nxnn.om: A Comprehensive Guide to ONNX to OM Model Conversion and Inference
Are you looking to convert your ONNX model into an OM model for better performance and efficiency? If so, you’ve come to the right place. In this detailed guide, I’ll walk you through the entire process, from setting up your environment to performing inference and optimizing your model. Let’s dive in!
Setting Up Your Environment
Before we begin, it’s essential to set up your environment correctly. This involves configuring environment variables to ensure that your system uses the appropriate settings when executing commands. To make these variables persistent, you can add them to your bashrc file. This way, they’ll remain effective even after closing your terminal window.
Converting ONNX to OM Model
Now that your environment is set up, it’s time to convert your ONNX model to an OM model. This process is made possible by the Ascend Tensor Compiler (ATC) tool, which optimizes operator scheduling, weight data layout, and memory usage to enhance the model’s performance on Ascend AI processors. To get a better understanding of ATC, refer to the CANN V100R020C10 development assistant tool guide (inference) and the ATC tool user guide.
Here’s a step-by-step guide to converting your ONNX model to an OM model:
- Use the ATC tool to convert your ONNX model to the OM format.
- Ensure that you have the necessary dependencies and libraries installed on your system.
- Run the ATC command with the appropriate parameters to convert your model.
- Verify that the conversion was successful by checking the output file.
Preprocessing Your Dataset
Once your model has been converted to the OM format, it’s crucial to preprocess your dataset to ensure that the model runs correctly. This involves normalizing the input data, resizing images, and performing any other necessary transformations. By doing so, you can improve the model’s accuracy and reduce the risk of errors during inference.
Performing Inference
Now that your model is ready, it’s time to perform inference. To do this, you can use the CANN V100R020C10 inference benchmark tool user guide (inference) to run your model on the Ascend310 processor. This tool will help you evaluate the model’s performance and identify any potential bottlenecks.
Here’s a step-by-step guide to performing inference:
- Load your dataset and preprocess it as described in the previous section.
- Load your OM model into memory.
- Pass the preprocessed data through the model to obtain the output.
- Evaluate the model’s performance by comparing the output to the expected results.
Optimizing Your Model
After performing inference, you may want to optimize your model further to improve its performance. This can be achieved by using performance analysis tools, such as the onnxtools script file by Zhang Dongyu. These tools can help you identify areas for improvement and make adjustments to your model.
Here’s a step-by-step guide to optimizing your model:
- Configure your environment variables to ensure that the Auto Tune tool works correctly in your terminal.
- Use the atc command to convert your model to the OM format.
- Run Auto Tune to optimize your model.
- Close the Auto Tune tool and run the atc command again to convert your model.
- Test the model’s performance and compare it to the previous results.
Table: ONNX to OM Conversion Parameters
| Parameter | Description |
|---|---|
| Input Shape | The shape of the input tensor. |
| Output Shape | The shape of the output tensor. |
| Input Type | The data type of the input tensor. |
| Output Type | The data type of the output tensor. |
