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import typing as t
from pathlib import Path
import keras
import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import (
convert_variables_to_constants_v2,
)
[docs]def freeze_model(
model: keras.Model, output_dir: str, file_name: str
) -> t.Tuple[str, t.List[str], t.List[str]]:
"""Freeze a Keras or TensorFlow Graph
to use a Keras or TensorFlow model in SmartSim, the model
must be frozen and the inputs and outputs provided to the
smartredis.client.set_model_from_file() method.
This utiliy function provides everything users need to take
a trained model and put it inside an ``orchestrator`` instance
:param model: TensorFlow or Keras model
:type model: tf.Module
:param output_dir: output dir to save model file to
:type output_dir: str
:param file_name: name of model file to create
:type file_name: str
:return: path to model file, model input layer names, model output layer names
:rtype: str, list[str], list[str]
"""
# TODO figure out why layer names don't match up to
# specified name in Model init.
if not file_name.endswith(".pb"):
file_name = file_name + ".pb"
full_model = tf.function(model)
full_model = full_model.get_concrete_function(
tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype)
)
frozen_func = convert_variables_to_constants_v2(full_model)
frozen_func.graph.as_graph_def()
input_names = [x.name.split(":")[0] for x in frozen_func.inputs]
output_names = [x.name.split(":")[0] for x in frozen_func.outputs]
tf.io.write_graph(
graph_or_graph_def=frozen_func.graph,
logdir=output_dir,
name=file_name,
as_text=False,
)
model_file_path = str(Path(output_dir, file_name).resolve())
return model_file_path, input_names, output_names
[docs]def serialize_model(model: keras.Model) -> t.Tuple[str, t.List[str], t.List[str]]:
"""Serialize a Keras or TensorFlow Graph
to use a Keras or TensorFlow model in SmartSim, the model
must be frozen and the inputs and outputs provided to the
smartredis.client.set_model() method.
This utiliy function provides everything users need to take
a trained model and put it inside an ``orchestrator`` instance.
:param model: TensorFlow or Keras model
:type model: tf.Module
:return: serialized model, model input layer names, model output layer names
:rtype: str, list[str], list[str]
"""
full_model = tf.function(model)
full_model = full_model.get_concrete_function(
tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype)
)
frozen_func = convert_variables_to_constants_v2(full_model)
frozen_func.graph.as_graph_def()
input_names = [x.name.split(":")[0] for x in frozen_func.inputs]
output_names = [x.name.split(":")[0] for x in frozen_func.outputs]
model_serialized = frozen_func.graph.as_graph_def().SerializeToString(
deterministic=True
)
return model_serialized, input_names, output_names