版权声明： https://blog.csdn.net/gwplovekimi/article/details/84622539
在之前的博客中已经介绍了ESPCN的原理了（学习笔记之——基于深度学习的图像超分辨率重构）本博文是对ESPCN进行实现。代码的框架仍然采用xintao前辈的代码，本博文会给出关键的实现部分
python train.py -opt options/train/train_sr.json
python test.py -opt options/test/test_sr.json
ESPCN的网络结构
代码
network.py
# Generator
def define_G(opt):
gpu_ids = opt['gpu_ids']
opt_net = opt['network_G']
which_model = opt_net['which_model_G']#hear decide which model, and thia para is in .json. if you add a new model, this part must be modified
if which_model == 'sr_resnet': # SRResNet
netG = arch.SRResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'], \
nb=opt_net['nb'], upscale=opt_net['scale'], norm_type=opt_net['norm_type'], \
act_type='relu', mode=opt_net['mode'], upsample_mode='pixelshuffle')
#############################################################################################################
elif which_model=='fsrcnn':#FSRCNN
netG=arch.FSRCNN(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'], \
nb=opt_net['nb'], upscale=opt_net['scale'], norm_type=opt_net['norm_type'], \
act_type='relu', mode=opt_net['mode'], upsample_mode='pixelshuffle')
#############################################################################################################
#############################################################################################################
elif which_model=='espcn':#ESPCN
netG=arch.ESPCN(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'], \
nb=opt_net['nb'], upscale=opt_net['scale'], norm_type=opt_net['norm_type'], \
act_type='relu', mode=opt_net['mode'], upsample_mode='pixelshuffle')
#############################################################################################################
#############################################################################################################
elif which_model=='srresnet':#SRResNet, the Original version
netG=arch.OSRRESNET(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'], \
nb=opt_net['nb'], upscale=opt_net['scale'], norm_type=opt_net['norm_type'], \
act_type='relu', mode=opt_net['mode'], upsample_mode='pixelshuffle')
#############################################################################################################
elif which_model == 'sft_arch': # SFT-GAN
netG = sft_arch.SFT_Net()
elif which_model == 'RRDB_net': # RRDB,this is ESRGAN
netG = arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
nb=opt_net['nb'], gc=opt_net['gc'], upscale=opt_net['scale'], norm_type=opt_net['norm_type'],
act_type='leakyrelu', mode=opt_net['mode'], upsample_mode='upconv')
else:
raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
if opt['is_train']:
init_weights(netG, init_type='kaiming', scale=0.1)###the weight initing. you can change this to change the method of init_weight
if gpu_ids:
assert torch.cuda.is_available()
netG = nn.DataParallel(netG)
return netG
architecture.py
#######################################################################################################3
#EPSCN
class ESPCN(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, upscale=2, norm_type='batch', act_type='relu', \
mode='NAC', res_scale=1, upsample_mode='upconv'):##play attention the upscales
super(ESPCN,self).__init__()
self.conv1=nn.Conv2d(in_channels=in_nc,out_channels=64,kernel_size=5,stride=1,padding=2)
self.conv2=nn.Conv2d(in_channels=64,out_channels=32,kernel_size=3,stride=1,padding=1)
self.conv3=nn.Conv2d(in_channels=32,out_channels=in_nc*(upscale ** 2),kernel_size=3,stride=1,padding=1)
self.pixel_shuffle=nn.PixelShuffle(upscale)
def forward(self, x):
out=F.tanh(self.conv1(x))
out=F.tanh(self.conv2(out))
out=F.sigmoid(self.pixel_shuffle(self.conv3(out)))
return outsetting
{
"name": "espcn_x4"//"001_RRDB_PSNR_x4_DIV2K" // please remove "debug_" during training or tensorboard wounld not work
, "use_tb_logger": true
, "model":"sr"
, "scale": 4
, "gpu_ids": [4]
, "datasets": {
"train": {
"name": "DIV2K800"
, "mode": "LRHR"
, "dataroot_HR": "/home/guanwp/BasicSR_datasets/DIV2K800_sub"
, "dataroot_LR": "/home/guanwp/BasicSR_datasets/DIV2K800_sub_bicLRx4"
, "subset_file": null
, "use_shuffle": true
, "n_workers": 8
, "batch_size": 16//how many samples in each iters
, "HR_size": 192 // 128 | 192
, "use_flip": true
, "use_rot": true
}
, "val": {
"name": "val_set5"
, "mode": "LRHR"
, "dataroot_HR": "/home/guanwp/BasicSR_datasets/val_set5/Set5"
, "dataroot_LR": "/home/guanwp/BasicSR_datasets/val_set5/Set5_sub_bicLRx4"
}
}
, "path": {
"root": "/home/guanwp/BasicSR-master",
"pretrain_model_G": null
,"experiments_root": "/home/guanwp/BasicSR-master/experiments/",
"models": "/home/guanwp/BasicSR-master/experiments/espcn_x4/models",
"log": "/home/guanwp/BasicSR-master/experiments/espcn_x4",
"val_images": "/home/guanwp/BasicSR-master/experiments/espcn_x4/val_images"
}
, "network_G": {
"which_model_G": "espcn"//"srresnet"//"sr_resnet"//"fsrcnn"//"sr_resnet" // RRDB_net | sr_resnet
, "norm_type": null
, "mode": "CNA"
, "nf": 64//56//64
, "nb": 23
, "in_nc": 3
, "out_nc": 3
, "gc": 32
, "group": 1
}
, "train": {
"lr_G": 1e-3//1e-3//2e-4
, "lr_scheme": "MultiStepLR"
, "lr_steps": [200000,400000,600000,800000,1000000,1500000]
, "lr_gamma": 0.5
, "pixel_criterion": "l1"//"l1"//'l2'//huber//Cross
, "pixel_weight": 1.0
, "val_freq": 5e3
, "manual_seed": 0
, "niter": 2e6//2e6//1e6
}
, "logger": {
"print_freq": 200
, "save_checkpoint_freq": 5e3
}
}
target
result
结果比原文的结果差一点点，感觉是由于学习率一开始太小带来的~