# Copyright 2017 The TensorFlow Authors. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""TensorFlow Lite tooling helper functionality."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import warnings

import enum

from six import PY3

from google.protobuf import text_format as _text_format

from google.protobuf.message import DecodeError

from tensorflow.core.framework import graph_pb2 as _graph_pb2

from tensorflow.lite.experimental.examples.lstm.rnn import dynamic_rnn # pylint: disable=unused-import

from tensorflow.lite.experimental.examples.lstm.rnn_cell import TFLiteLSTMCell # pylint: disable=unused-import

from tensorflow.lite.experimental.examples.lstm.rnn_cell import TfLiteRNNCell # pylint: disable=unused-import

from tensorflow.lite.experimental.microfrontend.python.ops import audio_microfrontend_op # pylint: disable=unused-import

from tensorflow.lite.experimental.tensorboard.ops_util import get_potentially_supported_ops # pylint: disable=unused-import

from tensorflow.lite.python import lite_constants as constants

from tensorflow.lite.python.convert import build_toco_convert_protos # pylint: disable=unused-import

from tensorflow.lite.python.convert import ConverterError # pylint: disable=unused-import

from tensorflow.lite.python.convert import OpsSet

from tensorflow.lite.python.convert import toco_convert # pylint: disable=unused-import

from tensorflow.lite.python.convert import toco_convert_graph_def as _toco_convert_graph_def

from tensorflow.lite.python.convert import toco_convert_impl as _toco_convert_impl

from tensorflow.lite.python.convert import toco_convert_protos # pylint: disable=unused-import

from tensorflow.lite.python.convert_saved_model import freeze_saved_model as _freeze_saved_model

from tensorflow.lite.python.interpreter import Interpreter # pylint: disable=unused-import

from tensorflow.lite.python.interpreter import load_delegate # pylint: disable=unused-import

from tensorflow.lite.python.op_hint import convert_op_hints_to_stubs # pylint: disable=unused-import

from tensorflow.lite.python.op_hint import OpHint # pylint: disable=unused-import

from tensorflow.lite.python.optimize import calibrator as _calibrator

from tensorflow.lite.python.util import build_debug_info_func as _build_debug_info_func

from tensorflow.lite.python.util import freeze_graph as _freeze_graph

from tensorflow.lite.python.util import get_debug_info as _get_debug_info

from tensorflow.lite.python.util import get_grappler_config as _get_grappler_config

from tensorflow.lite.python.util import get_tensor_name as _get_tensor_name

from tensorflow.lite.python.util import get_tensors_from_tensor_names as _get_tensors_from_tensor_names

from tensorflow.lite.python.util import is_frozen_graph as _is_frozen_graph

from tensorflow.lite.python.util import run_graph_optimizations as _run_graph_optimizations

from tensorflow.lite.python.util import set_tensor_shapes as _set_tensor_shapes

from tensorflow.python import keras as _keras

from tensorflow.python.client import session as _session

from tensorflow.python.eager import context

from tensorflow.python.eager import def_function as _def_function

from tensorflow.python.eager import function as _function

from tensorflow.python.framework import convert_to_constants as _convert_to_constants

from tensorflow.python.framework import dtypes as _dtypes

from tensorflow.python.framework import ops as _ops

from tensorflow.python.framework.errors_impl import NotFoundError as _NotFoundError

from tensorflow.python.framework.importer import import_graph_def as _import_graph_def

from tensorflow.python.keras.saving import saving_utils as _saving_utils

from tensorflow.python.lib.io import file_io as _file_io

from tensorflow.python.saved_model import signature_constants as _signature_constants

from tensorflow.python.saved_model import tag_constants as _tag_constants

from tensorflow.python.saved_model.load import load as _load

from tensorflow.python.util import deprecation as _deprecation

from tensorflow.python.util.tf_export import tf_export as _tf_export

@_tf_export("lite.Optimize")

class Optimize(enum.Enum):

"""Enum defining the optimizations to apply when generating tflite graphs.

Some optimizations may come at the cost of accuracy.

"""

# Default optimization strategy.

#

# Converter will do its best to improve size and latency based on the

# information provided.

# Enhanced optimizations can be gained by providing a representative_dataset.

# This is recommended, and is currently equivalent to the modes below.

# Currently, weights will be quantized and if representative_dataset is

# provided, activations for quantizable operations will also be quantized.

DEFAULT = "DEFAULT"

# Optimize for size.

#

# Optimizations that reduce the size of the model.

# The model size will be reduced.

# Currently, weights will be quantized and if representative_dataset is

# provided, activations for quantizable operations will also be quantized.

OPTIMIZE_FOR_SIZE = "OPTIMIZE_FOR_SIZE"

# Optimize for latency.

#

# Optimizations that reduce the latency of the model.

# Currently, weights will be quantized and if representative_dataset is

# provided, activations for quantizable operations will also be quantized.

OPTIMIZE_FOR_LATENCY = "OPTIMIZE_FOR_LATENCY"

def __str__(self):

return self.value

@_tf_export("lite.RepresentativeDataset")

class RepresentativeDataset(object):

"""Representative dataset to evaluate optimizations.

A representative dataset that can be used to evaluate optimizations by the

converter. E.g. converter can use these examples to estimate (min, max) ranges

by calibrating the model on inputs. This can allow converter to quantize a

converted floating point model.

"""

def __init__(self, input_gen):

"""Creates a representative dataset.

Args:

input_gen: an input generator that can be used to generate input samples

for the model. This must be a callable object that returns an object

that supports the `iter()` protocol (e.g. a generator function). The

elements generated must have same type and shape as inputs to the model.

"""

self.input_gen = input_gen

@_tf_export("lite.TargetSpec")

class TargetSpec(object):

"""Specification of target device.

Details about target device. Converter optimizes the generated model for

specific device.

Attributes:

supported_ops: Experimental flag, subject to change. Set of OpsSet options

supported by the device. (default set([OpsSet.TFLITE_BUILTINS]))

supported_types: List of types for constant values on the target device.

Supported values are types exported by lite.constants. Frequently, an

optimization choice is driven by the most compact (i.e. smallest)

type in this list (default [constants.FLOAT])

"""

def __init__(self, supported_ops=None, supported_types=None):

if supported_ops is None:

supported_ops = set([OpsSet.TFLITE_BUILTINS])

self.supported_ops = supported_ops

if supported_types is None:

supported_types = []

self.supported_types = supported_types

class TFLiteConverterBase(object):

"""Converter subclass to share functionality between V1 and V2 converters."""

def __init__(self):

self.allow_custom_ops = False

self.target_spec = TargetSpec()

self.optimizations = []

self.representative_dataset = None

self.experimental_enable_mlir_converter = False

self._debug_info = None

def _grappler_config(self):

is_only_flex_enabled = (

set([OpsSet.SELECT_TF_OPS]) == set(self.target_spec.supported_ops))

optimizers = ["constfold"]

if is_only_flex_enabled:

# The layout optimizer turns NHCW to NCHW. This provides performance

# optimizations when Flex mode is enabled. However, this is not compatible

# with builtin ops.

optimizers.append("layout")

return _get_grappler_config(optimizers)

def _validate_representative_dataset(self):

if self.representative_dataset:

if not isinstance(self.representative_dataset, RepresentativeDataset):

self.representative_dataset = RepresentativeDataset(

self.representative_dataset)

if self.representative_dataset.input_gen is None:

raise ValueError(

"Provide an input generator for representative_dataset")

elif self._is_int8_target_required():

raise ValueError("representative_dataset is required when specifying "

"TFLITE_BUILTINS_INT8 or INT8 supported types.")

def _validate_quantization(self):

if self._is_int8_target_required():

if self.target_spec.supported_types and (self._smallest_supported_type()

!= constants.INT8):

raise ValueError("TFLITE_BUILTINS_INT8 requires smallest supported "

"type to be INT8.")

def _is_int8_target_required(self):

return (set([OpsSet.TFLITE_BUILTINS_INT8]) == set(

self.target_spec.supported_ops) or

self._smallest_supported_type() == constants.INT8)

def _smallest_supported_type(self):

if self.target_spec.supported_types:

return min(self.target_spec.supported_types, key=lambda x: x.size)

else:

return None

def _any_optimization_enabled(self):

return bool(

set(self.optimizations).intersection([

Optimize.OPTIMIZE_FOR_LATENCY, Optimize.OPTIMIZE_FOR_SIZE,

Optimize.DEFAULT

]))

def _is_post_training_optimize(self):

return self._is_int8_target_required() or self._any_optimization_enabled()

def _is_int8_weight_only_quantize(self):

return (self._is_post_training_optimize() and

(self.representative_dataset is None))

def _is_float16_quantize(self):

return self._any_optimization_enabled() and (

self._smallest_supported_type() == constants.FLOAT16)

def _is_calibration_quantize(self):

return (self._is_post_training_optimize() and

self.representative_dataset and

self._smallest_supported_type() != constants.FLOAT16)

def _calibrate_quantize_model(self, result, inference_input_type,

inference_output_type):

allow_float = not self._is_int8_target_required()

calibrate_quantize = _calibrator.Calibrator(result)

return calibrate_quantize.calibrate_and_quantize(

self.representative_dataset.input_gen, inference_input_type,

inference_output_type, allow_float)

def _get_base_converter_args(self):

"""Returns the base converter args.

Returns:

{key str: val}

"""

float16_quantize = self._is_float16_quantize()

args = {

"input_format": constants.TENSORFLOW_GRAPHDEF,

"allow_custom_ops": self.allow_custom_ops,

"post_training_quantize": (self._is_int8_weight_only_quantize() or

float16_quantize),

"quantize_to_float16": float16_quantize,

"debug_info": self._debug_info,

"target_ops": self.target_spec.supported_ops,

"enable_mlir_converter": self.experimental_enable_mlir_converter,

}

return args

@_tf_export("lite.TFLiteConverter", v1=[])

class TFLiteConverterV2(TFLiteConverterBase):

"""Converts a TensorFlow model into TensorFlow Lite model.

Attributes:

allow_custom_ops: Boolean indicating whether to allow custom operations.

When false any unknown operation is an error. When true, custom ops are

created for any op that is unknown. The developer will need to provide

these to the TensorFlow Lite runtime with a custom resolver.

(default False)

target_spec: Experimental flag, subject to change. Specification of target

device.

optimizations: Experimental flag, subject to change. A list of optimizations

to apply when converting the model. E.g. `[Optimize.DEFAULT]

representative_dataset: A representative dataset that can be used to

generate input and output samples for the model. The converter can use the

dataset to evaluate different optimizations.

experimental_enable_mlir_converter: Experimental flag, subject to change.

Enables the MLIR converter instead of the TOCO converter.

Example usage:

```python

# Converting a SavedModel to a TensorFlow Lite model.

converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)

tflite_model = converter.convert()

# Converting a tf.Keras model to a TensorFlow Lite model.

converter = lite.TFLiteConverter.from_keras_model(model)

tflite_model = converter.convert()

# Converting ConcreteFunctions to a TensorFlow Lite model.

converter = lite.TFLiteConverter.from_concrete_functions([func])

tflite_model = converter.convert()

```

"""

def __init__(self, funcs, trackable_obj=None):

"""Constructor for TFLiteConverter.

Args:

funcs: List of TensorFlow ConcreteFunctions. The list should not contain

duplicate elements.

trackable_obj: tf.AutoTrackable object associated with `funcs`. A

reference to this object needs to be maintained so that Variables do not

get garbage collected since functions have a weak reference to

Variables. This is only required when the tf.AutoTrackable object is not

maintained by the user (e.g. `from_saved_model`).

"""

super(TFLiteConverterV2, self).__init__()

self._funcs = funcs

self._trackable_obj = trackable_obj

@classmethod

def from_concrete_functions(cls, funcs):

"""Creates a TFLiteConverter object from ConcreteFunctions.

Args:

funcs: List of TensorFlow ConcreteFunctions. The list should not contain

duplicate elements.

Returns:

TFLiteConverter object.

Raises:

Invalid input type.

"""

for func in funcs:

if not isinstance(func, _function.ConcreteFunction):

message = "This function takes in a list of ConcreteFunction."

if isinstance(func, _def_function.Function):

message += (" To get the ConcreteFunction from a Function,"

" call from_concrete_function.")

raise ValueError(message)

return cls(funcs)

@classmethod

def from_saved_model(cls, saved_model_dir, signature_keys=None, tags=None):

"""Creates a TFLiteConverter object from a SavedModel directory.

Args:

saved_model_dir: SavedModel directory to convert.

signature_keys: List of keys identifying SignatureDef containing inputs

and outputs. Elements should not be duplicated. By default the

`signatures` attribute of the MetaGraphdef is used. (default

saved_model.signatures)

tags: Set of tags identifying the MetaGraphDef within the SavedModel to

analyze. All tags in the tag set must be present. (default set(SERVING))

Returns:

TFLiteConverter object.

Raises:

Invalid signature keys.

"""

# Ensures any graphs created in Eager mode are able to run. This is required

# in order to create a tf.estimator.Exporter that exports a TFLite model.

with context.eager_mode():

saved_model = _load(saved_model_dir, tags)

if not signature_keys:

signature_keys = saved_model.signatures

funcs = []

for key in signature_keys:

if key not in saved_model.signatures:

raise ValueError("Invalid signature key '{}' found. Valid keys are "

"'{}'.".format(key, ",".join(saved_model.signatures)))

funcs.append(saved_model.signatures[key])

return cls(funcs, saved_model)

@classmethod

def from_keras_model(cls, model):

"""Creates a TFLiteConverter object from a Keras model.

Args:

model: tf.Keras.Model

Returns:

TFLiteConverter object.

"""

func = _saving_utils.trace_model_call(model)

concrete_func = func.get_concrete_function()

return cls([concrete_func])

def convert(self):

"""Converts a TensorFlow GraphDef based on instance variables.

Returns:

The converted data in serialized format.

Raises:

ValueError:

Multiple concrete functions are specified.

Input shape is not specified.

Invalid quantization parameters.

"""

# TODO(b/130297984): Add support for converting multiple function.

if len(self._funcs) != 1:

raise ValueError("This converter can only convert a single "

"ConcreteFunction. Converting multiple functions is "

"under development.")

frozen_func = _convert_to_constants.convert_variables_to_constants_v2(

self._funcs[0], lower_control_flow=False)

input_tensors = [

tensor for tensor in frozen_func.inputs

if tensor.dtype != _dtypes.resource

]

output_tensors = frozen_func.outputs

# Run a Grappler pass.

graph_def = frozen_func.graph.as_graph_def()

graph_def = _run_graph_optimizations(

graph_def,

input_tensors,

output_tensors,

config=self._grappler_config(),

graph=frozen_func.graph)

# Checks dimensions in input tensor.

for tensor in input_tensors:

# Note that shape_list might be empty for scalar shapes.

shape_list = tensor.shape.as_list()

if None in shape_list[1:]:

raise ValueError(

"None is only supported in the 1st dimension. Tensor '{0}' has "

"invalid shape '{1}'.".format(_get_tensor_name(tensor), shape_list))

elif shape_list and shape_list[0] is None:

# Set the batch size to 1 if undefined.

shape = tensor.shape.as_list()

shape[0] = 1

tensor.set_shape(shape)

self._validate_quantization()

self._validate_representative_dataset()

self._debug_info = _get_debug_info(

_build_debug_info_func(self._funcs[0].graph), graph_def)

converter_kwargs = self._get_base_converter_args()

# Converts model.

result = _toco_convert_impl(

input_data=graph_def,

input_tensors=input_tensors,

output_tensors=output_tensors,

**converter_kwargs)

if self._is_calibration_quantize():

result = self._calibrate_quantize_model(result, constants.FLOAT,

constants.FLOAT)

return result

@_tf_export(v1=["lite.TFLiteConverter"])

class TFLiteConverter(TFLiteConverterBase):

"""Convert a TensorFlow model into `output_format`.

This is used to convert from a TensorFlow GraphDef, SavedModel or tf.keras

model into either a TFLite FlatBuffer or graph visualization.

Attributes:

inference_type: Target data type of real-number arrays in the output file.

Must be `{tf.float32, tf.uint8}`. If `optimzations` are provided, this

parameter is ignored. (default tf.float32)

inference_input_type: Target data type of real-number input arrays. Allows

for a different type for input arrays.

If an integer type is provided and `optimizations` are not used,

`quantized_inputs_stats` must be provided.

If `inference_type` is tf.uint8, signaling conversion to a fully quantized

model from a quantization-aware trained input model, then

`inference_input_type` defaults to tf.uint8.

In all other cases, `inference_input_type` defaults to tf.float32.

Must be `{tf.float32, tf.uint8, tf.int8}`

inference_output_type: Target data type of real-number output arrays. Allows

for a different type for output arrays.

If `inference_type` is tf.uint8, signaling conversion to a fully quantized

model from a quantization-aware trained output model, then

`inference_output_type` defaults to tf.uint8.

In all other cases, `inference_output_type` must be tf.float32, an error

will be thrown otherwise.

Must be `{tf.float32, tf.uint8, tf.int8}`

output_format: Output file format. Currently must be `{TFLITE,

GRAPHVIZ_DOT}`. (default TFLITE)

quantized_input_stats: Dict of strings representing input tensor names

mapped to tuple of floats representing the mean and standard deviation

of the training data (e.g., {"foo" : (0., 1.)}). Only need if

`inference_input_type` is `QUANTIZED_UINT8`.

real_input_value = (quantized_input_value - mean_value) / std_dev_value.

(default {})

default_ranges_stats: Tuple of integers representing (min, max) range values

for all arrays without a specified range. Intended for experimenting with

quantization via "dummy quantization". (default None)

drop_control_dependency: Boolean indicating whether to drop control

dependencies silently. This is due to TFLite not supporting control

dependencies. (default True)

reorder_across_fake_quant: Boolean indicating whether to reorder FakeQuant

nodes in unexpected locations. Used when the location of the FakeQuant

nodes is preventing graph transformations necessary to convert the graph.

Results in a graph that differs from the quantized training graph,

potentially causing differing arithmetic behavior. (default False)

change_concat_input_ranges: Boolean to change behavior of min/max ranges for

inputs and outputs of the concat operator for quantized models. Changes

the ranges of concat operator overlap when true. (default False)

allow_custom_ops: Boolean indicating whether to allow custom operations.

When false any unknown operation is an error. When true, custom ops are

created for any op that is unknown. The developer will need to provide

these to the TensorFlow Lite runtime with a custom resolver.

(default False)

post_training_quantize: Deprecated. Please specify `[Optimize.DEFAULT]` for

`optimizations` instead. Boolean indicating whether to quantize the

weights of the converted float model. Model size will be reduced and

there will be latency improvements (at the cost of accuracy).

(default False)

dump_graphviz_dir: Full filepath of folder to dump the graphs at various

stages of processing GraphViz .dot files. Preferred over

--output_format=GRAPHVIZ_DOT in order to keep the requirements of the

output file. (default None)

dump_graphviz_video: Boolean indicating whether to dump the graph after

every graph transformation. (default False)

target_ops: Deprecated. Please specify `target_spec.supported_ops` instead.

Set of OpsSet options indicating which converter to use.

(default set([OpsSet.TFLITE_BUILTINS]))

target_spec: Experimental flag, subject to change. Specification of target

device.

optimizations: Experimental flag, subject to change. A list of optimizations

to apply when converting the model. E.g. `[Optimize.DEFAULT]`

representative_dataset: A representative dataset that can be used to

generate input and output samples for the model. The converter can use

the dataset to evaluate different optimizations.

experimental_enable_mlir_converter: Experimental flag, subject to change.

Enables the MLIR converter instead of the TOCO converter.

Example usage:

```python

# Converting a GraphDef from session.

converter = lite.TFLiteConverter.from_session(sess, in_tensors, out_tensors)

tflite_model = converter.convert()

open("converted_model.tflite", "wb").write(tflite_model)

# Converting a GraphDef from file.

converter = lite.TFLiteConverter.from_frozen_graph(

graph_def_file, input_arrays, output_arrays)

tflite_model = converter.convert()

open("converted_model.tflite", "wb").write(tflite_model)

# Converting a SavedModel.

converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)

tflite_model = converter.convert()

open("converted_model.tflite", "wb").write(tflite_model)

# Converting a tf.keras model.

converter = lite.TFLiteConverter.from_keras_model_file(keras_model)

tflite_model = converter.convert()

open("converted_model.tflite", "wb").write(tflite_model)

```

"""

def __init__(self,

graph_def,

input_tensors,

output_tensors,

input_arrays_with_shape=None,

output_arrays=None,

experimental_debug_info_func=None):

"""Constructor for TFLiteConverter.

Args:

graph_def: Frozen TensorFlow GraphDef.

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).

input_arrays_with_shape: Tuple of strings representing input tensor names

and list of integers representing input shapes

(e.g., [("foo" : [1, 16, 16, 3])]). Use only when graph cannot be loaded

into TensorFlow and when `input_tensors` and `output_tensors` are

None. (default None)

output_arrays: List of output tensors to freeze graph with. Use only when

graph cannot be loaded into TensorFlow and when `input_tensors` and

`output_tensors` are None. (default None)

experimental_debug_info_func: An experimental function to retrieve the

graph debug info for a set of nodes from the `graph_def`.

Raises:

ValueError: Invalid arguments.

"""

super(TFLiteConverter, self).__init__()

self._graph_def = graph_def

self._input_tensors = input_tensors

self._output_tensors = output_tensors

self.inference_type = constants.FLOAT

self.inference_input_type = None

self.inference_output_type = None

self.output_format = constants.TFLITE

self.quantized_input_stats = {}

self.default_ranges_stats = None

self.drop_control_dependency = True

self.reorder_across_fake_quant = False

self.change_concat_input_ranges = False

self._post_training_quantize = False

self.dump_graphviz_dir = None

self.dump_graphviz_video = False

self._debug_info_func = experimental_debug_info_func

# Attributes are used by models that cannot be loaded into TensorFlow.

if not self._has_valid_tensors():

if not input_arrays_with_shape or not output_arrays:

raise ValueError(

"If input_tensors and output_tensors are None, both "

"input_arrays_with_shape and output_arrays must be defined.")

self._input_arrays_with_shape = input_arrays_with_shape

self._output_arrays = output_arrays

@classmethod

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,

experimental_debug_info_func=_build_debug_info_func(sess.graph))

@classmethod

def from_frozen_graph(cls,

graph_def_file,

input_arrays,

output_arrays,

input_shapes=None):

"""Creates a TFLiteConverter class from a file containing a frozen GraphDef.

Args:

graph_def_file: Full filepath of file containing frozen GraphDef.

input_arrays: List of input tensors to freeze graph with.

output_arrays: List of output tensors to freeze graph with.

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)

Returns:

TFLiteConverter class.

Raises:

IOError:

File not found.

Unable to parse input file.

ValueError:

The graph is not frozen.

input_arrays or output_arrays contains an invalid tensor name.

input_shapes is not correctly defined when required

"""

with _ops.Graph().as_default():

with _session.Session() as sess:

# Read GraphDef from file.

if not _file_io.file_exists(graph_def_file):

raise IOError("File '{0}' does not exist.".format(graph_def_file))

with _file_io.FileIO(graph_def_file, "rb") as f:

file_content = f.read()

try:

graph_def = _graph_pb2.GraphDef()

graph_def.ParseFromString(file_content)

except (_text_format.ParseError, DecodeError):

try:

print("Ignore 'tcmalloc: large alloc' warnings.")

if not isinstance(file_content, str):

if PY3:

file_content = file_content.decode("utf-8")

else:

file_content = file_content.encode("utf-8")

graph_def = _graph_pb2.GraphDef()

_text_format.Merge(file_content, graph_def)

except (_text_format.ParseError, DecodeError):

raise IOError(

"Unable to parse input file '{}'.".format(graph_def_file))

# Handles models with custom TFLite ops that cannot be resolved in

# TensorFlow.

load_model_in_session = True

try:

_import_graph_def(graph_def, name="")

except _NotFoundError:

load_model_in_session = False

if load_model_in_session:

# Check if graph is frozen.

if not _is_frozen_graph(sess):

raise ValueError("Please freeze the graph using freeze_graph.py.")

# Get input and output tensors.

input_tensors = _get_tensors_from_tensor_names(

sess.graph, input_arrays)

output_tensors = _get_tensors_from_tensor_names(

sess.graph, output_arrays)

_set_tensor_shapes(input_tensors, input_shapes)

return cls(sess.graph_def, input_tensors, output_tensors)

else:

if not input_shapes:

raise ValueError("input_shapes must be defined for this model.")

if set(input_arrays) != set(input_shapes.keys()):

raise ValueError("input_shapes must contain a value for each item "

"in input_array.")

input_arrays_with_shape = [

(name, input_shapes[name]) for name in input_arrays

]

return cls(

graph_def,

input_tensors=None,

output_tensors=None,

input_arrays_with_shape=input_arrays_with_shape,

output_arrays=output_arrays)

@classmethod

def from_saved_model(cls,

saved_model_dir,

input_arrays=None,

input_shapes=None,

output_arrays=None,

tag_set=None,

signature_key=None):

"""Creates a TFLiteConverter class from a SavedModel.

Args:

saved_model_dir: SavedModel directory to convert.

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)

tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to

analyze. All tags in the tag set must be present. (default set("serve"))

signature_key: Key identifying SignatureDef containing inputs and outputs.

(default DEFAULT_SERVING_SIGNATURE_DEF_KEY)

Returns:

TFLiteConverter class.

"""

if tag_set is None:

tag_set = set([_tag_constants.SERVING])

if signature_key is None:

signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY

result = _freeze_saved_model(saved_model_dir, input_arrays, input_shapes,

output_arrays, tag_set, signature_key)

return cls(

graph_def=result[0],

input_tensors=result[1],

output_tensors=result[2],

experimental_debug_info_func=_build_debug_info_func(result[3]))

@classmethod

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 __setattr__(self, name, value):

if name == "post_training_quantize":

warnings.warn("Property %s is deprecated, "

"please use optimizations=[Optimize.DEFAULT]"

" instead." % name)

if value:

self.optimizations = [Optimize.DEFAULT]

else:

self.optimizations = []

return

if name == "target_ops":

warnings.warn("Property %s is deprecated, please use "

"target_spec.supported_ops instead." % name)

self.target_spec.supported_ops = value

return

object.__setattr__(self, name, value)

def __getattribute__(self, name):

if name == "post_training_quantize":

warnings.warn("Property %s is deprecated, "

"please use optimizations=[Optimize.DEFAULT]"

" instead." % name)

return Optimize.DEFAULT in set(self.optimizations)

if name == "target_ops":

warnings.warn("Property %s is deprecated, please use "

"target_spec.supported_ops instead." % name)

return self.target_spec.supported_ops

return object.__getattribute__(self, name)

def convert(self):

"""Converts a TensorFlow GraphDef based on instance variables.

Returns:

The converted data in serialized format. Either a TFLite Flatbuffer or a

Graphviz graph depending on value in `output_format`.

Raises:

ValueError:

Input shape is not specified.

None value for dimension in input_tensor.

"""

# Checks dimensions in input tensor.

if self._has_valid_tensors():

for tensor in self._input_tensors:

shape = tensor.shape

if not shape:

raise ValueError("Provide an input shape for input array "

"'{0}'.".format(_get_tensor_name(tensor)))

# Note that shape_list might be empty for scalar shapes.

shape_list = shape.as_list()

if None in shape_list[1:]:

raise ValueError(

"None is only supported in the 1st dimension. Tensor '{0}' has "

"invalid shape '{1}'.".format(

_get_tensor_name(tensor), shape_list))

elif shape_list and shape_list[0] is None:

self._set_batch_size(batch_size=1)

# Get quantization stats. Ensures there is one stat per name if the stats

# are specified.

if self.quantized_input_stats:

quantized_stats = []

invalid_stats = []

for name in self.get_input_arrays():

if name in self.quantized_input_stats:

quantized_stats.append(self.quantized_input_stats[name])

else:

invalid_stats.append(name)

if invalid_stats:

raise ValueError("Quantization input stats are not available for input "

"tensors '{0}'.".format(",".join(invalid_stats)))

else:

quantized_stats = None

self._validate_quantization()

self._validate_representative_dataset()

toco_inference_input_type = self.inference_input_type

inference_input_type = self.inference_input_type

inference_output_type = self.inference_output_type

post_training_optimize = self._is_post_training_optimize()

if post_training_optimize:

# Post training optimizations require that TOCO outputs a float model.

if self.inference_type != constants.FLOAT:

raise ValueError(

"`optimizations` require that `inference_type` is set to float.")

toco_inference_input_type = constants.FLOAT

# Set up default values.

if inference_input_type is None:

inference_input_type = constants.FLOAT

if inference_output_type is None:

inference_output_type = constants.FLOAT

weight_only_quantize = self._is_int8_weight_only_quantize()

if weight_only_quantize:

# Currently, weight only quantization requires float inputs and outputs.

if (inference_input_type != constants.FLOAT or

inference_output_type != constants.FLOAT):

raise ValueError(

"Provide an inference_input_type and inference_output_type of type "

"tf.float32.")

if not post_training_optimize and self.inference_output_type is not None:

raise ValueError(

"inference_output_type is currently not supported if optimizations "

"are not enabled.")

optimized_graph = self._graph_def

if self.inference_type != constants.QUANTIZED_UINT8:

try:

optimized_graph = _run_graph_optimizations(

self._graph_def,

self._input_tensors,

self._output_tensors,

config=self._grappler_config())

except Exception:

optimized_graph = self._graph_def

self._debug_info = _get_debug_info(self._debug_info_func, optimized_graph)

converter_kwargs = self._get_base_converter_args()

converter_kwargs.update({

"inference_type": self.inference_type,

"inference_input_type": toco_inference_input_type,

"output_format": self.output_format,

"quantized_input_stats": quantized_stats,

"default_ranges_stats": self.default_ranges_stats,

"drop_control_dependency": self.drop_control_dependency,

"reorder_across_fake_quant": self.reorder_across_fake_quant,

"change_concat_input_ranges": self.change_concat_input_ranges,

"dump_graphviz_dir": self.dump_graphviz_dir,

"dump_graphviz_video": self.dump_graphviz_video

})

# Converts model.

if self._has_valid_tensors():

result = _toco_convert_impl(

input_data=optimized_graph,

input_tensors=self._input_tensors,

output_tensors=self._output_tensors,

**converter_kwargs)

else:

result = _toco_convert_graph_def(

input_data=optimized_graph,

input_arrays_with_shape=self._input_arrays_with_shape,

output_arrays=self._output_arrays,

**converter_kwargs)

if self._is_calibration_quantize():

result = self._calibrate_quantize_model(result, inference_input_type,

inference_output_type)

return result

def get_input_arrays(self):

"""Returns a list of the names of the input tensors.

Returns: