onnx_runtime_model_inference_with_numpy_arrays.py

1import onnxruntime as ort
2import numpy as np
3
4# Load the model and create an InferenceSession
5# Replacing 'model.onnx' with your actual model file path
6session = ort.InferenceSession("model.onnx")
7
8# Get the name of the first input of the model
9input_name = session.get_inputs()[0].name
10
11# Create a dummy input (matching the shape and type of your model's requirements)
12# For example, if the model expects a float32 array of shape (1, 3, 224, 224)
13input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)
14
15# Run the model (inference)
16# The first argument is the list of output names (None retrieves all)
17# The second argument is a dictionary mapping input names to input data
18outputs = session.run(None, {input_name: input_data})
19
20# Print the output (usually a list of numpy arrays)
21print(outputs)