From 0c0555d94ba1f641bb722a918d3acf6fd4742fdc Mon Sep 17 00:00:00 2001
From: Jaesung Chung <jaesung@google.com>
Date: Wed, 6 May 2020 23:23:12 -0700
Subject: [PATCH] Split merged TFLiteConverter implementations into frozen
graph converter and saved model converter
PiperOrigin-RevId: 310301390
Change-Id: I021c1fa678d6367226e1a19e646bb6d0ff9769e3
---
tensorflow/lite/python/lite.py | 1394 ++++++++++-------
tensorflow/lite/python/lite_test.py | 37 +
tensorflow/lite/python/lite_v2_test.py | 21 +
.../tensorflow.lite.-t-f-lite-converter.pbtxt | 4 +-
.../tensorflow.lite.-t-f-lite-converter.pbtxt | 4 +-
5 files changed, 910 insertions(+), 550 deletions(-)
diff --git a/tensorflow/lite/python/lite.py b/tensorflow/lite/python/lite.py
index 15704a6da76..b2d58ec8746 100644
--- a/tensorflow/lite/python/lite.py
+++ b/tensorflow/lite/python/lite.py
@@ -296,9 +296,9 @@ class TFLiteConverterBase(object):
# The 'GraphDebugInfo' contains the stack traces of all the original nodes
# in the `GraphDef` to the converter.
self._debug_info = None
- self._saved_model_dir = None
+ self.saved_model_dir = None
self._saved_model_tags = None
- self._saved_model_version = None
+ self._saved_model_version = 0
self._saved_model_exported_names = []
self._experimental_sparsify_model = False
@@ -366,9 +366,9 @@ class TFLiteConverterBase(object):
"enable_mlir_converter": self.experimental_new_converter,
}
- if self._saved_model_dir:
+ if self.saved_model_dir:
args.update({
- "saved_model_dir": self._saved_model_dir,
+ "saved_model_dir": self.saved_model_dir,
"saved_model_version": self._saved_model_version,
"saved_model_tags": self._saved_model_tags,
"saved_model_exported_names": self._saved_model_exported_names,
@@ -387,19 +387,19 @@ class TFLiteConverterBase(object):
def _parse_saved_model_args(self):
"""Parses SavedModel arguments from the given Keras/RNN SavedModel."""
if not self.experimental_new_converter:
- self._saved_model_dir = None
+ self.saved_model_dir = None
return
- if self._saved_model_dir:
+ if self.saved_model_dir:
try:
saved_model_proto, _ = (
- _parse_saved_model_with_debug_info(self._saved_model_dir))
+ _parse_saved_model_with_debug_info(self.saved_model_dir))
except OSError:
# If it fails to read the given saved model, it will fall back to the
# frozen graph def path.
- self._saved_model_dir = None
+ self.saved_model_dir = None
return
if not self._contains_function_with_implements_attr(saved_model_proto):
- self._saved_model_dir = None
+ self.saved_model_dir = None
else:
self._saved_model_exported_names = []
self._saved_model_version = saved_model_proto.saved_model_schema_version
@@ -409,179 +409,18 @@ class TFLiteConverterBase(object):
self._saved_model_version))
-@_tf_export("lite.TFLiteConverter", v1=[])
-class TFLiteConverterV2(TFLiteConverterBase):
- """Converts a TensorFlow model into TensorFlow Lite model.
+class TFLiteConverterBaseV2(TFLiteConverterBase):
+ """Converter subclass to share functionality between V2 converters."""
- 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. Note that this is an optional
- attribute but it is necessary if INT8 is the only support builtin ops in
- target ops.
- experimental_new_converter: Experimental flag, subject to change.
- Enables MLIR-based conversion instead of TOCO conversion.
- 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,
- saved_model_dir=None,
- saved_model_tags=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`).
- saved_model_dir: Directory of the SavedModel. This argument can be null
- when it creates via the from_keras_model and from_concrete_function
- methods.
- saved_model_tags: Set of tags identifying the MetaGraphDef within the
- SavedModel to analyze. All tags in the tag set must be present. (default
- set(SERVING)). This argument will be available when the saved model dir
- argument is set.
- """
- super(TFLiteConverterV2, self).__init__()
- self._funcs = funcs
- self._trackable_obj = trackable_obj
- self._saved_model_dir = saved_model_dir
- self._saved_model_tags = saved_model_tags
-
- @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. Currently converter can only convert a single
- ConcreteFunction. Converting multiple functions is under development.
-
- 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 get_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.
- """
- # When run without eager enabled, this will return the legacy
- # TFLiteConverter.
- if not context.executing_eagerly():
- signature_key = None
- if signature_keys:
- if len(signature_keys) != 1:
- raise ValueError("Only support a single signature key.")
- else:
- signature_key = signature_keys[0]
- logging.warning("Invoking the TF1 implementation of TFLiteConverter "
- "because eager is disabled. Consider enabling eager.")
- return TFLiteConverter.from_saved_model(saved_model_dir,
- signature_key=signature_key,
- tag_set=tags)
-
- # 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.
- if tags is None:
- tags = set([_tag_constants.SERVING])
-
- 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, saved_model_dir, tags)
-
- @classmethod
- def from_keras_model(cls, model):
- """Creates a TFLiteConverter object from a Keras model.
-
- Args:
- model: tf.Keras.Model
-
- Returns:
- TFLiteConverter object.
- """
- input_signature = None
- # If the model's call is not a `tf.function`, then we need to first get its
- # input signature from `model_input_signature` method. We can't directly
- # call `trace_model_call` because otherwise the batch dimension is set
- # to None.
- # Once we have better support for dynamic shapes, we can remove this.
- if not isinstance(model.call, _def_function.Function):
- # Pass `keep_original_batch_size=True` will ensure that we get an input
- # signature including the batch dimension specified by the user.
- input_signature = _saving_utils.model_input_signature(
- model, keep_original_batch_size=True)
-
- func = _saving_utils.trace_model_call(model, input_signature)
- concrete_func = func.get_concrete_function()
- return cls([concrete_func])
-
- def convert(self):
+ def _convert(self, graph_def, input_tensors, output_tensors):
"""Converts a TensorFlow GraphDef based on instance variables.
+ 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).
+
Returns:
The converted data in serialized format.
@@ -592,62 +431,6 @@ class TFLiteConverterV2(TFLiteConverterBase):
Input shape is not specified.
Invalid quantization parameters.
"""
- # TODO(b/130297984): Add support for converting multiple function.
-
- if len(self._funcs) == 0:
- raise ValueError("No ConcreteFunction is specified.")
-
- if len(self._funcs) > 1:
- raise ValueError("This converter can only convert a single "
- "ConcreteFunction. Converting multiple functions is "
- "under development.")
-
- # Parses SavedModel argument.
- self._parse_saved_model_args()
-
- # graph_def is used here to preserve the node bug information
- if self._saved_model_dir:
- graph = _ops.Graph()
- saved_model = _loader_impl.SavedModelLoader(self._saved_model_dir)
- saved_model.load_graph(graph, tags=self._saved_model_tags)
- meta_graph = saved_model.get_meta_graph_def_from_tags(
- self._saved_model_tags)
- signature_def = meta_graph.signature_def[
- _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
- input_tensors = [
- graph.get_tensor_by_name(signature_def.inputs[key].name)
- for key in signature_def.inputs
- ]
- output_tensors = [
- graph.get_tensor_by_name(signature_def.outputs[key].name)
- for key in signature_def.outputs
- ]
- self._graph_def = graph_def = meta_graph.graph_def
- else:
- frozen_func, graph_def = (
- _convert_to_constants.convert_variables_to_constants_v2_as_graph(
- self._funcs[0], lower_control_flow=False))
- self._graph_def = graph_def
-
- input_tensors = [
- tensor for tensor in frozen_func.inputs
- if tensor.dtype != _dtypes.resource
- ]
- output_tensors = frozen_func.outputs
-
- # Run a Grappler pass.
- grappler_config = self._grappler_config()
- # Skip running grappler when there are no optimizers to run. If not,
- # grappler will run with the default optimizer set and it will lead to
- # causing an unexpected behavior.
- if grappler_config.graph_options.rewrite_options.optimizers:
- graph_def = _run_graph_optimizations(
- graph_def,
- input_tensors,
- output_tensors,
- config=grappler_config,
- graph=frozen_func.graph)
-
quant_mode = QuantizationMode(self.optimizations, self.target_spec,
self.representative_dataset, graph_def)
@@ -724,8 +507,820 @@ class TFLiteConverterV2(TFLiteConverterBase):
return result
+class TFLiteSavedModelConverterV2(TFLiteConverterBaseV2):
+ """Converts the given SavedModel into TensorFlow Lite model.
+
+ Attributes:
+ saved_model_dir: Directory of the SavedModel.
+ """
+
+ def __init__(self,
+ saved_model_dir,
+ saved_model_tags=None,
+ saved_model_exported_names=None,
+ trackable_obj=None):
+ """Constructor for TFLiteConverter.
+
+ Args:
+ saved_model_dir: Directory of the SavedModel.
+ saved_model_tags: Set of tags identifying the MetaGraphDef within the
+ SavedModel to analyze. All tags in the tag set must be present. (default
+ set(SERVING)).
+ saved_model_exported_names: Names to be exported (default: export all)
+ when the saved model import path is on.
+ 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(TFLiteSavedModelConverterV2, self).__init__()
+ self.saved_model_dir = saved_model_dir
+ self._saved_model_tags = saved_model_tags
+ self._saved_model_exported_names = saved_model_exported_names
+ self._trackable_obj = trackable_obj
+ self._parse_saved_model_args()
+
+ def convert(self):
+ """Converts a TensorFlow GraphDef based on instance variables.
+
+ Returns:
+ The converted data in serialized format.
+
+ Raises:
+ ValueError:
+ No concrete functions is specified.
+ Multiple concrete functions are specified.
+ Input shape is not specified.
+ Invalid quantization parameters.
+ """
+ graph = _ops.Graph()
+ saved_model = _loader_impl.SavedModelLoader(self.saved_model_dir)
+ saved_model.load_graph(graph, tags=self._saved_model_tags)
+ meta_graph = saved_model.get_meta_graph_def_from_tags(
+ self._saved_model_tags)
+ signature_def = meta_graph.signature_def[
+ _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
+ input_tensors = [
+ graph.get_tensor_by_name(signature_def.inputs[key].name)
+ for key in signature_def.inputs
+ ]
+ output_tensors = [
+ graph.get_tensor_by_name(signature_def.outputs[key].name)
+ for key in signature_def.outputs
+ ]
+ return self._convert(meta_graph.graph_def, input_tensors, output_tensors)
+
+
+class TFLiteFrozenGraphConverterV2(TFLiteConverterBaseV2):
+ """Converts the given frozen graph into TensorFlow Lite model."""
+
+ 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(TFLiteFrozenGraphConverterV2, self).__init__()
+ self._funcs = funcs
+ self._trackable_obj = trackable_obj
+
+ def convert(self):
+ """Converts a TensorFlow GraphDef based on instance variables.
+
+ Returns:
+ The converted data in serialized format.
+
+ Raises:
+ ValueError:
+ No concrete functions is specified.
+ 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) == 0:
+ raise ValueError("No ConcreteFunction is specified.")
+
+ if len(self._funcs) > 1:
+ raise ValueError("This converter can only convert a single "
+ "ConcreteFunction. Converting multiple functions is "
+ "under development.")
+
+ frozen_func, graph_def = (
+ _convert_to_constants.convert_variables_to_constants_v2_as_graph(
+ 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.
+ grappler_config = self._grappler_config()
+ # Skip running grappler when there are no optimizers to run. If not,
+ # grappler will run with the default optimizer set and it will lead to
+ # causing an unexpected behavior.
+ if grappler_config.graph_options.rewrite_options.optimizers:
+ graph_def = _run_graph_optimizations(
+ graph_def,
+ input_tensors,
+ output_tensors,
+ config=grappler_config,
+ graph=frozen_func.graph)
+
+ return self._convert(graph_def, input_tensors, output_tensors)
+
+
+@_tf_export("lite.TFLiteConverter", v1=[])
+class TFLiteConverterV2(TFLiteFrozenGraphConverterV2):
+ """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. Note that this is an optional
+ attribute but it is necessary if INT8 is the only support builtin ops in
+ target ops.
+ experimental_new_converter: Experimental flag, subject to change.
+ Enables MLIR-based conversion instead of TOCO conversion.
+ 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()
+ ```
+ """
+
+ # pylint: disable=useless-super-delegation
+ 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__(funcs, 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. Currently converter can only convert a single
+ ConcreteFunction. Converting multiple functions is under development.
+
+ 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 get_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.
+ """
+ # When run without eager enabled, this will return the legacy
+ # TFLiteConverter.
+ if not context.executing_eagerly():
+ signature_key = None
+ if signature_keys:
+ if len(signature_keys) != 1:
+ raise ValueError("Only support a single signature key.")
+ else:
+ signature_key = signature_keys[0]
+ logging.warning("Invoking the TF1 implementation of TFLiteConverter "
+ "because eager is disabled. Consider enabling eager.")
+ return TFLiteConverter.from_saved_model(saved_model_dir,
+ signature_key=signature_key,
+ tag_set=tags)
+
+ # 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.
+ if tags is None:
+ tags = set([_tag_constants.SERVING])
+
+ 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])
+
+ saved_model_converter = TFLiteSavedModelConverterV2(saved_model_dir, tags,
+ signature_keys,
+ saved_model)
+ if saved_model_converter.saved_model_dir:
+ return saved_model_converter
+
+ 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.
+ """
+ input_signature = None
+ # If the model's call is not a `tf.function`, then we need to first get its
+ # input signature from `model_input_signature` method. We can't directly
+ # call `trace_model_call` because otherwise the batch dimension is set
+ # to None.
+ # Once we have better support for dynamic shapes, we can remove this.
+ if not isinstance(model.call, _def_function.Function):
+ # Pass `keep_original_batch_size=True` will ensure that we get an input
+ # signature including the batch dimension specified by the user.
+ input_signature = _saving_utils.model_input_signature(
+ model, keep_original_batch_size=True)
+
+ func = _saving_utils.trace_model_call(model, input_signature)
+ concrete_func = func.get_concrete_function()
+ return cls([concrete_func])
+
+ # pylint: disable=useless-super-delegation
+ def convert(self):
+ """Converts a TensorFlow GraphDef based on instance variables.
+
+ Returns:
+ The converted data in serialized format.
+
+ Raises:
+ ValueError:
+ No concrete functions is specified.
+ Multiple concrete functions are specified.
+ Input shape is not specified.
+ Invalid quantization parameters.
+ """
+ return super(TFLiteConverterV2, self).convert()
+
+
+class TFLiteConverterBaseV1(TFLiteConverterBase):
+ """Converter subclass to share functionality between V1 converters.
+
+ 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)
+ conversion_summary_dir: A string indicating the path to the generated
+ conversion logs.
+ 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_new_converter: Experimental flag, subject to change. Enables
+ MLIR-based conversion instead of TOCO conversion.
+ """
+
+ def __init__(self, experimental_debug_info_func):
+ """Constructor for TFLiteConverter.
+
+ Args:
+ experimental_debug_info_func: An experimental function to retrieve the
+ graph debug info for a set of nodes from the `graph_def`.
+ """
+ super(TFLiteConverterBaseV1, self).__init__()
+ 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.dump_graphviz_dir = None
+ self.dump_graphviz_video = False
+ self.conversion_summary_dir = None
+ self._debug_info_func = experimental_debug_info_func
+ self._custom_opdefs = None
+
+ 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 _validate_quantized_input_stats(self, converter_kwargs):
+ """Ensure quantized_input_stats provided if required."""
+
+ quantized_types = frozenset({constants.INT8, constants.QUANTIZED_UINT8})
+
+ requires_quantized_input_stats = (
+ (converter_kwargs["inference_type"] in quantized_types or
+ converter_kwargs["inference_input_type"] in quantized_types) and
+ not converter_kwargs["post_training_quantize"])
+
+ if (requires_quantized_input_stats and
+ not converter_kwargs["quantized_input_stats"]):
+ raise ValueError("std_dev and mean must be defined when inference_type "
+ "or inference_input_type is QUANTIZED_UINT8 or INT8.")
+
+ 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.
+ """
+ quant_mode = QuantizationMode(self.optimizations, self.target_spec,
+ self.representative_dataset, self._graph_def)
+
+ if (not self._is_unknown_shapes_allowed() and self._has_valid_tensors()):
+ # Checks dimensions in input tensor.
+ 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
+
+ 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 = (
+ quant_mode.post_training_int8_no_float() or
+ quant_mode.post_training_int8_allow_float() or
+ quant_mode.post_training_dynamic_range_int8() or
+ quant_mode.post_training_fp16())
+ 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 = (
+ quant_mode.post_training_dynamic_range_int8() or
+ quant_mode.post_training_fp16())
+ 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 not self.saved_model_dir:
+ # if it is not uint8 or int8 with post-training quantization, it is not
+ # quantization aware training, then graph optimization is applied.
+ # Graph optimization is disabled for quantization aware training.
+ if (self.inference_type != constants.QUANTIZED_UINT8 or
+ (self.inference_type == constants.INT8 and
+ (post_training_optimize or weight_only_quantize))):
+ try:
+ # TODO(b/150163103): Merge `disabling lower using switch merge' calls.
+ # Grappler will also try to lower while loop into switch merge
+ # representation which is undesired for Ophints, so we simply remove
+ # those attributes to prevent Grappler from doing so.
+ graph_def = _convert_to_constants.disable_lower_using_switch_merge(
+ optimized_graph)
+ # Run function inlining optimization to ensure any models generated
+ # through the from_frozen_graph path have been inlined.
+ optimized_graph = _run_graph_optimizations(
+ graph_def,
+ self._input_tensors,
+ self._output_tensors,
+ config=self._grappler_config(["function"]))
+ except Exception: # pylint: disable=broad-except
+ optimized_graph = self._graph_def
+
+ self._debug_info = _get_debug_info(self._debug_info_func, optimized_graph)
+
+ converter_kwargs = self._get_base_converter_args()
+
+ if quant_mode.post_training_dynamic_range_int8():
+ converter_kwargs.update({
+ "post_training_quantize": True,
+ })
+ elif quant_mode.post_training_fp16():
+ converter_kwargs.update({
+ "post_training_quantize": True,
+ "quantize_to_float16": True,
+ })
+
+ 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,
+ "conversion_summary_dir": self.conversion_summary_dir,
+ "custom_opdefs": self._custom_opdefs,
+ })
+
+ if not self.experimental_new_converter:
+ logging.warning(
+ "Please consider switching to use new converter by setting "
+ "experimental_new_converter to true. "
+ "Old converter (TOCO) is deprecated and flow will be switched on "
+ "by default to use new converter soon.")
+ else:
+ logging.info("Using experimental converter: If you encountered a problem "
+ "please file a bug. You can opt-out "
+ "by setting experimental_new_converter=False")
+
+ self._validate_quantized_input_stats(converter_kwargs)
+
+ # 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 quant_mode.post_training_int8_no_float():
+ result = self._calibrate_quantize_model(result, inference_input_type,
+ inference_output_type, False)
+ elif quant_mode.post_training_int8_allow_float():
+ result = self._calibrate_quantize_model(result, inference_input_type,
+ inference_output_type, True)
+
+ if self._experimental_sparsify_model:
+ result = _mlir_sparsify(result)
+
+ return result
+
+ def get_input_arrays(self):
+ """Returns a list of the names of the input tensors.
+
+ Returns:
+ List of strings.
+ """
+ if self._has_valid_tensors():
+ return [_get_tensor_name(tensor) for tensor in self._input_tensors]
+ else:
+ return [name for name, _ in self._input_arrays_with_shape]
+
+ def _has_valid_tensors(self):
+ """Checks if the input and output tensors have been initialized.
+
+ Returns:
+ Bool.
+ """
+ return self._input_tensors and self._output_tensors
+
+ def _set_batch_size(self, batch_size):
+ """Sets the first dimension of the input tensor to `batch_size`.
+
+ Args:
+ batch_size: Batch size for the model. Replaces the first dimension of an
+ input size array if undefined. (default 1)
+
+ Raises:
+ ValueError: input_tensor is not defined.
+ """
+ if not self._has_valid_tensors():
+ raise ValueError("The batch size cannot be set for this model. Please "
+ "use input_shapes parameter.")
+
+ for tensor in self._input_tensors:
+ shape = tensor.shape.as_list()
+ if shape[0] is None:
+ shape[0] = batch_size
+ tensor.set_shape(shape)
+
+ def _is_unknown_shapes_allowed(self):
+ # Ophint Converted nodes will need the shapes to be known.
+ if _is_ophint_converted(self._graph_def):
+ return False
+
+ if not super(TFLiteConverterBaseV1, self)._is_unknown_shapes_allowed():
+ return False
+
+ # `conversion_summary_dir` calls TOCO. Unknown shapes are only supported by
+ # the MLIR converter.
+ if self.conversion_summary_dir:
+ logging.warning(
+ "`conversion_summary_dir` does not work with unknown shapes. "
+ "Graphs with unknown shapes might be different than when this flag "
+ "is disabled.")
+ return False
+ return True
+
+
+class TFLiteSavedModelConverter(TFLiteConverterBaseV1):
+ """Converts the given SavedModel into TensorFlow Lite model.
+
+ Attributes:
+ saved_model_dir: Directory of the SavedModel.
+ """
+
+ def __init__(self,
+ saved_model_dir,
+ saved_model_tags,
+ saved_model_exported_names,
+ experimental_debug_info_func=None):
+ """Constructor for TFLiteConverter.
+
+ Args:
+ saved_model_dir: Directory of the SavedModel.
+ saved_model_tags: Set of tags identifying the MetaGraphDef within the
+ SavedModel to analyze. All tags in the tag set must be present. (default
+ set(SERVING)).
+ saved_model_exported_names: Names to be exported (default: export all)
+ when the saved model import path is on.
+ 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(TFLiteSavedModelConverter,
+ self).__init__(experimental_debug_info_func)
+ self.saved_model_dir = saved_model_dir
+ self._saved_model_tags = saved_model_tags
+ self._saved_model_exported_names = saved_model_exported_names
+
+ signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
+
+ if len(self._saved_model_exported_names) != 1:
+ raise ValueError("Only support a single signature key.")
+
+ signature_key = self._saved_model_exported_names[0]
+
+ result = _freeze_saved_model(self.saved_model_dir, None, None, None,
+ self._saved_model_tags, signature_key)
+ self._graph_def = result[0]
+ self._input_tensors = result[1]
+ self._output_tensors = result[2]
+ self._parse_saved_model_args()
+
+ 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.
+ """
+ return self._convert()
+
+
+class TFLiteFrozenGraphConverter(TFLiteConverterBaseV1):
+ """Converts the given frozen graph def into TensorFlow Lite 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(TFLiteFrozenGraphConverter,
+ self).__init__(experimental_debug_info_func)
+ self._graph_def = graph_def
+ self._input_tensors = input_tensors
+ self._output_tensors = output_tensors
+
+ # 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
+
+ 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.
+ """
+ return self._convert()
+
+
@_tf_export(v1=["lite.TFLiteConverter"])
-class TFLiteConverter(TFLiteConverterBase):
+class TFLiteConverter(TFLiteFrozenGraphConverter):
"""Convert a TensorFlow model into `output_format`.
This is used to convert from a TensorFlow GraphDef, SavedModel or tf.keras
@@ -831,15 +1426,14 @@ class TFLiteConverter(TFLiteConverterBase):
```
"""
+ # pylint: disable=useless-super-delegation
def __init__(self,
graph_def,
input_tensors,
output_tensors,
input_arrays_with_shape=None,
output_arrays=None,
- experimental_debug_info_func=None,
- saved_model_dir=None,
- saved_model_tags=None):
+ experimental_debug_info_func=None):
"""Constructor for TFLiteConverter.
Args:
@@ -857,47 +1451,14 @@ class TFLiteConverter(TFLiteConverterBase):
`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`.
- saved_model_dir: Directory of the SavedModel. This argument can be null
- when it creates via the from_keras_model and from_concrete_function
- methods.
- saved_model_tags: Set of tags identifying the MetaGraphDef within the
- SavedModel to analyze. All tags in the tag set must be present. (default
- set(SERVING)). This argument will be available when the saved model dir
- argument is set.
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.conversion_summary_dir = None
- self._debug_info_func = experimental_debug_info_func
- self._custom_opdefs = None
- self._saved_model_dir = saved_model_dir
- self._saved_model_tags = saved_model_tags
-
- # 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
+ super(TFLiteConverter,
+ self).__init__(graph_def, input_tensors, output_tensors,
+ input_arrays_with_shape, output_arrays,
+ experimental_debug_info_func)
@classmethod
def from_session(cls, sess, input_tensors, output_tensors):
@@ -1045,15 +1606,19 @@ class TFLiteConverter(TFLiteConverterBase):
if signature_key is None:
signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
+ saved_model_converter = TFLiteSavedModelConverter(saved_model_dir, tag_set,
+ [signature_key])
+ if saved_model_converter.saved_model_dir:
+ return saved_model_converter
+
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]),
- saved_model_dir=saved_model_dir,
- saved_model_tags=tag_set)
+ experimental_debug_info_func=_build_debug_info_func(result[3]))
@classmethod
def from_keras_model_file(cls,
@@ -1128,50 +1693,7 @@ class TFLiteConverter(TFLiteConverterBase):
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 _validate_quantized_input_stats(self, converter_kwargs):
- """Ensure quantized_input_stats provided if required."""
-
- quantized_types = frozenset({constants.INT8, constants.QUANTIZED_UINT8})
-
- requires_quantized_input_stats = (
- (converter_kwargs["inference_type"] in quantized_types or
- converter_kwargs["inference_input_type"] in quantized_types) and
- not converter_kwargs["post_training_quantize"])
-
- if (requires_quantized_input_stats and
- not converter_kwargs["quantized_input_stats"]):
- raise ValueError("std_dev and mean must be defined when inference_type "
- "or inference_input_type is QUANTIZED_UINT8 or INT8.")
-
+ # pylint: disable=useless-super-delegation
def convert(self):
"""Converts a TensorFlow GraphDef based on instance variables.
@@ -1184,231 +1706,7 @@ class TFLiteConverter(TFLiteConverterBase):
Input shape is not specified.
None value for dimension in input_tensor.
"""
- # Parses SavedModel argument.
- self._parse_saved_model_args()
-
- quant_mode = QuantizationMode(self.optimizations, self.target_spec,
- self.representative_dataset, self._graph_def)
-
- # Checks dimensions in input tensor.
- if (not self._is_unknown_shapes_allowed() and 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
-
- 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 = (
- quant_mode.post_training_int8_no_float() or
- quant_mode.post_training_int8_allow_float() or
- quant_mode.post_training_dynamic_range_int8() or
- quant_mode.post_training_fp16())
- 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 = (
- quant_mode.post_training_dynamic_range_int8() or
- quant_mode.post_training_fp16())
- 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 not self._saved_model_dir:
- # if it is not uint8 or int8 with post-training quantization, it is not
- # quantization aware training, then graph optimization is applied.
- # Graph optimization is disabled for quantization aware training.
- if (self.inference_type != constants.QUANTIZED_UINT8 or
- (self.inference_type == constants.INT8 and
- (post_training_optimize or weight_only_quantize))):
- try:
- # TODO(b/150163103): Merge `disabling lower using switch merge' calls.
- # Grappler will also try to lower while loop into switch merge
- # representation which is undesired for Ophints, so we simply remove
- # those attributes to prevent Grappler from doing so.
- graph_def = _convert_to_constants.disable_lower_using_switch_merge(
- optimized_graph)
- # Run function inlining optimization to ensure any models generated
- # through the from_frozen_graph path have been inlined.
- optimized_graph = _run_graph_optimizations(
- graph_def,
- self._input_tensors,
- self._output_tensors,
- config=self._grappler_config(["function"]))
- 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()
-
- if quant_mode.post_training_dynamic_range_int8():
- converter_kwargs.update({
- "post_training_quantize": True,
- })
- elif quant_mode.post_training_fp16():
- converter_kwargs.update({
- "post_training_quantize": True,
- "quantize_to_float16": True,
- })
-
- 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,
- "conversion_summary_dir": self.conversion_summary_dir,
- "custom_opdefs": self._custom_opdefs,
- })
-
- if not self.experimental_new_converter:
- logging.warning(
- "Please consider switching to use new converter by setting "
- "experimental_new_converter to true. "
- "Old converter (TOCO) is deprecated and flow will be switched on "
- "by default to use new converter soon.")
- else:
- logging.info("Using experimental converter: If you encountered a problem "
- "please file a bug. You can opt-out "
- "by setting experimental_new_converter=False")
-
- self._validate_quantized_input_stats(converter_kwargs)
-
- # 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 quant_mode.post_training_int8_no_float():
- result = self._calibrate_quantize_model(result, inference_input_type,
- inference_output_type, False)
- elif quant_mode.post_training_int8_allow_float():
- result = self._calibrate_quantize_model(result, inference_input_type,
- inference_output_type, True)
-
- if self._experimental_sparsify_model:
- result = _mlir_sparsify(result)
-
- return result
-
- def get_input_arrays(self):
- """Returns a list of the names of the input tensors.
-
- Returns:
- List of strings.
- """
- if self._has_valid_tensors():
- return [_get_tensor_name(tensor) for tensor in self._input_tensors]
- else:
- return [name for name, _ in self._input_arrays_with_shape]
-
- def _has_valid_tensors(self):
- """Checks if the input and output tensors have been initialized.
-
- Returns:
- Bool.
- """
- return self._input_tensors and self._output_tensors
-
- def _set_batch_size(self, batch_size):
- """Sets the first dimension of the input tensor to `batch_size`.
-
- Args:
- batch_size: Batch size for the model. Replaces the first dimension of an
- input size array if undefined. (default 1)
-
- Raises:
- ValueError: input_tensor is not defined.
- """
- if not self._has_valid_tensors():
- raise ValueError("The batch size cannot be set for this model. Please "
- "use input_shapes parameter.")
-
- for tensor in self._input_tensors:
- shape = tensor.shape.as_list()
- if shape[0] is None:
- shape[0] = batch_size
- tensor.set_shape(shape)
-
- def _is_unknown_shapes_allowed(self):
- # Ophint Converted nodes will need the shapes to be known.
- if _is_ophint_converted(self._graph_def):
- return False
-
- if not super(TFLiteConverter, self)._is_unknown_shapes_allowed():
- return False
-
- # `conversion_summary_dir` calls TOCO. Unknown shapes are only supported by
- # the MLIR converter.
- if self.conversion_summary_dir:
- logging.warning(
- "`conversion_summary_dir` does not work with unknown shapes. "
- "Graphs with unknown shapes might be different than when this flag "
- "is disabled.")
- return False
- return True
+ return super(TFLiteConverter, self).convert()
@_tf_export(v1=["lite.TocoConverter"])
diff --git a/tensorflow/lite/python/lite_test.py b/tensorflow/lite/python/lite_test.py
index 3ff9fbc3710..530c514eb96 100644
--- a/tensorflow/lite/python/lite_test.py
+++ b/tensorflow/lite/python/lite_test.py
@@ -2318,5 +2318,42 @@ class ImportOpsUtilTest(LiteTest):
self.assertIsNotNone(lite.get_potentially_supported_ops())
+class DefaultConverterAttrsTest(LiteTest):
+
+ def testAttrs(self):
+ with ops.Graph().as_default():
+ in_tensor = array_ops.placeholder(shape=[2, 2], dtype=dtypes.float32)
+ out_tensor = in_tensor + in_tensor
+ sess = session.Session()
+
+ # Convert model.
+ converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+ [out_tensor])
+
+ # Assert output format.
+ self.assertEqual(converter.output_format, lite_constants.TFLITE)
+
+ # Assert the default inference type is float.
+ self.assertEqual(converter.inference_type, lite_constants.FLOAT)
+
+ # Assert the default inference type overrides are None.
+ self.assertIsNone(converter.inference_input_type)
+ self.assertIsNone(converter.inference_output_type)
+
+ # Assert the default quantization options are not set.
+ self.assertEqual(converter.quantized_input_stats, {})
+ self.assertIsNone(converter.default_ranges_stats)
+ self.assertFalse(converter.reorder_across_fake_quant)
+ self.assertFalse(converter.change_concat_input_ranges)
+
+ # Assert dropping control dependency is enabled by default.
+ self.assertTrue(converter.drop_control_dependency)
+
+ # Assert dumping extra information is disabled by default.
+ self.assertIsNone(converter.dump_graphviz_dir)
+ self.assertFalse(converter.dump_graphviz_video)
+ self.assertIsNone(converter.conversion_summary_dir)
+
+
if __name__ == '__main__':
test.main()
diff --git a/tensorflow/lite/python/lite_v2_test.py b/tensorflow/lite/python/lite_v2_test.py
index d351fd492f6..59f326d4b9f 100644
--- a/tensorflow/lite/python/lite_v2_test.py
+++ b/tensorflow/lite/python/lite_v2_test.py
@@ -981,6 +981,27 @@ class UnknownShapes(lite_v2_test_util.ModelTest):
np.testing.assert_almost_equal(
expected_value.numpy(), actual_value[0], decimal=4)
+ def testSizeInvalid(self):
+
+ @tf.function(input_signature=[
+ tf.TensorSpec(shape=[1, None, 16, 3], dtype=tf.float32)
+ ])
+ def model(in_tensor):
+ return in_tensor + in_tensor
+
+ concrete_func = model.get_concrete_function()
+
+ # Test invalid shape. None after 1st dimension. Run with TOCO in order to
+ # invoke shape checking code.
+ converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func])
+ converter.experimental_new_converter = False
+ with self.assertRaises(ValueError) as error:
+ converter.convert()
+ self.assertEqual(
+ 'None is only supported in the 1st dimension. Tensor '
+ '\'in_tensor\' has invalid shape \'[1, None, 16, 3]\'.',
+ str(error.exception))
+
if __name__ == '__main__':
test.main()
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.lite.-t-f-lite-converter.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.lite.-t-f-lite-converter.pbtxt
index 0c43fc556aa..e7689b4320f 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.lite.-t-f-lite-converter.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.lite.-t-f-lite-converter.pbtxt
@@ -1,11 +1,13 @@
path: "tensorflow.lite.TFLiteConverter"
tf_class {
is_instance: "<class \'tensorflow.lite.python.lite.TFLiteConverter\'>"
+ is_instance: "<class \'tensorflow.lite.python.lite.TFLiteFrozenGraphConverter\'>"
+ is_instance: "<class \'tensorflow.lite.python.lite.TFLiteConverterBaseV1\'>"
is_instance: "<class \'tensorflow.lite.python.lite.TFLiteConverterBase\'>"
is_instance: "<type \'object\'>"
member_method {
name: "__init__"
- argspec: "args=[\'self\', \'graph_def\', \'input_tensors\', \'output_tensors\', \'input_arrays_with_shape\', \'output_arrays\', \'experimental_debug_info_func\', \'saved_model_dir\', \'saved_model_tags\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], "
+ argspec: "args=[\'self\', \'graph_def\', \'input_tensors\', \'output_tensors\', \'input_arrays_with_shape\', \'output_arrays\', \'experimental_debug_info_func\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
}
member_method {
name: "convert"
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.lite.-t-f-lite-converter.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.lite.-t-f-lite-converter.pbtxt
index c575283b74d..c8c163d2f2a 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.lite.-t-f-lite-converter.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.lite.-t-f-lite-converter.pbtxt
@@ -1,11 +1,13 @@
path: "tensorflow.lite.TFLiteConverter"
tf_class {
is_instance: "<class \'tensorflow.lite.python.lite.TFLiteConverterV2\'>"
+ is_instance: "<class \'tensorflow.lite.python.lite.TFLiteFrozenGraphConverterV2\'>"
+ is_instance: "<class \'tensorflow.lite.python.lite.TFLiteConverterBaseV2\'>"
is_instance: "<class \'tensorflow.lite.python.lite.TFLiteConverterBase\'>"
is_instance: "<type \'object\'>"
member_method {
name: "__init__"
- argspec: "args=[\'self\', \'funcs\', \'trackable_obj\', \'saved_model_dir\', \'saved_model_tags\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+ argspec: "args=[\'self\', \'funcs\', \'trackable_obj\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "convert"