Repo for PyCon2018 Workshop- "Exploring PyTorch for AI assistance in Medical Imaging"
- Abhishek Kumar @abhi_kumar07 - Deep Learning Engineer at Predible Health
- Aditya Bagari- Final year Undergrad, IIT Madras
This repository will contain the teaching material and other info associated with our workshop at PyCon 2018 held Oct 05-09 in Hyderabad, India.
Session 1 will be introduction to PyTorch, while Session 2 will be directed to Introduction to CNN and hands on classification task
If you have a GitHub account, it is probably most convenient if you clone or fork the GitHub repository. You can clone the repository by running:
git clone https://github.com/vibrantabhi19/PyConIndia2018
If you are not familiar with git or don’t have an GitHub account, you can download the repository as a .zip file by heading over to the GitHub repository (https://github.com/vibrantabhi19/PyConIndia2018) in your browser and click the green “Download” button in the upper right.
Please note that we may add and improve the material until shortly before the tutorial session, and we recommend you to update your copy of the materials one day before the tutorials. If you have an GitHub account and cloned the repository via GitHub, you can sync your existing local repository with:
git pull origin masterIf you don’t have a GitHub account, you may have to re-download the .zip archive from GitHub.
This tutorial will require recent installations of
- NumPy
- SciPy
conda install -c anaconda scipy - PyTorch
conda install pytorch torchvision -c pytorch - imageio
conda install -c conda-forge imageioconda install -c conda-forge/label/gcc7 imageio - SimpleITK
conda install -c simpleitk simpleitkconda install -c simpleitk/label/dev simpleitk - Scikit Image
conda install -c anaconda scikit-image - Progressbar
conda install -c anaconda progressbar - matplotlib
- IPython
- Jupyter Notebook
Installation of PyTorch will take time and that is the main Library used in the workshop so its highly recommended to install it before you attend the workshop The last one is important, you should be able to type:
jupyter notebook
in your terminal window and see the notebook panel load in your web browser. Try opening and running a notebook from the material to see check that it works.
For users who do not yet have these packages installed, a relatively painless way to install all the requirements is to use a Python distribution such as Anaconda CE, which includes the most relevant Python packages for science, math, engineering, and data analysis; Anaconda can be downloaded and installed for free including commercial use and redistribution. The code examples in this tutorial should be compatible to Python 2.7, Python 3.4-3.6.
Although not required, we also recommend you to update the required Python packages to their latest versions to ensure best compatibility with the teaching material. Please upgrade already installed packages by executing
pip install [package-name] --upgrade- or
conda update [package-name]
The data for this tutorial is not included in the repository. We will be using custom data sets during the tutorial:
**Because the wireless network at conferences can often be spotty, it would be a good idea to download these data sets before arriving at the conference.
For models and the Data, please go to this [link] (https://drive.google.com/open?id=150snucenAUMI6Bj-y6ylevyujAjGf3vh) and download the required files.
to download all necessary data beforehand.
The download size of the data files are approx. 50 MB.