setup_instructions.txt

Setting up the project

1. Clone the repo
2. Run "init_config.sh" script. This will download the dataset and extract it. It will also setup the basic directory structure

CUDA Installation (if not installed) v11.0
1. Run "install_cuda.sh" script to perform a frsh install of CUDA 11.0. Comment lines 12-16 if you do not want to remove existing installation of CUDA.

cuDNN installation (if not installed) v8.0.4
1. Register on NVIDIA depeveloper program https://developer.nvidia.com/developer-program/signup
2. Download cuDNN v8.0.4.30 from https://developer.nvidia.com/cudnn
3. Run the "install_cuDNN.sh" script after copying the cuDNN zip file to the folder containing the "install_cuDNN.sh" script.

Python setup
1. Ensure your python version is anything between 3.6 and 3.8
2. Run "py_package_venv_setup.sh" script.
3. This will create a python virtual environment called "3dr_venv".
4. After this it will install all the necessary modules required to run the program.

Selecting the model and running the program (update this)
1.

Project file short descriptions
1. binvox_rw.py: read and write binvox files
2. binvox_viz.py: visualize binvox files
3. config.py: contains configuration details like dataset path and model training configuration information
4. data.py: model data generator
5. inference.py: run inference on the model
6. init_config.sh: sets up the file dierectory and downloads and extracts the dataset
7. install_cuda.sh: download and install CUDA v11.0
8. install_cuDNN.sh: download and install cuDNN v8.0.4
9. logger.py: datalogger setup
10. metrics.py: metrics used for the deep learning model
11. model.py: the deep learning model
12. py_package_venv_setup.sh: setup python environment and get required packages
13. requirements.txt: list of python modules used
14. save_data.py: save Loss/IoU values to csv file
15. saveiou.py: save Loss/IoU values to csv file
16. setup_instructions.txt: intsructions for project setup
17. ShapeNet_original.json: json file required for dataset
18. train.py: train model
19. utils.py: utility functions