export_onnx.py

"""
Export PriviGaze student to ONNX for on-device deployment.

Usage:
    python export_onnx.py --checkpoint ./checkpoints/student_best.pt --output privigaze.onnx
"""
import argparse
import torch
from models.student import PriviGazeStudent


def export(checkpoint_path, output_path, opset=11):
    model = PriviGazeStudent()
    ckpt = torch.load(checkpoint_path, map_location='cpu')
    model.load_state_dict(ckpt.get('student_state_dict', ckpt))
    model.eval()

    dummy = torch.randn(1, 1, 224, 224)
    torch.onnx.export(
        model,
        dummy,
        output_path,
        input_names=['face_gray'],
        output_names=['pitch', 'yaw', 'features'],
        dynamic_axes={'face_gray': {0: 'batch_size'},
                      'pitch': {0: 'batch_size'},
                      'yaw': {0: 'batch_size'},
                      'features': {0: 'batch_size'}},
        opset_version=opset,
        do_constant_folding=True,
    )
    print(f"Exported to {output_path}")

    # Verify
    import onnx
    m = onnx.load(output_path)
    onnx.checker.check_model(m)
    print("ONNX model validated OK")


def main():
    p = argparse.ArgumentParser()
    p.add_argument('--checkpoint', type=str, required=True)
    p.add_argument('--output', type=str, default='privigaze.onnx')
    p.add_argument('--opset', type=int, default=11)
    args = p.parse_args()
    export(args.checkpoint, args.output, args.opset)


if __name__ == "__main__":
    main()