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diff --git a/tensorflow/contrib/lite/g3doc/tflite_convert/cmdline_examples.md b/tensorflow/contrib/lite/g3doc/tflite_convert/cmdline_examples.md new file mode 100644 index 0000000000..d88acfae80 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/tflite_convert/cmdline_examples.md @@ -0,0 +1,360 @@ +# TensorFlow Lite Converter command-line examples + +This page shows how to use the TensorFlow Lite Converter in the command line. + +[TOC] + +## Command-line tools <a name="tools"></a> + +There are two approaches to running the converter in the command line. + +* `tflite_convert`: Starting from TensorFlow 1.9, the command-line tool + `tflite_convert` is installed as part of the Python package. All of the + examples below use `tflite_convert` for simplicity. + * Example: `tflite_convert --output_file=...` +* `bazel`: In order to run the latest version of the TensorFlow Lite Converter + either install the nightly build using + [pip](https://www.tensorflow.org/install/pip) or + [clone the TensorFlow repository](https://www.tensorflow.org/install/source) + and use `bazel`. + * Example: `bazel run + //tensorflow/contrib/lite/python:tflite_convert -- + --output_file=...` + +### Converting models prior to TensorFlow 1.9 <a name="pre-tensorflow-1.9"></a> + +The recommended approach for using the converter prior to TensorFlow 1.9 is the +[Python API](python_api.md#pre-tensorflow-1.9). If a command line tool is +desired, the `toco` command line tool was available in TensorFlow 1.7. Enter +`toco --help` in Terminal for additional details on the command-line flags +available. There were no command line tools in TensorFlow 1.8. + +## 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. + +### Convert a TensorFlow GraphDef <a name="graphdef"></a> + +The follow example converts a basic TensorFlow GraphDef (frozen by +[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)) +into a TensorFlow Lite FlatBuffer to perform floating-point inference. Frozen +graphs contain the variables stored in Checkpoint files as Const ops. + +``` +curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \ + | tar xzv -C /tmp +tflite_convert \ + --output_file=/tmp/foo.tflite \ + --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ + --input_arrays=input \ + --output_arrays=MobilenetV1/Predictions/Reshape_1 +``` + +The value for `input_shapes` is automatically determined whenever possible. + +### Convert a TensorFlow SavedModel <a name="savedmodel"></a> + +The follow example converts a basic TensorFlow SavedModel into a Tensorflow Lite +FlatBuffer to perform floating-point inference. + +``` +tflite_convert \ + --output_file=/tmp/foo.tflite \ + --saved_model_dir=/tmp/saved_model +``` + +[SavedModel](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators) +has fewer required flags than frozen graphs due to access to additional data +contained within the SavedModel. The values for `--input_arrays` and +`--output_arrays` are an aggregated, alphabetized list of the inputs and outputs +in the [SignatureDefs](https://www.tensorflow.org/serving/signature_defs) within +the +[MetaGraphDef](https://www.tensorflow.org/guide/saved_model#apis_to_build_and_load_a_savedmodel) +specified by `--saved_model_tag_set`. As with the GraphDef, the value for +`input_shapes` is automatically determined whenever possible. + +There is currently no support for MetaGraphDefs without a SignatureDef or for +MetaGraphDefs that use the [`assets/` +directory](https://www.tensorflow.org/guide/saved_model#structure_of_a_savedmodel_directory). + +### Convert a tf.Keras model <a name="keras"></a> + +The following example converts a `tf.keras` model into a TensorFlow Lite +Flatbuffer. The `tf.keras` file must contain both the model and the weights. + +``` +tflite_convert \ + --output_file=/tmp/foo.tflite \ + --keras_model_file=/tmp/keras_model.h5 +``` + +## Quantization + +### Convert a TensorFlow GraphDef for quantized inference <a name="graphdef-quant"></a> + +The TensorFlow Lite Converter is compatible with fixed point quantization models +described [here](https://www.tensorflow.org/performance/quantization). These are +float models with +[`FakeQuant*`](https://www.tensorflow.org/api_guides/python/array_ops#Fake_quantization) +ops inserted at the boundaries of fused layers to record min-max range +information. This generates a quantized inference workload that reproduces the +quantization behavior that was used during training. + +The following command generates a quantized TensorFlow Lite FlatBuffer from a +"quantized" TensorFlow GraphDef. + +``` +tflite_convert \ + --output_file=/tmp/foo.tflite \ + --graph_def_file=/tmp/some_quantized_graph.pb \ + --inference_type=QUANTIZED_UINT8 \ + --input_arrays=input \ + --output_arrays=MobilenetV1/Predictions/Reshape_1 \ + --mean_values=128 \ + --std_dev_values=127 +``` + +### Use \"dummy-quantization\" to try out quantized inference on a float graph <a name="dummy-quant"></a> + +In order to evaluate the possible benefit of generating a quantized graph, the +converter allows "dummy-quantization" on float graphs. The flags +`--default_ranges_min` and `--default_ranges_max` accept plausible values for +the min-max ranges of the values in all arrays that do not have min-max +information. "Dummy-quantization" will produce lower accuracy but will emulate +the performance of a correctly quantized model. + +The example below contains a model using Relu6 activation functions. Therefore, +a reasonable guess is that most activation ranges should be contained in [0, 6]. + +``` +curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \ + | tar xzv -C /tmp +tflite_convert \ + --output_file=/tmp/foo.cc \ + --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ + --inference_type=QUANTIZED_UINT8 \ + --input_arrays=input \ + --output_arrays=MobilenetV1/Predictions/Reshape_1 \ + --default_ranges_min=0 \ + --default_ranges_max=6 \ + --mean_values=128 \ + --std_dev_values=127 +``` + +## Specifying input and output arrays + +### Multiple input arrays + +The flag `input_arrays` takes in a comma-separated list of input arrays as seen +in the example below. This is useful for models or subgraphs with multiple +inputs. + +``` +curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \ + | tar xzv -C /tmp +tflite_convert \ + --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \ + --output_file=/tmp/foo.tflite \ + --input_shapes=1,28,28,96:1,28,28,16:1,28,28,192:1,28,28,64 \ + --input_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_3/MaxPool_0a_3x3/MaxPool,InceptionV1/InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/Relu \ + --output_arrays=InceptionV1/Logits/Predictions/Reshape_1 +``` + +Note that `input_shapes` is provided as a colon-separated list. Each input shape +corresponds to the input array at the same position in the respective list. + +### Multiple output arrays + +The flag `output_arrays` takes in a comma-separated list of output arrays as +seen in the example below. This is useful for models or subgraphs with multiple +outputs. + +``` +curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \ + | tar xzv -C /tmp +tflite_convert \ + --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \ + --output_file=/tmp/foo.tflite \ + --input_arrays=input \ + --output_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu +``` + +### Specifying subgraphs + +Any array in the input file can be specified as an input or output array in +order to extract subgraphs out of an input graph file. The TensorFlow Lite +Converter discards the parts of the graph outside of the specific subgraph. Use +[graph visualizations](#graph-visualizations) to identify the input and output +arrays that make up the desired subgraph. + +The follow command shows how to extract a single fused layer out of a TensorFlow +GraphDef. + +``` +curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \ + | tar xzv -C /tmp +tflite_convert \ + --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \ + --output_file=/tmp/foo.pb \ + --input_shapes=1,28,28,96:1,28,28,16:1,28,28,192:1,28,28,64 \ + --input_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_3/MaxPool_0a_3x3/MaxPool,InceptionV1/InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/Relu \ + --output_arrays=InceptionV1/InceptionV1/Mixed_3b/concat_v2 +``` + +Note that the final representation in TensorFlow Lite FlatBuffers tends to have +coarser granularity than the very fine granularity of the TensorFlow GraphDef +representation. For example, while a fully-connected layer is typically +represented as at least four separate ops in TensorFlow GraphDef (Reshape, +MatMul, BiasAdd, Relu...), it is typically represented as a single "fused" op +(FullyConnected) in the converter's optimized representation and in the final +on-device representation. As the level of granularity gets coarser, some +intermediate arrays (say, the array between the MatMul and the BiasAdd in the +TensorFlow GraphDef) are dropped. + +When specifying intermediate arrays as `--input_arrays` and `--output_arrays`, +it is desirable (and often required) to specify arrays that are meant to survive +in the final form of the graph, after fusing. These are typically the outputs of +activation functions (since everything in each layer until the activation +function tends to get fused). + +## Logging + + +## Graph visualizations + +The converter can export a graph to the Graphviz Dot format for easy +visualization using either the `--output_format` flag or the +`--dump_graphviz_dir` flag. The subsections below outline the use cases for +each. + +### Using `--output_format=GRAPHVIZ_DOT` <a name="using-output-format-graphviz-dot"></a> + +The first way to get a Graphviz rendering is to pass `GRAPHVIZ_DOT` into +`--output_format`. This results in a plausible visualization of the graph. This +reduces the requirements that exist during conversion from a TensorFlow GraphDef +to a TensorFlow Lite FlatBuffer. This may be useful if the conversion to TFLite +is failing. + +``` +curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \ + | tar xzv -C /tmp +tflite_convert \ + --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ + --output_file=/tmp/foo.dot \ + --output_format=GRAPHVIZ_DOT \ + --input_shape=1,128,128,3 \ + --input_arrays=input \ + --output_arrays=MobilenetV1/Predictions/Reshape_1 +``` + +The resulting `.dot` file can be rendered into a PDF as follows: + +``` +dot -Tpdf -O /tmp/foo.dot +``` + +And the resulting `.dot.pdf` can be viewed in any PDF viewer, but we suggest one +with a good ability to pan and zoom across a very large page. Google Chrome does +well in that respect. + +``` +google-chrome /tmp/foo.dot.pdf +``` + +Example PDF files are viewable online in the next section. + +### Using `--dump_graphviz_dir` + +The second way to get a Graphviz rendering is to pass the `--dump_graphviz_dir` +flag, specifying a destination directory to dump Graphviz rendering to. Unlike +the previous approach, this one retains the original output format. This +provides a visualization of the actual graph resulting from a specific +conversion process. + +``` +curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \ + | tar xzv -C /tmp +tflite_convert \ + --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \ + --output_file=/tmp/foo.tflite \ + --input_arrays=input \ + --output_arrays=MobilenetV1/Predictions/Reshape_1 \ + --dump_graphviz_dir=/tmp +``` + +This generates a few files in the destination directory. The two most important +files are `toco_AT_IMPORT.dot` and `/tmp/toco_AFTER_TRANSFORMATIONS.dot`. +`toco_AT_IMPORT.dot` represents the original graph containing only the +transformations done at import time. This tends to be a complex visualization +with limited information about each node. It is useful in situations where a +conversion command fails. + +`toco_AFTER_TRANSFORMATIONS.dot` represents the graph after all transformations +were applied to it, just before it is exported. Typically, this is a much +smaller graph with more information about each node. + +As before, these can be rendered to PDFs: + +``` +dot -Tpdf -O /tmp/toco_*.dot +``` + +Sample output files can be seen here below. Note that it is the same +`AveragePool` node in the top right of each image. + +<table><tr> + <td> + <a target="_blank" href="https://storage.googleapis.com/download.tensorflow.org/example_images/toco_AT_IMPORT.dot.pdf"> + <img src="https://www.tensorflow.org/images/tflite_convert/tflite_convert_before.png"/> + </a> + </td> + <td> + <a target="_blank" href="https://storage.googleapis.com/download.tensorflow.org/example_images/toco_AFTER_TRANSFORMATIONS.dot.pdf"> + <img src="https://www.tensorflow.org/images/tflite_convert/tflite_convert_after.png"/> + </a> + </td> +</tr> +<tr><td>before</td><td>after</td></tr> +</table> + +### Graph "video" logging + +When `--dump_graphviz_dir` is used, one may additionally pass +`--dump_graphviz_video`. This causes a graph visualization to be dumped after +each individual graph transformation, resulting in thousands of files. +Typically, one would then bisect into these files to understand when a given +change was introduced in the graph. + +### Legend for the graph visualizations <a name="graphviz-legend"></a> + +* Operators are red square boxes with the following hues of red: + * Most operators are + <span style="background-color:#db4437;color:white;border:1px;border-style:solid;border-color:black;padding:1px">bright + red</span>. + * Some typically heavy operators (e.g. Conv) are rendered in a + <span style="background-color:#c53929;color:white;border:1px;border-style:solid;border-color:black;padding:1px">darker + red</span>. +* Arrays are octagons with the following colors: + * Constant arrays are + <span style="background-color:#4285f4;color:white;border:1px;border-style:solid;border-color:black;padding:1px">blue</span>. + * Activation arrays are gray: + * Internal (intermediate) activation arrays are + <span style="background-color:#f5f5f5;border:1px;border-style:solid;border-color:black;border:1px;border-style:solid;border-color:black;padding:1px">light + gray</span>. + * Those activation arrays that are designated as `--input_arrays` or + `--output_arrays` are + <span style="background-color:#9e9e9e;border:1px;border-style:solid;border-color:black;padding:1px">dark + gray</span>. + * RNN state arrays are green. Because of the way that the converter + represents RNN back-edges explicitly, each RNN state is represented by a + pair of green arrays: + * The activation array that is the source of the RNN back-edge (i.e. + whose contents are copied into the RNN state array after having been + computed) is + <span style="background-color:#b7e1cd;border:1px;border-style:solid;border-color:black;padding:1px">light + green</span>. + * The actual RNN state array is + <span style="background-color:#0f9d58;color:white;border:1px;border-style:solid;border-color:black;padding:1px">dark + green</span>. It is the destination of the RNN back-edge updating + it. diff --git a/tensorflow/contrib/lite/g3doc/tflite_convert/cmdline_reference.md b/tensorflow/contrib/lite/g3doc/tflite_convert/cmdline_reference.md new file mode 100644 index 0000000000..d65912fea6 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/tflite_convert/cmdline_reference.md @@ -0,0 +1,159 @@ +# TensorFlow Lite Converter command-line glossary + +This page is complete reference of command-line flags used by the TensorFlow +Lite Converter's command line starting from TensorFlow 1.9 up until the most +recent build of TensorFlow. + +[TOC] + +## High-level flags + +The following high level flags specify the details of the input and output +files. The flag `--output_file` is always required. Additionally, either +`--graph_def_file`, `--saved_model_dir` or `--keras_model_file` is required. + +* `--output_file`. Type: string. Specifies the full path of the output file. +* `--graph_def_file`. Type: string. Specifies the full path of the input + GraphDef file frozen using + [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py). +* `--saved_model_dir`. Type: string. Specifies the full path to the directory + containing the SavedModel. +* `--keras_model_file`. Type: string. Specifies the full path of the HDF5 file + containing the tf.keras model. +* `--output_format`. Type: string. Default: `TFLITE`. Specifies the format of + the output file. Allowed values: + * `TFLITE`: TensorFlow Lite FlatBuffer format. + * `GRAPHVIZ_DOT`: GraphViz `.dot` format containing a visualization of the + graph after graph transformations. + * Note that passing `GRAPHVIZ_DOT` to `--output_format` leads to loss + of TFLite specific transformations. Therefore, the resulting + visualization may not reflect the final set of graph + transformations. To get a final visualization with all graph + transformations use `--dump_graphviz_dir` instead. + +The following flags specify optional parameters when using SavedModels. + +* `--saved_model_tag_set`. Type: string. Default: + [kSavedModelTagServe](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/tag_constants.h). + Specifies a comma-separated set of tags identifying the MetaGraphDef within + the SavedModel to analyze. All tags in the tag set must be specified. +* `--saved_model_signature_key`. Type: string. Default: + [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants). + Specifies the key identifying the SignatureDef containing inputs and + outputs. + +## Model flags + +*Model flags* provide additional information about the model stored in the input +file. + +* `--input_arrays`. Type: comma-separated list of strings. Specifies the list + of names of input activation tensors. +* `--output_arrays`. Type: comma-separated list of strings. Specifies the list + of names of output activation tensors. + +The following flags define properties of the input tensors. Each item in the +`--input_arrays` flag should correspond to each item in the following flags +based on index. + +* `--input_shapes`. Type: colon-separated list of comma-separated lists of + integers. Each comma-separated list of integers gives the shape of one of + the input arrays specified in + [TensorFlow convention](https://www.tensorflow.org/guide/dims_types#shape). + * Example: `--input_shapes=1,60,80,3` for a typical vision model means a + batch size of 1, an input image height of 60, an input image width of + 80, and an input image depth of 3 (representing RGB channels). + * Example: `--input_arrays=foo,bar --input_shapes=2,3:4,5,6` means "foo" + has a shape of [2, 3] and "bar" has a shape of [4, 5, 6]. +* `--std_dev_values`, `--mean_values`. Type: comma-separated list of floats. + These specify the (de-)quantization parameters of the input array, when it + is quantized. This is only needed if `inference_input_type` is + `QUANTIZED_UINT8`. + * The meaning of `mean_values` and `std_dev_values` is as follows: each + quantized value in the quantized input array will be interpreted as a + mathematical real number (i.e. as an input activation value) according + to the following formula: + * `real_value = (quantized_input_value - mean_value) / std_dev_value`. + * When performing float inference (`--inference_type=FLOAT`) on a + quantized input, the quantized input would be immediately dequantized by + the inference code according to the above formula, before proceeding + with float inference. + * When performing quantized inference + (`--inference_type=QUANTIZED_UINT8`), no dequantization is performed by + the inference code. However, the quantization parameters of all arrays, + including those of the input arrays as specified by `mean_value` and + `std_dev_value`, determine the fixed-point multipliers used in the + quantized inference code. `mean_value` must be an integer when + performing quantized inference. + +## Transformation flags + +*Transformation flags* specify options of the transformations to be applied to +the graph, i.e. they specify requested properties that the output file should +have. + +* `--inference_type`. Type: string. Default: `FLOAT`. Data type of all + real-number arrays in the output file except for input arrays (defined by + `--inference_input_type`). Must be `{FLOAT, QUANTIZED_UINT8}`. + + This flag only impacts real-number arrays including float and quantized + arrays. This excludes all other data types including plain integer arrays + and string arrays. Specifically: + + * If `FLOAT`, then real-numbers arrays will be of type float in the output + file. If they were quantized in the input file, then they get + dequantized. + * If `QUANTIZED_UINT8`, then real-numbers arrays will be quantized as + uint8 in the output file. If they were float in the input file, then + they get quantized. + +* `--inference_input_type`. Type: string. Data type of a real-number input + array in the output file. By default the `--inference_type` is used as type + of all of the input arrays. Flag is primarily intended for generating a + float-point graph with a quantized input array. A Dequantized operator is + added immediately after the input array. Must be `{FLOAT, QUANTIZED_UINT8}`. + + The flag is typically used for vision models taking a bitmap as input but + requiring floating-point inference. For such image models, the uint8 input + is quantized and the quantization parameters used for such input arrays are + their `mean_value` and `std_dev_value` parameters. + +* `--default_ranges_min`, `--default_ranges_max`. Type: floating-point. + Default value for the (min, max) range values used for all arrays without a + specified range. Allows user to proceed with quantization of non-quantized + or incorrectly-quantized input files. These flags produce models with low + accuracy. They are intended for easy experimentation with quantization via + "dummy quantization". + +* `--drop_control_dependency`. Type: boolean. Default: True. Indicates whether + to drop control dependencies silently. This is due to TensorFlow Lite not + supporting control dependencies. + +* `--reorder_across_fake_quant`. Type: boolean. Default: False. Indicates + 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. + +* `--allow_custom_ops`. Type: string. Default: False. Indicates 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. + +* `--post_training_quantize`. Type: boolean. Default: False. 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). + +## Logging flags + +The following flags generate graph visualizations of the graph as +[GraphViz](https://www.graphviz.org/) `.dot` files at various points during +graph transformations: + +* `--dump_graphviz_dir`. Type: string. Specifies the full path of the + directory to output GraphViz `.dot` files. Outputs the graph immediately + after reading in the graph and after all of the transformations have been + completed. +* `--dump_graphviz_video`. Type: boolean. Outputs GraphViz after every graph + transformation. Requires `--dump_graphviz_dir` to be specified. diff --git a/tensorflow/contrib/lite/g3doc/tflite_convert/index.md b/tensorflow/contrib/lite/g3doc/tflite_convert/index.md new file mode 100644 index 0000000000..12ba0225f6 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/tflite_convert/index.md @@ -0,0 +1,22 @@ +# TensorFlow Lite Converter + +The TensorFlow Lite Converter converts TensorFlow graphs into +TensorFlow Lite graphs. There are additional usages that are also detailed in +the usage documentation. + + +## Where the converter fits in the TensorFlow landscape + +Once an application developer has a trained TensorFlow model, the TensorFlow +Lite Converter will accept +that model and generate a TensorFlow Lite +[FlatBuffer](https://google.github.io/flatbuffers/) file. The converter currently supports +[SavedModels](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators), +frozen graphs (models generated via +[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)), +and `tf.Keras` model files. The TensorFlow Lite FlatBuffer file can be shipped +to client devices, generally mobile devices, where the TensorFlow Lite +interpreter handles them on-device. This flow is represented in the diagram +below. + +![drawing](toco_landscape.svg) diff --git a/tensorflow/contrib/lite/g3doc/tflite_convert/python_api.md b/tensorflow/contrib/lite/g3doc/tflite_convert/python_api.md new file mode 100644 index 0000000000..e1c0e0c240 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/tflite_convert/python_api.md @@ -0,0 +1,258 @@ +# TensorFlow Lite Converter & Interpreter Python API reference + +This page provides examples on how to use the TensorFlow Lite Converter and the +TensorFlow Lite interpreter using the Python API. + +[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 as of TensorFlow 1.9 +is `tf.contrib.lite.TFLiteConverter`. The API for calling the Python intepreter +is `tf.contrib.lite.Interpreter`. + +Note: Reference "Additional Instructions" sections for converting TensorFlow +models to TensorFlow Lite +[in TensorFlow 1.9 to TensorFlow 1.11](#pre-tensorflow-1.11) and +[prior to TensorFlow 1.9](#pre-tensorflow-1.9) + +`TFLiteConverter` provides class methods based on the original format of the +model. `TFLiteConverter.from_session()` is available for GraphDefs. +`TFLiteConverter.from_saved_model()` is available for SavedModels. +`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.contrib.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.contrib.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.contrib.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.contrib.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.contrib.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.contrib.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.contrib.lite.constants.<CONSTANT_NAME>` as seen below with +`QUANTIZED_UINT8`. Run `help(tf.contrib.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.contrib.lite.TFLiteConverter.from_session(sess, [img], [out]) + converter.inference_type = tf.contrib.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) +``` + +## TensorFlow Lite Python interpreter <a name="interpreter"></a> + +### Using the interpreter from a model file <a name="interpreter-file"></a> + +The following example shows how to use the TensorFlow Lite Python interpreter +when provided a TensorFlow Lite FlatBuffer file. The example also demonstrates +how to run inference on random input data. Run +`help(tf.contrib.lite.Interpreter)` in the Python terminal to get detailed +documentation on the interpreter. + +```python +import numpy as np +import tensorflow as tf + +# Load TFLite model and allocate tensors. +interpreter = tf.contrib.lite.Interpreter(model_path="converted_model.tflite") +interpreter.allocate_tensors() + +# Get input and output tensors. +input_details = interpreter.get_input_details() +output_details = interpreter.get_output_details() + +# Test 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() +output_data = interpreter.get_tensor(output_details[0]['index']) +print(output_data) +``` + +### Using the interpreter from model data <a name="interpreter-data"></a> + +The following example shows how to use the TensorFlow Lite Python interpreter +when starting with the TensorFlow Lite Flatbuffer model previously loaded. This +example shows an end-to-end use case, starting from building the TensorFlow +model. + +```python +import numpy as np +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.identity(val, name="out") + +with tf.Session() as sess: + converter = tf.contrib.lite.TFLiteConverter.from_session(sess, [img], [out]) + tflite_model = converter.convert() + +# Load TFLite model and allocate tensors. +interpreter = tf.contrib.lite.Interpreter(model_content=tflite_model) +interpreter.allocate_tensors() +``` + +## 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 in TensorFlow 1.9 to TensorFlow 1.11 <a name="#pre-tensorflow-1.11"></a> + +To convert TensorFlow models to TensorFlow Lite in TensorFlow 1.9 through +TensorFlow 1.11, use `TocoConverter`. `TocoConverter` is semantically +identically to `TFLiteConverter`. + +### Converting models prior to TensorFlow 1.9 <a name="pre-tensorflow-1.9"></a> + +To convert TensorFlow models to TensorFlow Lite in TensorFlow 1.7 and TensorFlow +1.8, use the `toco_convert` function. Run `help(tf.contrib.lite.toco_convert)` +to get details about accepted parameters. diff --git a/tensorflow/contrib/lite/g3doc/tflite_convert/toco_landscape.svg b/tensorflow/contrib/lite/g3doc/tflite_convert/toco_landscape.svg new file mode 100644 index 0000000000..335debde57 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/tflite_convert/toco_landscape.svg @@ -0,0 +1 @@ +<svg version="1.1" viewBox="0.0 0.0 720.0 540.0" fill="none" stroke="none" stroke-linecap="square" stroke-miterlimit="10" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><clipPath id="p.0"><path d="m0 0l720.0 0l0 540.0l-720.0 0l0 -540.0z" clip-rule="nonzero"/></clipPath><g clip-path="url(#p.0)"><path fill="#000000" fill-opacity="0.0" d="m0 0l720.0 0l0 540.0l-720.0 0z" fill-rule="evenodd"/><path fill="#f3f3f3" d="m19.375328 28.750656l361.6378 0l0 358.01575l-361.6378 0z" fill-rule="evenodd"/><path stroke="#cccccc" stroke-width="1.0" stroke-linejoin="round" stroke-linecap="butt" 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