onnxruntime_inference_session_load_model_and_run.py

1import onnxruntime as ort
2import numpy as np
3
4# Create an InferenceSession with the model file
5# Note: This assumes you have a model named 'model.onnx' in your directory
6# For this example, we will use a dummy model path or you can replace it with your model path
7session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
8
9# Get input metadata
10input_name = session.get_inputs()[0].name
11input_shape = session.get_inputs()[0].shape
12input_type = session.get_inputs()[0].type
13
14print(f"Input name: {input_name}, shape: {input_shape}, type: {input_type}")
15
16# Prepare input data (random data matching the model's input shape)
17# This example assumes a float32 input. Adjust based on your model's metadata.
18data = np.random.randn(1, 3, 224, 224).astype(np.float32)
19
20# Run the model
21# The first argument is the list of output names (None retrieves all)
22# The second argument is a dictionary mapping input names to data
23outputs = session.run(None, {input_name: data})
24
25# Print the output
26print("Inference successful. Output shape:", outputs[0].shape)
27print("Output values:", outputs)