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Update the Select TF Ops guide for iOS

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The updated documentation now includes how to use the prebuilt CocoaPods, and
also explains how the framework can be compiled manually with bazel.

PiperOrigin-RevId: 317038761
Change-Id: I0b7968cf614ce2522da2af618bc284fbc165bf0b
This commit is contained in:
YoungSeok Yoon 2020-06-17 22:47:49 -07:00 committed by TensorFlower Gardener
parent 899471f58f
commit 4654b512e7
1 changed files with 54 additions and 27 deletions
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81
tensorflow/lite/g3doc/guide/ops_select.md
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@ -172,38 +172,66 @@ dependencies {
### iOS
With XCode Command Line Tools installed, TensorFlow Lite with select TensorFlow
ops support can be built with the following command:
#### Using CocoaPods
```sh
tensorflow/contrib/makefile/build_all_ios_with_tflite.sh
We provide nightly prebuilt select TF ops CocoaPods, which you can depend on
alongside the `TensorFlowLiteSwift` or `TensorFlowLiteObjC` CocoaPods.
```ruby
# In your Podfile target:
pod 'TensorFlowLiteSwift' # or 'TensorFlowLiteObjC'
pod 'TensorFlowLiteSelectTfOps', '~> 0.0.1-nightly'
```
This will generate the required static linking libraries in the
`tensorflow/contrib/makefile/gen/lib/` directory.
After running `pod install`, you need to provide an additional linker flag to
force load the select TF ops framework into your project. In your Xcode project,
go to `Build Settings` -> `Other Linker Flags`, and add:
The TensorFlow Lite camera example app can be used to test this. A new
TensorFlow Lite XCode project with support for select TensorFlow ops has been
added to
`tensorflow/lite/examples/ios/camera/tflite_camera_example_with_select_tf_ops.xcodeproj`.
```text
-force_load $(SRCROOT)/Pods/TensorFlowLiteSelectTfOps/Frameworks/TensorFlowLiteSelectTfOps.framework/TensorFlowLiteSelectTfOps
```
To use this feature in your own project, either clone the example project or set
the project settings for a new or existing project to the following:
You should then be able to run any models converted with the `SELECT_TF_OPS` in
your iOS app. For example, you can modify the
[Image Classification iOS app](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios)
to test the select TF ops feature.
* In Build Phases -> Link Binary With Libraries, add the static libraries
under `tensorflow/contrib/makefile/gen/lib/` directory:
* `libtensorflow-lite.a`
* `libprotobuf.a`
* `nsync.a`
* In Build Settings -> Header Search Paths, add the following directories:
* `tensorflow/lite/`
* `tensorflow/contrib/makefile/downloads/flatbuffer/include`
* `tensorflow/contrib/makefile/downloads/eigen`
* In Build Settings -> Other Linker Flags, add `-force_load
tensorflow/contrib/makefile/gen/lib/libtensorflow-lite.a`.
* Replace the model file with the one converted with `SELECT_TF_OPS` enabled.
* Add `TensorFlowLiteSelectTfOps` dependency to the `Podfile` as instructed.
* Add the additional linker flag as above.
* Run the example app and see if the model works correctly.
A CocoaPod with support for select TensorFlow ops will also be released in the
future.
#### Using Bazel + Xcode
TensorFlow Lite with select TensorFlow ops for iOS can be built using Bazel.
First, follow the [iOS build instructions](build_ios.md) to configure your Bazel
workspace and `.bazelrc` file correctly.
Once you have configured the workspace with iOS support enabled, you can use the
following command to build the select TF ops addon framework, which can be added
on top of the regular `TensorFlowLiteC.framework`. Note that the select TF ops
framework cannot be built for `i386` architecture, so you need to explicitly
provide the list of target architectures excluding `i386`.
```sh
bazel build -c opt --config=ios --ios_multi_cpus=armv7,arm64,x86_64 \
//tensorflow/lite/experimental/ios:TensorFlowLiteSelectTfOps_framework
```
This will generate the framework under
`bazel-bin/tensorflow/lite/experimental/ios/` directory. You can add this new
framework to your Xcode project by following similar steps described under the
[Xcode project settings](./build_ios.md#modify_xcode_project_settings_directly)
section in the iOS build guide.
After adding the framework into your app project, an additional linker flag
should be specified in your app project to force load the select TF ops
framework. In your Xcode project, go to `Build Settings` -> `Other Linker
Flags`, and add:
```text
-force_load <path/to/your/TensorFlowLiteSelectTfOps.framework/TensorFlowLiteSelectTfOps>
```
### C++
@ -274,7 +302,6 @@ The following is a list of improvements to this pipeline that are in progress:
generate TFLite interpreter binaries that only contain the TensorFlow ops
required for a particular set of models.
* *Improved usability* - The conversion process will be simplified to only
require a single pass through the converter. Additionally, pre-built Android
AAR and iOS CocoaPod binaries will be provided.
require a single pass through the converter.
* *Improved performance* - Work is being done to ensure TensorFlow Lite with
TensorFlow ops has performance parity to TensorFlow Mobile.