onnx_runtime_inference_session_with_numpy_input.py

1import onnxruntime as ort
2import numpy as np
3
4# Load the model and create an InferenceSession
5# Note: This assumes you have a model file named 'model.onnx' in your directory
6# For this example, we use a placeholder; in a real scenario, provide the path to your .onnx file
7try:
8    session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
9
10    # Get the name of the first input of the model
11    input_name = session.get_inputs()[0].name
12
13    # Create dummy input data matching the expected shape and type (e.g., float32)
14    # This should be replaced with actual pre-processed data
15    input_shape = session.get_inputs()[0].shape
16    # Handle dynamic axes if present (replace None/string with 1)
17    refined_shape = [s if isinstance(s, int) else 1 for s in input_shape]
18    data = np.random.randn(*refined_shape).astype(np.float32)
19
20    # Run inference
21    results = session.run(None, {input_name: data})
22
23    # Print the output
24    print("Inference successful. Output shape:", results[0].shape)
25    print("Output data:", results[0])
26
27except Exception as e:
28    print(f"Error: {e}")
29    print("Please ensure 'model.onnx' exists in the current directory to run this script.")