Python 运行时错误：“；尺寸超出范围（预计在[-1,0]范围内，但得到1）”；

我希望你们一切都好。我是PyTorch的新手，我在网上遇到了一个代码，其中包含以下代码：

cuda = True if torch.cuda.is_available() else False
num_class = opt.num_class
num_repeats = opt.num_repeats
total=0
correct=0
acc=[]
acc1=[]
model = SHMnet(num_class)
# model = AlexNet1D(num_class)
learning_rate = opt.lr
print(model)
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,betas=(opt.b1,opt.b2))
if cuda:
    model=model.cuda()
    loss=loss.cuda()
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor

traindataset = []
for label in range(num_class):
    epdataset = epDataset(label,num_repeats)
    traindataset = ConcatDataset([traindataset,epdataset])

testdataset =[]
for label in range(num_class):
    eptdataset = eptDataset(label,6)
    testdataset = ConcatDataset([testdataset,eptdataset])
dataloader = DataLoader(traindataset, batch_size=opt.batch_size)
viddataloader = DataLoader(testdataset,batch_size = opt.batch_size)
loss_r=[]
start = time.time()

for epoch in range(opt.n_epochs):
    print('Epoch: %d' % epoch)
    total = 0
    correct = 0
    total1 = 0
    correct1 = 0
    for i, (sdata, label) in enumerate(dataloader):
        batch_size = sdata.shape[0]
        sdata = Variable(sdata.type(FloatTensor))
       noise=Variable(torch.from_numpy(np.random.normal(1, 0.2, sdata.shape))).type(FloatTensor)
        x = torch.mul(sdata,noise).view(sdata.size(0), 1, sdata.size(1))
        print(x)
        print(x.size())
        label = Variable (label.type(LongTensor))
        print(label)
        optimizer.zero_grad()
        output = model(x)
        print(output)
        print(output.size())
        loss1 = loss(input=output, target=label)
        loss1.backward()
        optimizer.step()
        _, predicted = torch.max(model(x).data, 1)
        temp = (predicted == label).sum()
        correct += temp
        total += batch_size
        print(i, loss1.item())
        loss_r.append(loss1.item())
    print('Accuracy of training data is: %d %%' % (100 * correct / total))
    acc.append(100 * correct / total)

我设法找到了一个数据集，但在运行代码时出现了以下错误：

RuntimeError: “dimension out of range (expected to be in range of [-1, 0], but got 1)”

所以，我不理解这个错误。原因是什么？如何解决

批量大小为60，类数为6

编辑1

输出和标签变量的输出为：

Labels:  tensor([0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5])
Labels Size/Shape:  torch.Size([12])
torch.Size([12, 512, 2])
Output:  tensor([0.0288, 0.0125, 0.0104, 0.0222, 0.0106, 0.0121],  grad_fn=<AddBackward0>)
Output Size/Shape:  torch.Size([6])

Input Size/Shape:  torch.Size([6, 1, 2192])
Labels:  tensor([0, 1, 2, 3, 4, 5])
Labels Size/Shape:  torch.Size([6])
torch.Size([6, 512, 2])
Output:  tensor([ 0.0228, -0.0088,  0.0047,  0.0224,  0.0146,  0.0357], grad_fn=<AddBackward0>)
Output Size/Shape:  torch.Size([6])

标签：张量（[0,0,1,1,2,2,3,3,4,4,5,5]） 标签尺寸/形状：火炬。尺寸（[12]） 火炬尺寸（[12,512,2]） 输出：张量（[0.0288,0.0125,0.0104,0.0222,0.0106,0.0121]，梯度fn=） 输出尺寸/形状：火炬。尺寸（[6]） 编辑2

我设法将批量大小更改为6，但是，输出和标签变量的输出为：

Labels:  tensor([0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5])
Labels Size/Shape:  torch.Size([12])
torch.Size([12, 512, 2])
Output:  tensor([0.0288, 0.0125, 0.0104, 0.0222, 0.0106, 0.0121],  grad_fn=<AddBackward0>)
Output Size/Shape:  torch.Size([6])

Input Size/Shape:  torch.Size([6, 1, 2192])
Labels:  tensor([0, 1, 2, 3, 4, 5])
Labels Size/Shape:  torch.Size([6])
torch.Size([6, 512, 2])
Output:  tensor([ 0.0228, -0.0088,  0.0047,  0.0224,  0.0146,  0.0357], grad_fn=<AddBackward0>)
Output Size/Shape:  torch.Size([6])

输入大小/形状：火炬大小（[6,1,2192]）
标签：张量（[0,1,2,3,4,5]）
标签尺寸/形状：火炬。尺寸（[6]）
火炬尺寸（[65125,2]）
输出：张量（[0.0228，-0.0088,0.0047,0.0224,0.0146,0.0357]，梯度fn=）
输出尺寸/形状：火炬。尺寸（[6]）

但是，我得到了相同的错误，批大小是6，类的数量是6

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谢谢

您在哪一行收到错误？@WasiAhmad这一行：loss1=loss（输入=输出，目标=标签）打印

输出的形状（
output.size（）
）和
label
（
label.size（）
）。@WasiAhmad请查看问题中的编辑，为什么您在loss时呼叫cuda？在pytorch或python中，尽量不要用保留的关键字来命名变量。现在回到你的错误，我想了一下为什么会发生这种情况，交叉熵损失期望第一个输入是形状：（batch_size，nb_类），第二个输入是形状（batch_size，1）。也许你需要检查一下