/BSD68_gray_Gaussian50"
// }
// , "val": {
// "name": "val_set5_x4_gray_mod4"
// , "mode": "LRHR"
// , "dataroot_HR": "/media/sdc/jwhe/BasicSR_v2/data/val/Set5_val/mod4/Set5_gray_mod4"
// , "dataroot_LR": "/media/sdc/jwhe/BasicSR_v2/data/val/Set5_val/mod4/Set5_gray_bicx4"
// }
//
// , "val": {
// "name": "val_set5_x45_mod18",
// "mode": "LRHR",
// "dataroot_HR": "/media/sdc/jwhe/BasicSR_v2/data/val/Set5_val/mod18/Set5_mod18",
// "dataroot_LR": "/media/sdc/jwhe/BasicSR_v2/data/val/Set5_val/mod18/Set5_bicx45"
// }
// , "val": {
// "name": "val_set5_x3_mod6"
// , "mode": "LRHR"
// , "dataroot_HR": "/media/sdc/jwhe/BasicSR_v2/data/val/Set5_val/mod6/Set5_mod6"
// , "dataroot_LR": "/media/sdc/jwhe/BasicSR_v2/data/val/Set5_val/mod6/Set5_bicx3"
// }
// }
, "val": {
"name": "SET5",
"mode": "LRHR",
"dataroot_HR": "/home/guanwp/BasicSR_datasets/val_set5/Set5",
"dataroot_LR": "/home/guanwp/BasicSR_datasets/val_set5/Set5_sub_bicLRx4_noiseALL"
}
//
// , "val": {
// "name": "val_set5_x3_gray_mod6"
// , "mode": "LRHR"
// , "dataroot_HR": "/media/sdc/jwhe/BasicSR_v2/data/val/Set5_val/mod6/Set5_gray_mod6"
// , "dataroot_LR": "/media/sdc/jwhe/BasicSR_v2/data/val/Set5_val/mod6/Set5_gray_bicx3"
// }
}
, "path": {
"root": "/home/guanwp/BasicSR-master/"
// , "pretrain_model_G": "../experiments/pretrained_models/sr_c16s06/LR_srx4_c16s06_resnet_denoise_DIV2K/c16s06_basicmodel_704000.pth"
, "pretrain_model_G": "/home/guanwp/BasicSR-master/experiments2/sr_resnet_x4_baesline/models/latest_G.pth"
// , "pretrain_model_G": "../experiments/pretrained_models/noise_c16s06/bicx4_nonorm_denoise_resnet_DIV2K/c16s06_basicmodel_992000.pth"
// , "pretrain_model_G": "../experiments/pretrained_models/sr_c16s06/LR_srx4_c16s06_resnet_denoise_DIV2K/c16s06_basicmodel_704000.pth"
// , "pretrain_model_G": "../noise_from15to75/experiments/gaussian_from15to75_resnet_denoise_DIV2K/models/986000_G.pth"
// , "pretrain_model_G": "../experiments/pretrained_models/noise_from15to75/gaussian_from15to75_resnet_denoise_DIV2K/basic_model_986000.pth"
// , "pretrain_model_sub": "../noise_from15to75/experiments/gaussion_from15to75_subnet_DIV2K/models/596000_G.pth"
, "pretrain_model_sub": "/home/guanwp/BasicSR-master/experiments/noise_estimation_gt_1e-3/models/203000_G.pth",
"experiments_root": "/home/guanwp/BasicSR-master/experiments/",
"models": "/home/guanwp/BasicSR-master/experiments/finetune_srresnet_dncnn_DIVIK800/models",
"log": "/home/guanwp/BasicSR-master/experiments/finetune_srresnet_dncnn_DIVIK800",
"val_images": "/home/guanwp/BasicSR-master/experiments/finetune_srresnet_dncnn_DIVIK800/val_images"
}
, "network_G": {
"which_model_G": "sr_resnet" // RRDB_net | sr_resnet
// , "norm_type": "adaptive_conv_res"
, "norm_type": null//"sft"
, "mode": "CNA"
, "nf": 64
, "nb": 16
, "in_nc": 3
, "out_nc": 3
// , "gc": 32
, "group": 1
, "gate_conv_bias": false
}
// , "network_G": {
// "which_model_G": "denoise_resnet" // RRDB_net | sr_resnet
//// , "norm_type": "adaptive_conv_res"
// , "norm_type": null
// , "mode": "CNA"
// , "nf": 64
// , "nb": 16
// , "in_nc": 6
// , "out_nc": 3
//// , "gc": 32
// , "group": 1
// , "down_scale": 2
// }
////
, "network_sub": {
"which_model_sub": "noise_estimation" // RRDB_net | sr_resnet | modulate_denoise_resnet |noise_subnet
// , "norm_type": "adaptive_conv_res"
, "norm_type": null//"batch"
, "mode": "CNA"
, "nf": 64
// , "nb": 16
, "in_nc": 3
, "out_nc": 3
, "group": 1
}
, "train": {
// "lr_G": 1e-3
"lr_G": 1e-4
, "lr_scheme": "MultiStepLR"
// , "lr_steps": [200000, 400000, 600000, 800000]
, "lr_steps": [500000]
// , "lr_steps": [600000]
// , "lr_steps": [1000000]
// , "lr_steps": [50000, 100000, 150000, 200000, 250000]
// , "lr_steps": [100000, 200000, 300000, 400000]
, "lr_gamma": 0.2
// , "lr_gamma": 0.5
, "pixel_criterion_basic": "l1"
, "pixel_criterion_noise": "l2"
, "pixel_weight_basic": 1.0
, "pixel_weight_noise": 1.0
, "val_freq": 1e3
, "manual_seed": 0
, "niter": 1e6
// , "niter": 6e5
}
, "logger": {
"print_freq": 200
, "save_checkpoint_freq": 1e3
}
}
结果如下图所示
