# CSC321 Programming Assignment 2: Convolutional Neural Networks soluion

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## Description

In this assignment, we will train a convolutional neural network for a task known as image colourization. That is, given a greyscale image, we wish to predict the colour at each pixel. This a
difficult problem for many reasons, one of which being that it is ill-posed: for a single greyscale
image, there can be multiple, equally valid colourings.

## Setting Up

We recommend that you use the Teaching Labs machines for the assignment, as all the required
libraries for the assignment are already installed there. Otherwise, if you are working on your own
environment, you will need to install Python 2, PyTorch (https://pytorch.org), SciPy, NumPy
and scikit-learn. Check out the websites of the course and relevant packages for more details.

## Dataset

We will use the CIFAR-10 data set, which consists of images of size 32×32 pixels. For most of the
questions we will use a subset of the dataset. The data loading script is included with the handout,
file, you can also do so manully from:
http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
To make the problem easier, we will only use the “Horse” category from this data set. Now
let’s learn to colour some horses!
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## A. Colourization as Regression (2 points)

There are many ways to frame the problem of image colourization as a machine learning problem.
One na¨ıve approach is to frame it as a regression problem, where we build a model to predict the
RGB intensities at each pixel given the greyscale input. In this case, the outputs are continuous,
and so squared error can be used to train the model.

A set of weights for such a model is included in the handout. Read the code in colour_regression.py

1. Describe the model RegressionCNN. How many convolution layers does it have? What are
the filter sizes and number of filters at each layer? Construct a table or draw a diagram.

2. Run colour_regression.py. This will load a set of trained weights, and should generate
some images showing validation outputs. Do the results look good to you? Why or why not?

3. A colour space [1] is a choice of mapping of colours into three-dimensional coordinates. Some
colours could be close together in one colour space, but further apart in others. The RGB
colour space is probably the most familiar to you, but most state of the art colourization
models do not use RGB colour space. The model used in colour_regression.py computes
squared error in RGB colour space. How could using the RGB colour space be problematic?

4. Most state of the art colourization models frame colourization as a classification problem instead of a regression problem. Why? (Hint: what does minimizing squared error encourage?)

## B. Colourization as Classification (2 points)

We will select a subset of 24 colours and frame colourization as a pixel-wise classification problem,
where we label each pixel with one of 24 colours. The 24 colours are selected using k-means
clustering3 over colours, and selecting cluster centers. This was already done for you, and cluster
centers are provided in colour/colour_kmeans*.npy files.

For simplicy, we still measure distance
in RGB space. This is not ideal but reduces the software dependencies for this assignment.

1. Complete the model CNN. This model should have the same layers and convolutional filters as
the RegressionCNN, with the exception of the output layer. Continue to use PyTorch layers
like nn.ReLU, nn.BatchNorm2d and nn.MaxPool2d, however we will not use nn.Conv2d. We
will use our own convolution layer MyConv2d included in the file to better understand its
internals.

2. Run the following command:
python colourization.py –model CNN –checkpoint weights/cnn_k3_f32.pkl –valid
This will load a set of trained weights for your CNN model, and should generate some images
showing the trained result. How do the result compare to the previous model?
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https://en.wikipedia.org/wiki/K-means_clustering
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## C. Skip Connections (3 points)

A skip connection in a neural network is a connection which skips one or more layer and connects
to a later layer. We will introduce skip connections.

1. Add a skip connection from the first layer to the last, second layer to the second last, etc.
That is, the final convolution should have both the output of the previous layer and the
initial greyscale input as input. This type of skip-connection is introduced by [3], and is
called a ”UNet”. Following the CNN class that you have completed, complete the __init__
and forward methods of the UNet class.

Hint: You will need to use the function torch.cat.

2. Train the model for at least 5 epochs and plot the training curve using a batch size of 10:
python colourization.py –model UNet –checkpoint unet_k3_f32.pkl -b 10 -e 5
This should take around 25 minutes on the Teaching Labs computer. If you train for 25
epochs, the results will be a lot better (but not necessary for this question). If you have a
gpu available you can use the –gpu flag to make training much, much faster.

3. How does the result compare to the previous model? Did skip connections improve the
validation loss and accuracy? Did the skip connections improve the output qualitatively?
How? Give at least two reasons why skip connections might improve the performance of our
CNN models.

Note: We recommend that you answer this question with the pre-trained weights provided,
especially if you did not train for 25 epochs in the previous question. Clearly state if you
choose to do so in your writeup. You can load the weights using the following command:
python colourization.py –model UNet –checkpoint weights/unet_k3_f32.pkl –valid

## D. Dilated Convolution (1 point)

In class, we have discussed convolutional filters that are contiguous – that is, they act on input
pixels that are next to one another. However, we can dilated the filters so that they have spaces
between input pixels [4], as follows:
Figure 1: (a) no dilation, (b) dilation=1, (c) dilation=3. Note that the dilation value used in this
assignment is slightly different from the original paper.
A dilated convolution is a way to increase the size of the receiptive field without increasing the
number of weights.

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1. We have been using 3×3 filters for most of the assignment. Compare between:
(a) 3×3 convolutions,
(b) 5×5 convolution,
(c) 3×3 convolution with dilation 1
How many weights (excluding biases) do they have? What is the size of the receptive field of
each?

2. The DilatedUNet class replaces the middle convolution with a dilated convolution with dilation 1. Why we might choose to add dilation here, instead of another convolution? (Hint:
think about impact on receptive field.)

## E. Visualizing Intermediate Activations (1 point)

We will visualize the intermediate activations for several inputs. The python script activation.py
has already been written for you, and outputs.

1. Visualize the activations of the CNN for a few test examples. How are the activation in the
first few layers different from the later layers? You do not need to attach the output images
to your writeup, only descriptions of what you see.

python activations.py –model CNN –checkpoint weights/cnn_k3_f32.pkl

2. Visualize the activations of the UNet for a few test examples. How do the activations differ
from the CNN activations?
python activations.py –model UNet –checkpoint weights/unet_k3_f32.pkl

## F. Conceptional Problems (1 point)

1. Data augmentation can be helpful when the training set size is small. Which of these data
augmentation methods do you think would have been helpful for our CNN models, and why?
(a) Augmenting via flipping each image upside down
(b) Augmenting via flipping each image left to right
(c) Augmenting via shifting each image one pixel left / right
(d) Augmenting via shifting each image one pixel up / down
(e) Augmenting via using other of the CIFAR-10 classes
2. We also did not tune any hyperparameters for this assignment. What are some hyperparameters that could be tuned? List five.
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## G. (Bonus) Dilated Convolution Implementation (1 point)

This is an optional portion of the assignment where we will implement the Dilated UNet.
1. Dilations are included as a paramter in PyTorch nn.Conv2d module and F.conv2d funcion.
For the purpose of this assignment we will not use the native implementation.

Instead, we will re-implement dilated convolution by directly manipulating the filters using PyTorch
tensor manipulation, and ensuring that the implemented dilated convolution is consistent
with Figure 1. The purpose is to (a) better understand PyTorch and (b) better understand
what the filters look like. Implemented the forward() method of MyDilatedConv2d function,
without using the dilation parameter of F.conv2d.

Hint: You can do a lot with PyTorch tensors that you can do with numpy arrays, like slicing
tensor[2:4], and assigning values to slices tensor[2:4] = 0. Bear in mind that PyTorch
will need to backpropagate through whatever operations that you use to manipulate the
tensors. You may also find the function F.upsample to be helpful.

Note: If you cannot complete this problem, use the dilation parameter of F.conv2d. You
will not receive credit for this section, but it will allow you to continue to the next section.

2. Using the pre-trained weights provided, use the following command to load the weights and
run validation step:
python colourization.py –model DUNet –checkpoint weights/dunet_k3_f32.pkl –valid
How does the result compare to the previous model, quantitatively (loss and accuracy) and
qualitatively? You may or may not see an improvement in this case. In what circumstances

#### What To Submit

For reference, here is everything you need to hand in:
• A PDF file a2-writeup.pdf, typeset using LATEX, containing your answers to the conceptual
questions and requested outputs.

#### References

[1] https://en.wikipedia.org/wiki/Color_space
[2] Zhang, R., Isola, P., and Efros, A. A. (2016, October). Colorful image colorization. In European
Conference on Computer Vision (pp. 649-666). Springer International Publishing.
[3] Ronneberger, O., Fischer, P., and Brox, T. (2015, October). U-net: Convolutional networks for
biomedical image segmentation. In International Conference on Medical Image Computing and
Computer-Assisted Intervention (pp. 234-241). Springer, Cham.
[4] Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv
preprint arXiv:1511.07122. Chicago
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[5] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., … and Rabinovich, A.
(2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer
vision and pattern recognition (pp. 1-9).
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