python - TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not str

Question

当我尝试训练模型时，出现以下错误：

TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not str

我正在使用的代码是：

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
        self.dropout = nn.Dropout2d()
        self.fc1 = nn.Linear(256, 64)
        self.fc2 = nn.Linear(64, 1)
        self.hybrid = Hybrid(qiskit.Aer.get_backend('qasm_simulator'), 100, np.pi / 2)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = self.dropout(x)
        x = x.view(1, -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x = self.hybrid(x)
        return torch.cat((x, 1 - x), -1)

model = Net()
optimizer = optim.Adam(model.parameters(), lr=0.001)
loss_func = nn.NLLLoss()

epochs = 20
loss_list = []

model.train()

for epoch in range(epochs):
    total_loss = []

    for i, data in enumerate(train_ldr, 0):
        # get the inputs; data is a list of [inputs, labels]
        X_train, Y_train = data
        print(data)
        optimizer.zero_grad()
         
        # Forward pass
        output = model(X_train)
        # Calculating loss
        loss = loss_func(output, Y_train)
        # Backward pass
        loss.backward()
        # Optimize the weights
        optimizer.step()
        
        total_loss.append(loss.item())
    loss_list.append(sum(total_loss)/len(total_loss))
    print('Training [{:.0f}%]\tLoss: {:.4f}'.format(
        100. * (epoch + 1) / epochs, loss_list[-1]))

完整的追溯是：

{'data': tensor([[715.9147, 679.4994, 131.4772,   9.4777,   9.4777,  13.8722,  85.8577,
           2.5333]]), 'Target': tensor([0])}

TypeError                                 Traceback (most recent call last)
<ipython-input-52-7c8c9f3a38b7> in <module>
     20 
     21         # Forward pass
---> 22         output = model(X_train)
     23         # Calculating loss
     24         loss = loss_func(output, Y_train)

~\anaconda3\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

<ipython-input-39-6b9a402c220d> in forward(self, x)
     10 
     11     def forward(self, x):
---> 12         x = F.relu(self.conv1(x))
     13         x = F.max_pool2d(x, 2)
     14         x = F.relu(self.conv2(x))

~\anaconda3\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~\anaconda3\lib\site-packages\torch\nn\modules\conv.py in forward(self, input)
    397 
    398     def forward(self, input: Tensor) -> Tensor:
--> 399         return self._conv_forward(input, self.weight, self.bias)
    400 
    401 class Conv3d(_ConvNd):

~\anaconda3\lib\site-packages\torch\nn\modules\conv.py in _conv_forward(self, input, weight, bias)
    393                             weight, bias, self.stride,
    394                             _pair(0), self.dilation, self.groups)
--> 395         return F.conv2d(input, weight, bias, self.stride,
    396                         self.padding, self.dilation, self.groups)
    397 

TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not str

当我尝试调试代码时，X_train并Y_train按住“数据”和“目标”。我不明白为什么 for 循环中的枚举（数据加载器）没有获取张量值。

否则，数据加载器中存在值。如果数据集中的每个张量行前面都有“数据”和“目标”前缀，我该如何删除它。请提出任何解决方案。

标签： pythonpytorchpytorch-dataloader

Answer 1

问题是这data是一个字典，当您按照您的方式 ( X_train, Y_train = data) 解压缩它时，您会在对值感兴趣的同时解压缩键。

参考这个简单的例子：

d = {'a': [1,2], 'b': [3,4]}
x, y = d
print(x,y) # a b

所以你应该改变这个：

X_train, Y_train = data

进入这个：

X_train, Y_train = data.values()