HelloWorld is a simple image classification application that demonstrates how to use PyTorch Android API. This application runs TorchScript serialized TorchVision pretrained MobileNet v3 model on static image which is packaged inside the app as android asset.

Let’s start with model preparation. If you are familiar with PyTorch, you probably should already know how to train and save your model. In case you don’t, we are going to use a pre-trained image classification model(MobileNet v3), which is packaged in TorchVision. To install it, run the command below:

pip install torch torchvision

To serialize and optimize the model for Android, you can use the Python script in the root folder of HelloWorld app:

import torch
import torchvision
from torch.utils.mobile_optimizer import optimize_for_mobile

model = torchvision.models.mobilenet_v3_small(pretrained=True)
model.eval()
example = torch.rand(1, 3, 224, 224)
traced_script_module = torch.jit.trace(model, example)
optimized_traced_model = optimize_for_mobile(traced_script_module)
optimized_traced_model._save_for_lite_interpreter("app/src/main/assets/model.ptl")

If everything works well, we should have our scripted and optimized model - model.pt generated in the assets folder of android application. That will be packaged inside android application as asset and can be used on the device.

By using the new MobileNet v3 model instead of the old Resnet18 model, and by calling the optimize_for_mobile method on the traced model, the model inference time on a Pixel 3 gets decreased from over 230ms to about 40ms.

More details about TorchScript you can find in tutorials on pytorch.org

git clone https://github.com/pytorch/android-demo-app.git
cd HelloWorldApp

If Android SDK and Android NDK are already installed you can install this application to the connected android device or emulator with:

./gradlew installDebug

We recommend you to open this project in Android Studio 3.5.1+ (At the moment PyTorch Android and demo applications use android gradle plugin of version 3.5.0, which is supported only by Android Studio version 3.5.1 and higher), in that case you will be able to install Android NDK and Android SDK using Android Studio UI.

Pytorch android is added to the HelloWorld as gradle dependencies in build.gradle:

repositories {
    jcenter()
}

dependencies {
    implementation 'org.pytorch:pytorch_android_lite:1.9.0'
    implementation 'org.pytorch:pytorch_android_torchvision:1.9.0'
}

Where org.pytorch:pytorch_android is the main dependency with PyTorch Android API, including libtorch native library for all 4 android abis (armeabi-v7a, arm64-v8a, x86, x86_64). Further in this doc you can find how to rebuild it only for specific list of android abis.

org.pytorch:pytorch_android_torchvision - additional library with utility functions for converting android.media.Image and android.graphics.Bitmap to tensors.

All the logic happens in org.pytorch.helloworld.MainActivity. As a first step we read image.jpg to android.graphics.Bitmap using the standard Android API.

Bitmap bitmap = BitmapFactory.decodeStream(getAssets().open("image.jpg"));

Module module = LiteModuleLoader.load(assetFilePath(this, "model.pt"));

org.pytorch.Module represents torch::jit::script::Module that can be loaded with load method specifying file path to the serialized to file model.

Tensor inputTensor = TensorImageUtils.bitmapToFloat32Tensor(bitmap,
    TensorImageUtils.TORCHVISION_NORM_MEAN_RGB, TensorImageUtils.TORCHVISION_NORM_STD_RGB);

org.pytorch.torchvision.TensorImageUtils is part of org.pytorch:pytorch_android_torchvision library. The TensorImageUtils#bitmapToFloat32Tensor method creates tensors in the torchvision format using android.graphics.Bitmap as a source.

All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]

inputTensor's shape is 1x3xHxW, where H and W are bitmap height and width appropriately.

Tensor outputTensor = module.forward(IValue.from(inputTensor)).toTensor();
float[] scores = outputTensor.getDataAsFloatArray();

org.pytorch.Module.forward method runs loaded module's forward method and gets result as org.pytorch.Tensor outputTensor with shape 1x1000.

Its content is retrieved using org.pytorch.Tensor.getDataAsFloatArray() method that returns java array of floats with scores for every image net class.

After that we just find index with maximum score and retrieve predicted class name from ImageNetClasses.IMAGENET_CLASSES array that contains all ImageNet classes.

float maxScore = -Float.MAX_VALUE;
int maxScoreIdx = -1;
for (int i = 0; i < scores.length; i++) {
  if (scores[i] > maxScore) {
    maxScore = scores[i];
    maxScoreIdx = i;
  }
}
String className = ImageNetClasses.IMAGENET_CLASSES[maxScoreIdx];

In the following sections you can find detailed explanations of PyTorch Android API, code walk through for a bigger demo application, implementation details of the API, how to customize and build it from source.