In this exercise repo for DL@MBL you will learn about the U-Net and implement a configurable version yourself.
In this notebook, we will implement a U-Net architecture. Through this exercise you should gain an understanding of the U-Net architecture in particular as well as learn how to approach the implementation of an architecture in general and familiarize yourself a bit more with the inner workings of pytorch.
The exercise is split into three parts:
In part 1 you will implement the building blocks of the U-Net. That includes the convolutions, downsampling, upsampling and skip connections. We will go in the order of how difficult they are to implement.
In part 2 you will combine the modules you've built in part 1 to implement the U-Net module.
In part 3 and 4 are light on coding tasks but you will learn about two important concepts: receptive fields and translational equivariance.
Finally, in part 5 you will train your first U-Net of the course! This will just be a first flavor though since you will learn much more about that in the next exercise.
Please run the setup script to create the environment for this exercise and download data.
source setup.shNow open the exercise.ipynb notebook in your preferred tool and make sure to select the 02-unet-exercise kernel.
pre-commit to run black and ruff.solution.py. A github action will generate the exercise and solution notebooks when you push your changes.solution.ipynb and the tag "task" to cells that should only be in exercise.ipynb.