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https://github.com/tensorflow/flutter-tflite


https://github.com/tensorflow/flutter-tflite

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## Announcement



Update: 26 April, 2023



This repo is a TensorFlow managed fork of the [tflite_flutter_plugin](https://github.com/am15h/tflite_flutter_plugin) project by the amazing Amish Garg. The goal of this project is to support our Flutter community in creating machine-learning backed apps with the TensorFlow Lite framework.



This project is currently a work-in-progress as we update it to create a working plugin that meets the latest and greatest Flutter and TensorFlow Lite standards. That said, *pull requests and contributions are more than welcome* and will be reviewed by TensorFlow or Flutter team members. We thank you for your understanding as we make progress on this update.



Feel free to reach out to us by posting in the issues or discussion areas.



Thanks!



- PaulTR



## Overview



TensorFlow Lite Flutter plugin provides a flexible and fast solution for accessing TensorFlow Lite interpreter and performing inference. The API is similar to the TFLite Java and Swift APIs. It directly binds to TFLite C API making it efficient (low-latency). Offers acceleration support using NNAPI, GPU delegates on Android, Metal and CoreML delegates on iOS, and XNNPack delegate on Desktop platforms.



## Key Features



- Multi-platform Support for Android and iOS

- Flexibility to use any TFLite Model.

- Acceleration using multi-threading.

- Similar structure as TensorFlow Lite Java API.

- Inference speeds close to native Android Apps built using the Java API.

- Run inference in different isolates to prevent jank in UI thread.



## (Important) Initial setup : Add dynamic libraries to your app



### Android & iOS



Examples and support now support dynamic library downloads! iOS samples can be run with the commands



`flutter build ios` & `flutter install ios` from their respective iOS folders.



Android can be run with the commands



`flutter build android` & `flutter install android`



while devices are plugged in.



Note: This requires a device with a minimum API level of 26.



Note: TFLite may not work in the iOS simulator. It's recommended that you test with a physical device.



When creating a release archive (IPA), the symbols are stripped by Xcode, so the command `flutter build ipa` may throw a `Failed to lookup symbol ... symbol not found` error. To work around this:



1. In Xcode, go to **Target Runner > Build Settings > Strip Style**

2. Change from **All Symbols** to **Non-Global Symbols**



### MacOS



For MacOS a TensorFlow Lite dynamic library needs to be added to the project manually.

For this, first a `.dylib` needs to be built. You can follow the [Bazel build guide](https://www.tensorflow.org/lite/guide/build_arm) or the [CMake build guide](https://www.tensorflow.org/lite/guide/build_cmake) to build the libraries.



**CMake Note:**



- cross compiling in CMake can be achieved using:

`-DCMAKE_OSX_ARCHITECTURES=x86_64|arm64`



- bundling two architectures (arm / x86) using lipo:

`lipo -create arm64/libtensorflowlite_c.dylib x86/libtensorflowlite_c.dylib -output libtensorflowlite_c.dylib`



As a second step, the library needs to be added to your application's XCode project. For this, you can follow the step 1 and 2 of the [official Flutter guide on adding dynamic libraries](https://docs.flutter.dev/platform-integration/macos/c-interop#compiled-dynamic-library-macos).



### Linux



For Linux a TensorFlow Lite dynamic library needs to be added to the project manually.

For this, first a `.so` needs to be built. You can follow the [Bazel build guide](https://www.tensorflow.org/lite/guide/build_arm) or the [CMake build guide](https://www.tensorflow.org/lite/guide/build_cmake) to build the libraries.



As a second step, the library needs to be added to your application's project. This is a simple procedure



1. Create a folder called `blobs` in the top level of your project

2. Copy the `libtensorflowlite_c-linux.so` to this folder

3. Append following lines to your `linux/CMakeLists.txt`



``` Make

...



# get tf lite binaries

install(

  FILES ${PROJECT_BUILD_DIR}/../blobs/libtensorflowlite_c-linux.so

  DESTINATION ${INSTALL_BUNDLE_DATA_DIR}/../blobs/

)

```



### Windows



For Windows a TensorFlow Lite dynamic library needs to be added to the project manually.

For this, first a `.dll` needs to be built. You can follow the [Bazel build guide](https://www.tensorflow.org/lite/guide/build_arm) or the [CMake build guide](https://www.tensorflow.org/lite/guide/build_cmake) to build the libraries.



As a second step, the library needs to be added to your application's project. This is a simple procedure



1. Create a folder called `blobs` in the top level of your project

2. Copy the `libtensorflowlite_c-win.dll` to this folder

3. Append following lines to your `windows/CMakeLists.txt`



``` Make

...



# get tf lite binaries

install(

  FILES ${PROJECT_BUILD_DIR}/../blobs/libtensorflowlite_c-win.dll 

  DESTINATION ${INSTALL_BUNDLE_DATA_DIR}/../blobs/

)

```



## TFLite Flutter Helper Library



The helper library has been deprecated. New development underway for a replacement at . Current timeline is to have wide support by the end of August, 2023.



## Import



```dart

import 'package:tflite_flutter/tflite_flutter.dart';

```



## Usage instructions



### Import the libraries



In the dependency section of `pubspec.yaml` file, add `tflite_flutter: ^0.10.1` (adjust the version accordingly based on the latest release)



### Creating the Interpreter



- **From asset**



    Place `your_model.tflite` in `assets` directory. Make sure to include assets in `pubspec.yaml`.



    ```dart

    final interpreter = await Interpreter.fromAsset('assets/your_model.tflite');

    ```



Refer to the documentation for info on creating interpreter from buffer or file.



### Performing inference



- **For single input and output**



    Use `void run(Object input, Object output)`.



    ```dart

    // For ex: if input tensor shape [1,5] and type is float32

    var input = [[1.23, 6.54, 7.81, 3.21, 2.22]];



    // if output tensor shape [1,2] and type is float32

    var output = List.filled(1*2, 0).reshape([1,2]);



    // inference

    interpreter.run(input, output);



    // print the output

    print(output);

    ```

  

- **For multiple inputs and outputs**



    Use `void runForMultipleInputs(List inputs, Map outputs)`.



    ```dart

    var input0 = [1.23];  

    var input1 = [2.43];  



    // input: List

    var inputs = [input0, input1, input0, input1];  



    var output0 = List.filled(1, 0);  

    var output1 = List.filled(1, 0);



    // output: Map

    var outputs = {0: output0, 1: output1};



    // inference  

    interpreter.runForMultipleInputs(inputs, outputs);



    // print outputs

    print(outputs)

    ```



### Closing the interpreter



```dart

interpreter.close();

```



### Asynchronous Inference with `IsolateInterpreter`



To utilize asynchronous inference, first create your `Interpreter` and then wrap it with `IsolateInterpreter`.



```dart

final interpreter = await Interpreter.fromAsset('assets/your_model.tflite');

final isolateInterpreter =

        await IsolateInterpreter.create(address: interpreter.address);

```



Both `run` and `runForMultipleInputs` methods of `isolateInterpreter` are asynchronous:



```dart

await isolateInterpreter.run(input, output);

await isolateInterpreter.runForMultipleInputs(inputs, outputs);

```



By using `IsolateInterpreter`, the inference runs in a separate isolate. This ensures that the main isolate, responsible for UI tasks, remains unblocked and responsive.



## Contribute to this package



This package is managed using [melos](https://pub.dev/packages/melos). Before starting to work on the project, make sure to run the bootstrap command.



```sh

dart pub global activate melos # Install or activate melos globally

melos bootstrap # Initialize the workspace and bootstrap the package

```



### Generated code



This package uses [ffigen](https://pub.dev/packages/ffigen) to generate FFI bindings. To run code generation, you can use the following melos command:



```sh

melos run ffigen 

```