Move TFLite converter 2.0 docs to default.
PiperOrigin-RevId: 270805335
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Nupur Garg
TensorFlower Gardener
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@ -36,22 +36,14 @@ upper_tabs:
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path: /lite/convert/
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- title: "Python API"
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path: /lite/convert/python_api
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- title: "Command line examples"
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path: /lite/convert/cmdline_examples
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- title: "Command line reference"
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path: /lite/convert/cmdline_reference
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- title: "Command line"
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path: /lite/convert/cmdline
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- title: "Convert quantized models"
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path: /lite/convert/quantization
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- title: "Convert RNN models"
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path: /lite/convert/rnn
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- heading: "Convert a TensorFlow 2.0 model"
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- title: "TensorFlow Lite converter and 2.0"
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path: /lite/r2/convert/
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- title: "Generate a concrete function"
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path: /lite/r2/convert/concrete_function
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- title: "Python API"
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path: /lite/r2/convert/python_api
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path: /lite/convert/concrete_function
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- heading: "Inference"
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- title: "Overview"
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@ -41,7 +41,7 @@ install the nightly build using [pip](https://www.tensorflow.org/install/pip) or
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use `bazel`. An example can be seen below.
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```
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bazel run //third_party/tensorflow/lite/python:tflite_convert -- \
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bazel run //tensorflow/lite/python:tflite_convert -- \
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--saved_model_dir=/tmp/mobilenet_saved_model \
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--output_file=/tmp/mobilenet.tflite
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```
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@ -1,48 +1,28 @@
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# TensorFlow Lite converter
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The TensorFlow Lite converter is used to convert TensorFlow models into an
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optimized [FlatBuffer](https://google.github.io/flatbuffers/) format, so that
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they can be used by the TensorFlow Lite interpreter.
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The TensorFlow Lite converter takes a TensorFlow model and generates a
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TensorFlow Lite [`FlatBuffer`](https://google.github.io/flatbuffers/) file
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(`.tflite`). The converter supports
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[SavedModel directories](https://www.tensorflow.org/alpha/guide/saved_model),
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[`tf.keras` models](https://www.tensorflow.org/alpha/guide/keras/overview), and
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[concrete functions](concrete_function.md).
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Note: This page contains documentation on the converter API for TensorFlow 1.x.
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The API for TensorFlow 2.0 is available
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[here](https://www.tensorflow.org/lite/r2/convert/).
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Note: This page contains documentation on the converter API for TensorFlow 2.0.
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The API for TensorFlow 1.X is available
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[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/r1/convert/index.md).
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## FlatBuffers
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## Device deployment
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FlatBuffer is an efficient open-source cross-platform serialization library. It
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is similar to
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[protocol buffers](https://developers.google.com/protocol-buffers), with the
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distinction that FlatBuffers do not need a parsing/unpacking step to a secondary
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representation before data can be accessed, avoiding per-object memory
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allocation. The code footprint of FlatBuffers is an order of magnitude smaller
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than protocol buffers.
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## From model training to device deployment
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The TensorFlow Lite converter generates a TensorFlow Lite
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[FlatBuffer](https://google.github.io/flatbuffers/) file (`.tflite`) from a
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TensorFlow model.
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The converter supports the following input formats:
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* [SavedModels](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators)
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* Frozen `GraphDef`: Models generated by
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[freeze_graph.py](https://www.tensorflow.org/code/tensorflow/python/tools/freeze_graph.py).
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* `tf.keras` HDF5 models.
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* Any model taken from a `tf.Session` (Python API only).
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The TensorFlow Lite `FlatBuffer` file is then deployed to a client device, and
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the TensorFlow Lite interpreter uses the compressed model for on-device
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inference. This conversion process is shown in the diagram below:
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The TensorFlow Lite `FlatBuffer` file is then deployed to a client device (e.g.
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mobile, embedded) and run locally using the TensorFlow Lite interpreter. This
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conversion process is shown in the diagram below:
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## Options
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## Converting models
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The TensorFlow Lite Converter can be used from either of these two options:
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* [Python](python_api.md) (**Preferred**): Using the Python API makes it
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easier to convert models as part of a model development pipeline, and helps
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mitigate [compatibility](../tf_ops_compatibility.md) issues early on.
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* [Command line](cmdline_examples.md)
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The TensorFlow Lite converter should be used from the
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[Python API](python_api.md). Using the Python API makes it easier to convert
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models as part of a model development pipeline and helps mitigate
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[compatibility](../guide/ops_compatibility.md) issues early on. Alternatively,
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the [command line tool](cmdline.md) supports basic models.
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@ -1,187 +1,280 @@
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# Converter Python API guide
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This page describes how to convert TensorFlow models into the TensorFlow Lite
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format using the TensorFlow Lite Converter Python API.
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If you're looking for information about how to run a TensorFlow Lite model,
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see [TensorFlow Lite inference](../guide/inference.md).
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Note: This page describes the converter in the TensorFlow nightly release,
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installed using `pip install tf-nightly`. For docs describing older versions
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reference ["Converting models from TensorFlow 1.12"](#pre_tensorflow_1.12).
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This page provides examples on how to use the
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[TensorFlow Lite converter](index.md) using the Python API in TensorFlow 2.0.
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[TOC]
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## Python API
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## High-level overview
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The Python API for converting TensorFlow models to TensorFlow Lite in TensorFlow
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2.0 is `tf.lite.TFLiteConverter`. `TFLiteConverter` provides the following
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classmethods to convert a model based on the original model format:
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While the TensorFlow Lite Converter can be used from the command line, it is
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often convenient to use in a Python script as part of the model development
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pipeline. This allows you to know early that you are designing a model that can
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be targeted to devices with mobile.
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* `TFLiteConverter.from_saved_model()`: Converts
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[SavedModel directories](https://www.tensorflow.org/alpha/guide/saved_model).
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* `TFLiteConverter.from_keras_model()`: Converts
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[`tf.keras` models](https://www.tensorflow.org/alpha/guide/keras/overview).
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* `TFLiteConverter.from_concrete_functions()`: Converts
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[concrete functions](concrete_function.md).
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## API
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Note: The TensorFlow Lite 2.0 alpha had a different version of the
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`TFLiteConverter` API which only contained the classmethod
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[`from_concrete_function`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/lite/TFLiteConverter#from_concrete_function).
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The API detailed in this document can be installed using the
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[`tf-nightly-2.0-preview`](#installing_the_tensorflow_20_nightly_) pip install.
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The API for converting TensorFlow models to TensorFlow Lite is
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`tf.lite.TFLiteConverter`, which provides class methods based on the original
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format of the model. For example, `TFLiteConverter.from_session()` is available
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for GraphDefs, `TFLiteConverter.from_saved_model()` is available for
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SavedModels, and `TFLiteConverter.from_keras_model_file()` is available for
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`tf.Keras` files.
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This document contains [example usages](#examples) of the API, a detailed list
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of [changes in the API between 1.X and 2.0](#differences), and
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[instructions](#versioning) on running the different versions of TensorFlow.
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Example usages for simple float-point models are shown in
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[Basic Examples](#basic). Examples usages for more complex models is shown in
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[Complex Examples](#complex).
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## Examples <a name="examples"></a>
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## Basic examples <a name="basic"></a>
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### Converting a SavedModel <a name="saved_model"></a>
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The following section shows examples of how to convert a basic float-point model
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from each of the supported data formats into a TensorFlow Lite FlatBuffers.
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### Exporting a GraphDef from tf.Session <a name="basic_graphdef_sess"></a>
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The following example shows how to convert a TensorFlow GraphDef into a
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TensorFlow Lite FlatBuffer from a `tf.Session` object.
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The following example shows how to convert a
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[SavedModel](https://www.tensorflow.org/alpha/guide/saved_model) into a
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TensorFlow Lite [`FlatBuffer`](https://google.github.io/flatbuffers/).
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```python
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import tensorflow as tf
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img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
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var = tf.get_variable("weights", dtype=tf.float32, shape=(1, 64, 64, 3))
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val = img + var
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out = tf.identity(val, name="out")
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# Construct a basic model.
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root = tf.train.Checkpoint()
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root.v1 = tf.Variable(3.)
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root.v2 = tf.Variable(2.)
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root.f = tf.function(lambda x: root.v1 * root.v2 * x)
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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converter = tf.lite.TFLiteConverter.from_session(sess, [img], [out])
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tflite_model = converter.convert()
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open("converted_model.tflite", "wb").write(tflite_model)
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```
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# Save the model.
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export_dir = "/tmp/test_saved_model"
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input_data = tf.constant(1., shape=[1, 1])
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to_save = root.f.get_concrete_function(input_data)
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tf.saved_model.save(root, export_dir, to_save)
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### Exporting a GraphDef from file <a name="basic_graphdef_file"></a>
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The following example shows how to convert a TensorFlow GraphDef into a
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TensorFlow Lite FlatBuffer when the GraphDef is stored in a file. Both `.pb` and
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`.pbtxt` files are accepted.
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The example uses
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[Mobilenet_1.0_224](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz).
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The function only supports GraphDefs frozen using
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[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py).
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```python
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import tensorflow as tf
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graph_def_file = "/path/to/Downloads/mobilenet_v1_1.0_224/frozen_graph.pb"
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input_arrays = ["input"]
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output_arrays = ["MobilenetV1/Predictions/Softmax"]
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converter = tf.lite.TFLiteConverter.from_frozen_graph(
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graph_def_file, input_arrays, output_arrays)
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# Convert the model.
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converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
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tflite_model = converter.convert()
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open("converted_model.tflite", "wb").write(tflite_model)
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```
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### Exporting a SavedModel <a name="basic_savedmodel"></a>
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This API does not have the option of specifying the input shape of any input
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arrays. If your model requires specifying the input shape, use the
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[`from_concrete_functions`](#concrete_function) classmethod instead. The code
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looks similar to the following:
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The following example shows how to convert a SavedModel into a TensorFlow Lite
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FlatBuffer.
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```python
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model = tf.saved_model.load(export_dir)
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concrete_func = model.signatures[
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tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
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concrete_func.inputs[0].set_shape([1, 256, 256, 3])
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converter = TFLiteConverter.from_concrete_functions([concrete_func])
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```
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### Converting a Keras model <a name="keras"></a>
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The following example shows how to convert a
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[`tf.keras` model](https://www.tensorflow.org/alpha/guide/keras/overview) into a
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TensorFlow Lite [`FlatBuffer`](https://google.github.io/flatbuffers/).
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```python
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import tensorflow as tf
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converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
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# Create a simple Keras model.
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x = [-1, 0, 1, 2, 3, 4]
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y = [-3, -1, 1, 3, 5, 7]
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model = tf.keras.models.Sequential(
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[tf.keras.layers.Dense(units=1, input_shape=[1])])
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model.compile(optimizer='sgd', loss='mean_squared_error')
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model.fit(x, y, epochs=50)
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# Convert the model.
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converter = tf.lite.TFLiteConverter.from_keras_model(model)
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tflite_model = converter.convert()
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open("converted_model.tflite", "wb").write(tflite_model)
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```
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For more complex SavedModels, the optional parameters that can be passed into
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`TFLiteConverter.from_saved_model()` are `input_arrays`, `input_shapes`,
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`output_arrays`, `tag_set` and `signature_key`. Details of each parameter are
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available by running `help(tf.lite.TFLiteConverter)`.
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### Converting a concrete function <a name="concrete_function"></a>
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### Exporting a tf.keras File <a name="basic_keras_file"></a>
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The following example shows how to convert a `tf.keras` model into a TensorFlow
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Lite FlatBuffer. This example requires
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[`h5py`](http://docs.h5py.org/en/latest/build.html) to be installed.
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The following example shows how to convert a TensorFlow
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[concrete function](concrete_function.md) into a TensorFlow Lite
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[`FlatBuffer`](https://google.github.io/flatbuffers/).
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```python
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import tensorflow as tf
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converter = tf.lite.TFLiteConverter.from_keras_model_file("keras_model.h5")
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# Construct a basic model.
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root = tf.train.Checkpoint()
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root.v1 = tf.Variable(3.)
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root.v2 = tf.Variable(2.)
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root.f = tf.function(lambda x: root.v1 * root.v2 * x)
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# Create the concrete function.
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input_data = tf.constant(1., shape=[1, 1])
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concrete_func = root.f.get_concrete_function(input_data)
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# Convert the model.
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#
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# `from_concrete_function` takes in a list of concrete functions, however,
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# currently only supports converting one function at a time. Converting multiple
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# functions is under development.
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converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
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tflite_model = converter.convert()
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open("converted_model.tflite", "wb").write(tflite_model)
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```
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The `tf.keras` file must contain both the model and the weights. A comprehensive
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example including model construction can be seen below.
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### End-to-end MobileNet conversion <a name="mobilenet"></a>
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The following example shows how to convert and run inference on a pre-trained
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`tf.keras` MobileNet model to TensorFlow Lite. It compares the results of the
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TensorFlow and TensorFlow Lite model on random data. In order to load the model
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from file, use `model_path` instead of `model_content`.
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```python
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import numpy as np
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import tensorflow as tf
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# Generate tf.keras model.
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model = tf.keras.models.Sequential()
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model.add(tf.keras.layers.Dense(2, input_shape=(3,)))
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model.add(tf.keras.layers.RepeatVector(3))
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model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(3)))
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model.compile(loss=tf.keras.losses.MSE,
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optimizer=tf.keras.optimizers.RMSprop(lr=0.0001),
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metrics=[tf.keras.metrics.categorical_accuracy],
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sample_weight_mode='temporal')
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# Load the MobileNet tf.keras model.
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model = tf.keras.applications.MobileNetV2(
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weights="imagenet", input_shape=(224, 224, 3))
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x = np.random.random((1, 3))
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y = np.random.random((1, 3, 3))
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model.train_on_batch(x, y)
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model.predict(x)
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# Save tf.keras model in HDF5 format.
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keras_file = "keras_model.h5"
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tf.keras.models.save_model(model, keras_file)
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# Convert to TensorFlow Lite model.
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converter = tf.lite.TFLiteConverter.from_keras_model_file(keras_file)
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# Convert the model.
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converter = tf.lite.TFLiteConverter.from_keras_model(model)
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tflite_model = converter.convert()
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open("converted_model.tflite", "wb").write(tflite_model)
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# Load TFLite model and allocate tensors.
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interpreter = tf.lite.Interpreter(model_content=tflite_model)
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interpreter.allocate_tensors()
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# Get input and output tensors.
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input_details = interpreter.get_input_details()
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output_details = interpreter.get_output_details()
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# Test the TensorFlow Lite model on random input data.
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input_shape = input_details[0]['shape']
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input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
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interpreter.set_tensor(input_details[0]['index'], input_data)
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interpreter.invoke()
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# The function `get_tensor()` returns a copy of the tensor data.
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# Use `tensor()` in order to get a pointer to the tensor.
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tflite_results = interpreter.get_tensor(output_details[0]['index'])
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# Test the TensorFlow model on random input data.
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tf_results = model(tf.constant(input_data))
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# Compare the result.
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for tf_result, tflite_result in zip(tf_results, tflite_results):
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np.testing.assert_almost_equal(tf_result, tflite_result, decimal=5)
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```
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## Complex examples <a name="complex"></a>
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## Summary of changes in Python API between 1.X and 2.0 <a name="differences"></a>
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For models where the default value of the attributes is not sufficient, the
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attribute's values should be set before calling `convert()`. In order to call
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any constants use `tf.lite.constants.<CONSTANT_NAME>` as seen below with
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`QUANTIZED_UINT8`. Run `help(tf.lite.TFLiteConverter)` in the Python
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terminal for detailed documentation on the attributes.
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The following section summarizes the changes in the Python API from 1.X to 2.0.
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If any of the changes raise concerns, please file a
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[GitHub issue](https://github.com/tensorflow/tensorflow/issues).
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Although the examples are demonstrated on GraphDefs containing only constants.
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The same logic can be applied irrespective of the input data format.
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### Formats supported by `TFLiteConverter`
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### Exporting a quantized GraphDef <a name="complex_quant"></a>
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`TFLiteConverter` in 2.0 supports SavedModels and Keras model files generated in
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both 1.X and 2.0. However, the conversion process no longer supports frozen
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`GraphDefs` generated in 1.X. Users who want to convert frozen `GraphDefs` to
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TensorFlow Lite should use `tf.compat.v1.TFLiteConverter`.
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The following example shows how to convert a quantized model into a TensorFlow
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Lite FlatBuffer.
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### Quantization-aware training
|
||||
|
||||
The following attributes and methods associated with
|
||||
[quantization-aware training](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize)
|
||||
have been removed from `TFLiteConverter` in TensorFlow 2.0:
|
||||
|
||||
* `inference_type`
|
||||
* `inference_input_type`
|
||||
* `quantized_input_stats`
|
||||
* `default_ranges_stats`
|
||||
* `reorder_across_fake_quant`
|
||||
* `change_concat_input_ranges`
|
||||
* `post_training_quantize` - Deprecated in the 1.X API
|
||||
* `get_input_arrays()`
|
||||
|
||||
The rewriter function that supports quantization-aware training does not support
|
||||
models generated by TensorFlow 2.0. Additionally, TensorFlow Lite’s quantization
|
||||
API is being reworked and streamlined in a direction that supports
|
||||
quantization-aware training through the Keras API. These attributes will be
|
||||
removed in the 2.0 API until the new quantization API is launched. Users who
|
||||
want to convert models generated by the rewriter function can use
|
||||
`tf.compat.v1.TFLiteConverter`.
|
||||
|
||||
### Changes to `TFLiteConverter` attributes
|
||||
|
||||
The `target_ops` attribute has become an attribute of `TargetSpec` and renamed
|
||||
to `supported_ops` in line with future additions to the optimization framework.
|
||||
|
||||
Additionally, the following attributes have been removed:
|
||||
|
||||
* `drop_control_dependency` (default: `True`) - Control flow is currently not
|
||||
supported by TFLite so it is always `True`.
|
||||
* _Graph visualization_ - The recommended approach for visualizing a
|
||||
TensorFlow Lite graph in TensorFlow 2.0 will be to use
|
||||
[visualize.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tools/visualize.py).
|
||||
Unlike GraphViz, it enables users to visualize the graph after post training
|
||||
quantization has occurred. The following attributes related to graph
|
||||
visualization will be removed:
|
||||
* `output_format`
|
||||
* `dump_graphviz_dir`
|
||||
* `dump_graphviz_video`
|
||||
|
||||
### General API changes
|
||||
|
||||
#### Conversion methods
|
||||
|
||||
The following methods that were previously deprecated in 1.X will no longer be
|
||||
exported in 2.0:
|
||||
|
||||
* `lite.toco_convert`
|
||||
* `lite.TocoConverter`
|
||||
|
||||
#### `lite.constants`
|
||||
|
||||
The `lite.constants` API was removed in 2.0 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, `lite.constants.TFLITE` and `lite.constants.GRAPHVIZ_DOT` were
|
||||
removed due to the deprecation of the `output_format` flag in `TFLiteConverter`.
|
||||
|
||||
#### `lite.OpHint`
|
||||
|
||||
The `OpHint` API is currently not available in 2.0 due to an incompatibility
|
||||
with the 2.0 APIs. This API enables conversion of LSTM based models. Support for
|
||||
LSTMs in 2.0 is being investigated. All related `lite.experimental` APIs have
|
||||
been removed due to this issue.
|
||||
|
||||
## Installing TensorFlow <a name="versioning"></a>
|
||||
|
||||
### Installing the TensorFlow 2.0 nightly <a name="2.0-nightly"></a>
|
||||
|
||||
The TensorFlow 2.0 nightly can be installed using the following command:
|
||||
|
||||
```
|
||||
pip install tf-nightly-2.0-preview
|
||||
```
|
||||
|
||||
### Using TensorFlow 2.0 from a 1.X installation <a name="use-2.0-from-1.X"></a>
|
||||
|
||||
TensorFlow 2.0 can be enabled from recent 1.X installations using the following
|
||||
code snippet.
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
import tensorflow.compat.v2 as tf
|
||||
|
||||
img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
|
||||
const = tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
|
||||
val = img + const
|
||||
out = tf.fake_quant_with_min_max_args(val, min=0., max=1., name="output")
|
||||
|
||||
with tf.Session() as sess:
|
||||
converter = tf.lite.TFLiteConverter.from_session(sess, [img], [out])
|
||||
converter.inference_type = tf.lite.constants.QUANTIZED_UINT8
|
||||
input_arrays = converter.get_input_arrays()
|
||||
converter.quantized_input_stats = {input_arrays[0] : (0., 1.)} # mean, std_dev
|
||||
tflite_model = converter.convert()
|
||||
open("converted_model.tflite", "wb").write(tflite_model)
|
||||
tf.enable_v2_behavior()
|
||||
```
|
||||
|
||||
|
||||
## Additional instructions
|
||||
|
||||
### Build from source code <a name="latest_package"></a>
|
||||
|
||||
In order to run the latest version of the TensorFlow Lite Converter Python API,
|
||||
@ -189,14 +282,3 @@ either install the nightly build with
|
||||
[pip](https://www.tensorflow.org/install/pip) (recommended) or
|
||||
[Docker](https://www.tensorflow.org/install/docker), or
|
||||
[build the pip package from source](https://www.tensorflow.org/install/source).
|
||||
|
||||
### Converting models from TensorFlow 1.12 <a name="pre_tensorflow_1.12"></a>
|
||||
|
||||
Reference the following table to convert TensorFlow models to TensorFlow Lite in
|
||||
and before TensorFlow 1.12. Run `help()` to get details of each API.
|
||||
|
||||
TensorFlow Version | Python API
|
||||
------------------ | ---------------------------------
|
||||
1.12 | `tf.contrib.lite.TFLiteConverter`
|
||||
1.9-1.11 | `tf.contrib.lite.TocoConverter`
|
||||
1.7-1.8 | `tf.contrib.lite.toco_convert`
|
||||
|
||||
File diff suppressed because one or more lines are too long
|
Before Width: | Height: | Size: 109 KiB After Width: | Height: | Size: 98 KiB |
@ -18,7 +18,7 @@ There are two approaches to running the converter in the command line.
|
||||
[clone the TensorFlow repository](https://www.tensorflow.org/install/source)
|
||||
and use `bazel`.
|
||||
* Example: `bazel run
|
||||
//tensorflow/lite/python:tflite_convert --
|
||||
//third_party/tensorflow/lite/python:tflite_convert --
|
||||
--output_file=...`
|
||||
|
||||
### Converting models prior to TensorFlow 1.9 <a name="pre_tensorflow_1.9"></a>
|
||||
48
tensorflow/lite/g3doc/r1/convert/index.md
Normal file
48
tensorflow/lite/g3doc/r1/convert/index.md
Normal file
@ -0,0 +1,48 @@
|
||||
# TensorFlow Lite converter
|
||||
|
||||
The TensorFlow Lite converter is used to convert TensorFlow models into an
|
||||
optimized [FlatBuffer](https://google.github.io/flatbuffers/) format, so that
|
||||
they can be used by the TensorFlow Lite interpreter.
|
||||
|
||||
Note: This page contains documentation on the converter API for TensorFlow 1.x.
|
||||
The API for TensorFlow 2.0 is available
|
||||
[here](https://www.tensorflow.org/lite/r2/convert/).
|
||||
|
||||
## FlatBuffers
|
||||
|
||||
FlatBuffer is an efficient open-source cross-platform serialization library. It
|
||||
is similar to
|
||||
[protocol buffers](https://developers.google.com/protocol-buffers), with the
|
||||
distinction that FlatBuffers do not need a parsing/unpacking step to a secondary
|
||||
representation before data can be accessed, avoiding per-object memory
|
||||
allocation. The code footprint of FlatBuffers is an order of magnitude smaller
|
||||
than protocol buffers.
|
||||
|
||||
## From model training to device deployment
|
||||
|
||||
The TensorFlow Lite converter generates a TensorFlow Lite
|
||||
[FlatBuffer](https://google.github.io/flatbuffers/) file (`.tflite`) from a
|
||||
TensorFlow model.
|
||||
|
||||
The converter supports the following input formats:
|
||||
|
||||
* [SavedModels](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators)
|
||||
* Frozen `GraphDef`: Models generated by
|
||||
[freeze_graph.py](https://www.tensorflow.org/code/tensorflow/python/tools/freeze_graph.py).
|
||||
* `tf.keras` HDF5 models.
|
||||
* Any model taken from a `tf.Session` (Python API only).
|
||||
|
||||
The TensorFlow Lite `FlatBuffer` file is then deployed to a client device, and
|
||||
the TensorFlow Lite interpreter uses the compressed model for on-device
|
||||
inference. This conversion process is shown in the diagram below:
|
||||
|
||||
|
||||
|
||||
## Options
|
||||
|
||||
The TensorFlow Lite Converter can be used from either of these two options:
|
||||
|
||||
* [Python](python_api.md) (**Preferred**): Using the Python API makes it
|
||||
easier to convert models as part of a model development pipeline, and helps
|
||||
mitigate [compatibility](../tf_ops_compatibility.md) issues early on.
|
||||
* [Command line](cmdline_examples.md)
|
||||
202
tensorflow/lite/g3doc/r1/convert/python_api.md
Normal file
202
tensorflow/lite/g3doc/r1/convert/python_api.md
Normal file
@ -0,0 +1,202 @@
|
||||
# Converter Python API guide
|
||||
|
||||
This page describes how to convert TensorFlow models into the TensorFlow Lite
|
||||
format using the TensorFlow Lite Converter Python API.
|
||||
|
||||
If you're looking for information about how to run a TensorFlow Lite model,
|
||||
see [TensorFlow Lite inference](../guide/inference.md).
|
||||
|
||||
Note: This page describes the converter in the TensorFlow nightly release,
|
||||
installed using `pip install tf-nightly`. For docs describing older versions
|
||||
reference ["Converting models from TensorFlow 1.12"](#pre_tensorflow_1.12).
|
||||
|
||||
[TOC]
|
||||
|
||||
|
||||
## High-level overview
|
||||
|
||||
While the TensorFlow Lite Converter can be used from the command line, it is
|
||||
often convenient to use in a Python script as part of the model development
|
||||
pipeline. This allows you to know early that you are designing a model that can
|
||||
be targeted to devices with mobile.
|
||||
|
||||
## API
|
||||
|
||||
The API for converting TensorFlow models to TensorFlow Lite is
|
||||
`tf.lite.TFLiteConverter`, which provides class methods based on the original
|
||||
format of the model. For example, `TFLiteConverter.from_session()` is available
|
||||
for GraphDefs, `TFLiteConverter.from_saved_model()` is available for
|
||||
SavedModels, and `TFLiteConverter.from_keras_model_file()` is available for
|
||||
`tf.Keras` files.
|
||||
|
||||
Example usages for simple float-point models are shown in
|
||||
[Basic Examples](#basic). Examples usages for more complex models is shown in
|
||||
[Complex Examples](#complex).
|
||||
|
||||
## Basic examples <a name="basic"></a>
|
||||
|
||||
The following section shows examples of how to convert a basic float-point model
|
||||
from each of the supported data formats into a TensorFlow Lite FlatBuffers.
|
||||
|
||||
### Exporting a GraphDef from tf.Session <a name="basic_graphdef_sess"></a>
|
||||
|
||||
The following example shows how to convert a TensorFlow GraphDef into a
|
||||
TensorFlow Lite FlatBuffer from a `tf.Session` object.
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
|
||||
img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
|
||||
var = tf.get_variable("weights", dtype=tf.float32, shape=(1, 64, 64, 3))
|
||||
val = img + var
|
||||
out = tf.identity(val, name="out")
|
||||
|
||||
with tf.Session() as sess:
|
||||
sess.run(tf.global_variables_initializer())
|
||||
converter = tf.lite.TFLiteConverter.from_session(sess, [img], [out])
|
||||
tflite_model = converter.convert()
|
||||
open("converted_model.tflite", "wb").write(tflite_model)
|
||||
```
|
||||
|
||||
### Exporting a GraphDef from file <a name="basic_graphdef_file"></a>
|
||||
|
||||
The following example shows how to convert a TensorFlow GraphDef into a
|
||||
TensorFlow Lite FlatBuffer when the GraphDef is stored in a file. Both `.pb` and
|
||||
`.pbtxt` files are accepted.
|
||||
|
||||
The example uses
|
||||
[Mobilenet_1.0_224](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz).
|
||||
The function only supports GraphDefs frozen using
|
||||
[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py).
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
|
||||
graph_def_file = "/path/to/Downloads/mobilenet_v1_1.0_224/frozen_graph.pb"
|
||||
input_arrays = ["input"]
|
||||
output_arrays = ["MobilenetV1/Predictions/Softmax"]
|
||||
|
||||
converter = tf.lite.TFLiteConverter.from_frozen_graph(
|
||||
graph_def_file, input_arrays, output_arrays)
|
||||
tflite_model = converter.convert()
|
||||
open("converted_model.tflite", "wb").write(tflite_model)
|
||||
```
|
||||
|
||||
### Exporting a SavedModel <a name="basic_savedmodel"></a>
|
||||
|
||||
The following example shows how to convert a SavedModel into a TensorFlow Lite
|
||||
FlatBuffer.
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
|
||||
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
|
||||
tflite_model = converter.convert()
|
||||
open("converted_model.tflite", "wb").write(tflite_model)
|
||||
```
|
||||
|
||||
For more complex SavedModels, the optional parameters that can be passed into
|
||||
`TFLiteConverter.from_saved_model()` are `input_arrays`, `input_shapes`,
|
||||
`output_arrays`, `tag_set` and `signature_key`. Details of each parameter are
|
||||
available by running `help(tf.lite.TFLiteConverter)`.
|
||||
|
||||
### Exporting a tf.keras File <a name="basic_keras_file"></a>
|
||||
|
||||
The following example shows how to convert a `tf.keras` model into a TensorFlow
|
||||
Lite FlatBuffer. This example requires
|
||||
[`h5py`](http://docs.h5py.org/en/latest/build.html) to be installed.
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model_file("keras_model.h5")
|
||||
tflite_model = converter.convert()
|
||||
open("converted_model.tflite", "wb").write(tflite_model)
|
||||
```
|
||||
|
||||
The `tf.keras` file must contain both the model and the weights. A comprehensive
|
||||
example including model construction can be seen below.
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
|
||||
# Generate tf.keras model.
|
||||
model = tf.keras.models.Sequential()
|
||||
model.add(tf.keras.layers.Dense(2, input_shape=(3,)))
|
||||
model.add(tf.keras.layers.RepeatVector(3))
|
||||
model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(3)))
|
||||
model.compile(loss=tf.keras.losses.MSE,
|
||||
optimizer=tf.keras.optimizers.RMSprop(lr=0.0001),
|
||||
metrics=[tf.keras.metrics.categorical_accuracy],
|
||||
sample_weight_mode='temporal')
|
||||
|
||||
x = np.random.random((1, 3))
|
||||
y = np.random.random((1, 3, 3))
|
||||
model.train_on_batch(x, y)
|
||||
model.predict(x)
|
||||
|
||||
# Save tf.keras model in HDF5 format.
|
||||
keras_file = "keras_model.h5"
|
||||
tf.keras.models.save_model(model, keras_file)
|
||||
|
||||
# Convert to TensorFlow Lite model.
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model_file(keras_file)
|
||||
tflite_model = converter.convert()
|
||||
open("converted_model.tflite", "wb").write(tflite_model)
|
||||
```
|
||||
|
||||
## Complex examples <a name="complex"></a>
|
||||
|
||||
For models where the default value of the attributes is not sufficient, the
|
||||
attribute's values should be set before calling `convert()`. In order to call
|
||||
any constants use `tf.lite.constants.<CONSTANT_NAME>` as seen below with
|
||||
`QUANTIZED_UINT8`. Run `help(tf.lite.TFLiteConverter)` in the Python
|
||||
terminal for detailed documentation on the attributes.
|
||||
|
||||
Although the examples are demonstrated on GraphDefs containing only constants.
|
||||
The same logic can be applied irrespective of the input data format.
|
||||
|
||||
### Exporting a quantized GraphDef <a name="complex_quant"></a>
|
||||
|
||||
The following example shows how to convert a quantized model into a TensorFlow
|
||||
Lite FlatBuffer.
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
|
||||
img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
|
||||
const = tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
|
||||
val = img + const
|
||||
out = tf.fake_quant_with_min_max_args(val, min=0., max=1., name="output")
|
||||
|
||||
with tf.Session() as sess:
|
||||
converter = tf.lite.TFLiteConverter.from_session(sess, [img], [out])
|
||||
converter.inference_type = tf.lite.constants.QUANTIZED_UINT8
|
||||
input_arrays = converter.get_input_arrays()
|
||||
converter.quantized_input_stats = {input_arrays[0] : (0., 1.)} # mean, std_dev
|
||||
tflite_model = converter.convert()
|
||||
open("converted_model.tflite", "wb").write(tflite_model)
|
||||
```
|
||||
|
||||
|
||||
## Additional instructions
|
||||
|
||||
### Build from source code <a name="latest_package"></a>
|
||||
|
||||
In order to run the latest version of the TensorFlow Lite Converter Python API,
|
||||
either install the nightly build with
|
||||
[pip](https://www.tensorflow.org/install/pip) (recommended) or
|
||||
[Docker](https://www.tensorflow.org/install/docker), or
|
||||
[build the pip package from source](https://www.tensorflow.org/install/source).
|
||||
|
||||
### Converting models from TensorFlow 1.12 <a name="pre_tensorflow_1.12"></a>
|
||||
|
||||
Reference the following table to convert TensorFlow models to TensorFlow Lite in
|
||||
and before TensorFlow 1.12. Run `help()` to get details of each API.
|
||||
|
||||
TensorFlow Version | Python API
|
||||
------------------ | ---------------------------------
|
||||
1.12 | `tf.contrib.lite.TFLiteConverter`
|
||||
1.9-1.11 | `tf.contrib.lite.TocoConverter`
|
||||
1.7-1.8 | `tf.contrib.lite.toco_convert`
|
||||
1
tensorflow/lite/g3doc/r1/images/convert/workflow.svg
Normal file
1
tensorflow/lite/g3doc/r1/images/convert/workflow.svg
Normal file
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 109 KiB |
@ -1,28 +0,0 @@
|
||||
# TensorFlow Lite converter
|
||||
|
||||
The TensorFlow Lite converter takes a TensorFlow model and generates a
|
||||
TensorFlow Lite [`FlatBuffer`](https://google.github.io/flatbuffers/) file
|
||||
(`.tflite`). The converter supports
|
||||
[SavedModel directories](https://www.tensorflow.org/alpha/guide/saved_model),
|
||||
[`tf.keras` models](https://www.tensorflow.org/alpha/guide/keras/overview), and
|
||||
[concrete functions](concrete_function.md).
|
||||
|
||||
Note: This page contains documentation on the converter API for TensorFlow 2.0.
|
||||
The API for TensorFlow 1.X is available
|
||||
[here](https://www.tensorflow.org/lite/convert/).
|
||||
|
||||
## Device deployment
|
||||
|
||||
The TensorFlow Lite `FlatBuffer` file is then deployed to a client device (e.g.
|
||||
mobile, embedded) and run locally using the TensorFlow Lite interpreter. This
|
||||
conversion process is shown in the diagram below:
|
||||
|
||||
|
||||
|
||||
## Converting models
|
||||
|
||||
The TensorFlow Lite converter should be used from the
|
||||
[Python API](python_api.md). Using the Python API makes it easier to convert
|
||||
models as part of a model development pipeline and helps mitigate
|
||||
[compatibility](../../guide/ops_compatibility.md) issues early on.
|
||||
Alternatively, the [command line tool](cmdline.md) supports basic models.
|
||||
@ -1,284 +0,0 @@
|
||||
# Converter Python API guide
|
||||
|
||||
This page provides examples on how to use the
|
||||
[TensorFlow Lite converter](index.md) using the Python API in TensorFlow 2.0.
|
||||
|
||||
[TOC]
|
||||
|
||||
## Python API
|
||||
|
||||
The Python API for converting TensorFlow models to TensorFlow Lite in TensorFlow
|
||||
2.0 is `tf.lite.TFLiteConverter`. `TFLiteConverter` provides the following
|
||||
classmethods to convert a model based on the original model format:
|
||||
|
||||
* `TFLiteConverter.from_saved_model()`: Converts
|
||||
[SavedModel directories](https://www.tensorflow.org/alpha/guide/saved_model).
|
||||
* `TFLiteConverter.from_keras_model()`: Converts
|
||||
[`tf.keras` models](https://www.tensorflow.org/alpha/guide/keras/overview).
|
||||
* `TFLiteConverter.from_concrete_functions()`: Converts
|
||||
[concrete functions](concrete_function.md).
|
||||
|
||||
Note: The TensorFlow Lite 2.0 alpha had a different version of the
|
||||
`TFLiteConverter` API which only contained the classmethod
|
||||
[`from_concrete_function`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/lite/TFLiteConverter#from_concrete_function).
|
||||
The API detailed in this document can be installed using the
|
||||
[`tf-nightly-2.0-preview`](#installing_the_tensorflow_20_nightly_) pip install.
|
||||
|
||||
This document contains [example usages](#examples) of the API, a detailed list
|
||||
of [changes in the API between 1.X and 2.0](#differences), and
|
||||
[instructions](#versioning) on running the different versions of TensorFlow.
|
||||
|
||||
## Examples <a name="examples"></a>
|
||||
|
||||
### Converting a SavedModel <a name="saved_model"></a>
|
||||
|
||||
The following example shows how to convert a
|
||||
[SavedModel](https://www.tensorflow.org/alpha/guide/saved_model) into a
|
||||
TensorFlow Lite [`FlatBuffer`](https://google.github.io/flatbuffers/).
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
|
||||
# Construct a basic model.
|
||||
root = tf.train.Checkpoint()
|
||||
root.v1 = tf.Variable(3.)
|
||||
root.v2 = tf.Variable(2.)
|
||||
root.f = tf.function(lambda x: root.v1 * root.v2 * x)
|
||||
|
||||
# Save the model.
|
||||
export_dir = "/tmp/test_saved_model"
|
||||
input_data = tf.constant(1., shape=[1, 1])
|
||||
to_save = root.f.get_concrete_function(input_data)
|
||||
tf.saved_model.save(root, export_dir, to_save)
|
||||
|
||||
# Convert the model.
|
||||
converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
|
||||
tflite_model = converter.convert()
|
||||
```
|
||||
|
||||
This API does not have the option of specifying the input shape of any input
|
||||
arrays. If your model requires specifying the input shape, use the
|
||||
[`from_concrete_functions`](#concrete_function) classmethod instead. The code
|
||||
looks similar to the following:
|
||||
|
||||
```python
|
||||
model = tf.saved_model.load(export_dir)
|
||||
concrete_func = model.signatures[
|
||||
tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
|
||||
concrete_func.inputs[0].set_shape([1, 256, 256, 3])
|
||||
converter = TFLiteConverter.from_concrete_functions([concrete_func])
|
||||
```
|
||||
|
||||
### Converting a Keras model <a name="keras"></a>
|
||||
|
||||
The following example shows how to convert a
|
||||
[`tf.keras` model](https://www.tensorflow.org/alpha/guide/keras/overview) into a
|
||||
TensorFlow Lite [`FlatBuffer`](https://google.github.io/flatbuffers/).
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
|
||||
# Create a simple Keras model.
|
||||
x = [-1, 0, 1, 2, 3, 4]
|
||||
y = [-3, -1, 1, 3, 5, 7]
|
||||
|
||||
model = tf.keras.models.Sequential(
|
||||
[tf.keras.layers.Dense(units=1, input_shape=[1])])
|
||||
model.compile(optimizer='sgd', loss='mean_squared_error')
|
||||
model.fit(x, y, epochs=50)
|
||||
|
||||
# Convert the model.
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(model)
|
||||
tflite_model = converter.convert()
|
||||
```
|
||||
|
||||
### Converting a concrete function <a name="concrete_function"></a>
|
||||
|
||||
The following example shows how to convert a TensorFlow
|
||||
[concrete function](concrete_function.md) into a TensorFlow Lite
|
||||
[`FlatBuffer`](https://google.github.io/flatbuffers/).
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
|
||||
# Construct a basic model.
|
||||
root = tf.train.Checkpoint()
|
||||
root.v1 = tf.Variable(3.)
|
||||
root.v2 = tf.Variable(2.)
|
||||
root.f = tf.function(lambda x: root.v1 * root.v2 * x)
|
||||
|
||||
# Create the concrete function.
|
||||
input_data = tf.constant(1., shape=[1, 1])
|
||||
concrete_func = root.f.get_concrete_function(input_data)
|
||||
|
||||
# Convert the model.
|
||||
#
|
||||
# `from_concrete_function` takes in a list of concrete functions, however,
|
||||
# currently only supports converting one function at a time. Converting multiple
|
||||
# functions is under development.
|
||||
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
|
||||
tflite_model = converter.convert()
|
||||
```
|
||||
|
||||
### End-to-end MobileNet conversion <a name="mobilenet"></a>
|
||||
|
||||
The following example shows how to convert and run inference on a pre-trained
|
||||
`tf.keras` MobileNet model to TensorFlow Lite. It compares the results of the
|
||||
TensorFlow and TensorFlow Lite model on random data. In order to load the model
|
||||
from file, use `model_path` instead of `model_content`.
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
|
||||
# Load the MobileNet tf.keras model.
|
||||
model = tf.keras.applications.MobileNetV2(
|
||||
weights="imagenet", input_shape=(224, 224, 3))
|
||||
|
||||
# Convert the model.
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(model)
|
||||
tflite_model = converter.convert()
|
||||
|
||||
# Load TFLite model and allocate tensors.
|
||||
interpreter = tf.lite.Interpreter(model_content=tflite_model)
|
||||
interpreter.allocate_tensors()
|
||||
|
||||
# Get input and output tensors.
|
||||
input_details = interpreter.get_input_details()
|
||||
output_details = interpreter.get_output_details()
|
||||
|
||||
# Test the TensorFlow Lite model on random input data.
|
||||
input_shape = input_details[0]['shape']
|
||||
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
|
||||
interpreter.set_tensor(input_details[0]['index'], input_data)
|
||||
|
||||
interpreter.invoke()
|
||||
|
||||
# The function `get_tensor()` returns a copy of the tensor data.
|
||||
# Use `tensor()` in order to get a pointer to the tensor.
|
||||
tflite_results = interpreter.get_tensor(output_details[0]['index'])
|
||||
|
||||
# Test the TensorFlow model on random input data.
|
||||
tf_results = model(tf.constant(input_data))
|
||||
|
||||
# Compare the result.
|
||||
for tf_result, tflite_result in zip(tf_results, tflite_results):
|
||||
np.testing.assert_almost_equal(tf_result, tflite_result, decimal=5)
|
||||
```
|
||||
|
||||
## Summary of changes in Python API between 1.X and 2.0 <a name="differences"></a>
|
||||
|
||||
The following section summarizes the changes in the Python API from 1.X to 2.0.
|
||||
If any of the changes raise concerns, please file a
|
||||
[GitHub issue](https://github.com/tensorflow/tensorflow/issues).
|
||||
|
||||
### Formats supported by `TFLiteConverter`
|
||||
|
||||
`TFLiteConverter` in 2.0 supports SavedModels and Keras model files generated in
|
||||
both 1.X and 2.0. However, the conversion process no longer supports frozen
|
||||
`GraphDefs` generated in 1.X. Users who want to convert frozen `GraphDefs` to
|
||||
TensorFlow Lite should use `tf.compat.v1.TFLiteConverter`.
|
||||
|
||||
### Quantization-aware training
|
||||
|
||||
The following attributes and methods associated with
|
||||
[quantization-aware training](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize)
|
||||
have been removed from `TFLiteConverter` in TensorFlow 2.0:
|
||||
|
||||
* `inference_type`
|
||||
* `inference_input_type`
|
||||
* `quantized_input_stats`
|
||||
* `default_ranges_stats`
|
||||
* `reorder_across_fake_quant`
|
||||
* `change_concat_input_ranges`
|
||||
* `post_training_quantize` - Deprecated in the 1.X API
|
||||
* `get_input_arrays()`
|
||||
|
||||
The rewriter function that supports quantization-aware training does not support
|
||||
models generated by TensorFlow 2.0. Additionally, TensorFlow Lite’s quantization
|
||||
API is being reworked and streamlined in a direction that supports
|
||||
quantization-aware training through the Keras API. These attributes will be
|
||||
removed in the 2.0 API until the new quantization API is launched. Users who
|
||||
want to convert models generated by the rewriter function can use
|
||||
`tf.compat.v1.TFLiteConverter`.
|
||||
|
||||
### Changes to `TFLiteConverter` attributes
|
||||
|
||||
The `target_ops` attribute has become an attribute of `TargetSpec` and renamed
|
||||
to `supported_ops` in line with future additions to the optimization framework.
|
||||
|
||||
Additionally, the following attributes have been removed:
|
||||
|
||||
* `drop_control_dependency` (default: `True`) - Control flow is currently not
|
||||
supported by TFLite so it is always `True`.
|
||||
* _Graph visualization_ - The recommended approach for visualizing a
|
||||
TensorFlow Lite graph in TensorFlow 2.0 will be to use
|
||||
[visualize.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tools/visualize.py).
|
||||
Unlike GraphViz, it enables users to visualize the graph after post training
|
||||
quantization has occurred. The following attributes related to graph
|
||||
visualization will be removed:
|
||||
* `output_format`
|
||||
* `dump_graphviz_dir`
|
||||
* `dump_graphviz_video`
|
||||
|
||||
### General API changes
|
||||
|
||||
#### Conversion methods
|
||||
|
||||
The following methods that were previously deprecated in 1.X will no longer be
|
||||
exported in 2.0:
|
||||
|
||||
* `lite.toco_convert`
|
||||
* `lite.TocoConverter`
|
||||
|
||||
#### `lite.constants`
|
||||
|
||||
The `lite.constants` API was removed in 2.0 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, `lite.constants.TFLITE` and `lite.constants.GRAPHVIZ_DOT` were
|
||||
removed due to the deprecation of the `output_format` flag in `TFLiteConverter`.
|
||||
|
||||
#### `lite.OpHint`
|
||||
|
||||
The `OpHint` API is currently not available in 2.0 due to an incompatibility
|
||||
with the 2.0 APIs. This API enables conversion of LSTM based models. Support for
|
||||
LSTMs in 2.0 is being investigated. All related `lite.experimental` APIs have
|
||||
been removed due to this issue.
|
||||
|
||||
## Installing TensorFlow <a name="versioning"></a>
|
||||
|
||||
### Installing the TensorFlow 2.0 nightly <a name="2.0-nightly"></a>
|
||||
|
||||
The TensorFlow 2.0 nightly can be installed using the following command:
|
||||
|
||||
```
|
||||
pip install tf-nightly-2.0-preview
|
||||
```
|
||||
|
||||
### Using TensorFlow 2.0 from a 1.X installation <a name="use-2.0-from-1.X"></a>
|
||||
|
||||
TensorFlow 2.0 can be enabled from recent 1.X installations using the following
|
||||
code snippet.
|
||||
|
||||
```python
|
||||
import tensorflow.compat.v2 as tf
|
||||
|
||||
tf.enable_v2_behavior()
|
||||
```
|
||||
|
||||
### Build from source code <a name="latest_package"></a>
|
||||
|
||||
In order to run the latest version of the TensorFlow Lite Converter Python API,
|
||||
either install the nightly build with
|
||||
[pip](https://www.tensorflow.org/install/pip) (recommended) or
|
||||
[Docker](https://www.tensorflow.org/install/docker), or
|
||||
[build the pip package from source](https://www.tensorflow.org/install/source).
|
||||
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Block a user