class SimpleCNN(nn.Module):
def __init__(self):
    super(SimpleCNN, self).__init__()
    
    self.conv1 = nn.Sequential(
        nn.Conv2d(3, 16, kernel_size=5, padding=2),
        nn.BatchNorm2d(16),
        nn.ReLU(),
        nn.MaxPool2d(2)
    )
    
    self.conv2 = nn.Sequential(
        nn.Conv2d(16, 32, kernel_size=5, padding=2),
        nn.BatchNorm2d(32),
        nn.ReLU(),
        nn.MaxPool2d(2)
    )
    
    self.fc = nn.Linear(56 * 56 * 32, 2)
    
def forward(self, x):
    out = self.conv1(x)
    out = self.conv2(out)            # (bs, C, H,  W)
    out = out.view(out.size(0), -1)  # (bs, C * H, W)
    out = self.fc(out)
    return out

model = SimpleCNN()
if use_gpu:
model = model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.002, momentum=0.9)
num_epochs = 10
losses = []
for epoch in range(num_epochs):
for i, (inputs, targets) in enumerate(train_dl):
inputs = to_var(inputs)
targets = to_var(targets)
    # forwad pass
    optimizer.zero_grad()
    outputs = model(inputs)
    
    # loss
    loss = criterion(outputs, targets)
    losses += [loss.data[0]]
    # backward pass
    loss.backward()
    
    # update parameters
    optimizer.step()
    
    # report
    if (i + 1) % 50 == 0:
        print('Epoch [%2d/%2d], Step [%3d/%3d], Loss: %.4f'
              % (epoch + 1, num_epochs, i + 1, len(train_ds) // [0], loss.data[0]))

IndexError                                Traceback (most recent call last)
 in 
11
12         # loss
—> 13         loss = criterion(outputs, targets)
14         losses += [loss.data[0]]
15         # backward pass
~\anaconda3\lib\site-packages\torch\nn\modules\module.py in call(self, *input, **kwargs)
548             result = self._slow_forward(*input, **kwargs)
549         else:
–> 550             result = self.forward(*input, **kwargs)
551         for hook in self._forward_hooks.values():
552             hook_result = hook(self, input, result)
~\anaconda3\lib\site-packages\torch\nn\modules\loss.py in forward(self, input, target)
930     def forward(self, input, target):
931         return F.cross_entropy(input, target, weight=self.weight,
–> 932                                ignore_index=self.ignore_index, reduction=self.reduction)
933
934
~\anaconda3\lib\site-packages\torch\nn\functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2315     if size_average is not None or reduce is not None:
2316         reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2317     return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
2318
2319
~\anaconda3\lib\site-packages\torch\nn\functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
2113                          .format(input.size(0), target.size(0)))
2114     if dim == 2:
-> 2115         ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
2116     elif dim == 4:
2117         ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
IndexError: Target 3 is out of bounds.
thanks