错误：在训练训练 CNN 模型时，维度超出范围（预计在 [-1, 0] 范围内，但得到 1）

原文标题 ：error: Dimension out of range (expected to be in range of [-1, 0], but got 1) when training to train a CNN model

我正在尝试遵循使用 resnet18 来训练二进制分类模型的代码片段，因为我应该训练多分类模型，我通过更改损失函数、激活函数对代码进行了一些修改

import torchmetrics

class CheastCancer(pl.LightningModule):

  def __init__(self,init_weights=True):
    super().__init__()

    self.model = torchvision.models.resnet18()
    self.model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    self.model.fc = torch.nn.Linear(in_features=512, out_features=3, bias=True)

    self.optimizer = torch.optim.Adam(self.model.parameters(), lr = 1e-4)
    self.loss_fn = torch.nn.CrossEntropyLoss()

    self.train_acc = torchmetrics.Accuracy()
    self.val_acc = torchmetrics.Accuracy()

  def forward(self, data):
    pred = self.model(data)
    return pred

  def training_step(self, batch, batch_idx):
    img, label = batch
    label = label.float()
    pred = self(img)[:,0]
    loss = self.loss_fn(pred,label)

    self.log("Train Loss", loss)
    self.log("Step Train ACC", self.train_acc(torch.softmax(pred), label.int()))

    return loss
  
  def training_epoch_end(self, outs):
    self.log("Train ACC", self.train_acc.compute())

  def validation_step(self, batch, batch_idx):
    img, label = batch
    label = label.float()
    pred = self(img)[:,0]
    loss = self.loss_fn(pred,label)

    self.log("Train Loss", loss)
    self.log("Step Train ACC", self.val_acc(torch.poisson(pred), label.int()))

    return loss
  
  def training_epoch_end(self, outs):
    self.log("Train ACC", self.val_acc.compute())

  def configure_optimizers(self):
      return [self.optimizer]

但是，我收到以下错误消息：

LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

  | Name      | Type             | Params
-----------------------------------------------
0 | model     | ResNet           | 11.2 M
1 | loss_fn   | CrossEntropyLoss | 0     
2 | train_acc | Accuracy         | 0     
3 | val_acc   | Accuracy         | 0     
-----------------------------------------------
11.2 M    Trainable params
0         Non-trainable params
11.2 M    Total params
44.687    Total estimated model params size (MB)
Sanity Checking:
0/? [00:00<?, ?it/s]
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:245: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  category=PossibleUserWarning,
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-132-5af489ac4112> in <module>()
----> 1 trainer.fit(model, train_loader, val_loader)

17 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)
   2844     if size_average is not None or reduce is not None:
   2845         reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2846     return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
   2847 
   2848 

IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

从我到目前为止的搜索来看，当标签的维度与预测不同时，通常会出现此错误。但我不确定我该如何解决？有人可以帮忙吗。