I don't think this is a fix as the python garbage collector would automatically handle this.

The garbage collector does not handle this, as the objects live at least until the variables fall out of scope or are reassigned. A for-loop doesn't create a new scope. That means in the current code memory usage of input tensors is doubled.

More information here: #4 (comment)

This would not be an issue unless your GPU does not have the mem to hold 2x of the data, no? A standard deep learning GPU (1080Ti/Titan) would be able to handle the default batch-size.

That's right; it's likely only an issue when trying the code on simple development machines. Do note that train_birdview.py, for the code's default batch size of 256, "wastes" 3GB vmem. It's good practice to del tensors when you no longer need them.