Thanks again for the super helpful answers. Now I understand the logic of modifying the tensors prior to passing to the loss function. As a sidenote, I discovered that the CrossEntropyLoss function has an optional parameter weights which seems to have the same effect (I can set the weight to 0 for the unlabeled class and 1 for all other classes). I tested this with the CamVid course example and got some encouraging results! I only used the building, car, tree, pedestrian, and cyclist classes (weight=1) and left all other classes unlabeled (weight=0).
I agree that throwing away pixels is not ideal (this might be why the model gets confused between cyclists and pedestrians?), but I think this is realistically the only way I can approach this problem right now. Digging through the forums, I gather that an alternative might be to do an unsupervised segmentation first, then give the resulting classes names and possibly train more (against my incomplete mask). That may be a longer-term goal. First I will see how well this incomplete-masking approach works with my vegetation data.