601.482/682 Deep Learning
Please submit 1) a single zip file containing your Jupyter Notebook and PDF of your
Jupyter Notebook to “Homework 6 - Notebook” and 2) your written report (LaTeX
generated PDF) to “Homework 6 - Report” on Gradescope (Entry Code: BBVDNN)
Important: You must program this homework using the PyTorch framework. We highly recom- mend using Google Colaboratory.
Important: If you don’t have local GPU access, you should port the provided Python scripts to
Colaboratory and enable GPU in that environment (under Edit->Notebook Settings). Training
should converge in less than 30 min. If your model does not make significant updates in that time,
you should re-examine your code. Either way, this is a reminder to start the assignment early.
1. Unsupervised Pre-training. In this problem, you will attempt the 2017 Endoscopic Instrument Challenge.1 You are given a pre-processed dataset consisting of endoscopic frame images (not in sequential order). The goal is to train a network which takes each RGB frame as an input and predicts a pixel-wise segmentation mask that labels the target instrument type and background tissue. Additionally, we introduce an unsupervised pre-training method and compare the performance of training on a small labeled dataset with/without pre-training. This is relevant for real-life medical image problems, where there is usually a shortage of data labels.
Data Folder We have provided a well-structured dataset. It consists of ‘/segmentation’ and
‘/colorization’. In each sub-folder, there are ‘/train’ and ‘/validation’ for training purposes.
The main goals of the homework are as follows. Concrete TODOs are enumerated on the next page.
• Complete the Network structure for segmentation task (a-c). The network structure we provide is a simplified U-Net, which is a very popular framework in medical image segmentation task. Read and understand the code in unet.py, the implementation is missing the last layer and the last activation function. Next, fill in the missing components for 1(a). For the segmentation task, train ONLY with the frames in ‘/seg- mentation/train’ and validate and test with the frames in ‘/segmentation/validation’ and ‘/segmentation/test’ respectively. The original input image is a 256 × 320 × 3 RGB image. The ground truth label is a grey-scale image that has the same dimension, where different gray values indicate the instrument type or background tissue.
• Pre-training by self-supervised colorization (d). Image colorization training2 is a com- mon way of self-supervised learning.3 The idea is to take grey-scale images as an input and predict colorized images, similar to filling in colors in a draft painting. You must use the same U-Net structure as above to train a model for this colorization task. Then use the pretrained weights as initialization for the segmentation task.
For the colorization task, you need to train on ‘/colorization/train_cor’ and validate on
‘/colorization/validation_cor’. You are given mapping.json that contains the label of
each grey level. We have also provided grey-scale image for each input in each subfolder
of ‘/colorization’ for your colorization task input.
Now that you know the broad goals and data sets, please complete the following TODOs.
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(a) Train a segmentation network using the frames in the ‘/segmentation/train’ folder. Please complete the DICE score function to evaluate your model, and write from scratch a DICE loss function as your network loss4. (Hint: You need to convert the grey-scale label mask to one-hot encoding of the label and then calculate the DICE score for each label). Please train the network until convergence (should take around 30 min) using the default provided hyperparameters and provide a figure of training loss and validation loss w.r.t. epochs (in a single figure). Please report your performance (DICE score) on the test dataset, you should expect a DICE score > 0.5. (Hint: Using BatchNorm might help you achieve better performance.)
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(b) Introduce meaningful data augmentation (e.g. vertical and horizontal flips) and train the network until convergence using the same hyperparameters as (a). Please plot the training loss and validation loss on a single figure again and report test dataset performance, you should expect a DICE score > 0.6.
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(c) Train on the colorization task using frames from the ‘/colorization/train_cor’ folder. Use hyperparameters that seem reasonable (based on your previous experiments) and mean squared error as your loss function. Please provide a figure of training loss w.r.t. epochs until your model converges. Then save your model to initialize the network for the next task.
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(d) Load the colorization pre-trained model and start training for the segmentation task using the frames in the ‘/segmentation/train’ folder. Make sure you are using the same hyperparameters as you did in the former task, and please clearly state them in your report. Plot the figure of training loss and validation loss. Report test dataset performance. Do you see a difference with the former result in (b)? (Hint: Since this is a relatively simple dataset, you might not actually observe differences in performance.)
2. Transfer Learning. Please download the fashion MNIST dataset 5 as used in HW4 and download the VGG16 model (https://pytorch.org/docs/stable/torchvision/models. html).
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(a) Randomly initialize all parameters in VGG16 and try to train your model to learn the Fashion MNIST classification task. What’s the accuracy you achieve? Please report your test accuracy on the test dataset. You should expect an accuracy > 85%.
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(b) Load the pre-trained VGG16 model from torch vision models. Freeze all but the last layer: randomly initialize the last layer of your network and fine-tune this. What accuracy do you get now? Please again report your test accuracy on the test dataset. You should expect an accuracy > 60%.
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(c) Now, imagine a scenario in which you want to train the VGG16 model on an entirely new dataset and will fine-tune either the model from (2a) or (2b). Which pre-trained model is the preferred starting point for your new use case?
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