Converting PyTorch Models to TensorFlow Lite: A Step-by-Step Guide

Bridging Frameworks: From PyTorch to TensorFlow Lite with ONNX

Are you looking to deploy your PyTorch machine learning model to mobile or embedded devices using TensorFlow Lite? Converting models between different deep learning frameworks can be a bit challenging, but fear not! In this guide, we'll walk you through the process of converting a PyTorch model into a TensorFlow Lite model using the ONNX format as an intermediary step.

Step 1: Export PyTorch Model to ONNX Format

The first step is to export your PyTorch model to the ONNX (Open Neural Network Exchange) format, which serves as an intermediate representation that can be used to bridge the gap between different deep learning frameworks. Here's how you can do it:

import torch
import torchvision

# Load your PyTorch model
model = torchvision.models.resnet18(pretrained=True)
model.eval()

# Example input (adjust according to your model's input)
dummy_input = torch.randn(1, 3, 224, 224)

# Export to ONNX format
torch.onnx.export(model, dummy_input, "model.onnx", verbose=True)

Step 2: Convert ONNX to TensorFlow GraphDef

Next, you'll need to convert the ONNX model to TensorFlow's GraphDef format. You can use the tf2onnx library to achieve this. If you don't have it installed, you can install it using the following command:

pip install tf2onnx

Here's the code to convert the ONNX model to TensorFlow GraphDef:

import tf2onnx

# Load the ONNX model
onnx_model = onnx.load("model.onnx")

# Convert ONNX model to TensorFlow GraphDef
tf_rep = tf2onnx.tfonnx.process_tf_graph(onnx_model)
tf_rep.export_graph("model.pb")

Step 3: Convert TensorFlow GraphDef to TensorFlow Lite

The final step is to convert the TensorFlow GraphDef model to TensorFlow Lite format using the TensorFlow Lite Converter:

import tensorflow as tf

# Load the TensorFlow GraphDef model
with tf.compat.v1.gfile.GFile("model.pb", "rb") as f:
   graph_def = tf.compat.v1.GraphDef()
   graph_def.ParseFromString(f.read())

# Convert to TensorFlow Lite format
converter = tf.compat.v1.lite.TFLiteConverter.from_graph_def(graph_def)
tflite_model = converter.convert()

# Save the TensorFlow Lite model
with open("model.tflite", "wb") as f:
   f.write(tflite_model)

Conclusion

Converting a PyTorch model to TensorFlow Lite might seem like a daunting task, but with the help of the ONNX format and a few intermediate steps, you can easily achieve this. By following this step-by-step guide, you'll be able to deploy your PyTorch models on mobile and embedded devices using TensorFlow Lite, opening up new possibilities for your machine learning applications. Happy converting!