HW7: Convolutional Neural Networks

HW7: Convolutional Neural Networks

Assignment Goals
Compare creating a CNN to a basic neural network as in HW6
Design your own deep network for scene recognition
Summary
Your implementation in this assignment might take one or two hours to run. We highly recommend to
start working on this assignment early! In this homework, we will explore building deep neural
networks, including Convolutional Neural Networks (CNNs), using PyTorch. Helper code is provided in
this assignment and can be downloaded from this zip file code_hw7-2.zip
(https://canvas.wisc.edu/courses/230450/files/19086157/download?download_frd=1) .
In this HW, you are still expected to use the Pytorch backend and virtual environment for programming
where you can find the tutorials in HW6.
Design a CNN model for MiniPlaces Dataset
In this part, you will design a simple CNN model along with your own convolutional network for a more
realistic dataset -- MiniPlaces (https://github.com/CSAILVision/miniplaces) , again using PyTorch.
Dataset
MiniPlaces is a scene recognition dataset developed by MIT. This dataset has 120K images from 100
scene categories. The categories are mutually exclusive. The dataset is split into 100K images for
training, 10K images for validation, and 10K for testing.
Helper Code
We provide helper functions in train_miniplaces.py and dataloader.py, and skeleton code in
student_code.py. See the comments in these files for more implementation details.
The original image resolution for images in MiniPlaces is 128x128. To make the training feasible, our
data loader reduces the image resolution to 32x32. You can always assume this input resolution. Our
data loader will also download the full dataset the first time you run train_miniplaces.py
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Before the training procedure, we define the dataloader, model, optimizer, image transform and criterion.
We execute the training and testing in function train_model and test_model, which is similar to what
we have for HW6.
Part I Creating LeNet-5
In this part, you have to implement a classic CNN model, called LeNet-5 in Pytorch for the MiniPlaces
dataset. We use the following layers in this order:
1. One convolutional layer with the number of output channels to be 6, kernel size to be 5, stride to be
1, padding to be 0 followed by a relu activation layer and then a 2D max pooling layer (kernel size to
be 2, padding to be 0 and stride to be 2).
2. One convolutional layer with the number of output channels to be 16, kernel size to be 5, stride to be
1, padding to be 0 followed by a relu activation layer and then a 2D max pooling layer (kernel size to
be 2, padding to be 0 and stride to be 2).
3. A Flatten layer to convert the 3D tensor to a 1D tensor.
4. A Linear layer with output dimension to be 256, followed by a relu activation function.
5. A Linear layer with output dimension to be 128, followed by a relu activation function.
6. A Linear layer with output dimension to be the number of classes (in our case, 100).
You have to fill in the LeNet class in student_code.py. You are expected to create the model following
this tutorial (https://pytorch.org/tutorials/beginner/examples_nn/two_layer_net_module.html) , which is
different from using nn.Sequential() in the last HW.
In addition, given a batch of inputs with shape [N, C, W, H], where N is the batch size, C is the input
channel and W, H are the width and height of the image (both 32 in our case), you are expected to
return both the output of the model along with the shape of the intermediate outputs for the above 6
stages. The shape should be a dictionary with the keys to be 1,2,3,4,5,6 (integers) denoting each stage,
and the corresponding value to be a list that denotes the shape of the intermediate outputs.
Hint: The expected model has the following form:
class LeNet(nn.Module):
 def __init__(self, input_shape=(32, 32), num_classes=100):
 super(LeNet, self).__init__()
 # certain definitions
 def forward(self, x):
 shape_dict = {}
 # certain operations
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 return out, shape_dict
Shape_dict should have the following form: {1: [a,b,c,d], 2:[e,f,g,h], .., 6: [x,y]}
The linear layer and the convolutional layer have bias terms.
Part II Count the number of trainable parameters of LeNet-5
In this part, you are expected to return the number of trainable parameters of your created LeNet model
in the previous part. You have to fill in the function count_model_params in student_code.py.
The function output is in the unit of Million(1e6). Please do not use any external library which directly
calculates the number of parameters (other libraries, such as NumPy can be used)!
Hint: You can use the model.named_parameters() to gain the name and the corresponding parameters
of a model. Please do not do any rounding to the result.
Part III Training LeNet-5 under different configurations
Based on the LeNet-5 model created in the previous parts, in this section, you are expected to train the
LeNet-5 model under different configurations.
You will use similar implementations of train_model and test_model as you did for HW6 (which we
provide in student_code.py). When you run train_miniplaces.py, the python script will save two files in
the "outputs" folder.
checkpoint.pth.tar is the model checkpoint at the latest epoch.
model_best.pth.tar is the model weights that has highest accuracy on the validation set.
Our code supports resuming from a previous checkpoint, such that you can pause the training and
resume later. This can be achieved by running
python train_miniplaces.py --resume ./outputs/checkpoint.pth.tar
After training, we provide eval_miniplaces.py to help you evaluate the model on the validation set and
also help you in timing your model. This script will grab a pre-trained model and evaluate it on the
validation set of 10K images. For example, you can run
python eval_miniplaces.py --load ./outputs/model_best.pth.tar
The output shows the validation accuracy and also the model evaluation time in seconds (see an
example below).
= Loading from cached file ./data/miniplaces/cached_val.pkl ...
= loading checkpoint './outputs/model_best.pth.tar'
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= loaded checkpoint './outputs/model_best.pth.tar' (epoch x)
Evaluting the model ...
[Test set] Epoch: xx, Accuracy: xx.xx%
Evaluation took 2.26 sec
You can run this script a few times to see the average runtime of your model.
Please train the model under the following different configurations:
1. The default configuration provided in the code, which means you do not have to make modifications.
2. Set the batch size to 8, the remaining configurations are kept the same.
3. Set the batch size to 16, the remaining configurations are kept the same.
4. Set the learning rate to 0.05, the remaining configurations are kept the same.
5. Set the learning rate to 0.01, the remaining configurations are kept the same.
6. Set the epochs to 20, the remaining configurations are kept the same.
7. Set the epochs to 5, the remaining configurations are kept the same.
After training, you are expected to get the validation accuracy for each configuration using the best
model (model_best.pth.tar), then save these accuracies into a results.txt file, where the accuracy of
each configuration is placed in one line in order. Your .txt file will end up looking like this:
18.00
17.39
12.31
…
The exact accuracy may not align well with your results. They are just for illustration purposes.
You have to submit the results.txt file together with your student_code.py.
Optional: Profiling Your Model
You might find that the training or evaluation of your model is a bit slower than expected. Fortunately,
PyTorch has its own profiling tool. Here is a quick tutorial
(https://pytorch.org/tutorials/recipes/recipes/profiler.html) of using PyTorch profiler. You can easily inject
the profiler into train_miniplaces.py to inspect the runtime and memory consumption of different parts
of your model. A general principle is that a deep (many layers) and wide (many feature channels)
network will train much slower. It is your design choice to balance between the efficiency and the
accuracy.
Optional: Training on CSL
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Scoring Rubric
We recommend training the model on your local machine or CSL machine and time your model (using
eval_miniplaces.py) on CSL machines. If you decide to train the model on a CSL machine, you will need
to find a way to allow your remote session to remain active when you are disconnected from CSL. In this
case, we recommend using tmux, a terminal multiplexer for Unix-like systems. tmux is already installed
on CSL. To use tmux, simply type tmux in the terminal. Now you can run your code in a tmux session.
And the session will remain active even if you are disconnected.
If you want to detach a tmux session without closing it, press "ctrl + b" then "d" (detach) within a tmux
session. This will exist to the terminal while keeping the session active. Later you can re-attach the
session.
If you want to enter an active tmux session, type "tmux a" to attach to the last session in the terminal
(outside of tmux).
If you want to close a tmux session, press "ctrl + b" then "x" (exit) within a tmux session. You won't
be able to enter this session again. Please make sure that you close your tmux sessions after this
assignment.
See here for a brief tutorial (https://linuxize.com/post/getting-started-with-tmux/) on the powerful
tool of tmux.
Deliverable
You will need to submit student_code.py together with your results.txt for Part III. The validation
accuracy for different configurations must be in .txt format and named as results.txt.
Submission Notes
You will need to submit a single zip file named hw7_<netid.zip, where you replace <netid with your
netID (your wisc.edu login). This file will include student_code.py and the validation accuracy file with the
proper name as mentioned above. Do not submit a Jupyter notebook .ipynb file.
Be sure to remove all debugging output before submission. Failure to remove debugging output will
be penalized (10pts).
If a regrading request isn't justifiable (the initial grade is correct and clear, subject to the
instructors' judgment), the request for regrading will be penalized (10 pts).
This assignment is due on March 30, 2:30 PM. We highly recommend to start early. It is
preferable to first submit a version well before the deadline (at least one hour before) and check
the content/format of the submission to make sure it's the right version. Then, later update the
submission until the deadline if needed.
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Total Points: 100
Criteria Ratings Pts
30 pts
20 pts
50 pts
Correctly create the LeNet model with the right output shape. 30 pts
Full
Marks
0 pts
No
Marks
Correctly calculate the number of parameters of LeNet. 20 pts
Full
Marks
0 pts
No
Marks
Correctly train the model under different configurations and report the right
accuracy.
50 pts
Full
Marks
0 pts
No
Marks