Homework 1

starter code data

In this homework, we will train a simple deep network to classify images from SuperTuxKart.

This assignment should be solved individually. No collaboration, sharing of solutions, or exchange of models is allowed. Please, do not directly copy existing code from anywhere other than your previous solutions, or the previous master solution. We will check assignments for duplicates. See below for more details.

Running your assignment on google colab

You might need a GPU to train your models. You can get a free one on google colab. We provide you with a ipython notebook that can get you started on colab for each homework.

If you’ve never used colab before, go through colab notebook (tutorial)
When you’re comfortable with the workflow, feel free to use colab notebook (shortened)

Follow the instructions below to use it.

Starter code and dataset

The starter code for this assignment can be found here. The starter code contains several useful scripts:

The starter code also contains a data directory where you’ll copy (or symlink) the SuperTuxKart classification dataset. Unzip the data directly into the homework folder, replacing the existing data directory completely. Make sure you see the following directories and files inside your main directory


You will run all scripts from inside this main directory.

Data Loader (15 pts)

As a first step, we will need to implement a data loader for the SuperTuxKart dataset. Complete the __init__, __len__, and the __getitem__ of the SuperTuxDataset class in the utils.py.

Once you finish, you can visualize some of the images by their classes using:

python3 -m homework.visualize_data data/valid


Hint: We recommend using the csv package to read csv files and the PIL library (Pillow fork) to read images in Python.

Hint: Use torchvision.transforms.ToTensor() to convert the PIL image to a pytorch tensor.

Hint: You have (at least) two options on how to load the dataset. You can load all images in the __init__ function, or you can lazily load them in __getitem__. If you load all images in __init__, make sure you convert the image to a tensor in the constructor, otherwise, you might get an OSError: [Errno 24] Too many open files.

Congratulations, you finished the first part. Time to grade your data loader.

python3 -m grader homework -v

Relevant Operations

Linear Model (15 pts)

Implement the LinearClassifier class in models.py. Define the linear model and all layers in the __init__ function, then implement forward. Your forward function receives a (B,3,64,64) tensor as an input and should return a (B,6) torch.Tensor (one value per class). You can earn these full credits without training the model, just from the correct model definition.

You can grade your linear model using

python3 -m grader homework -v

Relevant Operations

Classification Loss (10 pts)

Next, we’ll implement the ClassificationLoss in models.py. We will later use this loss to train our classifiers. You should implement the log-likelihood of a softmax classifier.

\(-\log\left(\frac{\exp(x_l) }{ \sum_j \exp(x_j)} \right),\) where \(x\) are the logits and \(l\) is the label. You may use existing pytorch functions to implement this.

You can grade your loss using

python3 -m grader homework -v

Relevant Operations

Training the linear model (30 pts)

Train your linear model in train.py. You should implement the full training procedure

You can train your network using

python3 -m homework.train -m linear

Hint: You might find it useful to store optimization parameters in the ArgumentParser, and quickly try a few from the command-line.

Hint: You might find it useful to allow training of an existing model to continue. Use the torch.load function for that. See load_model.

Hint: Try to write your training code model agnostic. We will swap out the model below.

We will use this checkpoint to grade your linear model’s performance. You can grade your trained model using

python3 -m grader homework -v

Relevant Operations

MLP Model (30 pts)

Implement the MLPClassifier class in models.py. The inputs and outputs to the multi-layer perceptron are the same as the linear classifier. However, now you’re learning a non-linear function.

You can train your network using

python3 -m homework.train -m mlp

Hint: This part might require some tuning of your training code. Try to move most modifications to command-line arguments in ArgumentParser

Hint: Use ReLU layers as non-linearities.

Hint: Two layers are sufficient.

Hint: Keep the first layer small to save parameters.

You can test your trained model using

python3 -m grader homework -v

Relevant Operations


The test grader we provide

python3 -m grader homework -v

will run a subset of test cases we use during the actual testing. The point distributions will be the same, but we will use additional test cases. More importantly, we evaluate your model on the test set. The performance on the test grader may vary. Try not to overfit to the validation set too much.


Once you finished the assignment, create a submission bundle using

python3 bundle.py homework [YOUR UT ID]

and submit the zip file on canvas. Please note that the maximum file size our grader accepts is 20MB. Please keep your model compact. Please double-check that your zip file was properly created, by grading it again

python3 -m grader [YOUR UT ID].zip

Online grader

We will use an automated grader through canvas to grade all your submissions. There is a soft limit of 5 submisisons per assignment. Please contact the course staff before going over this limit, otherwise your submission might be counted as invalid.

The online grading system will use a slightly modified version of python and the grader:

Please do not try to break or hack the grader. Doing so will have negative consequences for your standing in this class and the program.

Honor code

This assignment should be solved individually.

What interaction with classmates is allowed?

What interaction is not allowed?

Ways students failed in past years (do not do this):

Installation and setup

Installing python 3

Go to https://www.python.org/downloads/ to download python 3. Alternatively, you can install a python distribution such as Anaconda. Please select python 3 (not python 2).

Installing the dependencies

Install all dependencies using

python3 -m pip install -r requirements.txt

Note: On some systems, you might be required to use pip3 instead of pip for python 3.

If you’re using conda use

conda env create environment.yml

The test grader will not have any dependencies installed, other than native python3 libraries and libraries mentioned in requirements.txt. This includes packages like pandas. If you use additional dependencies ask on piazza first, or risk the test grader failing.

Manual installation of pytorch

Go to https://pytorch.org/get-started/locally/ then select the stable Pytorch build, your OS, package (pip if you installed python 3 directly, conda if you installed Anaconda), python version, cuda version. Run the provided command. Note that cuda is not required, you can select cuda = None if you don’t have a GPU or don’t want to do GPU training locally. We will provide instruction for doing remote GPU training on Google Colab for free.

Manual installation of the Python Imaging Library (PIL)

The easiest way to install the PIL is through pip or conda.

python3 -m pip install -U Pillow

There are a few important considerations when using PIL. First, make sure that your OS uses libjpeg-turbo and not the slower libjpeg (all modern Ubuntu versions do by default). Second, if you’re frustrated with slow image transformations in PIL use Pillow-SIMD instead:

CC="cc -mavx2" python3 -m pip install -U --force-reinstall Pillow-SIMD

The CC="cc -mavx2" is only needed if your CPU supports AVX2 instructions. pip will most likely complain a bit about missing dependencies. Install them, either through conda, or your favorite package manager (apt, brew, …).