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What’s new in TensorFlow Lite from DevSummit 2020

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Posted by Edge devices, such as smartphones, have become more powerful each year and enable an increasing number of on-device machine learning use cases. in March 2020.

Pushing the limits of on-device machine learning

Enabling state-of-the-art models

Machine learning is a fast-moving field with new models that break the state-of-the-art records every few months. We put a lot of effort into making these state-of-the-art models run well on TensorFlow Lite. As examples, we now support ), a family of image classification models, ), and ), a light-weight version of BERT (.
model that achieved state-of-the-art accuracy on a range of NLP tasks, including question and answer, natural language inference, and others. MobileBERT is about 4x faster and smaller than BERT and retains similar accuracy. Meanwhile, ALBERT-Lite is 6x smaller than BERT, but slower than MobileBERT.
, Google's cutting edge compiler technology for ML, which makes it easier to troubleshoot conversion errors and extend to accommodate feature requests
The new converter is fully backward compatible and is enabled by default since TensorFlow 2.2, while the old converter is still available via a flag. See the Find out more in our ), built from the ground up to deliver better performance on the classes of CPU hardware and models typically used in mobile environments. As of TensorFlow 1.15, this library is enabled by default for all ARM devices and has helped deliver latency improvements anywhere from 1.2x to 5x across an extremely broad range of models and use cases.
, as well as a new highly optimized floating-point convolutional kernel library (.
Core ML is the machine learning framework available on Apple’s devices and provides the API to run ML models on Apple’s Neural Engine. The new TensorFlow Lite Core ML delegate allows running TensorFlow Lite models on Core ML and Neural Engine, if available, to achieve faster inference with better power consumption efficiency. On iPhone XS and newer devices, where Neural Engine is available, we have observed performance gains from 1.3x to 11x on various computer vision models. More details can be found in our , achieving approximately 4-6x speed-up over CPU and approximately 2x speed-up over OpenGL on a variety of computer vision models. Here is a snapshot of the OpenCL backend performance on Pixel 4. . The new profiling data allows you to identify performance bottlenecks.
Here are some examples of insights that you can get from the profiler and potential solutions to improve performance:
  • If the number of available CPU cores is smaller than the number of inference threads, then the CPU scheduling overhead can lead to subpar performance. You can reschedule other CPU intensive tasks in your application to avoid overlapping with your model inference or tweak the number of interpreter threads.
  • If the operators are not fully delegated, then some parts of the model graph are executed on the CPU rather than the expected hardware accelerator. You can substitute the unsupported operators with similar supported operators.
This feature is available now in TensorFlow Lite Android library nightly build. More details can be found

Make ML easier to use

Model creation with no ML expertise

TensorFlow Lite , including the EfficientNet-Lite models mentioned above. It currently supports image classification () with more computer-vision and NLP use cases coming soon.

Model sharing made easy with metadata

Traditionally, running inference with TensorFlow Lite means working with the raw tensors. This presented two hurdles:
  1. The consumer of the TensorFlow Lite model will need to know exactly what the tensor shape means (e.g. 1 x 224 x 224 x 3). Is it a bitmap? If so, is it in red, blue, and green channels or some other scheme? This poses a problem if the team creating the model is not the same team consuming it.
  2. The need to use a lot of error-prone boilerplate code to convert from high-level data types, such as Bitmap to an RGB float array or a ByteArray, before it can be used.
To solve the first problem, we , which reads the TensorFlow Lite metadata and creates the appropriate wrapper code to resize, normalize, and convert to and from ByteArray. This means you can now interact with the TensorFlow Lite model using high-level objects that you are familiar with:
PYTHON 
// 1. Initializing the Model    
MyClassifierModel myImageClassifier = new MyClassifierModel(activity);

// 2. Setting the input with a Bitmap called inputBitmap
MyClassifierModel.Inputs inputs = myImageClassifier.createInputs();
inputs.loadImage(inputBitmap));

// 3. Running the model
MyClassifierModel.Outputs outputs = myImageClassifier.run(inputs);

// 4. Retrieving the result
Map labeledProbability = outputs.getProbability();
This is currently an experimental feature and only supports image-based models. We added metadata support to most TensorFlow Lite vision models on . Going forward, the project is expanding in three ways:
  1. Support input types beyond images to enable more use-cases
  2. Build an Android Studio and , We love to see the engagement in the TensorFlow Lite community. Recently, one member collected pretrained models, samples, and tutorials created by the community and curated them on .
    for their Tensilica HiFi DSP family.

    How Google is using TensorFlow Lite

    TensorFlow Lite is used extensively within Google in many of our key products, including YouTube, Google Assistant, and Google Photos.
    The Google Lens team shared how they migrated from a server-based model to a client-based on-device model to improve the user experience.

    The Live Perception team showed how to build a machine learning pipeline to process live camera feed in real-time.

    What’s next

    We have new features and improvements coming in a few months:
    • .
      Vollständiger Original-Bericht
      Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.
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