代码的框架仍然是——《https://github.com/xinntao/BasicSR》

原理

先再次缕一缕SRGAN的loss。本部分主要是对于《Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial
Network》第二部分的理解

SRGAN的目标就是利用G网络来作为SR网络。所以目标就是要训练一个好的G网络。这是SR网络的loss function

为G网络；

首先介绍Adversarial network architecture

The general idea behind this formulation is that it allows one to train a generative model G with the goal of fooling a differentiable discriminator D that is trained to distinguish super-resolved images from real images.G网络生成让D网络以为是真的超分的图片。

This encourages perceptually superior solutions residing in the subspace, the manifold, of natural images.这种做法可以产生更加natural的图片。

G网络和D网络的结构如下图所示

对于Perceptual loss——就是SR的loss，是用于评判G网络的性能的。

Content loss——内容上的损失

对于基于像素维度的MSE loss，就是通过下面公式来计算的。大部分的超分算法（非GAN）都是采用这个，正如本人的其他博文提到的那样，这样的loss会使得SR结果过平滑

而本文不采用这种loss，本文定义了一种称为VGG的loss

Adversarial loss——对抗损失。This encourages our network to favor solutions that reside on the manifold of natural images

理解如下，一般对于SR任务，loss会分为三种。MSE为代表的loss、perceptual loss，以及GAN的loss（Adversarial loss）。而GAN的loss就是用来训练G网络的loss，而perceptual loss就是G网络用于SR任务上，用于评估G网络的性能的loss。

训练过程的理解：

LR输入G网络，G网络输出一个SR的结果，SR跟HR在D网络上得到一个对抗损失（Adversarial loss）；SR跟HR在VGG19上上得到contest loss（或者一般现在会把他称为perceptual loss）Content loss损失（或者一般现在会把他称为感知损失Perceptual loss）。两个loss同时作为优化的函数，故此会有原文中的

代码

好接下来给出代码的注释

python train.py -opt options/train/train_esrgan.json

python test.py -opt options/test/test_esrgan.json

先给出setting

{
  "name": "SRGAN_x4_DIV2K" //  please remove "debug_" during training
  , "use_tb_logger": true
  , "model":"srgan"
  , "scale": 4
  , "gpu_ids": [3,4,5]

  , "datasets": {
    "train": {
      "name": "DIV2K"
      , "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
      , "HR_size": 128
      , "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/SRGAN_x4_DIV2K/models",
    "log": "/home/guanwp/BasicSR-master/experiments/SRGAN_x4_DIV2K",
    "val_images": "/home/guanwp/BasicSR-master/experiments/SRGAN_x4_DIV2K/val_images"
  }

  , "network_G": {
    "which_model_G": "sr_resnet" // RRDB_net | sr_resnet
    , "norm_type": null
    , "mode": "CNA"
    , "nf": 64
    , "nb": 16// number of residual block
    , "in_nc": 3
    , "out_nc": 3
    , "gc": 32
    , "group": 1
  }
  , "network_D": {
    "which_model_D": "discriminator_vgg_128"
    , "norm_type": "batch"
    , "act_type": "leakyrelu"
    , "mode": "CNA"
    , "nf": 64
    , "in_nc": 3
  }

  , "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": 0//1e-2//just for the NIQE, you should set to 0
    , "feature_criterion": "l1"
    , "feature_weight": 1
    , "gan_type": "vanilla"
    , "gan_weight": 5e-3

    //for wgan-gp
     , "D_update_ratio": 1//for the D network
     , "D_init_iters": 0
    // , "gp_weigth": 10

    , "manual_seed": 0
    , "niter": 6e5//5e5
    , "val_freq": 2000//5e3
  }

  , "logger": {
    "print_freq": 200
    , "save_checkpoint_freq": 5e3
  }
}

先开始实验

下面解读代码

见setting中采用srgan，为此打开__init__.py文件，可以发现应该阅读SRGAN_model.py文件

def create_model(opt):
    model = opt['model']##this para is came from the .json file
    
    #the model in jason, decided which modl import
    #so if you add a new model, this .py must be modified
    if model == 'sr':###this is the SR model
        from .SR_model import SRModel as M#take sr as an example
    elif model == 'srgan':###this is the SRGAN
        from .SRGAN_model import SRGANModel as M
    elif model == 'srragan':
        from .SRRaGAN_model import SRRaGANModel as M
    elif model == 'sftgan':
        from .SFTGAN_ACD_model import SFTGAN_ACD_Model as M
    else:
        raise NotImplementedError('Model [{:s}] not recognized.'.format(model))
    m = M(opt)
    print('Model [{:s}] is created.'.format(m.__class__.__name__))
    return m#return the model

G网络的结构。此处的G网络也可以直接采用之前博文中给出的结构（基于pytorch的SRResNet的复现）

#####################SRResNet########################################################
class SRResNet(nn.Module):#read my CSDN for the nn.Module
#nn.Module is contain the forward and each layyer
    def __init__(self, in_nc, out_nc, nf, nb, upscale=4, norm_type='batch', act_type='relu', \
            mode='NAC', res_scale=1, upsample_mode='upconv'):#the .jason file decide the mode is "CNA"
        #input channels\output channels\the number of filters in the first layer\thw number of resduial block\upscale\   \relu\Conv -> Norm -> Act\
        super(SRResNet, self).__init__()#for the super(),read my CSDN
        n_upscale = int(math.log(upscale, 2))
        if upscale == 3:
            n_upscale = 1

        fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None)#read the block.py.
        resnet_blocks = [B.ResNetBlock(nf, nf, nf, norm_type=norm_type, act_type=act_type,\
            mode=mode, res_scale=res_scale) for _ in range(nb)]#'nb' is the number of block, and there is 23 in the .jason
        LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)#here use the BN

        if upsample_mode == 'upconv':
            upsample_block = B.upconv_blcok##Deconvolution
        elif upsample_mode == 'pixelshuffle':##there are 'pixelshuffle' in the network.py
            upsample_block = B.pixelshuffle_block##the espcn
        else:
            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
        if upscale == 3:
            upsampler = upsample_block(nf, nf, 3, act_type=act_type)
        else:
            upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)]
        HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
        HR_conv1 = B.conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None)

        self.model = B.sequential(fea_conv, B.ShortcutBlock(B.sequential(*resnet_blocks, LR_conv)),\
            *upsampler, HR_conv0, HR_conv1)

    def forward(self, x):
        x = self.model(x)
        return x

D网络的结构

# VGG style Discriminator with input size 128*128
class Discriminator_VGG_128(nn.Module):
    def __init__(self, in_nc, base_nf, norm_type='batch', act_type='leakyrelu', mode='CNA'):
        super(Discriminator_VGG_128, self).__init__()
        # features
        # hxw, c
        # 128, 64
        conv0 = B.conv_block(in_nc, base_nf, kernel_size=3, norm_type=None, act_type=act_type, \
            mode=mode)
        conv1 = B.conv_block(base_nf, base_nf, kernel_size=4, stride=2, norm_type=norm_type, \
            act_type=act_type, mode=mode)
        # 64, 64
        conv2 = B.conv_block(base_nf, base_nf*2, kernel_size=3, stride=1, norm_type=norm_type, \
            act_type=act_type, mode=mode)
        conv3 = B.conv_block(base_nf*2, base_nf*2, kernel_size=4, stride=2, norm_type=norm_type, \
            act_type=act_type, mode=mode)
        # 32, 128
        conv4 = B.conv_block(base_nf*2, base_nf*4, kernel_size=3, stride=1, norm_type=norm_type, \
            act_type=act_type, mode=mode)
        conv5 = B.conv_block(base_nf*4, base_nf*4, kernel_size=4, stride=2, norm_type=norm_type, \
            act_type=act_type, mode=mode)
        # 16, 256
        conv6 = B.conv_block(base_nf*4, base_nf*8, kernel_size=3, stride=1, norm_type=norm_type, \
            act_type=act_type, mode=mode)
        conv7 = B.conv_block(base_nf*8, base_nf*8, kernel_size=4, stride=2, norm_type=norm_type, \
            act_type=act_type, mode=mode)
        # 8, 512
        conv8 = B.conv_block(base_nf*8, base_nf*8, kernel_size=3, stride=1, norm_type=norm_type, \
            act_type=act_type, mode=mode)
        conv9 = B.conv_block(base_nf*8, base_nf*8, kernel_size=4, stride=2, norm_type=norm_type, \
            act_type=act_type, mode=mode)
        # 4, 512
        self.features = B.sequential(conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\
            conv9)

        # classifier
        self.classifier = nn.Sequential(
            nn.Linear(512 * 4 * 4, 100), nn.LeakyReLU(0.2, True), nn.Linear(100, 1))

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x