Chen-Yifan / pcb-auto-grader

cs543 group project

PCB Auto Grader

This readme helps you use this autograder of ECE 445 EAGLE (pcb) assignment.

Prerequisites

• Conda / pip
• Python3

Packages

• torch == 1.5
• torchvision == 0.6
• pcb-tools
• detecto

Install pytorch

• OSX

# conda
conda install pytorch==1.5.1 torchvision==0.6.1 -c pytorch
# pip
pip install torch==1.5.1 torchvision==0.6.1

• Linux and Windows

Commands for CPU only

# conda 
conda install pytorch==1.5.1 torchvision==0.6.1 cpuonly -c pytorch
# pip
pip install torch==1.5.1+cpu torchvision==0.6.1+cpu -f https://download.pytorch.org/whl/torch_stable.html

If you have CUDA, check this out for how to install pytorch on your machine: https://pytorch.org/get-started/previous-versions/

Install other packages with pip.

pip install pcb-tools
pip install detecto

Download the code

This repo uses computer vision technique to autonomously grade the pcb assignment.

git clone https://github.com/Chen-Yifan/pcb-auto-grader

How to grade

1. Put a ZIP file containing the student submissions in pcb-auto-grader/grader/upload folder. The zip file should include the following files at least:

ECE445_EagleHW.brd
ECE445_EagleHW.dri
ECE445_EagleHW.GBL
ECE445_EagleHW.GBO
ECE445_EagleHW.GBS
ECE445_EagleHW.GML
ECE445_EagleHW.gpi
ECE445_EagleHW.GTL
ECE445_EagleHW.GTO
ECE445_EagleHW.GTP
ECE445_EagleHW.GTS
ECE445_EagleHW.TXT

2. Go to grader folder and run the code grade.py to batch grade all the files in upload folder.

cd pcb-auto-grader/grader
python grader.py 

3. Done! Output are final grades.