saved_model to pb

import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
import numpy as np

# Load saved_model
imported = tf.saved_model.load('saved_model')
f = imported.signatures["serving_default"]

# Get frozen ConcreteFunction
frozen_func = convert_variables_to_constants_v2(f)
frozen_func.graph.as_graph_def()

layers = [op.name for op in frozen_func.graph.get_operations()]
print("-" * 50)
print("Frozen model layers: ")
for layer in layers:
    print(layer)

# Save frozen graph from frozen ConcreteFunction to hard drive
tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
                    logdir='saved_model',
                    name='faceboxes.pb',
                    as_text=False)

https://stackoverflow.com/questions/59657166/convert-frozen-model-pb-to-savedmodel

import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph
from tensorflow.lite.python.util import run_graph_optimizations, get_grappler_config
import numpy as np
def frozen_keras_graph(func_model):
    frozen_func, graph_def = convert_variables_to_constants_v2_as_graph(func_model)

    input_tensors = [
        tensor for tensor in frozen_func.inputs
        if tensor.dtype != tf.resource
    ]
    output_tensors = frozen_func.outputs
    graph_def = run_graph_optimizations(
        graph_def,
        input_tensors,
        output_tensors,
        config=get_grappler_config(["constfold", "function"]),
        graph=frozen_func.graph)

    return graph_def


def convert_keras_model_to_pb():

    keras_model = train_model()
    func_model = tf.function(keras_model).get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
    graph_def = frozen_keras_graph(func_model)
    tf.io.write_graph(graph_def, '/tmp/tf_model3', 'frozen_graph.pb')

def convert_saved_model_to_pb():
    model_dir = '/tmp/saved_model'
    model = tf.saved_model.load(model_dir)
    func_model = model.signatures["serving_default"]
    graph_def = frozen_keras_graph(func_model)
    tf.io.write_graph(graph_def, '/tmp/tf_model3', 'frozen_graph.pb')

or

def convert_saved_model_to_pb(output_node_names, input_saved_model_dir, output_graph_dir):
    from tensorflow.python.tools import freeze_graph

    output_node_names = ','.join(output_node_names)

    freeze_graph.freeze_graph(input_graph=None, input_saver=None,
                              input_binary=None,
                              input_checkpoint=None,
                              output_node_names=output_node_names,
                              restore_op_name=None,
                              filename_tensor_name=None,
                              output_graph=output_graph_dir,
                              clear_devices=None,
                              initializer_nodes=None,
                              input_saved_model_dir=input_saved_model_dir)


def save_output_tensor_to_pb():
    output_names = ['StatefulPartitionedCall']
    save_pb_model_path = '/tmp/pb_model/freeze_graph.pb'
    model_dir = '/tmp/saved_model'
    convert_saved_model_to_pb(output_names, model_dir, save_pb_model_path)