ExecuTorch Android Demo App

This guide explains how to setup ExecuTorch for Android using a demo app. The app employs a DeepLab v3 model for image segmentation tasks. Models are exported to ExecuTorch using XNNPACK FP32 backend.

Prerequisites

Download and install Android Studio and SDK 34.
(For exporting the DL3 model) Python 3.10+ with executorch package installed.

Step 1: Export the model

Run the script in dl3/python/export.py to export the model.

Step 2: Set up your device or emulator

You can run the app on either a physical device or an emulator. To set up your device or emulator, follow these steps:

Using a Physical Device

Connect your device to your computer via USB.
Enable USB debugging on your device.

Using an Emulator

Open Android Studio and create a new virtual device.
Start the emulator by clicking the "Play" button next to the device name.

Step 3: Build, install, and run the app on your phone

On your terminal

(cd dl3/android/DeepLabV3Demo first)

./gradlew installDebug
adb shell am start -W -S -n org.pytorch.executorchexamples.dl3/.MainActivity

On Android Studio

Open Android Studio and open the project path dl3/android/DeepLabV3Demo. Wait for gradle sync to complete. Then simply press "Run app" button (Control + r) to run the app either on physical device / emulator.

Step 4: Push the model to the phone or emulator

The app loads a hardcoded model path (/data/local/tmp/dl3_xnnpack_fp32.pte) on the phone. Run the following adb command to push the model.

adb push dl3_xnnpack_fp32.pte /data/local/tmp/dl3_xnnpack_fp32.pte

Note: If you want to use a QNN lowered model, you mush modify the maven executorch dependency to executorch-qnn and rebuild the app.

Step 5: Load and Test Custom Images (No APK Rebuild Needed)

You can now test image segmentation on your own images (supported formats: .jpg, .jpeg, .png) without rebuilding the APK. The app supports loading .jpg, .jpeg, and .png images from the /sdcard/Pictures/ directory, with user-granted permissions.

How to Use

a. Push your image to the device:

adb push <path to your image> /sdcard/Pictures/

b. After the push, In the app:

Tap the "Load And Refresh" button.
If prompted, grant the app permission to access the /sdcard/Pictures/ folder.
The image should appear immediately in the app.
You can now run dynamic image segmentation and view the results.

Tip: You can use images containing Dog, Sheep, Person, or any combination of these classes.

This feature enhances the testing/validation process by allowing users to quickly test different images without the need to rebuild the application.

Step 6: Run unit test

On your terminal

./gradlew connectedAndroidTest

On Android Studio

In Android Studio project, open file app/src/androidTest/java/org/pytorch/executorchexamples/dl3/SanityCheck.java, and click the "Play" button for public class SanityCheck (Control + Shift + r).