In that case nn.NLLLoss should work fine.
Sorry for missing the shape information, but your mask shouldn’t have the channel dimension, but store the class indices instead.
Here is a small example code:
batch_size = 1    
nb_classes = 3
h, w = 24, 24
output = torch.randn(batch_size, nb_classes, h, w)
target = torch.empty(batch_size, h, w, dtype=torch.long).random_(nb_classes)
criterion = nn.NLLLoss()
loss = criterion(output, target)