How to write Python inference onnx weights.

In Python, you can use the onnxruntime library to load ONNX models for inference. After that, you can use the numpy library to convert the weights into NumPy arrays and pass them to the ONNX model for inference.

Here is a sample code demonstrating how to perform ONNX weight inference in Python.

import onnxruntime
import numpy as np

# 加载ONNX模型
onnx_model_path = 'model.onnx'
sess = onnxruntime.InferenceSession(onnx_model_path)

# 加载权重
weight_path = 'weights.npy'
weights = np.load(weight_path)

# 获取输入和输出名称
input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name

# 创建用于推理的输入数据
input_data = np.random.randn(*sess.get_inputs()[0].shape).astype(np.float32)

# 将权重设置为ONNX模型
sess.set_tensor(input_name, input_data)
sess.set_tensor(output_name, np.zeros(sess.get_outputs()[0].shape).astype(np.float32))
sess.set_tensor('weight', weights)

# 进行推理
sess.run([output_name])

# 获取输出结果
output_data = sess.get_tensor(output_name)

print(output_data)

In this example, start by loading the ONNX model using the onnxruntime library and loading the weight file using the numpy library. Then, get the input and output names, create a NumPy array for input data. Next, use the sess.set_tensor() method to set the input data and weight data. Finally, perform inference using the sess.run() method and retrieve the output result using the sess.get_tensor() method.