onnxruntime_inference_session_with_dummy_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)
6session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
7
8# Get the name of the input and output nodes
9input_name = session.get_inputs()[0].name
10output_name = session.get_outputs()[0].name
11
12# Create a dummy input (matching the shape and type of your model's input)
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 inference
17outputs = session.run([output_name], {input_name: input_data})
18
19# Print the output
20print(outputs)