OnepassModel.py 文件源码

def forward(self, input, inputV):
        x1 = F.leaky_relu(self.down1(input), 0.2, True)
        x2 = F.leaky_relu(self.down2(x1), 0.2, True)
        x3 = F.leaky_relu(self.down3(x2), 0.2, True)
        x4 = F.leaky_relu(self.down4(x3), 0.2, True)
        x5 = F.leaky_relu(self.down5(x4), 0.2, True)
        x6 = F.leaky_relu(self.down6(x5), 0.2, True)
        x7 = F.leaky_relu(self.down7(x6), 0.2, True)
        x8 = F.relu(self.down8(x7), True)

        v1 = F.leaky_relu(self.downV1(inputV), 0.2, True)
        v2 = F.leaky_relu(self.downV2(v1), 0.2, True)
        v3 = F.leaky_relu(self.downV3(v2), 0.2, True)
        v4 = F.leaky_relu(self.downV4(v3), 0.2, True)
        v5 = F.leaky_relu(self.downV5(v4), 0.2, True)
        v6 = F.leaky_relu(self.downV6(v5), 0.2, True)
        v7 = F.leaky_relu(self.downV7(v6), 0.2, True)
        v8 = F.relu(self.downV8(v7), True)

        x = F.relu(self.up8(torch.cat([x8, v8], 1)), True)
        x = F.relu(self.up7(torch.cat([x, x7, v7], 1)), True)
        x = F.relu(self.up6(torch.cat([x, x6, v6], 1)), True)
        x = F.relu(self.up5(torch.cat([x, x5, v5], 1)), True)
        x = F.relu(self.up4(torch.cat([x, x4, v4], 1)), True)
        x = F.relu(self.up3(torch.cat([x, x3, v3], 1)), True)
        x = F.relu(self.up2(torch.cat([x, x2], 1)), True)
        x = F.tanh(self.up1(torch.cat([x, x1], 1)))
        return x


############################
# D network
###########################