nxnn.om,nxnn.om: A Comprehensive Guide to ONNX Model Conversion and Offline Inference

nxnn.om: A Comprehensive Guide to ONNX Model Conversion and Offline Inference

In the realm of artificial intelligence, the conversion and optimization of models are crucial steps to enhance computational efficiency and performance. This article aims to provide you with a detailed and multi-dimensional introduction to converting an ONNX model to an nxnn.om model, as well as the associated inference process. Let’s delve into the intricacies of this process.

Setting Up the Environment

Before we begin, it is essential to set up the environment variables. Environment variables play a vital role in allowing the system to use specific configurations when executing commands. If you set them directly in the command line, they will only be effective for the current window. To ensure that environment variables remain valid, you can add them to the bashrc file.

Converting ONNX Model to nxnn.om

Here are the steps to convert an ONNX model to an nxnn.om format:

1. Use the ATC (Ascend Tensor Compiler) tool to convert the model to OM format. The ATC tool optimizes operator scheduling, weight data arrangement, and memory usage, enabling the model to run more efficiently on the Ascend AI processor. For a comprehensive understanding of the tool’s usage, refer to the CANN V100R020C10 development auxiliary tool guide (inference) 01, as well as the ATC tool user guide.
2. After the model conversion, perform data preprocessing to ensure the model runs correctly.
3. Utilize the CANN V100R020C10 inference benchmark tool user guide (inference) 01 to perform offline inference. This tool can help you measure the model’s performance on the Ascend310.
4. To further optimize the model’s performance, you can use performance analysis tools, such as the Zhang Dongyu/onnxtools script file. These tools allow you to fine-tune the model.

Performance Tuning

The process of tuning the model includes:

1. Configure the environment variables to ensure that the Auto Tune tool can work correctly in the current terminal, and remain valid even after closing the terminal.
2. Use the atc command to convert the model. After executing Auto Tune, close the tool and run atc conversion again, followed by testing the performance.
3. By repeatedly testing and adjusting, you will find that the model’s running time has improved. At this point, you can use profili

ONNX Model Conversion from PyTorch

Converting a PyTorch .pth model to ONNX format can be achieved using the following steps:

1. Install the ONNX package by running pip install onnx in the terminal.
2. Load the PyTorch model and convert it to ONNX format. For example, you can use the following code to load a PyTorch model:

import torchimport torchvisionmodel = torchvision.models.resnet18(pretrained=True)model.eval()input_tensor = torch.randn(1, 3, 224, 224)torch.onnx.export(model, input_tensor, "resnet18.onnx")

In this example, we loaded a pre-trained ResNet18 model and converted it to ONNX format. We also created a virtual input tensor that will be used for exporting the model.

Verifying the ONNX Model

You can verify the exported ONNX model using ONNX Runtime or other ONNX-compatible inference engines. For example, you can use the following code to verify the exported ResNet18 model:

import onnximport onnxruntimeonnx_model = onnx.load("resnet18.onnx")onnx.checker.checkmodel(onnx_model)

Conclusion

Converting an ONNX model to an nxnn.om format and performing offline inference can significantly enhance the performance of your AI applications. By following the steps outlined in this article, you can optimize your models for the Ascend AI processor and achieve better results in your projects.