Pushing and executing binaries directly on Android is a valid approach to benchmarking, but it can result in subtle (but observable) differences in performance relative to execution within an actual Android app. In particular, Android's scheduler tailors behavior based on thread and process priorities, which differ between a foreground Activity/Application and a regular background binary executed via adb shell .... This tailored behavior is most evident when enabling multi-threaded CPU execution with TensorFlow Lite.

To that end, this app offers perhaps a more faithful view of runtime performance that developers can expected when deploying TensorFlow Lite with their application.

To build/install/run

(0) Refer to https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android to edit the WORKSPACE to configure the android NDK/SDK.

(1) Build for your specific platform, e.g.:

bazel build -c opt \
  --config=android_arm64 \
  --cxxopt='--std=c++11' \
  tensorflow/lite/tools/benchmark/android:benchmark_model

(2) Connect your phone. Install the benchmark APK to your phone with adb:

adb install -r -d bazel-bin/tensorflow/lite/tools/benchmark/android/benchmark_model.apk

(3) Push the compute graph that you need to test.

adb push mobilenet_quant_v1_224.tflite /data/local/tmp

(4) Run the benchmark. Additional command-line flags are documented here and can be appended to the args string alongside the required --graph flag (note that all args must be nested in the single quoted string that follows the args key).

adb shell am start -S -n \
  org.tensorflow.lite.benchmark/org.tensorflow.lite.benchmark.BenchmarkModelActivity \
  --es args '"--graph=/data/local/tmp/mobilenet_quant_v1_224.tflite --num_threads=4"'

(5) The results will be available in Android's logcat, e.g.:

adb logcat | grep "Average inference"

... tflite  : Average inference timings in us: Warmup: 91471, Init: 4108, Inference: 80660.1