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)