model.py 文件源码

def forward(self,x):
        output = self.Scale(x) # for original scale
        output_size = output.size()[2]
        input_size = x.size()[2]

        self.interp1 = nn.Upsample(size=(int(input_size*0.75)+1, int(input_size*0.75)+1), mode='bilinear')
        self.interp2 = nn.Upsample(size=(int(input_size*0.5)+1, int(input_size*0.5)+1), mode='bilinear')
        self.interp3 = nn.Upsample(size=(output_size, output_size), mode='bilinear')

        x75 = self.interp1(x)
        output75 = self.interp3(self.Scale(x75)) # for 0.75x scale

        x5 = self.interp2(x)
        output5 = self.interp3(self.Scale(x5))  # for 0.5x scale

        out_max = torch.max(torch.max(output, output75), output5)
        return [output, output75, output5, out_max]