Update TOCO command line documentation.

PiperOrigin-RevId: 201756935
author: Nupur Garg <nupurgarg@google.com> 2018-06-22 16:12:02 -0700
committer: TensorFlower Gardener <gardener@tensorflow.org> 2018-06-22 16:14:54 -0700
commit: aa63c6b0209c1194a392e4327f6211c7c13f40dc (patch)
tree: 4ef182b6607447fd19ed6e4625ab79a732fe4b23 /tensorflow/contrib/lite/toco/g3doc
parent: 8e1a4a5b03357c0397c578611482c69065e7bdc6 (diff)
4 files changed, 275 insertions, 378 deletions
diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
index 8e93f02ef1..767cf48a9f 100644
--- a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
+++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
@@ -9,57 +9,49 @@ complemented by the following documents:
 
 Table of contents:
 
-*   [Convert a TensorFlow SavedModel to TensorFlow Lite](#savedmodel)
-*   [Convert a TensorFlow GraphDef to TensorFlow Lite for float
-    inference](#graphdef-float)
+*   [Command-line tools](#tools)
+    *   [Converting models prior to TensorFlow 1.9.](#pre-tensorflow-1.9)
+*   [Convert a TensorFlow GraphDef](#graphdef)
+*   [Convert a TensorFlow SavedModel](#savedmodel)
 *   [Quantization](#quantization)
-    *   [Convert a TensorFlow GraphDef to TensorFlow Lite for quantized
-        inference](#graphdef-quant)
+    *   [Convert a TensorFlow GraphDef for quantized inference](#graphdef-quant)
     *   [Use "dummy-quantization" to try out quantized inference on a float
         graph](#dummy-quant)
 *   [Specifying input and output arrays](#specifying-input-and-output-arrays)
-    *   [Multiple output arrays](#multiple-output-arrays)
     *   [Multiple input arrays](#multiple-input-arrays)
+    *   [Multiple output arrays](#multiple-output-arrays)
     *   [Specifying subgraphs](#specifying-subgraphs)
-*   [Other conversions supported by TOCO](#other-conversions)
-    *   [Optimize a TensorFlow GraphDef](#optimize-graphdef)
-    *   [Convert a TensorFlow Lite FlatBuffer back into TensorFlow GraphDef
-        format](#to-graphdef)
-*   [Logging](#logging)
-    *   [Graph "video" logging](#graph-video-logging)
 *   [Graph visualizations](#graph-visualizations)
     *   [Using --output_format=GRAPHVIZ_DOT](#using-output-formatgraphviz-dot)
     *   [Using --dump_graphviz](#using-dump-graphviz)
+    *   [Graph "video" logging](#graph-video-logging)
     *   [Legend for the graph visualizations](#graphviz-legend)
 
-## Convert a TensorFlow SavedModel to TensorFlow Lite <a name="savedmodel"></a>
+## Command-line tools <a name="tools"></a>
 
-The follow example converts a basic TensorFlow SavedModel into a Tensorflow Lite
-FlatBuffer to perform floating-point inference.
+There are two approaches to running TOCO via command line.
 
-```
-bazel run --config=opt \
-  third_party/tensorflow/contrib/lite/toco:toco -- \
-  --savedmodel_directory=/tmp/saved_model \
-  --output_file=/tmp/foo.tflite
-```
+*   `tflite_convert`: Starting from TensorFlow 1.9, the command-line tool
+    `tflite_convert` will be installed as part of the Python package. All of the
+    examples below use `tflite_convert` for simplicity.
+    *   Example: `tflite --output_file=...`
+*   `bazel`: In order to run the latest version of TOCO, [clone the TensorFlow
+    repository](https://www.tensorflow.org/install/install_sources#clone_the_tensorflow_repository)
+    and use `bazel`. This is the recommended approach for converting models that
+    utilize new features that were not supported by TOCO in TensorFlow 1.9.
+    *   Example: `bazel run
+        //tensorflow/contrib/lite/python:tflite_convert --
+        --output_file=...`
 
-[SavedModel](https://www.tensorflow.org/programmers_guide/saved_model#using_savedmodel_with_estimators)
-has fewer required flags than frozen graphs (described [below](#graphdef-float))
-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/programmers_guide/saved_model#apis_to_build_and_load_a_savedmodel)
-specified by `--savedmodel_tagset`. The value for `input_shapes` is
-automatically determined from the MetaGraphDef whenever possible. The default
-value for `--inference_type` for SavedModels is `FLOAT`.
+### Converting models prior to TensorFlow 1.9. <a name="pre-tensorflow-1.9"></a>
 
-There is currently no support for MetaGraphDefs without a SignatureDef or for
-MetaGraphDefs that use the [`assets/`
-directory](https://www.tensorflow.org/programmers_guide/saved_model#structure_of_a_savedmodel_directory).
+The recommended approach for using TOCO 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.
 
-## Convert a TensorFlow GraphDef to TensorFlow Lite for float inference <a name="graphdef-float"></a>
+## 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))
@@ -69,19 +61,43 @@ 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
-bazel run --config=opt \
-  //tensorflow/contrib/lite/toco:toco -- \
-  --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
+tflite_convert \
   --output_file=/tmp/foo.tflite \
-  --inference_type=FLOAT \
-  --input_shape=1,128,128,3 \
-  --input_array=input \
-  --output_array=MobilenetV1/Predictions/Reshape_1
+  --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/programmers_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/programmers_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/programmers_guide/saved_model#structure_of_a_savedmodel_directory).
+
 ## Quantization
 
-### Convert a TensorFlow GraphDef to TensorFlow Lite for quantized inference <a name="graphdef-quant"></a>
+### Convert a TensorFlow GraphDef for quantized inference <a name="graphdef-quant"></a>
 
 TOCO is compatible with fixed point quantization models described
 [here](https://www.tensorflow.org/performance/quantization). These are float
@@ -95,18 +111,14 @@ The following command generates a quantized TensorFlow Lite FlatBuffer from a
 "quantized" TensorFlow GraphDef.
 
 ```
-bazel run --config=opt \
-  //tensorflow/contrib/lite/toco:toco -- \
-  --input_file=/tmp/some_quantized_graph.pb \
+tflite_convert \
   --output_file=/tmp/foo.tflite \
-  --input_format=TENSORFLOW_GRAPHDEF \
-  --output_format=TFLITE \
+  --graph_def_file=/tmp/some_quantized_graph.pb \
   --inference_type=QUANTIZED_UINT8 \
-  --input_shape=1,128,128,3 \
-  --input_array=input \
-  --output_array=MobilenetV1/Predictions/Reshape_1 \
-  --mean_value=128 \
-  --std_value=127
+  --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>
@@ -124,45 +136,20 @@ 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
-bazel run --config=opt \
-  //tensorflow/contrib/lite/toco:toco -- \
-  --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
+tflite_convert \
   --output_file=/tmp/foo.cc \
-  --input_format=TENSORFLOW_GRAPHDEF \
-  --output_format=TFLITE \
+  --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
   --inference_type=QUANTIZED_UINT8 \
-  --input_shape=1,128,128,3 \
-  --input_array=input \
-  --output_array=MobilenetV1/Predictions/Reshape_1 \
+  --input_arrays=input \
+  --output_arrays=MobilenetV1/Predictions/Reshape_1 \
   --default_ranges_min=0 \
   --default_ranges_max=6 \
-  --mean_value=127.5 \
-  --std_value=127.5
+  --mean_values=128 \
+  --std_dev_values=127
 ```
 
 ## Specifying input and output arrays
 
-### 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
-bazel run --config=opt \
-  //tensorflow/contrib/lite/toco:toco -- \
-  --input_file=/tmp/inception_v1_2016_08_28_frozen.pb \
-  --output_file=/tmp/foo.tflite \
-  --input_format=TENSORFLOW_GRAPHDEF \
-  --output_format=TFLITE \
-  --inference_type=FLOAT \
-  --input_shape=1,224,224,3 \
-  --input_array=input \
-  --output_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu
-```
-
 ### Multiple input arrays
 
 The flag `input_arrays` takes in a comma-separated list of input arrays as seen
@@ -172,21 +159,33 @@ inputs.
 ```
 curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \
   | tar xzv -C /tmp
-bazel run --config=opt \
-  //tensorflow/contrib/lite/toco:toco -- \
-  --input_file=/tmp/inception_v1_2016_08_28_frozen.pb \
+tflite_convert \
+  --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \
   --output_file=/tmp/foo.tflite \
-  --input_format=TENSORFLOW_GRAPHDEF \
-  --output_format=TFLITE \
-  --inference_type=FLOAT \
   --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_array=InceptionV1/Logits/Predictions/Reshape_1
+  --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
@@ -201,115 +200,57 @@ GraphDef.
 ```
 curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \
   | tar xzv -C /tmp
-bazel run --config=opt \
-  //tensorflow/contrib/lite/toco:toco -- \
-  --input_file=/tmp/inception_v1_2016_08_28_frozen.pb \
+tflite_convert \
+  --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \
   --output_file=/tmp/foo.pb \
-  --input_format=TENSORFLOW_GRAPHDEF \
-  --output_format=TENSORFLOW_GRAPHDEF \
   --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_array=InceptionV1/InceptionV1/Mixed_3b/concat_v2
+  --output_arrays=InceptionV1/InceptionV1/Mixed_3b/concat_v2
 ```
 
-Note that the final representation of an on-device inference workload (say, in
-TensorFlow Lite FlatBuffers format) 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 (e.g. in TensorFlow
-Lite FlatBuffer format). As the level of granularity gets coarser, some
+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` / `--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).
-
-## Other conversions supported by TOCO <a name="other-conversions"></a>
-
-The converter accepts both TENSORFLOW_GRAPHDEF and TFLITE file formats as both
-`--input_format` and `--output_format`. This means that conversion to and from
-any supported format is possible.
+TensorFlow GraphDef) are dropped.
 
-### Optimize a TensorFlow GraphDef <a name="optimize-graphdef"></a>
-
-Same-format "conversions" can be used to optimize and simplify a graph or be
-used to [get a subgraph](#specifying-subgraphs) of a graph. The flag
-`--inference_type` is not required because TensorFlow graphs, including those
-containing the
-[`FakeQuant*`](https://www.tensorflow.org/api_guides/python/array_ops#Fake_quantization)
-ops are always float graphs.
-
-```
-curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \
-  | tar xzv -C /tmp
-bazel run --config=opt \
-  //tensorflow/contrib/lite/toco:toco -- \
-  --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
-  --output_file=/tmp/foo.pb \
-  --input_format=TENSORFLOW_GRAPHDEF \
-  --output_format=TENSORFLOW_GRAPHDEF \
-  --input_shape=1,128,128,3 \
-  --input_array=input \
-  --output_array=MobilenetV1/Predictions/Reshape_1
-```
-
-### Convert a TensorFlow Lite FlatBuffer back into TensorFlow GraphDef format <a name="to-graphdef"></a>
-
-The converter supports file format conversions from TensorFlow Lite, back into
-TensorFlow GraphDef format.
-
-```
-bazel run --config=opt \
-  //tensorflow/contrib/lite/toco:toco -- \
-  --input_file=/tmp/foo.tflite \
-  --output_file=/tmp/foo.pb \
-  --input_format=TFLITE \
-  --output_format=TENSORFLOW_GRAPHDEF \
-  --input_shape=1,128,128,3 \
-  --input_array=input \
-  --output_array=MobilenetV1/Predictions/Reshape_1
-```
+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 "video" logging
-
-When `--dump_graphviz=` is used (see the section on [graph
-visualizations](#graph-visualizations)), one may additionally pass
-`--dump_graphviz_video`, which causes a graph visualization to be dumped after
-each individual graph transformation. This results in thousands of files.
-Typically, one would then bisect into these files to understand when a given
-change was introduced in the graph.
 
 ## Graph visualizations
 
 TOCO can export a graph to the GraphViz Dot format for easy visualization via
-either the `--output_format` flag or the `--dump_graphviz` flag. The subsections
-below outline the use cases for each.
+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`
 
 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 normally exist during conversion between other
-input and output formats. For example, this may be useful if conversion from
-TENSORFLOW_GRAPHDEF to TFLITE is failing.
+reduces the requirements that exist during conversion between other input and
+output formats. This may be useful if conversion from TENSORFLOW_GRAPHDEF to
+TFLITE is failing.
 
 ```
 curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \
   | tar xzv -C /tmp
-bazel run --config=opt \
-  //tensorflow/contrib/lite/toco:toco -- \
-  --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
+tflite_convert \
+  --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
   --output_file=/tmp/foo.dot \
-  --input_format=TENSORFLOW_GRAPHDEF \
   --output_format=GRAPHVIZ_DOT \
   --input_shape=1,128,128,3 \
-  --input_array=input \
-  --output_array=MobilenetV1/Predictions/Reshape_1
+  --input_arrays=input \
+  --output_arrays=MobilenetV1/Predictions/Reshape_1
 ```
 
 The resulting `.dot` file can be rendered into a PDF as follows:
@@ -330,49 +271,35 @@ Example PDF files are viewable online in the next section.
 
 ### Using `--dump_graphviz`
 
-The second way to get a graphviz rendering is to pass the `--dump_graphviz=`
+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 allows you to keep your real command-line (with
-your real `--output_format` and other flags) unchanged, just appending a
-`--dump_graphviz=` flag to it. This provides a visualization of the actual graph
-during a specific conversion process.
+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
-bazel run --config=opt \
-  //tensorflow/contrib/lite/toco:toco -- \
-  --input_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
+tflite_convert \
+  --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
   --output_file=/tmp/foo.tflite \
-  --input_format=TENSORFLOW_GRAPHDEF \
-  --output_format=TFLITE \
-  --inference_type=FLOAT \
-  --input_shape=1,128,128,3 \
-  --input_array=input \
-  --output_array=MobilenetV1/Predictions/Reshape_1 \
-  --dump_graphviz=/tmp
-```
-
-This generates a few files in the destination directory, here `/tmp`. The two
-most important files are:
-
-```
-/tmp/toco_AT_IMPORT.dot
-/tmp/toco_AFTER_TRANSFORMATIONS.dot
+  --input_arrays=input \
+  --output_arrays=MobilenetV1/Predictions/Reshape_1 \
+  --dump_graphviz_dir=/tmp
 ```
 
-`toco_AT_IMPORT.dot` represents the graph as it was imported from
-`--input_file`, before any transformation was applied to it (besides some
-transformations that are applied immediately while importing). This tends to be
-a complex visualization with limited information, but is useful especially in
-situations where a conversion command fails (this file is generated even if the
-conversion subsequently fails).
+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 was exported to the `--output_file`.
-Typically, this is a much smaller graph with more information about each node.
+were applied to it, just before it is exported. Typically, this is a much
+smaller graph with more information about each node.
 
-Again, these can be rendered to PDFs:
+As before, these can be rendered to PDFs:
 
 ```
 dot -Tpdf -O /tmp/toco_*.dot
@@ -383,6 +310,14 @@ Sample output files can be seen here:
 *   [toco_AT_IMPORT.dot.pdf](https://storage.googleapis.com/download.tensorflow.org/example_images/toco_AT_IMPORT.dot.pdf)
 *   [toco_AFTER_TRANSFORMATIONS.dot.pdf](https://storage.googleapis.com/download.tensorflow.org/example_images/toco_AFTER_TRANSFORMATIONS.dot.pdf).
 
+### 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:
diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_reference.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_reference.md
index 8085ae0748..2d44b871c6 100644
--- a/tensorflow/contrib/lite/toco/g3doc/cmdline_reference.md
+++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_reference.md
@@ -1,7 +1,8 @@
 # TensorFlow Lite Optimizing Converter command-line glossary
 
-This page is complete reference of command-line flags. It is complemented by the
-following other documents:
+This page is complete reference of command-line flags used by TOCO's command
+line starting from TensorFlow 1.9 up until the most recent build of TensorFlow.
+It is complemented by the following other documents:
 
 *   [README](../README.md)
 *   [Command-line examples](cmdline_examples.md)
@@ -16,116 +17,79 @@ Table of contents:
 
 ## High-level flags
 
-The following high level flags specify the location of the input and output
+The following high level flags specify the details of the input and output
 files. The flag `--output_file` is always required. Additionally, either
-`--input_file` or `--savedmodel_directory` is required.
-
-*   `--savedmodel_directory`. Type: string. Specifies the full path to the
-    directory containing the SavedModel.
-*   `--savedmodel_tagset`. Type: string. Default:
+`--graph_def_file` or `--saved_model_dir` 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.
+*   `--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 containg 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` 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.
-*   `--input_file`. Type: string. Specifies the path of the input file. This may
-    be either an absolute or a relative path.
-*   `--output_file`. Type: string. Specifies the path of the output file.
-
-The following high level flags specify the types of the input and output files:
-
-*   `--input_format`. Type: string. Default: `TENSORFLOW_GRAPHDEF`. Specifies
-    the format of the input file. Allowed values:
-    *   `TENSORFLOW_GRAPHDEF` &mdash; The TensorFlow GraphDef format. Both
-        binary and text proto formats are allowed.
-    *   `TFLITE` &mdash; The TensorFlow Lite FlatBuffers format.
-*   `--output_format`. Type: string. Default: `TFLITE`. Specifies the format of
-    the output file. Allowed values:
-    *   `TENSORFLOW_GRAPHDEF` &mdash; The TensorFlow GraphDef format. Always
-        produces a file in binary (not text) proto format.
-    *   `TFLITE` &mdash; The TensorFlow Lite FlatBuffers format.
-        *   Whether a float or quantized TensorFlow Lite file will be produced
-            depends on the `--inference_type` flag.
-    *   `GRAPHVIZ_DOT` &mdash; The GraphViz `.dot` format. This asks the
-        converter to generate a reasonable graphical representation of the graph
-        after simplification by a generic set of transformation.
-        *   A typical `dot` command line to view the resulting graph might look
-            like: `dot -Tpdf -O file.dot`.
-        *   Note that since passing this `--output_format` means losing the
-            information of which output format you actually care about, and
-            since the converter's transformations depend on the specific output
-            format, the resulting visualization may not fully reflect what you
-            would get on the actual output format that you are using. To avoid
-            that concern, and generally to get a visualization of exactly what
-            you get in your actual output format as opposed to just a merely
-            plausible visualization of a model, consider using `--dump_graphviz`
-            instead and keeping your true `--output_format`.
+*   `--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.
 
-*   `--output_array`. Type: string. Specifies a single array as the output
-    activations. Incompatible with `--output_arrays`.
-*   `--output_arrays`. Type: comma-separated list of strings. Specifies a list
-    of arrays as the output activations, for models with multiple outputs.
-    Incompatible with `--output_array`.
-*   `--input_array`. Type: string. Specifies a single array as the input
-    activations. Incompatible with `--input_arrays`.
-*   `--input_arrays`. Type: comma-separated list of strings. Specifies a list of
-    arrays as the input activations, for models with multiple inputs.
-    Incompatible with `--input_array`.
-*   `--batch_size`. Type: integer. Default: 1. Specifies the batch size for the
-    model. Replaces the first dimension of an input size array if undefined. Use
-    only with SavedModels when neither `--input_shape` nor `input_shapes` flags
-    are specified. Incompatible with GraphDefs.
-
-When `--input_array` is used, the following flags are available to provide
-additional information about the single input array:
-
-*   `--input_shape`. Type: comma-separated list of integers. Specifies the shape
-    of the input array, in TensorFlow convention: starting with the outer-most
-    dimension (the dimension corresponding to the largest offset stride in the
-    array layout), ending with the inner-most dimension (the dimension along
-    which array entries are typically laid out contiguously in memory).
-    *   For example, a typical vision model might pass
-        `--input_shape=1,60,80,3`, meaning a batch size of 1 (no batching), an
-        input image height of 60, an input image width of 80, and an input image
-        depth of 3, for the typical case where the input image is a RGB bitmap
-        (3 channels, depth=3) stored by horizontal scanlines (so 'width' is the
-        next innermost dimension after 'depth').
-*   `--mean_value` and `--std_value`. Type: floating-point. The decimal point
-    character is always the dot (`.`) regardless of the locale. These specify
-    the (de-)quantization parameters of the input array, when it is quantized.
-    *   The meaning of mean_value and std_value 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:
+*   `--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/versions/r1.2/programmers_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 integers.
+    These specify the (de-)quantization parameters of the input array, when it
+    is quantized.
+    *   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_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 ever to be
-        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_value, all participate in the determination of the
-        fixed-point multipliers used in the quantized inference code.
-
-When `--input_arrays` is used, the following flags are available to provide
-additional information about the multiple input arrays:
-
-*   `--input_shapes`. Type: colon-separated list of comma-separated lists of
-    integers. Each comma-separated list of integer gives the shape of one of the
-    input arrays specified in `--input_arrays`, in the same order. See
-    `--input_shape` for details.
-    *   Example: `--input_arrays=foo,bar --input_shapes=2,3:4,5,6` means that
-        there are two input arrays. The first one, "foo", has shape [2,3]. The
-        second one, "bar", has shape [4,5,6].
-*   `--mean_values`, `--std_values`. Type: comma-separated lists of
-    floating-point numbers. Each number gives the corresponding value for one of
-    the input arrays specified in `--input_arrays`, in the same order. See
-    `--mean_value`, `--std_value` for details.
+        (`--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.
 
 ## Transformation flags
 
@@ -133,21 +97,13 @@ additional information about the multiple input arrays:
 the graph, i.e. they specify requested properties that the output file should
 have.
 
-*   `--inference_type`. Type: string. Sets the type of real-number arrays in the
-    output file, that is, controls the representation (quantization) of real
-    numbers in the output file, except for input arrays, which are controlled by
-    `--inference_input_type`.
-
-    This flag only impacts real-number arrays. By "real-number" we mean float
-    arrays, and quantized arrays. This excludes plain integer arrays, strings
-    arrays, and every other data type.
+*   `--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}`.
 
-    For real-number arrays, the impact of this flag is to allow the output file
-    to choose a different real-numbers representation (quantization) from what
-    the input file used. For any other types of arrays, changing the data type
-    would not make sense.
-
-    Specifically:
+    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
@@ -155,66 +111,54 @@ have.
     *   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.
-    *   If not set, then all real-numbers arrays retain the same type in the
-        output file as they have in the input file.
-
-*   `--inference_input_type`. Type: string. Similar to inference_type, but
-    allows to control specifically the quantization of input arrays, separately
-    from other arrays.
-
-    If not set, then the value of `--inference_type` is implicitly used, i.e. by
-    default input arrays are quantized like other arrays.
-
-    Like `--inference_type`, this only affects real-number arrays. By
-    "real-number" we mean float arrays, and quantized arrays. This excludes
-    plain integer arrays, strings arrays, and every other data type.
-
-    The typical use for this flag is for vision models taking a bitmap as input,
-    typically with uint8 channels, yet still requiring floating-point inference.
-    For such image models, the uint8 input is quantized, i.e. the uint8 values
-    are interpreted as real numbers, and the quantization parameters used for
-    such input arrays are their `mean_value`, `std_value` parameters.
-
-*   `--default_ranges_min`, `--default_ranges_max`. Type: floating-point. The
-    decimal point character is always the dot (`.`) regardless of the locale.
-    These flags enable what is called "dummy quantization". If defined, their
-    effect is to define fallback (min, max) range values for all arrays that do
-    not have a properly specified (min, max) range in the input file, thus
-    allowing to proceed with quantization of non-quantized or
-    incorrectly-quantized input files. This enables easy performance prototyping
-    ("how fast would my model run if I quantized it?") but should never be used
-    in production as the resulting quantized arithmetic is inaccurate.
-
-*   `--drop_fake_quant`. Type: boolean. Default: false. Causes fake-quantization
-    nodes to be dropped from the graph. This may be used to recover a plain
-    float graph from a fake-quantized graph.
-
-*   `--reorder_across_fake_quant`. Type: boolean. Default: false. Normally,
-    fake-quantization nodes must be strict boundaries for graph transformations,
-    in order to ensure that quantized inference has the exact same arithmetic
-    behavior as quantized training --- which is the whole point of quantized
-    training and of FakeQuant nodes in the first place. However, that entails
-    subtle requirements on where exactly FakeQuant nodes must be placed in the
-    graph. Some quantized graphs have FakeQuant nodes at unexpected locations,
-    that prevent graph transformations that are necessary in order to generate a
-    well-formed quantized representation of these graphs. Such graphs should be
-    fixed, but as a temporary work-around, setting this
-    reorder_across_fake_quant flag allows the converter to perform necessary
-    graph transformations on them, at the cost of no longer faithfully matching
-    inference and training arithmetic.
-
-*   `--quantize_weights`. Type: boolean. Default: false. Store weights as
-    quantized weights followed by dequantize operations. Computation is still
-    done in float, but reduces model size (at the cost of accuracy and latency).
+
+*   `--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.
+
+*   `--quantize_weights`. Type: boolean. Default: False. Indicates whether to
+    store weights as quantized weights followed by dequantize operations.
+    Computation is still done in float, but reduces model size (at the cost of
+    accuracy and latency).
 
 ## Logging flags
 
-The following flags allow to generate graph visualizations of the actual graph
-at various points during transformations:
+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=/path` enables dumping of the graphs at various stages of
-    processing as GraphViz `.dot` files. Generally preferred over
-    `--output_format=GRAPHVIZ_DOT` as this allows you to keep your actually
-    relevant `--output_format`.
-*   `--dump_graphviz_video` enables dumping of the graph after every single
-    graph transformation (for debugging purposes).
+*   `--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/toco/g3doc/python_api.md b/tensorflow/contrib/lite/toco/g3doc/python_api.md
index a7841a6855..afa6fd6957 100644
--- a/tensorflow/contrib/lite/toco/g3doc/python_api.md
+++ b/tensorflow/contrib/lite/toco/g3doc/python_api.md
@@ -20,6 +20,9 @@ Table of contents:
 *   [TensorFlow Lite Python interpreter](#interpreter)
     *   [Using the interpreter from a model file](#interpreter-file)
     *   [Using the interpreter from model data](#interpreter-data)
+*   [Additional instructions](#additional-instructions)
+    *   [Build from source code](#latest-package)
+    *   [Converting models prior to TensorFlow 1.9.](#pre-tensorflow-1.9)
 
 ## High-level overview
 
@@ -31,8 +34,8 @@ designing a model that can be targeted to devices with mobile.
 
 ## API
 
-The API for converting TensorFlow models to TensorFlow Lite is
-`tf.contrib.lite.TocoConverter`. The API for calling the Python intepreter is
+The API for converting TensorFlow models to TensorFlow Lite as of TensorFlow 1.9
+is `tf.contrib.lite.TocoConverter`. The API for calling the Python intepreter is
 `tf.contrib.lite.Interpreter`.
 
 `TocoConverter` provides class methods based on the original format of the
@@ -200,3 +203,18 @@ with tf.Session() as sess:
 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 TOCO Python API, clone the TensorFlow
+repository, configure the installation, and build and install the pip package.
+Detailed instructions are available
+[here](https://www.tensorflow.org/install/install_sources).
+
+### Converting models prior to TensorFlow 1.9. <a name="pre-tensorflow-1.9"></a>
+
+To use TOCO 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/toco/g3doc/toco_landscape.svg b/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg
index a47c088991..262e13a591 100644
--- a/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg
+++ b/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg
@@ -1 +1 @@
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+\ No newline at end of file
author	Nupur Garg <nupurgarg@google.com>	2018-06-22 16:12:02 -0700
committer	TensorFlower Gardener <gardener@tensorflow.org>	2018-06-22 16:14:54 -0700
commit	aa63c6b0209c1194a392e4327f6211c7c13f40dc (patch)
tree	4ef182b6607447fd19ed6e4625ab79a732fe4b23 /tensorflow/contrib/lite/toco/g3doc
parent	8e1a4a5b03357c0397c578611482c69065e7bdc6 (diff)