def from_session(cls, sess, input_tensors, output_tensors):
"""Creates a TFLiteConverter class from a TensorFlow Session.
Args:
sess: TensorFlow Session.
input_tensors: List of input tensors. Type and shape are computed using
`foo.shape` and `foo.dtype`.
output_tensors: List of output tensors (only .name is used from this).
Returns:
TFLiteConverter class.
"""
graph_def = _freeze_graph(sess, input_tensors, output_tensors)
return cls(graph_def, input_tensors, output_tensors)
def from_keras_model_file(cls,
model_file,
input_arrays=None,
input_shapes=None,
output_arrays=None,
custom_objects=None):
"""Creates a TFLiteConverter class from a tf.keras model file.
Args:
model_file: Full filepath of HDF5 file containing the tf.keras model.
input_arrays: List of input tensors to freeze graph with. Uses input
arrays from SignatureDef when none are provided. (default None)
input_shapes: Dict of strings representing input tensor names to list of
integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}).
Automatically determined when input shapes is None (e.g., {"foo" :
None}). (default None)
output_arrays: List of output tensors to freeze graph with. Uses output
arrays from SignatureDef when none are provided. (default None)
custom_objects: Dict mapping names (strings) to custom classes or
functions to be considered during model deserialization. (default None)
Returns:
TFLiteConverter class.
"""
_keras.backend.clear_session()
_keras.backend.set_learning_phase(False)
keras_model = _keras.models.load_model(model_file, custom_objects)
sess = _keras.backend.get_session()
# Get input and output tensors.
if input_arrays:
input_tensors = _get_tensors_from_tensor_names(
sess.graph, input_arrays)
else:
input_tensors = keras_model.inputs
if output_arrays:
output_tensors = _get_tensors_from_tensor_names(
sess.graph, output_arrays)
else:
output_tensors = keras_model.outputs
_set_tensor_shapes(input_tensors, input_shapes)
graph_def = _freeze_graph(sess, input_tensors, output_tensors)
return cls(graph_def, input_tensors, output_tensors)
def from_keras_model_file(cls,
model_file,
input_arrays=None,
input_shapes=None,
output_arrays=None,
custom_objects=None):
"""Creates a TFLiteConverter class from a tf.keras model file.
Args:
model_file: Full filepath of HDF5 file containing the tf.keras model.
input_arrays: List of input tensors to freeze graph with. Uses input
arrays from SignatureDef when none are provided. (default None)
input_shapes: Dict of strings representing input tensor names to list of
integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}).
Automatically determined when input shapes is None (e.g., {"foo" :
None}). (default None)
output_arrays: List of output tensors to freeze graph with. Uses output
arrays from SignatureDef when none are provided. (default None)
custom_objects: Dict mapping names (strings) to custom classes or
functions to be considered during model deserialization. (default None)
Returns:
TFLiteConverter class.
"""
_keras.backend.clear_session()
_keras.backend.set_learning_phase(False)
keras_model = _keras.models.load_model(model_file, custom_objects)
sess = _keras.backend.get_session()
# Get input and output tensors.
if input_arrays:
input_tensors = _get_tensors_from_tensor_names(sess.graph, input_arrays)
else:
input_tensors = keras_model.inputs
if output_arrays:
output_tensors = _get_tensors_from_tensor_names(sess.graph, output_arrays)
else:
output_tensors = keras_model.outputs
_set_tensor_shapes(input_tensors, input_shapes)
graph_def = _freeze_graph(sess, input_tensors, output_tensors)
return cls(graph_def, input_tensors, output_tensors)
def from_keras_model_file(cls,
model_file,
input_arrays=None,
input_shapes=None,
output_arrays=None,
custom_objects=None):
"""Creates a TFLiteConverter class from a tf.keras model file.
Args:
model_file: Full filepath of HDF5 file containing the tf.keras model.
input_arrays: List of input tensors to freeze graph with. Uses input
arrays from SignatureDef when none are provided. (default None)
input_shapes: Dict of strings representing input tensor names to list of
integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}).
Automatically determined when input shapes is None (e.g., {"foo" :
None}). (default None)
output_arrays: List of output tensors to freeze graph with. Uses output
arrays from SignatureDef when none are provided. (default None)
custom_objects: Dict mapping names (strings) to custom classes or
functions to be considered during model deserialization. (default None)
Returns:
TFLiteConverter class.
"""
# Handles Keras when Eager mode is enabled.
if context.executing_eagerly():
if input_arrays or output_arrays:
raise ValueError(
"`input_arrays` and `output_arrays` are unsupported "
"with Eager mode. If your model requires any of these "
"parameters, please use disable_eager_execution().")
_keras.backend.set_learning_phase(False)
keras_model = _keras.models.load_model(model_file, custom_objects)
function = _saving_utils.trace_model_call(keras_model)
concrete_func = function.get_concrete_function()
frozen_func = _convert_to_constants.convert_variables_to_constants_v2(
concrete_func, lower_control_flow=False)
_set_tensor_shapes(frozen_func.inputs, input_shapes)
return cls(frozen_func.graph.as_graph_def(),
frozen_func.inputs,
frozen_func.outputs,
experimental_debug_info_func=_build_debug_info_func(
frozen_func.graph))
# Handles Keras when Eager mode is disabled.
_keras.backend.clear_session()
_keras.backend.set_learning_phase(False)
keras_model = _keras.models.load_model(model_file, custom_objects)
sess = _keras.backend.get_session()
# Get input and output tensors.
if input_arrays:
input_tensors = _get_tensors_from_tensor_names(
sess.graph, input_arrays)
else:
input_tensors = keras_model.inputs
if output_arrays:
output_tensors = _get_tensors_from_tensor_names(
sess.graph, output_arrays)
else:
output_tensors = keras_model.outputs
_set_tensor_shapes(input_tensors, input_shapes)
graph_def = _freeze_graph(sess, input_tensors, output_tensors)
return cls(graph_def,
input_tensors,
output_tensors,
experimental_debug_info_func=_build_debug_info_func(
sess.graph))
def from_keras_model_file(cls,
model_file,
input_arrays=None,
input_shapes=None,
output_arrays=None,
custom_objects=None):
"""Creates a TFLiteConverter class from a tf.keras model file.
Args:
model_file: Full filepath of HDF5 file containing the tf.keras model.
input_arrays: List of input tensors to freeze graph with. Uses input
arrays from SignatureDef when none are provided. (default None)
input_shapes: Dict of strings representing input tensor names to list of
integers representing input shapes (e.g., {"foo" : [1, 16, 16, 3]}).
Automatically determined when input shapes is None (e.g., {"foo" :
None}). (default None)
output_arrays: List of output tensors to freeze graph with. Uses output
arrays from SignatureDef when none are provided. (default None)
custom_objects: Dict mapping names (strings) to custom classes or
functions to be considered during model deserialization. (default None)
Returns:
TFLiteConverter class.
"""
# Handles Keras when Eager mode is enabled.
if context.executing_eagerly():
if input_arrays or output_arrays:
raise ValueError("`input_arrays` and `output_arrays` are unsupported "
"with Eager mode. If your model requires any of these "
"parameters, please use disable_eager_execution().")
_keras.backend.set_learning_phase(False)
keras_model = _keras.models.load_model(model_file, custom_objects)
function = _saving_utils.trace_model_call(keras_model)
concrete_func = function.get_concrete_function()
frozen_func = _convert_to_constants.convert_variables_to_constants_v2(
concrete_func)
_set_tensor_shapes(frozen_func.inputs, input_shapes)
return cls(frozen_func.graph.as_graph_def(), frozen_func.inputs,
frozen_func.outputs)
# Handles Keras when Eager mode is disabled.
_keras.backend.clear_session()
_keras.backend.set_learning_phase(False)
keras_model = _keras.models.load_model(model_file, custom_objects)
sess = _keras.backend.get_session()
# Get input and output tensors.
if input_arrays:
input_tensors = _get_tensors_from_tensor_names(sess.graph, input_arrays)
else:
input_tensors = keras_model.inputs
if output_arrays:
output_tensors = _get_tensors_from_tensor_names(sess.graph, output_arrays)
else:
output_tensors = keras_model.outputs
_set_tensor_shapes(input_tensors, input_shapes)
graph_def = _freeze_graph(sess, input_tensors, output_tensors)
return cls(graph_def, input_tensors, output_tensors)