Pytorch 运行时错误：预期的标量类型为 Float，但找到了 Byte

Question

我正在研究带有数字的经典示例。我想创建我的第一个神经网络来预测数字图像 {0,1,2,3,4,5,6,7,8,9} 的标签。所以第一列train.txt有标签，所有其他列是每个标签的特征。我定义了一个类来导入我的数据：

class DigitDataset(Dataset):
    """Digit dataset."""

    def __init__(self, file_path, transform=None):
        """
        Args:
            csv_file (string): Path to the csv file with annotations.
            root_dir (string): Directory with all the images.
            transform (callable, optional): Optional transform to be applied
                on a sample.
        """
        self.data = pd.read_csv(file_path, header = None, sep =" ")
        self.transform = transform

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        labels = self.data.iloc[idx,0]
        images = self.data.iloc[idx,1:-1].values.astype(np.uint8).reshape((1,16,16))

        if self.transform is not None:
            sample = self.transform(sample)
        return images, labels

然后我运行这些命令将我的数据集拆分为批次，以定义模型和损失：

train_dataset = DigitDataset("train.txt")
train_loader = DataLoader(train_dataset, batch_size=64,
                        shuffle=True, num_workers=4)

# Model creation with neural net Sequential model
model=nn.Sequential(nn.Linear(256, 128), # 1 layer:- 256 input 128 o/p
                    nn.ReLU(),          # Defining Regular linear unit as activation
                    nn.Linear(128,64),  # 2 Layer:- 128 Input and 64 O/p
                    nn.Tanh(),          # Defining Regular linear unit as activation
                    nn.Linear(64,10),   # 3 Layer:- 64 Input and 10 O/P as (0-9)
                    nn.LogSoftmax(dim=1) # Defining the log softmax to find the probablities 
for the last output unit 
                  ) 

# defining the negative log-likelihood loss for calculating loss
criterion = nn.NLLLoss()

images, labels = next(iter(train_loader))
images = images.view(images.shape[0], -1)

logps = model(images) #log probabilities
loss = criterion(logps, labels) #calculate the NLL-loss

我接受错误：

---------------------------------------------------------------------------
   RuntimeError                              Traceback (most recent call last) 
    <ipython-input-2-7f4160c1f086> in <module>
     47 images = images.view(images.shape[0], -1)
     48 
---> 49 logps = model(images) #log probabilities
     50 loss = criterion(logps, labels) #calculate the NLL-loss

~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, 
*input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input)
    115     def forward(self, input):
    116         for module in self:
--> 117             input = module(input)
    118         return input
    119 

~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, 
*input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

 ~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/linear.py in forward(self, input)
     91 
     92     def forward(self, input: Tensor) -> Tensor:
---> 93         return F.linear(input, self.weight, self.bias)
     94 
     95     def extra_repr(self) -> str:

 ~/anaconda3/lib/python3.8/site-packages/torch/nn/functional.py in linear(input, weight, bias)
   1688     if input.dim() == 2 and bias is not None:
   1689         # fused op is marginally faster
-> 1690         ret = torch.addmm(bias, input, weight.t())
   1691     else:
   1692         output = input.matmul(weight.t())

RuntimeError: expected scalar type Float but found Byte

你知道有什么问题吗？感谢您的耐心和帮助！

Answer 1

这一行是你的错误的原因：

images = self.data.iloc[idx, 1:-1].values.astype(np.uint8).reshape((1, 16, 16))

images是uint8( byte) 而神经网络需要输入作为浮点数才能计算梯度（您不能使用整数计算反向传播的梯度，因为它们不是连续且不可微的）。

您可以使用torchvision.transforms.functional.to_tensor将图像转换float成[0, 1]这样：

import torchvision

images = torchvision.transforms.functional.to_tensor(
    self.data.iloc[idx, 1:-1].values.astype(np.uint8).reshape((1, 16, 16))
)

或简单地除以将255值放入[0, 1].

Answer 2

这是一个简单的解决方案。

只需将 .float() 添加到图像张量即可。像这样：

# Forward Pass
outputs = model(images.float())