FSRCNN四倍放大

版权声明： https://blog.csdn.net/gwplovekimi/article/details/83993394

代码：

#######################################################################################################3
#FSRCNN
class FSRCNN(nn.Module):
    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'):##play attention the upscales
        super(FSRCNN,self).__init__()
        #Feature extractionn
        self.conv1=nn.Conv2d(in_channels=in_nc,out_channels=nf,kernel_size=5,stride=1,padding=2)#nf=56.add padding ,make the data alignment
        self.prelu1=nn.PReLU()
 
        #Shrinking
        self.conv2=nn.Conv2d(in_channels=nf,out_channels=12,kernel_size=1,stride=1,padding=0)
        self.prelu2 = nn.PReLU()
 
        # Non-linear Mapping
        self.conv3=nn.Conv2d(in_channels=12,out_channels=12,kernel_size=3,stride=1,padding=1)
        self.prelu3 = nn.PReLU()
        self.conv4=nn.Conv2d(in_channels=12,out_channels=12,kernel_size=3,stride=1,padding=1)
        self.prelu4 = nn.PReLU()
        self.conv5=nn.Conv2d(in_channels=12,out_channels=12,kernel_size=3,stride=1,padding=1)
        self.prelu5 = nn.PReLU()
        self.conv6=nn.Conv2d(in_channels=12,out_channels=12,kernel_size=3,stride=1,padding=1)
        self.prelu6 = nn.PReLU()
 
        # Expanding
        self.conv7=nn.Conv2d(in_channels=12,out_channels=nf,kernel_size=1,stride=1,padding=0)
        self.prelu7 = nn.PReLU()
 
        # Deconvolution
        self.last_part= nn.ConvTranspose2d(in_channels=nf,out_channels=in_nc,kernel_size=9,stride=upscale, padding=4, output_padding=3)
 
 
    def forward(self, x):#
         out = self.prelu1(self.conv1(x))
         out = self.prelu2(self.conv2(out))
         out = self.prelu3(self.conv3(out))
         out = self.prelu4(self.conv4(out))
         out = self.prelu5(self.conv5(out))
         out = self.prelu6(self.conv6(out))
         out = self.prelu7(self.conv7(out))
         out = self.last_part(out)
 
         return out
 
 
##########################################################################################################

setting跟《基于pytorch的FSRCNN》一样

结果（原文为30.55）

训练了一个多小时就达到原文得效果哈~

（四个不同得测试集得结果）