Convert a TensorFlow model into output_format.
tf.compat.v1.lite.TFLiteConverter(
graph_def, input_tensors, output_tensors, input_arrays_with_shape=None,
output_arrays=None, experimental_debug_info_func=None
)
This is used to convert from a TensorFlow GraphDef, SavedModel or tf.keras
model into either a TFLite FlatBuffer or graph visualization.
Example usage:
# Converting a GraphDef from session.
converter = tf.compat.v1.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 = tf.compat.v1.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 = tf.compat.v1.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 = tf.compat.v1.TFLiteConverter.from_keras_model_file(keras_model)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
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.
|
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. (default True)
|
Methods
convert
View source
convert()
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.
|
from_frozen_graph
View source
@classmethod
from_frozen_graph(
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
|
from_keras_model_file
View source
@classmethod
from_keras_model_file(
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.
|
from_saved_model
View source
@classmethod
from_saved_model(
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.
|
from_session
View source
@classmethod
from_session(
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.
|
View source
get_input_arrays()
Returns a list of the names of the input tensors.
View source on GitHub