onnx_runtime_inference_with_numpy_input_preparation.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)
6# If you don't have one, you can download a sample like 'squeezenet' from the ONNX Model Zoo
7session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
8
9# Get the name of the input node
10input_name = session.get_inputs()[0].name
11
12# Create dummy input data matching the model's expected shape and type
13# For example, if the model expects a float32 tensor of shape [1, 3, 224, 224]
14input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)
15
16# Run the model
17outputs = session.run(None, {input_name: input_data})
18
19# Print the output (typically a list of numpy arrays)
20print(outputs)