请问cyclegan训练时候 ，生成图片一直是白板。后来发现Dloss一直是0.5，Gloss则有相应改变，有知道怎么解决吗？

我的loss感觉没写错呢！
with torch.cuda.amp.autocast():
#train D
fake_horse = gen_H(zebra)
D_H_real = disc_H(horse)
D_H_fake = disc_H(fake_horse.detach())
# H_reals += D_H_real.mean().item()
# H_fakes += D_H_fake.mean().item()
D_H_real_loss = mse(D_H_real, torch.ones_like(D_H_real))
D_H_fake_loss = mse(D_H_fake, torch.zeros_like(D_H_fake))
D_H_loss = D_H_real_loss + D_H_fake_loss

        fake_zebra = gen_Z(horse)
        D_Z_real = disc_Z(zebra)
        D_Z_fake = disc_Z(fake_zebra.detach())
        D_Z_real_loss = mse(D_Z_real, torch.ones_like(D_Z_real))
        D_Z_fake_loss = mse(D_Z_fake, torch.zeros_like(D_Z_fake))
        D_Z_loss = D_Z_real_loss + D_Z_fake_loss
        
        D_loss = (D_H_loss + D_Z_loss)/2

opt_disc.zero_grad()
d_scaler.scale(D_loss).backward()
d_scaler.step(opt_disc)
d_scaler.update()

#train G
with torch.cuda.amp.autocast():
# adversarial loss for both generators
D_H_fake = disc_H(fake_horse)
D_Z_fake = disc_Z(fake_zebra)
loss_G_H = mse(D_H_fake, torch.zeros_like(D_H_fake))
loss_G_Z = mse(D_Z_fake, torch.zeros_like(D_Z_fake))

        # cycle loss
        cycle_zebra = gen_Z(fake_horse)
        cycle_horse = gen_H(fake_zebra)
        cycle_zebra_loss = l1(zebra, cycle_zebra)
        cycle_horse_loss = l1(horse, cycle_horse)

        # identity loss (remove these for efficiency if you set lambda_identity=0)
        identity_zebra = gen_Z(zebra)
        identity_horse = gen_H(horse)
        identity_zebra_loss = l1(zebra, identity_zebra)
        identity_horse_loss = l1(horse, identity_horse)

        # add all togethor
        G_loss = (
            loss_G_Z
            + loss_G_H
            + cycle_zebra_loss * config.LAMBDA_CYCLE
            + cycle_horse_loss * config.LAMBDA_CYCLE
            + identity_horse_loss * config.LAMBDA_IDENTITY
            + identity_zebra_loss * config.LAMBDA_IDENTITY
        )

opt_gen.zero_grad()
g_scaler.scale(G_loss).backward()
g_scaler.step(opt_gen)
g_scaler.update()

no pain no gan