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Learn how to efficiently apply multiple transformations, such as `Resize()` and `ToTensor()`, to your dataset in PyTorch for image processing.
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How to Apply Multiple Transformations on Your Dataset Using PyTorch

When working with images in deep learning applications, it’s essential to prepare your dataset correctly. A common requirement for preparing image data is applying transformations. For instance, you might want to resize images and convert them to tensors in preparation for a Convolutional Neural Network (CNN). If you're unfamiliar with how to combine transformations in PyTorch, this post will guide you through the process step-by-step.

The Problem at Hand

You have a directory of images stored in "/content/sub_folder" and you wish to apply two transformations:

Resize the images to a uniform size of 224x224 pixels.

Convert the images to PyTorch tensors using ToTensor().

Separately, these transformations work well. You can apply Resize() like this:

[[See Video to Reveal this Text or Code Snippet]]

And you can also apply ToTensor() like so:

[[See Video to Reveal this Text or Code Snippet]]

However, the challenge arises when you attempt to combine both transformations and apply them simultaneously to the dataset. In the following sections, we will discuss how to accomplish this elegantly.

Solution: Using Compose to Combine Transformations

Fortunately, PyTorch provides a simple and efficient way to apply multiple transformations using the Compose method from torchvision.transforms. This allows you to chain multiple transformations together, ensuring that they are applied in the order specified.

Step-by-Step Instructions

Import the Required Libraries: Start by importing the necessary modules from torchvision:

[[See Video to Reveal this Text or Code Snippet]]

Create a Composition of Transformations: Use T.Compose() to combine Resize and ToTensor into one transformation sequence.

[[See Video to Reveal this Text or Code Snippet]]

Load the Dataset with Transformed Images: Now load your dataset using ImageFolder() and pass the composed transformations to the transform parameter.

[[See Video to Reveal this Text or Code Snippet]]

Ready for Use: Your dataset is now prepared with both transformations applied to each image. You can proceed to use dataset for your CNN application.

Code Example

Here’s the complete code snippet that incorporates all the steps mentioned above:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By using T.Compose, you can easily apply multiple transformations to your dataset simultaneously in PyTorch. This approach not only saves you time but also ensures that your images are uniformly pre-processed to meet the input requirements of your neural network.

Now that you understand how to apply multiple transformations, you can experiment with additional transformations like normalization, cropping, or augmentation as per the requirements of your deep learning task. Happy coding!