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# (Replace 'model.onnx' with the path to your actual ONNX model file)
6session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
7
8# Get model input details
9input_name = session.get_inputs()[0].name
10input_shape = session.get_inputs()[0].shape
11input_type = session.get_inputs()[0].type
12
13print(f"Input name: {input_name}, shape: {input_shape}, type: {input_type}")
14
15# Prepare dummy input data based on the model's requirements
16# This example assumes a float32 input. Adjust shape/dtype as needed for your model.
17data = np.random.random(input_shape).astype(np.float32)
18
19# Run inference
20outputs = session.run(None, {input_name: data})
21
22# Print results
23print("Inference results:")
24print(outputs)