History

YoungSeok Yoon 75f58fcdde Fix the iOS benchmark app build by adding CoreML framework PiperOrigin-RevId: 308748643 Change-Id: I5d5d1d73325a0f0d60893aea6dc3f664bd13cca6		2020-04-27 20:00:59 -07:00
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TFLiteBenchmark	Fix the iOS benchmark app build by adding CoreML framework	2020-04-27 20:00:59 -07:00
build_benchmark_framework.sh	Port TFLite from the gemmlowp profiler to the ruy profiler.	2020-01-14 12:19:32 -08:00
README.md

README.md

TFLite iOS benchmark app.

Description

An iOS app to benchmark TFLite models.

The app reads benchmark parameters from a JSON file named benchmark_params.json in its benchmark_data directory. Any downloaded models for benchmarking should also be placed in benchmark_data directory.

The JSON file specifies the name of the model file and other benchmarking parameters like inputs to the model, type of inputs, number of iterations, number of threads. The default values in the JSON file are for the Mobilenet_1.0_224 model (paper, tflite&pb).

Building / running the app

Follow the iOS build instructions to configure the Bazel workspace and .bazelrc file correctly.
Run build_benchmark_framework.sh script to build the benchmark framework. This script will build the benchmark framework for iOS and put it under TFLiteBenchmark/TFLiteBenchmark/Frameworks directory.
If you want more detailed profiling, run the build script with -p option: build_benchmark_framework.sh -p.
Modify benchmark_params.json change the input_layer, input_layer_shape and other benchmark parameters.
Change Build Phases -> Copy Bundle Resources and add the model file to the resources that need to be copied.
Ensure that Build Phases -> Link Binary With Library contains the Accelerate framework and TensorFlowLiteBenchmarkC.framework.
Now try running the app. The app has a single button that runs the benchmark on the model and displays results in a text view below. You can also see the console output section in your Xcode to see more detailed benchmark information.