PyTorch conv2d: A Practical Guide

Filed Under: Python Modules
Conv2d Pytorch

What is PyTorch conv2d? Working with Data Science and building Machine Learning models, we often come across a need to parse data. This can be in multiple forms, and organizing data to be relevant and usable to us, is very important.

PyTorch is a Framework that allows for a number of implementations for the data that is being processed. It is split up into a few sections dedicated to the different areas of Data Science, such as the torch.nn, torch.Tensor, torch.cuda, and torch.backends.

All of these different packages, serve various purposes, but we’ll be dealing with torch.nn in this article.

We’ll be discussing a lot of those in our articles, but, this article focuses on a single component of the torch.nn module ecosystem.

What is the PyTorch conv2d class?

The PyTorch conv2d class performs a convolution operation on the 2D matrix that is provided to it.

This means that matrix inversion, and MAC operations on the matrix, can be done through a single function included in the torch.nn module.

Which module is the conv2d part of?

The Conv2d class is a part of the torch.nn package in the PyTorch module. The torch.nn package is one that mainly consists of a large number of functions that focus on neural networks.

This package tends to offer methods that help in creating neural networks using mathematical operations.

One of these mathematical operations is convolution, of which the Conv2d class is an important part.

Use cases for PyTorch conv2d

While intended for utilization in building neural networks, the use cases for the Conv2d function also works in Image Processing.

This mostly is only used for multiplying two images together and yet ensure that the resulting matrix has the same dimensions as required.

How do we use conv2d?

In order to use Conv2d, we’ll first have to import the function.

Note that we’ll have to first install PyTorch and then import the modules,

from torch.nn import Conv2d

Now, we can use the Conv2d class! We’ll need to provide inputs and a few parameters to the convolution class in order to process the data.

The syntax of the function is,

m = Conv2d(in_channels, out_channels, kernel_size=(n, n), stride, padding, bias)

All of these parameters change up the convolution’s output, and each of them has a specific purpose,

  • in_channels – Refers to the number of channels that are in the input image.
  • out_channels – This field refers to the number of channels produced by the convolution.
  • kernel_size – Used to provide the width and height of the filter mask.
  • stride – This field controls the stride for the convolution.
  • padding – It controls the amount of implicit zero-paddings on both sides for padding number of points for each dimension.
  • bias – This field checks whether there exists a learnable bias to the output.

An example of using the Conv2d class is as follows,

# importing the class from the package
from torch.nn import Conv2d

# using square kernels with an equal stride
m = Conv2d(16, 33, 3, stride=2)

Something of note, is that the output that we receive is of the type Tensor.

Other PyTorch features

Convolution is an important topic to look into and working with neural networks is an important milestone in Data Science.

As part of the data wrangling process, convolution does provide us with a great method to work with a large variety of data.

Look into our other articles on Getting started with PyTorch, and its components – Tensor, torchmax, and the PyTorch dataloader.


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