The trained neural network is extracted from a checkpoint and dumped into a protobuf(.pb) file. This process is called "freezing" a model. The idea and part of our code are from Morgan. To freeze a model, typically one does
$ dp freeze -o graph.pbin the folder where the model is trained. The output model is called graph.pb.
In multi-task mode, this process will output several models, each of which contains the common descriptor and
one of the user-defined fitting nets in {ref}fitting_net_dict <model/fitting_net_dict>, let's name it fitting_key, together frozen in graph_{fitting_key}.pb.
Those frozen models are exactly the same as single-task output with fitting net fitting_key.