Master ONNX Model Training with NVIDIA TAO Toolkit

Master ONNX Model Training with NVIDIA TAO Toolkit

Table of Contents:

1. Introduction
2. Preparing the Model 2.1 Downloading the ResNet18 Model 2.2 Understanding the ImageNet Dataset
3. Installing Required Dependencies 3.1 Installing Nvidia TAO for Training and Optimization 3.2 Installing Nvidia's TensorFlow Package 3.3 Installing the TAO BYOM Package
4. Converting the Model to ONNX
5. Visualizing the ONNX Graph 5.1 Selecting the Activation Node
6. Importing the ONNX Model into TAO 6.1 Using the TAO BYOM Script 6.2 Specifying the Output Directory 6.3 Specifying the Penultimate Node
7. Training on the Pascal VOC Dataset 7.1 Preparing and Formatting the Dataset 7.2 Reviewing the Training Config
8. Monitoring the Training Progress 8.1 Using TensorBoard Visualization 8.2 Analyzing Scalar Values 8.3 Examining the Graph
9. Evaluating the Trained Model 9.1 Calculating Overall Accuracy 9.2 Analyzing Accuracy on Subclasses
10. testing the Model on Sample Images 10.1 Evaluating Model Performance
11. Conclusion

Introduction

In this article, we will explore the process of converting an important open-source model into ONNX format and fine-tuning it using TAO (Train, Adapt, and Optimize). We'll focus on computer vision and classification tasks, using a pre-trained ResNet18 model trained on the ImageNet dataset. By following the steps outlined in this guide, you'll be able to optimize and train your own models effectively.

Preparing the Model

Before we dive into the conversion and optimization process, we need to obtain the necessary model and understand the dataset it was trained on. We'll start by downloading the ResNet18 model, which is trained on the ImageNet dataset containing millions of images from 1000 different classes. This pre-trained model is ideal for transfer learning, as it provides a solid foundation for convergence and accuracy.

Installing Required Dependencies

To proceed with the conversion and training process, we need to ensure that all the required dependencies are installed. We'll begin by installing Nvidia TAO, a powerful tool for training and optimization. Additionally, we'll install Nvidia's TensorFlow Package, as it is necessary for model conversion. Finally, we'll install the TAO BYOM package, a script specially designed to convert and import models into TAO.

Converting the Model to ONNX

The first step in the process is converting the TensorFlow or PyTorch model into ONNX format. However, since we already have an ONNX model, we can skip this step and move on to the next.

Visualizing the ONNX Graph

Before importing the ONNX model into TAO, we need to select the appropriate activation node. This is necessary because the imported model was trained on a different number of classes compared to the model we'll be training. By analyzing the ONNX graph, we can identify the last activation layer's name and ensure that TAO attaches a matching head for our specific dataset during the training process.

Importing the ONNX Model into TAO

Now that we've identified the correct activation layer, we can proceed with importing the ONNX model into TAO using the TAO BYOM script. This script allows us to specify the ONNX file, model name, output directory, and penultimate node (the last activation layer). The conversion process is quick, and once it's complete, we can check the output directory to ensure the successful conversion of the model.

Training on the Pascal VOC Dataset

To demonstrate the training process, we'll use the popular Pascal VOC dataset. This dataset consists of 20 different classes, including people, animals, vehicles, and objects. We'll need to download and prepare the dataset, as well as copy the imported model into the local project directory. Additionally, we'll review the training configuration, where we can specify the model name, training and validation data paths, optimizer, learning rate, and other hyperparameters.

Monitoring the Training Progress

While the training job is running, it's essential to monitor its progress. We'll use the TensorBoard visualization tool for this purpose. By opening TensorBoard in our browser, we can view scalar values such as accuracy and loss, providing insights into the training performance. Furthermore, for advanced users, TensorBoard allows us to analyze the graph structure and plot histograms of model weights and biases.

Evaluating the Trained Model

Once the training is complete, we'll evaluate the accuracy of our model on the test dataset. This will give us an overall understanding of its performance. Additionally, we'll analyze the accuracy on subclasses to identify areas for improvement.

Testing the Model on Sample Images

To further assess the model's performance, we'll test it on a few sample images. By examining the predicted results, we can get a better sense of the model's strengths and weaknesses.

Conclusion

In conclusion, this article has provided a step-by-step guide on converting, optimizing, and training models using TAO. By following these instructions, you can leverage pre-trained models, fine-tune them for your specific tasks, and achieve improved accuracy. With TAO's advanced features and the ability to add custom layers, you have the flexibility to create highly optimized models for various machine learning applications.

Highlights:

• Converting and fine-tuning models using TAO
• Using pre-trained ResNet18 model
• Understanding the ImageNet dataset
• Installing Nvidia TAO and TensorFlow packages
• Visualizing and selecting the activation node in the ONNX graph
• Importing the ONNX model into TAO
• Training on the Pascal VOC dataset
• Monitoring training progress with TensorBoard
• Evaluating the trained model's accuracy
• Testing the model on sample images

FAQ: Q: What is TAO? A: TAO stands for Train, Adapt, and Optimize, and it is a comprehensive tool for converting, optimizing, and training models.

Q: What is the benefit of using a pre-trained model like ResNet18? A: Pre-trained models like ResNet18 are trained on large datasets and have already learned useful features. This allows for faster convergence and higher accuracy when fine-tuning for specific tasks.

Q: Can I use my own dataset for training? A: Yes, TAO allows you to train models on your own custom datasets. Simply prepare and format the dataset, and specify the data path in the training configuration.

Q: Can I add custom layers to my model in TAO? A: Yes, TAO provides the option to add custom layers using the -c flag in the TAO BYOM script. This allows for greater flexibility in model architecture.

Q: How long does training typically take? A: The training duration depends on the GPU and the size of the dataset. It can range from half an hour to a few hours.

Resources:

• Nvidia TAO
• ResNet18 Model
• ImageNet Dataset
• Pascal VOC Dataset