I used 2 methods to build a U-Net in Pytorch. But the performance of the 2 nets are different. I want to know if the 2 methods are the same or not?
First method to build U-Net:
class UNet_1(nn.Module):
  def __init__(self):
  ......
  def forward(self, x):
            x0 = self.conv_0(x)

        x1 = self.down_1(x0)
        x2 = self.down_2(x1)
        x3 = self.down_3(x2)
        x4 = self.down_4(x3)

        u4 = self.upcat_4(x4, x3)
        u3 = self.upcat_3(u4, x2)
        u2 = self.upcat_2(u3, x1)
        u1 = self.upcat_1(u2, x0)

        logits = self.final_conv(u1)
        return logits
  

net = UNet_1()

Second method to build U-Net:
class Encoder(nn.Module):
  def __init__(self):
  ......
  def forward(self, x):
    x0 = self.conv_0(x)
    x1 = self.down_1(x0)
    x2 = self.down_2(x1)
    x3 = self.down_3(x2)
    x4 = self.down_4(x3)
    return x0, x1, x2, x3, x4

class Decoder(nn.Module):
  def __init__(self):
  ......
  def forward(self, x0, x1, x2, x3, x4):
    u4 = self.upcat_4(x4, x3)
    u3 = self.upcat_3(u4, x2)
    u2 = self.upcat_2(u3, x1)
    u1 = self.upcat_1(u2, x0)

    logits = self.final_conv(u1)
    return logits


class UNet_2(nn.Module):
    def __init__(self, enc, dec):
        super().__init__()
        self.enc = enc
        self.dec = dec

    def forward(self, x):
        x0, x1, x2, x3, x4 = self.enc(x)
        out = self.dec(x4)
        return out

enc = Encoder()
dec = Decoder()
unet= UNet_2(enc, dec)

The training would be like:
opt = torch.optim.Adam(unet.parameters(), lr=1e-4)
x, y = dataloader()
pred = unet(x)
loss = cretera(pred, y)
opt.zero_grad()
loss.backward()
opt.step()

I found the second method cannot work normally according to the results. Because during the training of the second method I found the training loss decreased in the first several epochs, but increased again afterwards. finally the validation dice result is 0. I donot know why. Maybe because of net.parameters()? Any ideads would be appreciated.