STT-tensorflow/tensorflow/lite/g3doc/convert/1x_compatibility.md

TensorFlow 1.x Compatibility

The tf.lite.TFLiteConverter Python API was updated between TensorFlow 1.x and 2.x. This document explains the differences between the two versions, and provides information about how to use the 1.x version if required.

If any of the changes raise concerns, please file a GitHub Issue.

Note: We highly recommend that you migrate your TensorFlow 1.x code to TensorFlow 2.x code .

Model formats

SavedModel and Keras

The tf.lite.TFLiteConverter API supports SavedModel and Keras HDF5 files generated in both TensorFlow 1.x and 2.x.

Frozen Graph

Note: TensorFlow 2.x no longer supports the generation of frozen graph models.

The tf.compat.v1.lite.TFLiteConverter API supports frozen graph models generated in TensorFlow 1.x, as shown below:

import tensorflow as tf
# Path to the frozen graph file
graph_def_file = 'frozen_graph.pb'
# A list of the names of the model's input tensors
input_arrays = ['input_name']
# A list of the names of the model's output tensors
output_arrays = ['output_name']
# Load and convert the frozen graph
converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(
  graph_def_file, input_arrays, output_arrays)
tflite_model = converter.convert()
# Write the converted model to disk
open("converted_model.tflite", "wb").write(tflite_model)

Converter attributes

Renamed attributes

The following 1.x attribute has been renamed in 2.x.

• target_ops has been renamed to target_spec.supported_ops - In 2.x, in line with future additions to the optimization framework, it has become an attribute of TargetSpec and has been renamed to supported_ops.

Unsupported attributes

The following 1.x attributes have been removed in 2.x.

• Quantization - In 2.x, quantize aware training is supported through the Keras API and post training quantization uses fewer streamlined converter flags. Thus, the following attributes and methods related to quantization have been removed:
  • inference_type
  • quantized_input_stats
  • post_training_quantize
  • default_ranges_stats
  • reorder_across_fake_quant
  • change_concat_input_ranges
  • get_input_arrays()
• Visualization - In 2.x, the recommended approach for visualizing a TensorFlow Lite graph is to use visualize.py . Unlike GraphViz, it enables users to visualize the graph after post training quantization has occurred. Thus, the following attributes related to graph visualization have been removed:
  • output_format
  • dump_graphviz_dir
  • dump_graphviz_video
• Frozen graph - In 2.x, the frozen graph model format has been removed. Thus, the following attribute related to frozen graphs has been removed:
  • drop_control_dependency

Unsupported APIs

The following section explains several significant features in 1.x that have been removed in 2.x.

Conversion APIs

The following methods were deprecated in 1.x and have been removed in 2.x:

• lite.toco_convert
• lite.TocoConverter

lite.constants API

The lite.constants API was removed in 2.x in order to decrease duplication between TensorFlow and TensorFlow Lite. The following list maps the lite.constant type to the TensorFlow type:

• lite.constants.FLOAT: tf.float32
• lite.constants.INT8: tf.int8
• lite.constants.INT32: tf.int32
• lite.constants.INT64: tf.int64
• lite.constants.STRING: tf.string
• lite.constants.QUANTIZED_UINT8: tf.uint8

Additionally, the deprecation of the output_format flag in TFLiteConverter led to the removal of the following constants:

• lite.constants.TFLITE
• lite.constants.GRAPHVIZ_DOT

lite.OpHint API

The OpHint API is currently unsupported due to an incompatibility with the 2.x APIs. This API enables conversion of LSTM based models. Support for LSTMs in 2.x is being investigated. All related lite.experimental APIs have been removed due to this issue.