如何在教育模式培训中处理NotImplementedError？

Question

def train_fn(data_loader, model, optimizer):

model.train()
total_loss = 0.0

for images, masks in tqdm(data_loader):

  images = images.to(DEVICE)
  masks = masks.to(DEVICE)

  optimizer.zero_grad()
  logits, loss = model(images,masks)
  loss.backward()
  optimizer.step()

  total_loss += loss.item()



return total_loss/ len(data_loader)


def eval_fn(data_loader, model):

model.eval()
total_loss = 0.0

with torch.no_grad():

  for images, masks in tqdm(data_loader):

    images = images.to(DEVICE)
    masks = masks.to(DEVICE)

    logits, loss = model(images,masks)


    total_loss += loss.item()


return total_loss/ len(data_loader)

optimizer = torch.optim.Adam(model.parameters(), lr = LR)

best_valid_loss = np.Inf

for i in range(EPOCHS):


train_loss = train_fn(trainloader, model, optimizer)
valid_loss = eval_fn(validloader, model)

if valid_loss < best_valid_loss:
  torch.save(model.state_dict(), 'best_model.pt')
  print("SAVED_MODEL")
  best_valid_loss = valid_loss

打印(f“时代：{i+1} Train_loss：{train_loss} Valid_loss：{valid_loss}")

当我试图训练模型时，我得到了以下错误：

0%\x{e76f} 0/15 00:00<？

NotImplementedError跟踪(最近一次调用) in () 4 5->6 train_loss =train_fn(列车装载器、模型、优化器)7 valid_loss =eval_fn(验证加载程序，模型)8

2帧/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py在_forward_unimplemented(self，*input) 199注册钩子，而后者无声地忽略它们。200“”-> 201 #举重NotImplementedError 202 203

NotImplementedError：

我该怎么处理呢？

Answer 1

查看注释中提供的链接，您的模型定义如下所示：

class SegmentationModel(nn.Module):

  def __init__(self):
    super(SegmentationModel,self).__init__()

    self.arc = smp.Unet(
        encoder_name = ENCODER,
        encoder_weights = WEIGHTS,
        in_channels = 3,
        classes = 1,
        activation = None
    )

    def forward(self, images, masks = None):
      logits = self.arc(images)

      if masks != None:
        loss1 = DiceLoss(mode = 'binary')(logits, masks)
        loss2 = nn.BCEWithLogitsLoss()(logits,masks)
        return logits, loss1 + loss2

      return logits

如果您仔细观察，您将看到forward()有一个不规则的额外缩进，使它成为__init__()内部的一个函数，而不是SegmentationModel的一个方法。把它往左移一点，它应该工作得很好：

class SegmentationModel(nn.Module):

  def __init__(self):
    super(SegmentationModel,self).__init__()

    self.arc = smp.Unet(
        encoder_name = ENCODER,
        encoder_weights = WEIGHTS,
        in_channels = 3,
        classes = 1,
        activation = None
    )

  def forward(self, images, masks = None):
    logits = self.arc(images)

    if masks != None:
      loss1 = DiceLoss(mode = 'binary')(logits, masks)
      loss2 = nn.BCEWithLogitsLoss()(logits,masks)
      return logits, loss1 + loss2

    return logits