ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) throws IndexError: Target 42 is out of bounds
After loading CIFAR 100, I try to train my neural network. But I don't know why I get the out of bounds error shown below

Optimizing the network with batch size 25
Epoch: 0  of  30 Average loss: -
/mnt_home/klee/LBSBGenGapSharpnessResearch/vgg.py:43: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
  return F.log_softmax(x)
Traceback (most recent call last):
  File "plot_parametric_pytorch_cifar100.py", line 130, in &lt;module&gt;
    loss_fn = F.nll_loss(ops, tgts)
  File "/home/klee/anaconda3/envs/sharpenv/lib/python3.7/site-packages/torch/nn/functional.py", line 2115, in nll_loss
    ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
IndexError: Target 42 is out of bounds.


Here is the script I'm running:


cudnn.benchmark = True
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train.astype('float32')
X_train = np.transpose(X_train, axes=(0, 3, 1, 2))
X_test = X_test.astype('float32')
X_test = np.transpose(X_test, axes=(0, 3, 1, 2))
X_train /= 255
X_test /= 255
device = torch.device('cuda:0')

# This is where you can load any model of your choice.
# I stole PyTorch Vision's VGG network and modified it to work on CIFAR-10.
# You can take this line out and add any other network and the code
# should run just fine.
model = vgg.vgg11_bn()
#model.to(device)


# Forward pass
opfun = lambda X: model.forward(Variable(torch.from_numpy(X)))

# Forward pass through the network given the input
predsfun = lambda op: np.argmax(op.data.numpy(), 1)

# Do the forward pass, then compute the accuracy
accfun = lambda op, y: np.mean(np.equal(predsfun(op), y.squeeze()))*100

# Initial point
x0 = deepcopy(model.state_dict())

# Number of epochs to train for
# Choose a large value since LB training needs higher values
# Changed from 150 to 30
nb_epochs = 30 
batch_range = [25, 40, 50, 64, 80, 128, 256, 512, 625, 1024, 1250, 1750, 2048, 2500, 3125, 4096, 4500, 5000]

# parametric plot (i.e., don't train the network)
hotstart = False

if not hotstart:
    for batch_size in batch_range:
        optimizer = torch.optim.Adam(model.parameters())
        model.load_state_dict(x0)
        #model.to(device)
        average_loss_over_epoch = '-'
        print('Optimizing the network with batch size %d' % batch_size)
        np.random.seed(1337) #So that both networks see same sequence of batches
        for e in range(nb_epochs):
            model.eval()
            print('Epoch:', e, ' of ', nb_epochs, 'Average loss:', average_loss_over_epoch)
            average_loss_over_epoch = 0
            # Checkpoint the model every epoch
            torch.save(model.state_dict(), "./models/30EpochCIFAR100ExperimentBatchSize" + str(batch_size) + ".pth")
            array = np.random.permutation(range(X_train.shape[0]))
            slices = X_train.shape[0] // batch_size
            beginning = 0
            end = 1

            # Training loop!
            for _ in range(slices):
                start_index = batch_size * beginning 
                end_index = batch_size * end
                smpl = array[start_index:end_index]
                model.train()
                optimizer.zero_grad()
                ops = opfun(X_train[smpl])
                tgts = Variable(torch.from_numpy(y_train[smpl]).long().squeeze())
                loss_fn = F.nll_loss(ops, tgts) &lt;--- errorring linne
                average_loss_over_epoch += loss_fn.data.numpy() / (X_train.shape[0] // batch_size)
                loss_fn.backward()
                optimizer.step()
                beginning += 1
                end += 1




and here's the VGG model:


__all__ = [
    'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
    'vgg19_bn', 'vgg19',
]


model_urls = {
    'vgg11': 'https://s3.amazonaws.com/pytorch/models/vgg11-fb7e83b2.pth',
    'vgg13': 'https://s3.amazonaws.com/pytorch/models/vgg13-58758d87.pth',
    'vgg16': 'https://s3.amazonaws.com/pytorch/models/vgg16-82412952.pth',
    'vgg19': 'https://s3.amazonaws.com/pytorch/models/vgg19-341d7465.pth',
}


class VGG(nn.Module):
    def __init__(self, features):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(512, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Linear(4096, 10),
        )
        self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return F.log_softmax(x)

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                n = m.weight.size(1)
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()


def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)


cfg = {
    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}


def vgg11(pretrained=False, **kwargs):
    """VGG 11-layer model (configuration "A")

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = VGG(make_layers(cfg['A']), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg11']))
    return model


def vgg11_bn(**kwargs):
    """VGG 11-layer model (configuration "A") with batch normalization"""
    return VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)



I'm not sure how to fix the index error. I think it has to do with the number of classes, but I'm  not sure where to fix that (in the above code): I read https://discuss.pytorch.org/t/indexerror-target-2-is-out-of-bounds/69614 but not sure where to go from here.

You are using CIFAR-100, which has 100 classes (hence the name). But your model only predicts 10 classes. Naturally, any class above 10 will result in an error.

The output of the last linear linear in the model's classifier needs to be changed to 100:

self.classifier = nn.Sequential(
    nn.Dropout(),
    nn.Linear(512, 4096),
    nn.ReLU(True),
    nn.Dropout(),
    nn.Linear(4096, 4096),
    nn.ReLU(True),
    nn.Linear(4096, 100),
)

