PyTorch to CoreML

Converting PyTorch Model to CoreML Using ONNX

This section provides a detailed guide on converting a PyTorch model to CoreML format using ONNX. This conversion is useful for deploying machine learning models in iOS applications.

Prerequisites

Install Python, PyTorch, ONNX, and CoreMLTools. Use commands like pip install torch onnx coremltools.
Ensure you have the model file and weights for the PyTorch model you wish to convert.

Step 1: Determine Model Input Requirements

Understand the input shape and type required by your PyTorch model. This information is crucial for creating dummy inputs and for successful conversion.

Step 2: Export PyTorch Model to ONNX Format

export.py


        import torch
        from models.wav2lip import Wav2Lip
        
        # Initialize and load your model
        model = Wav2Lip()
        checkpoint = torch.load('/path/to/checkpoint.pth', map_location='cpu')
        if 'state_dict' in checkpoint:
            state_dict = checkpoint['state_dict']
            model.load_state_dict(state_dict)
        else:
            model.load_state_dict(checkpoint)
        model.eval()
        
        # Create dummy inputs
        dummy_audio_input = torch.randn(1, 1, 80, 16)
        dummy_face_input = torch.randn(1, 6, 96, 96)
        
        # Export the model to ONNX
        torch.onnx.export(model, (dummy_audio_input, dummy_face_input), 'wav2lip.onnx')

Run python export.py in your command line to execute this script.

Step 3: Convert ONNX Model to CoreML Format

coremlconversion.py


        import onnx
        from onnx_coreml import convert
        
        # Path to the ONNX model
        onnx_model_path = '/path/to/wav2lip.onnx'
        
        # Load the ONNX model
        onnx_model = onnx.load(onnx_model_path)
        
        # Convert ONNX to CoreML
        coreml_model = convert(onnx_model)
        
        # Save the CoreML model
        coreml_model.save('wav2lip.mlmodel')

Run python coremlconversion.py to perform the conversion.

Additional Notes

Review the generated CoreML model to ensure it has the correct input and output layers as expected.
Test the CoreML model in a development environment before deploying it in a production application.

Summary

This guide covers the conversion of a PyTorch model to CoreML using ONNX, which facilitates the deployment of machine learning models in iOS applications.