目前，我正在尝试复制一个De模糊GanV2网络。目前，我正在执行培训。以下是我的培训管道的当前状态：

from torch.autograd import Variable
torch.autograd.set_detect_anomaly(mode=True)
total_generator_loss = 0
total_discriminator_loss = 0
psnr_score = 0.0
used_loss_function = 'wgan_gp_loss'
for epoch in range(n_epochs):

      #set to train mode
      generator.train(); discriminator.train()
      tqdm_bar = tqdm(train_loader, desc=f'Training Epoch {epoch} ', total=int(len(train_loader)))
      for batch_idx, imgs in enumerate(tqdm_bar):
        
        #load imgs to cpu
        blurred_images = imgs["blurred"].cuda()
        sharped_images = imgs["sharp"].cuda()
        
        # generator output
        deblurred_img = generator(blurred_images)
    
        # denormalize
        with torch.no_grad():
          denormalized_blurred = denormalize(blurred_images)
          denormalized_sharp = denormalize(sharped_images)
          denormalized_deblurred = denormalize(deblurred_img)
    
        # get D's output
        sharp_discriminator_out = discriminator(sharped_images)
        deblurred_discriminator_out = discriminator(deblurred_img)
    
        # set critic_updates
        if used_loss_function== 'wgan_gp_loss':
          critic_updates = 5
        else:
            critic_updates = 1
    
        #train discriminator
        discriminator_loss = 0
        for i in range(critic_updates):
          discriminator_optimizer.zero_grad()
          # train discriminator on real and fake
          if used_loss_function== 'wgan_gp_loss':
            gp_lambda = 10
            alpha = random.random()
            interpolates = alpha * sharped_images + (1 - alpha) * deblurred_img
            interpolates_discriminator_out = discriminator(interpolates)
            kwargs = {'gp_lambda': gp_lambda,
                       'interpolates': interpolates,
                       'interpolates_discriminator_out': interpolates_discriminator_out,
                       'sharp_discriminator_out': sharp_discriminator_out,
                       'deblurred_discriminator_out': deblurred_discriminator_out
                        }
            wgan_loss_d, gp_d = wgan_gp_loss('D', **kwargs)
            discriminator_loss_per_update = wgan_loss_d + gp_d
    
          discriminator_loss_per_update.backward(retain_graph=True)
          discriminator_optimizer.step()
          discriminator_loss += discriminator_loss_per_update.item()

但是当我运行此代码时，我会收到以下错误消息：

RuntimeError：梯度计算所需的变量之一已被就地操作修改：[torch. cuda.FloatTensor[1,512,4,4]]处于版本2；而不是预期的版本1。提示：上面的回溯显示了未能计算其梯度的操作。有问题的变量在那里或以后的任何地方都被更改了。祝你好运！

RuntimeError Traceback（最近一次调用）in（）62#discriminator_loss_per_update=gan_loss_d63-

1帧 /usr/local/lib/python3.7/dist-packages/torch/tensor.py向后（自我，梯度，retain_graph，create_graph，输入）243create_graph=create_graph，244输入=输入）→245 torch. autograd.back（自我，梯度，retain_graph，create_graph，输入=输入）246 247 defregister_hook（自我，挂钩）：

/usr/local/lib/python3.7/dist-packages/torch/autograd/init.py（张量，grad_tensors，retain_graph，create_graph，grad_variables，输入）145变量。execution_engine.run_backward（146张量，grad_tensors，retain_graph，create_graph，输入，→147allow_unreachable=True，accumulate_grad=True）#allow_unreachable标志148 149

不幸的是，我无法真正跟踪会导致此错误的就地操作。有人可能对我有想法或建议吗？我将感谢任何输入：slight_smile：