IndexError: Target 3 is out of bounds

1

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

2

Hello Milad!

Your last (fully-connected) layer has an output size of 2.
Therefore your model will output a tensor of shape
[batchSize, 2]. This is passed to CrossEntropyLoss,
so your targets must be a tensor of shape [batchSize]
consisting of integer class labels that range from 0 to 1.

One of your target labels has value 3, hence the error.

It appears that you are working on a classification problem.
How many classes does your problem actually have?

Best.

K. Frank

1 Like
3

Thanks K.Frank Dear
But 4 Classes(Dog , Cat , sheep , Fish)

4

I encountered this issue when I was using the Hugging face ViTForImageClassification model.

  File "/opt/miniconda/envs/jupyter/lib/python3.7/site-packages/torch/nn/functional.py", line 2846, in cross_entropy
    return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
IndexError: Target 2 is out of bounds.
  0%|

I solved the issue by providing the label2id and id2label parameters.

    labels = train_dataset.features["label"].names
    label2id, id2label = dict(), dict()
    for i, label in enumerate(labels):
        label2id[label] = i
        id2label[i] = label
model = ViTForImageClassification.from_pretrained(args.model, label2id=label2id, id2label=id2label)