Readme
Overview
This is a custom implementation of Detectron2 Fast-RCNN, which can find elements - buttons, titles, different input fields and much more - in any web design prototype or web UI image.
The implementation comes with a client, which can be used locally, to quickly and easily upload images for inference and get visualised results. The client creates directories and manages images, runs shell scripts and helps to visualise output data. It also serves as a proof-of-concept implementation of complex machine learning technologies in a user-friendly and highly accessible format for all users.
This project aims to show that it is possible to visually analyse UI design prototype structure automatically using RCNN technology. Furthermore, it shows that this functionality and its value can be served online (if hosted on a webserver) and can be used by interested users (designers) in the browser.
Installation
Install Cuda and cuDNN
Below is the setup process. Assuming you have Ubuntu 20.04 installed with an Nvidia GPU available.
To make pytorch work with GPU, it is necessary to install CUDA and cuDNN. If working from Ubuntu 20.04 the following guide is suitable.
After installing both CUDA and cuDNN check CUDA and cuDNN are version 10.1 and 7.6.5 respectively:
CUDA:
$ nvcc -V
cuDNN:
get where cuda installed:
$ whereis cuda
then navigate to that folder's /include folder and:
$ cat cudnn.h | grep CUDNN_MAJOR -A 2
Activate conda venv
ensure that conda is initialised with Python 3.8.3 (set it within conda environment creation).
See cheatsheet !
With conda environment activated:
$ pip install tensorflow-gpu==2.2.0
To install correct pytorch and torchvision do this with conda activated environment:
$ conda install -c pytorch torchvision cudatoolkit=10.1 pytorch
Check that GPU is available and working. In virtual environment $ python:
import torch
torch.cuda.current_device()
torch.cuda.get_device_name(0)
Then install some Detectron2 dependencies:
$ pip install cython pyyaml==5.1
$ pip install pycocotools
$ pip install opencv-python
Then install Detectron2:
$ python -m pip install detectron2==0.2.1 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.6/index.html
For more information see also Detectron2 installation guide.
Note: If using a different dataset than the one provided in this repository, ensure that the eval set contains at least 1 instance of all classes that can be encountered in the training dataset:
cfg.DATASETS.TEST = ("my_dataset_val",
Otherwise the validation step will fail at EVAL_PERIOD.
Also, if you want to use a different dataset than the one provided, make sure that your data is marked according to COCO standard. A good tool to use is the VGG annotator (export annotations to COCO json).
Run from conda virtual env: The model weights are not included in this repository, since the trained weights are too large. Hence, it is necessary to train the model before launching the web client and doing inference:
$ python main.py
Alternatively the weights and checkpoint folder can be downloaded here: 'link' - all contents to be placed within 'output/' directory. Output: to graphically visualise and get very detailed data about model performance, after training completes, run:
$ tensorboard --logdir output
To Launch Web Client (NodeJS,ExpressJS)
$ npm install
then:
$ npm run dev
The app will then be ready and serve on port 3000. Important note: if the webserver is launched from a virtual environment like conda, you must activate the environment. Otherwise, the python scripts called by the webserver will not work.