python - CNN Pytorch错误: Input type (torch. cuda.ByteTensor)和权重类型(torch.cuda.FloatTensor)应该相同

Question

我收到错误，

Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same

以下是我的代码，

device    = torch.device('cuda:0')

trainData = torchvision.datasets.FashionMNIST('/content/', train=True, transform=None, target_transform=None, download=True)
testData  = torchvision.datasets.FashionMNIST('/content/', train=False, transform=None, target_transform=None, download=True)

class Net(nn.Module):
  def __init__(self):
    super().__init__()

    '''
    Network Structure:

    input > 
    (1)Conv2D > (2)MaxPool2D > 
    (3)Conv2D > (4)MaxPool2D > 
    (5)Conv2D > (6)MaxPool2D > 
    (7)Linear > (8)LinearOut

    '''

    # Creating the convulutional Layers
    self.conv1 = nn.Conv2d(in_channels=CHANNELS, out_channels=32, kernel_size=3)
    self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3)
    self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3)

    self.flatten = None
    # Creating a Random dummy sample to get the Flattened Dimensions
    x = torch.randn(CHANNELS, DIM, DIM).view(-1, CHANNELS, DIM, DIM)
    x = self.convs(x)

    # Creating the Linear Layers
    self.fc1   = nn.Linear(self.flatten, 512)
    self.fc2   = nn.Linear(512, CLASSES)

  def convs(self, x):

    # Creating the MaxPooling Layers
    x = F.max_pool2d(F.relu(self.conv1(x)), kernel_size=(2, 2))
    x = F.max_pool2d(F.relu(self.conv2(x)), kernel_size=(2, 2))
    x = F.max_pool2d(F.relu(self.conv3(x)), kernel_size=(2, 2))

    if not self.flatten:
      self.flatten = x[0].shape[0] * x[0].shape[1] * x[0].shape[2]
    return x

  # FORWARD PASS
  def forward(self, x):
    x = self.convs(x)
    x = x.view(-1, self.flatten)
    sm = F.relu(self.fc1(x))
    x = F.softmax(self.fc2(sm), dim=1)
    return x, sm


  x_train, y_train = training_set
  x_train, y_train = x_train.to(device), y_train.to(device)
  optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE)
  loss_func = nn.MSELoss()
  loss_log  = []

  for epoch in range(EPOCHS):
    for i in tqdm(range(0, len(x_train), BATCH_SIZE)):
        x_batch = x_train[i:i+BATCH_SIZE].view(-1, CHANNELS, DIM, DIM).to(device)
        y_batch = y_train[i:i+BATCH_SIZE].to(device)

        net.zero_grad()
        output, sm = net(x_batch)
        loss = loss_func(output, y_batch.float())
        loss.backward()
        optimizer.step()
    loss_log.append(loss)
    # print(f"Epoch : {epoch} || Loss : {loss}")

  return loss_log


train_set = (trainData.train_data, trainData.train_labels)
test_set  = (testData.test_data, testData.test_labels)

EPOCHS        = 5
LEARNING_RATE = 0.001
BATCH_SIZE    = 32

net = Net().to(device)

loss_log = train(net, train_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)

这是我遇到的错误，

RuntimeError                              Traceback (most recent call last)
<ipython-input-8-0db1a1b4e37d> in <module>()
      5 net = Net().to(device)
      6 
----> 7 loss_log = train(net, train_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)

6 frames
<ipython-input-6-7de4a78e3736> in train(net, training_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)
     13 
     14         net.zero_grad()
---> 15         output, sm = net(x_batch)
     16         loss = loss_func(output, y_batch.float())
     17         loss.backward()

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--> 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

<ipython-input-5-4fddc427892a> in forward(self, x)
     41   # FORWARD PASS
     42   def forward(self, x):
---> 43     x = self.convs(x)
     44     x = x.view(-1, self.flatten)
     45     sm = F.relu(self.fc1(x))

<ipython-input-5-4fddc427892a> in convs(self, x)
     31 
     32     # Creating the MaxPooling Layers
---> 33     x = F.max_pool2d(F.relu(self.conv1(x)), kernel_size=(2, 2))
     34     x = F.max_pool2d(F.relu(self.conv2(x)), kernel_size=(2, 2))
     35     x = F.max_pool2d(F.relu(self.conv3(x)), kernel_size=(2, 2))

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--> 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in forward(self, input)
    343 
    344     def forward(self, input):
--> 345         return self.conv2d_forward(input, self.weight)
    346 
    347 class Conv3d(_ConvNd):

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in conv2d_forward(self, input, weight)
    340                             _pair(0), self.dilation, self.groups)
    341         return F.conv2d(input, weight, self.bias, self.stride,
--> 342                         self.padding, self.dilation, self.groups)
    343 
    344     def forward(self, input):

RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same

我仔细检查了我的神经网络和输入都在 GPU 中。我仍然收到此错误，但我不明白为什么!

有人请帮我摆脱这个错误。

Answer 1

将您的输入x_batch转换为 float 。在将其传递到模型之前使用 x_batch = x_batch.float()。