不同loss function之間的對比（基於FSRCNN）
阿新 • • 發佈：2018-11-30
對於L2、huber和Cross三種不同的損失函式形式進行測試。（之前都是用L1）
將SR_model.py程式碼修改如下：
# loss loss_type = train_opt['pixel_criterion'] if loss_type == 'l1': self.cri_pix = nn.L1Loss().to(self.device) elif loss_type == 'l2': self.cri_pix = nn.MSELoss().to(self.device) ####################################################################### elif loss_type=='huber': self.cri_pix = nn.SmoothL1Loss().to(self.device) elif loss_type=='Cross': self.cri_pix = nn.CrossEntropyLoss().to(self.device) ####################################################################3### else: raise NotImplementedError('Loss type [{:s}] is not recognized.'.format(loss_type))
放大兩倍
#######################################################################################################3 #FSRCNN class FSRCNN(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(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=1) 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 ##########################################################################################################
結果對比如下：
在初始的時候，huber function的效果比L2的要好，或者說比L2的PSNR上升得更快。baseline中採用得為L1，那麼上升得效果也是較差得~
結果如下圖所示
cross function會報錯。。。。
由此看來，如果採用cross需要改比較多的資料結構。。。為此這裡就不展開了。。。。
一個不錯得pytorch手冊