SavedModel doesn't have SignatureDefs

Conversion of TFJS model (PoseNet ResNet50) to SavedModel is successful but the produced SavedModel doesn’t seem to contain any signature keys, inputs and outputs.

saved_model_cli show --dir /posenet/savedmodel --all
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

# end out output

It means that TensorFlow Serving cannot serve this model.

My idea was to load the produced model as GraphDef

def get_graph_def_from_saved_model(saved_model_dir):
    with tf.Session() as session:
        meta_graph_def = tf.saved_model.loader.load(
            session,
            tags=['serve'],
            export_dir=saved_model_dir
        )
    return meta_graph_def.graph_def
graph_def = get_graph_def_from_saved_model('/posenet/savedmodel')

find name of the input

input_nodes = ([node.name for node in graph_def.node if node.op == 'Placeholder'])
print(input_nodes)  # ['sub_2']

and then save it again

with tf.Session(graph=tf.Graph()) as session:
    tf.import_graph_def(graph_def, name='')
    inputs = {input_name: session.graph.get_tensor_by_name(f'{input_name}:0') for input_name in input_nodes}
    outputs =  # how do I find the outputs?
    tf.saved_model.simple_save(
        session,
        '/posenet/savedmodel_2',
        inputs=inputs,
        outputs=outputs
    )

but I don’t know how to find names of the output nodes. Can you please suggest? Or can you think of a better way of generating SignatureDefs for the SavedModel?

Thank you for this useful tool!

[floe]

Additional comment: if you want to continue to convert the result to TFLite (which I did), then the following code snippet from the converter API guide may be helpful (this can basically be appended right below the code example from @glenvorel ):

model = tf.saved_model.load(export_dir)
concrete_func = model.signatures[
  tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
concrete_func.inputs[0].set_shape([1, 256, 256, 3])
converter = TFLiteConverter.from_concrete_functions([concrete_func])

[floe]

I only used the output node float_segments and classified everything above 0.65 as body part, nothing more.