How To Convert A QAT Quantization Aware Trained Tensorflow Graph Into Tflite Model?

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Introduction

Quantization aware training (QAT) is a technique used to train neural networks with quantized weights and activations, which can significantly reduce the computational resources and memory requirements of the model. However, the resulting model is still in the TensorFlow format and needs to be converted into the TFLite format to be used on mobile devices or other platforms that support TFLite. In this article, we will discuss how to convert a QAT quantization aware trained TensorFlow graph into a TFLite model.

Understanding QAT and TFLite

Quantization Aware Training (QAT)

Quantization aware training is a technique used to train neural networks with quantized weights and activations. This is done by adding quantization nodes to the skeleton graph of the model, which allows the model to learn the quantization parameters along with the weights and biases. The resulting model is a quantization aware model that can be used for inference with quantized weights and activations.

TFLite

TFLite is a lightweight, open-source framework developed by Google for mobile and embedded devices. It is designed to run machine learning models on devices with limited resources, such as smartphones and smart home devices. TFLite models are typically smaller and faster than their TensorFlow counterparts, making them ideal for use on mobile devices.

Converting QAT Model to TFLite

To convert a QAT model to TFLite, you need to follow these steps:

Step 1: Load the Trained QAT Model

First, you need to load the trained QAT model using the TensorFlow API. You can do this by creating a tf.Session object and loading the model using the tf.saved_model.load() function.

import tensorflow as tf

qat_model = tf.saved_model.load('path/to/qat/model')

Step 2: Convert the QAT Model to TFLite

Next, you need to convert the QAT model to TFLite using the tf.lite.TFLiteConverter class. This class provides a simple way to convert a TensorFlow model to TFLite.

# Convert the QAT model to TFLite
converter = tf.lite.TFLiteConverter.from_keras_model(qat_model)
tflite_model = converter.convert()

Step 3: Save the TFLite Model

Finally, you need to save the TFLite model to a file using the tf.io.write_bytes() function.

# Save the TFLite model to a file
with open('path/to/tflite/model.tflite', 'wb') as f:
    f.write(tflite_model)

Tips and Tricks

Here are some tips and tricks to keep in mind when converting a QAT model to TFLite:

Use the tf.lite.Optimize Function

The tf.lite.Optimize function can be used to optimize the TFLite model for mobile devices. This function can be used to remove unnecessary nodes and optimize the model for better performance.

# Optimize the TFLite model
optimized_model = tf.l.Optimize(tflite_model)

Use the tf.lite.TFLiteConverter Options

The tf.lite.TFLiteConverter class provides several options that can be used to customize the conversion process. For example, you can use the target_ops option to specify the target operations for the TFLite model.

# Specify the target operations for the TFLite model
converter = tf.lite.TFLiteConverter.from_keras_model(qat_model, target_ops=['add', 'mul'])

Conclusion

Converting a QAT model to TFLite is a straightforward process that can be done using the TensorFlow API. By following the steps outlined in this article, you can convert your QAT model to TFLite and use it on mobile devices or other platforms that support TFLite. Remember to use the tf.lite.Optimize function and the tf.lite.TFLiteConverter options to optimize the TFLite model for better performance.

Example Use Case

Here is an example use case for converting a QAT model to TFLite:

Suppose you have a QAT model that is trained to classify images into different categories. You want to use this model on a mobile device to classify images in real-time. To do this, you need to convert the QAT model to TFLite using the steps outlined in this article.

import tensorflow as tf


qat_model = tf.saved_model.load('path/to/qat/model')



converter = tf.lite.TFLiteConverter.from_keras_model(qat_model)
tflite_model = converter.convert()



with open('path/to/tflite/model.tflite', 'wb') as f:
f.write(tflite_model)

Once you have converted the QAT model to TFLite, you can use it on a mobile device to classify images in real-time. This is just one example use case for converting a QAT model to TFLite, but the possibilities are endless.

Frequently Asked Questions

Here are some frequently asked questions about converting a QAT model to TFLite:

Q: What is the difference between QAT and TFLite?

A: QAT is a technique used to train neural networks with quantized weights and activations, while TFLite is a lightweight, open-source framework developed by Google for mobile and embedded devices.

Q: How do I convert a QAT model to TFLite?

A: To convert a QAT model to TFLite, you need to follow the steps outlined in this article. This includes loading the trained QAT model, converting the model to TFLite using the tf.lite.TFLiteConverter class, and saving the TFLite model to a file.

Q: What are the benefits of converting a QAT model to TFLite?

A: The benefits of converting a QAT model to TFLite include reduced computational resources and memory requirements, improved performance, and the ability to use the model on mobile devices or other platforms that support TFLite.

Q: Can I use the tf.lite.Optimize function to optimize the Tite model?

Q: What is the difference between QAT and TFLite?

A: QAT (Quantization Aware Training) is a technique used to train neural networks with quantized weights and activations, which can significantly reduce the computational resources and memory requirements of the model. TFLite (TensorFlow Lite) is a lightweight, open-source framework developed by Google for mobile and embedded devices. While QAT is a training technique, TFLite is a framework for deploying and running machine learning models on mobile devices.

Q: How do I convert a QAT model to TFLite?

A: To convert a QAT model to TFLite, you need to follow these steps:

  1. Load the trained QAT model using the TensorFlow API.
  2. Convert the QAT model to TFLite using the tf.lite.TFLiteConverter class.
  3. Save the TFLite model to a file.

Here is an example code snippet that demonstrates how to convert a QAT model to TFLite:

import tensorflow as tf


qat_model = tf.saved_model.load('path/to/qat/model')



converter = tf.lite.TFLiteConverter.from_keras_model(qat_model)
tflite_model = converter.convert()



with open('path/to/tflite/model.tflite', 'wb') as f:
f.write(tflite_model)

Q: What are the benefits of converting a QAT model to TFLite?

A: The benefits of converting a QAT model to TFLite include:

  • Reduced computational resources and memory requirements
  • Improved performance
  • Ability to use the model on mobile devices or other platforms that support TFLite

Q: Can I use the tf.lite.Optimize function to optimize the TFLite model?

A: Yes, you can use the tf.lite.Optimize function to optimize the TFLite model for better performance. This function can be used to remove unnecessary nodes and optimize the model for better performance.

Here is an example code snippet that demonstrates how to use the tf.lite.Optimize function:

import tensorflow as tf


tflite_model = tf.lite.load_model('path/to/tflite/model.tflite')



optimized_model = tf.l.Optimize(tflite_model)

Q: What are the limitations of converting a QAT model to TFLite?

A: The limitations of converting a QAT model to TFLite include:

  • Reduced accuracy due to quantization
  • Limited support for certain operations and functions
  • May require additional optimization and tuning to achieve optimal performance

Q: Can I use the TFLite model on multiple platforms?

A: Yes, you can use the TFLite model on multiple platforms, including Android, iOS, and Linux. TFLite is a cross-platform framework that supports a wide range of devices and platforms.

Q: How do I deploy the TFLite model on a mobile device?

A: To deploy the TFLite model on a mobile device, you need to follow these steps:

  1. Convert the QAT model to TFLite using the tf.lite.TFLiteConverter class.
  2. Save the TFLite model to a file.
  3. Use a mobile app development framework such as Android Studio or Xcode to create a mobile app that uses the TFLite model.
  4. Deploy the mobile app on a mobile device.

Here is an example code snippet that demonstrates how to deploy the TFLite model on an Android device:

import tensorflow as tf


tflite_model = tf.lite.load_model('path/to/tflite/model.tflite')



app = tf.lite.MobileApp(tflite_model)



app.deploy('path/to/android/device')

Q: Can I use the TFLite model for real-time inference?

A: Yes, you can use the TFLite model for real-time inference. TFLite is designed to support real-time inference on mobile devices and other platforms.

Here is an example code snippet that demonstrates how to use the TFLite model for real-time inference:

import tensorflow as tf


tflite_model = tf.lite.load_model('path/to/tflite/model.tflite')



engine = tf.lite.RealTimeInferenceEngine(tflite_model)



predictions = engine.predict('path/to/input/data')

Q: How do I optimize the TFLite model for better performance?

A: To optimize the TFLite model for better performance, you need to follow these steps:

  1. Use the tf.lite.Optimize function to optimize the TFLite model.
  2. Use the tf.lite.TFLiteConverter class to convert the QAT model to TFLite.
  3. Use a mobile app development framework such as Android Studio or Xcode to create a mobile app that uses the TFLite model.
  4. Deploy the mobile app on a mobile device.

Here is an example code snippet that demonstrates how to optimize the TFLite model for better performance:

import tensorflow as tf


tflite_model = tf.lite.load_model('path/to/tflite/model.tflite')



optimized_model = tf.l.Optimize(tflite_model)



with open('path/to/optimized/tflite/model.tflite', 'wb') as f:
f.write(optimized_model)

Q: Can I use the TFLite model for other applications?

A: Yes, you can use the TFLite model for other applications, such as:

  • Image classification
  • Object detection
  • Speech recognition
  • Natural language processing

Here is an example code snippet that demonstrates how to use the TFLite model for image classification:

import tensorflow as tf


tflite_model = tf.lite.load_model('path/to/tflite/model.tflite')



engine = tf.lite.ImageClassificationEngine(tflite_model)



predictions = engine.predict('path/to/input/image')