Using TensorFlow Lite’s Experimental GPU Delegate for Coding in TensorFlow

TensorFlow Lite is a lightweight and efficient deep learning framework developed by Google that allows you to run machine learning models on mobile and embedded devices. One of the features of TensorFlow Lite is the Experimental GPU Delegate, which enables GPU acceleration of inference operations on supported devices. In this tutorial, we will guide you through the process of coding TensorFlow Lite with the Experimental GPU Delegate.

Step 1: Install TensorFlow Lite

Before we can start using the Experimental GPU Delegate, we need to install TensorFlow Lite on our system. You can install TensorFlow Lite using pip by running the following command in your terminal:

pip install tensorflow

Step 2: Import necessary libraries

Next, we need to import the necessary libraries in our Python script. We will need to import TensorFlow Lite Interpreter to load and run our TensorFlow Lite model, and the Experimental GPU Delegate to enable GPU acceleration. It is essential to check if the GPU delegate is available on the system by using the experimental module provided by TensorFlow Lite.

import tensorflow as tf
from tensorflow.lite.python import interpreter as interpreter_wrapper
from tensorflow.lite.experimental import load_delegate

Step 3: Load the TensorFlow Lite model

Now, we need to load our TensorFlow Lite model using the Interpreter class from TensorFlow Lite. You can load your model using the from_path or from_buffer method, depending on where your model file is located.

model_path = "path/to/your/tflite/model"
interpreter = interpreter_wrapper.Interpreter(model_path)

Step 4: Enable GPU delegate

After loading the model, we need to enable the Experimental GPU Delegate on our interpreter. We can do this by creating a delegate and setting it as the delegate of the interpreter.

interpreter.allocate_tensors()

if load_delegate.supports_delegate():
    interpreter.add_delegate(load_delegate.GpuDelegate())

Step 5: Run inference on the model

Now that we have our model loaded and the Experimental GPU Delegate enabled, we can run inference on our model. We can feed input data to our model using the set_tensor method and retrieve the output data using the get_tensor method.

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# feed input data
interpreter.set_tensor(input_details[0]['index'], input_data)

# run inference
interpreter.invoke()

# get output data
output_data = interpreter.get_tensor(output_details[0]['index'])

Step 6: Perform post-processing

Finally, you can perform any necessary post-processing on the output data to get the desired results. You can also measure the performance of your model by calculating the inference time using the time module.

import time

start_time = time.time()
# run inference
interpreter.invoke()
end_time = time.time()

inference_time = end_time - start_time
print(f"Inference time: {inference_time} seconds")

That’s it! You have successfully coded TensorFlow Lite with the Experimental GPU Delegate. Experiment with different models and input data to see the benefits of GPU acceleration on your machine learning tasks.