onnx_runtime_inference.py

import numpy as np
import onnxruntime as rt

labels = ['hot_dog', 'not_hot_dog']

# get image as tensor - 1 x 3 x 256 x 256 dimensions
hot_dog_tensor_onnx = image_transform_onnx('test_images/hot_dog_114.jpg', 256)

# initialize onnx runtime inference session
sess = rt.InferenceSession('models/hot_dog_model_resnet18_256_256.onnx')

# input & output names
input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name

# input dimensions (important for debugging)
input_dims = sess.get_inputs()[0].shape

print(input_name, output_name, input_dims)

results = sess.run([output_name], {input_name: not_hot_dog_tensor_onnx})[0]
print(labels[np.argmax(results)], results)

# prints
# ('hot_dog',array([[0.9986059 , 0.00139411]], dtype=float32))