Pytorch: RuntimeError: result type Float can't be cast to the desired output type Long

Question

我有一个模型，如下所示：

IMG_WIDTH = IMG_HEIGHT = 224

class AlexNet(nn.Module):
  def __init__(self, output_dim):
    super(AlexNet, self).__init__()
    self._to_linear = None
    self.x = torch.randn(3, IMG_WIDTH, IMG_HEIGHT).view(-1, 3, IMG_WIDTH, IMG_HEIGHT)
    self.features = nn.Sequential(
        nn.Conv2d(3, 64, 3, 2, 1), # in_channels, out_channels, kernel_size, stride, padding
        nn.MaxPool2d(2),
        nn.ReLU(inplace=True),
        nn.Conv2d(64, 192, 3, padding=1),
        nn.MaxPool2d(2),
        nn.ReLU(inplace=True), 
        nn.Conv2d(192, 384, 3, padding=1),
        nn.MaxPool2d(2),
        nn.ReLU(inplace=True), 
        nn.Conv2d(384, 256, 3, padding=1),
        nn.MaxPool2d(2),
        nn.ReLU(inplace=True),
        nn.Conv2d(256, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 256, 3, padding=1),
        nn.MaxPool2d(2),
        nn.ReLU(inplace=True)
  )
    self.conv(self.x)
    self.classifier = nn.Sequential(
        nn.Dropout(.5),
        nn.Linear(self._to_linear, 4096),
        nn.ReLU(inplace=True),
        nn.Dropout(.5),
        nn.Linear(4096, 4096),
        nn.ReLU(inplace=True),
        nn.Linear(4096, output_dim),
    )

  def conv(self, x):
    x = self.features(x)
    if self._to_linear is None:
        self._to_linear = x.shape[1] * x.shape[2] * x.shape[3]
    return x

  def forward(self, x):
    x = self.conv(x)
    h = x.view(x.shape[0], -1)
    x = self.classifier(h)
    return x, h

这是我的优化器和损失函数：

optimizer = torch.optim.Adam(model.parameters())
criterion = nn.BCEWithLogitsLoss().to(device)

这是我的 train 和 evaluate 函数：

def train(model, iterator, optimizer, criterion, device):
  epoch_loss, epoch_acc = 0, 0
  model.train()
  for (x, y) in iterator:
    # features and labels to the device
    x = x.to(device)
    y = y.to(device).long()
    # Zero the gradients
    optimizer.zero_grad()
    y_pred, _ = model(x)
  
    # Calculate the loss and accuracy
    loss = criterion(y_pred.squeeze(), y)
    acc = binary_accuracy(y_pred, y)
    # Backward propagate
    loss.backward()
    # Update the weights
    optimizer.step()

    epoch_loss +=loss.item()
    epoch_acc += acc.item()

  return epoch_loss/len(iterator), epoch_acc/len(iterator)

def evaluate(model, iterator, criterion, device):
  epoch_loss, epoch_acc = 0, 0
  model.eval()
  with torch.no_grad():
    for (x, y) in iterator:
      x = x.to(device)
      y = y.to(device).long()
      y_pred, _ = model(x)
      loss = criterion(y_pred, y)
      acc = binary_accuracy(y_pred, y)

      epoch_loss += loss.item()
      epoch_acc += acc.item()
  return epoch_loss/len(iterator), epoch_acc/len(iterator)

这是我遇到的错误：

RuntimeError: result type Float can't be cast to the desired output type Long

可能是我的问题，因为我已尝试将标签转换为 long 张量，如下所示：

y = y.to(device).long()

不过好像不行。

Answer 1

我在执行此操作时遇到了同样的错误：

loss_fn(output, target)

其中 output 是张量 torch.float32 和 target 是 Tensor torch.int64。解决这个问题的方法是这样调用损失函数：

loss_fn(output, target.float())

Pytorch: RuntimeError: result type Float can't be cast to the desired output type Long

Pytorch: RuntimeError: result type Float can't be cast to the desired output type Long

python

deep-learning

pytorch