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Diego Rivera: Neural Network models in Virtual Reality

Project: Neural Network in Virtual Reality through Unity 3D

Mentors: Lie Xie, Tian Cai, Wole Oyekoya

Week 1:

For week one research over transformers were done and reading how to implement google cardboard in unity and have it working. Also researching on how Pytorch works was done this week. Next week a Unity scene will be developed in order for the neural network models to be implemented in the scene. More research and development will be done this week to have a presentable prototype next week.

Week 2:

This week development on the unity scene was started, and a majority of the visual aspect finished. I was able to use Unity’s new input system that allows XR controller and a regular console controller to move the object for an easy way to implement in other platforms. Creating the transformer model is still in production more debugging is needed.

The video above showcase the placement model and how the controls will work in controller. XR controller is added in the game but testing must be done in order to see if it works and calibrated correctly. The box is shown to rotate on its x and y axis, decrease, and increase in size as well. UI elements will be added and continue debugging and creating a functional Transformer model in Pytorch will be next.

 

Week 3:

I was able to obtain a quest and able to test out the game however there are many bugs and errors I need to fix which is the main objective to get the project working and running. Next week bugs should be fixed, and the project should be able to run and work properly.

 

Week 4:

Debugging was finished and the CNN scene was developed. For the Development of the Transformer scene a ONNX file is created and ready to use in Unity for a similar experience as the CNN scene. Audio is set, controller are interactive, clipping issues is found in the CNN scene but that will be fixed later as developing the Transformer Scene is next and should be priority. Downside of CNN scene and possibly the Transformer scene is the need to be in Link mode and the standalone application will not work because File explorer is need in order to get the models to work.

Basic load of CNN modelCNN model with inputs and outputs

The models above shows the model before and after receiving weights and inputs

 

Week 5:

The project has started development in The transformer scene using the ONNX file, some issues were encountered and bugging issues appeared. Once the scene is implemented, quality assurance will begin.

 

Week 6:

Developing a working Transformer model is a success, using the ONNX file allows for a simple interactive transformer model, however I am unable to display a visual model of the transformer like the CNN visualizer. The ONNX file was able to transform into a JSON file but the code used in the CNN scene is not compatible with it, as a result a visual interactive scene was created using the ONNX file. The scene allows the user to drag a photo and place it on a black square which takes in the data and allows the user to run the model and get an output.

  

Further QA will be done and implementing more information about the models.

 

Week 7:

The final touches of the Transformer scene has been made, a small demo shows how the model runs through. Not included in the video a scroll bar with information about the Transformer model was created to give more background to the model and project.

 

Week 8:

Finished development, presentation was today July 29th, 2022. I learned a lot in this REU and understand more about machine learning and more information about deep learning. No further updates were made this week just preparation on presentation and finishing writing report.

Final Report submitted and accepted as a 2-pages paper (poster presentation) at VRST 2022:
Diego Rivera. 2022. Visualizing Machine Learning in 3D. In 28th ACM Symposium on Virtual Reality Software and Technology (VRST ’22), November 29-December 1, 2022, Tsukuba, Japan. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3562939.3565688 – pdf

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