Fine-tuning in model_main_tf2.py

Prerequisites

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1. The entire URL of the documentation with the issue

https://github.com/tensorflow/models/tree/master/research/object_detection

2. Describe the issue

I have been looking around the source code, but I can't find how the actual fine-tuning is done. I know there is an option to train from a "detection" and from a "classification" checkpoint and I understand that this involves loading the weights from a previous checkpoint. But when it comes to training what layers are actually trained? Do you do transfer learning or do pertaining on the entire architecture?

I looked at the tutorials: https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/inference_from_saved_model_tf2_colab.ipynb and I understand that here you are fine-tuning only the final 6 layers from the classification and regression heads and that makes sense, but when you train using the model_main_tf2.py file I can't find how the trainable_variables are selected.

There doesn't seem to be a selection class or function in the project. Can you customize in the model_main_tf2.py the layers that you want to fine-tune?