Pytorch: Step by Step implementation 3D Convolution Neural Network

Pytorch: Step by Step implementation 3D Convolution Neural Network

Lern on how to code a PyTorch implementation of 3d CNN

In this article, we will be briefly explaining what a 3d CNN is, and how it is different from a generic 2d CNN. Then we will teach you step by step how to implement your own 3D Convolutional Neural Network using Pytorch .

A very dominant part of this article can be found again on my other article about 3d CNN implementation in Keras. So if you tend to code with Tensorflow/Keras instead then this link might be appropriate.

This article will be written around these 4 parts :

• 1] What is a 3D Convolutional Neural Network?
• 2] How does 3d datas look like? (e.g MNIST)
• 3] How to Implement it now?!
• 4] But then a 3d? What for?

1] What is a 3D Convolutional Neural Network?

A 3d CNN remains regardless of what we say a CNN that is very much similar to 2d CNN. Except that it differs in these following points (non-exhaustive listing):

3d Convolution Layers

Originally a 2d Convolution Layer is an entry per entry multiplication between the input and the different filters, where filters and inputs are 2d matrices. (fig.1)

In a 3d Convolution Layer, the same operations are used. We do these operations on multiple pairs of 2d matrices. (fig.2)

Padding options and slides step options work the same way.

3d MaxPool Layers

2d Maxpool Layers (2×2 filter) is about taking the maximum element of a small 2×2 square that we delimitate from the input. (fig.3)

Now in a 3d Maxpool (2x2x2), we look for the maximum element in a width 2 cube. This cube represents the space delimited by the 2x2x2 zone from the input. (fig.4)

Note that the number of operations (compared to 2d CNN layers) is multiplied by the size of the filters used (regardless of the layer being Maxpool or Convolution) and also multiplied by the size of the input itself.

2]How does 3d datas look like?

So how does a data point for a 3d CNN look like?

One way to picture it is by using the following image (fig.5):

Other existing datasets that you can use for your CNN are:

3]How to Implement it now?!

You can try for yourself the code on this dataset from Kaggle that we are using.

Multiple libraries will be used throughout the the notebook. Here are the lists of it.

To begin with, since the dataset is a bit specific, we use the following to helper functions to process them before giving them to the network.

Plus, the dataset is stored as h5 file, so to extract the actual data points, we are required to read from h5 file, and use the to_categorical function to transform it into vectors. In this step, we also prepare for cross-validation.

Supposedly, the variables X_train/X_test should have respectively shape (10000, 16, 16, 16, 3) and (2000, 16, 16, 16, 3) and targets_train/targets_test respectively (10000,) (2000,). But again we now convert all of that to PyTorch tensor format. Which we do the following way.

For the model here is the architecture that we will be using:

2 sets of ConvMake:

• a 3d Convolution Layer with filter size (3x3x3) and stride (1x1x1) for both sets
• a Leaky Relu Activation function
• a 3d MaxPool Layer with filters size (2x2x2) and stride (2x2x2)

2 FC Layerswith respectively 512 and 128 nodes.

1 Dropout Layerafter first FC layer.

The model is then translated into the code the following way:

In terms of parameters pay attention to the number of input nodes on your first Fully Convolutional Layer. Our data set being of shape (16,16,16,3), that is how we are getting filtered outputs of size (2x2x2).

If you are not familiar with CNN on PyTorch (i.e parameters or training of model) then consider reading this introduction to CNN on PyTorch!

Here is the code for training. Nothing really special, you can obviously optimise (WAY MORE!) it by changing the optimizer to Adam for instance, tweak on the learning rate (with some momentum) and much more…

For your information, after a small sample training, we got the following accuracies and losses. (fig.6 and fig.7)

4] But then a 3d? What for?

There happens to have many applications for a 3d CNN that are for instance:

• IRM data processing and therefore the inference
• self-driving
• Distance estimation

Alright, that’s pretty much all. I hope you will try this technology out! The source code is over here !