minimal.py

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#!/usr/bin/env python3
 
from onert import infer
import numpy as np
import sys
 
 
def main(nnpackage_path, backends="cpu"):
    # Create session and load nnpackage
    # The default value of backends is "cpu".
    session = infer.session(nnpackage_path, backends)
 
    # Prepare input. Here we just allocate dummy input arrays.
    input_infos = session.get_inputs_tensorinfo()
    dummy_inputs = []
    for info in input_infos:
        # Retrieve the dimensions list from tensorinfo property.
        dims = list(info.dims)
        # Build the shape tuple from tensorinfo dimensions.
        shape = tuple(dims[:info.rank])
        # Create a dummy numpy array filled with zeros.
        dummy_inputs.append(np.zeros(shape, dtype=info.dtype))
 
    outputs = session.infer(dummy_inputs)
 
    print(f"nnpackage {nnpackage_path.split('/')[-1]} runs successfully.")
    return
 
 
if __name__ == "__main__":
    argv = sys.argv[1:]
    main(*argv)