TypeError： conv2d()： 参数 'input'(位置 1) 必须是张量，而不是 str

Question

当我尝试训练模型时，我会得到以下错误：

TypeError:conv2d((：参数"input"(位置1(必须是张量，而不是str

我使用的代码是：

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]))

完整的回溯是：

{'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)
~anaconda3libsite-packagestorchnnmodulesmodule.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))
~anaconda3libsite-packagestorchnnmodulesmodule.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(),
~anaconda3libsite-packagestorchnnmodulesconv.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):
~anaconda3libsite-packagestorchnnmodulesconv.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和Y_train保持"Data"&amp目标"。我不明白为什么for循环中的enumerate(dataloader(没有获取张量值。

与其他情况一样，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前缀"Data"one_answers"Target"，我该如何删除它。请提出任何解决方案。

Answer 1

问题是data是一个字典，当您以(X_train, Y_train = data(的方式对其进行解包时，您可以在对值感兴趣的情况下对键进行解包。

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()