How do you convert a .onnx to tflite?

Step 1: Install Required Libraries

Before starting the conversion, ensure all necessary libraries are installed, including onnx, tensorflow, tf2onnx, and tensorflow-lite. These can be installed via pip:

bash
pip install onnx tensorflow tf2onnx
pip install --extra-index-url https://google-coral.github.io/py-repo/ tflite_runtime

Step 2: Convert ONNX Model to TensorFlow Model

Use the tf2onnx tool to convert the ONNX model to a TensorFlow SavedModel or GraphDef format. The command is:

bash
python -m tf2onnx.convert --opset 11 --input model.onnx --output model.pb --inputs INPUTS --outputs OUTPUTS

The --inputs and --outputs parameters must be replaced with the actual names of the input and output layers of your model. After conversion, you will obtain a TensorFlow model file.

Step 3: Convert from TensorFlow to TensorFlow Lite

Once you have the TensorFlow model, use the TensorFlow Lite Converter to convert it to TFLite format. Example code:

python
import tensorflow as tf

# Load the model
converter = tf.lite.TFLiteConverter.from_saved_model("saved_model_path")
# Configure conversion parameters, e.g., quantization
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# Save the converted model
with open('model.tflite', 'wb') as f:
  f.write(tflite_model)

Finally: Test the TFLite Model

After conversion, test the TFLite model's performance and correctness on your target device or environment. Use the TensorFlow Lite Interpreter to load and run the model, ensuring it operates as expected.

python
import numpy as np
import tensorflow as tf

# Load TFLite model and allocate tensors
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()

# Retrieve input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Execute model test
input_shape = input_details[0]['shape']
inputs = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], inputs)
interpreter.invoke()
output = interpreter.get_tensor(output_details[0]['index'])

print(output)

Summary

By following these steps, you can convert ONNX models to TensorFlow Lite models for efficient inference on edge devices. This process requires careful attention to model compatibility and potential issues during conversion, such as unsupported operations or performance optimization challenges.