I recently switched from PyTorch to TF (1 and 2) and I am trying to acquire a good workflow with it.

The simple things I want to do are the following:

1. Load a full pretrained object detection model from TF1 zoo or TF2 zoo
2. Use model.summary() to inspect the network architecture of the loaded model.
3. Do inference with a pretrained loaded model.
4. Modify (e.g. reshape, drop, add) the layers and weights of the loaded model.
5. Retraining the modified loaded model.

I know that TF has the concept of graph and weights as opposed to PyTorch which just have models encompassing everything.
Nevertheless I could not find an easy and best way to load a pretraind model and the internet is full of different answers for different tf versions.

I am really confused because to achieve the points above I have so many different files available when I download a pretrained model from TF1 zoo (or TF2 zoo).

Take this one for instance, the first in the list of the TF1 zoo.
I have the saved_model folder with the saved_model.pb and the variables (empty) folder, the frozen_inference_graph.pb the model.ckpt files, the pipeline.config and in some cases an event file.
Are all these different files really necessary to encode graph structure and weights?Am I missing something or this is just more complicated than necessary?
In addition, the file/folder structure is different iF you download a model from TF2 zoo (see image below)

What I tried

import tensorflow as tf #(v2.4)
def load_pretrained_model(self, saved_model_sub_folder,
                          mode):
    # 1. this only load an AutoTrackable object that can be use for inference but no graph
    if mode == '.pb':
        model_dir = str(TRAINED_MODEL_DIR) + saved_model_sub_folder
        model_dir = pathlib.Path(model_dir) / "saved_model"
        model = tf.saved_model.load(str(model_dir), None, '.')
        detection_model = model.signatures['serving_default']

    # 2. this returns None
    elif mode == '.graph':
        def load_graph(frozen_graph_filename):
            with tf.compat.v1.gfile.GFile(frozen_graph_filename, "rb") as f:
                graph_def = tf.compat.v1.GraphDef()
                graph_def.ParseFromString(f.read())
                return graph_def
        detection_model = tf.compat.v1.import_graph_def(load_graph(frozen_graph_filename))
    else:
        detection_model = None

    return detection_model

Tl;dr

Can someone answer some of the points (1 to 5) above regarding how to load a complete (graph, weights, everything..) customizable tensorflow1 or tensorflow2 model in python3?