Скачать или смотреть How to Use transforms.FiveCrop() Outside the DataLoader in PyTorch

Discover how to apply `transforms.FiveCrop()` and other transformations outside a PyTorch DataLoader, ensuring proper tensor shapes for your neural networks.
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Mastering Image Transformations in PyTorch: transforms.FiveCrop()

In deep learning, especially when working with computer vision tasks, manipulating image data correctly is crucial. One common scenario arises when using PyTorch's DataLoader, where we might want to apply specific transformations to image batches. A particular challenge emerges when you need to send one version of the images to one network and a transformed version to another.

In this guide, we'll explore how to effectively implement the transforms.FiveCrop() function outside the DataLoader, ensuring that you maintain the expected tensor shapes for your neural networks.

The Problem at Hand

You have a DataLoader returning batches of images with the shape torch.Size([bs, c, h, w]) where:

bs = Batch Size (e.g., 4)

c = Number of Channels (e.g., 1 for grayscale)

h, w = Height and Width of the images (e.g., 128)

Your goal is to apply a series of transformations including:

Center Crop (to size 100)

Five Crop (to size 16)

Resize (to size 128)

Convert to Tensor

Normalize the images

However, the challenge is that the output of your transformations results in tensors losing their batch size dimension. Instead of the expected shape of torch.Size([bs, ncrops, c, h, w]), you only get a tensor shape of torch.Size([ncrops, c, h, w]).

Solution Overview

To manage your transformations properly and maintain the desired tensor shape, you will need to modify how you're stacking the transformed images. Let’s break down the steps involved in achieving this:

Step 1: Transforming Your Images

You will define a function to handle the transformations. Here's how to do it:

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

Step 2: Understanding the Function

Image Transformations: The img_t variable combines multiple transformations into a single composition. It handles converting the image to PIL format, applying CenterCrop, and then FiveCrop.

Patch Transformations: The patch_t variable deals with resizing, converting to a tensor, and normalizing the images.

Looping Through Images: By iterating through each image in orig_img, you apply img_t to get the five cropped images and then apply patch_t to each of those.

Step 3: Stacking the Output

The critical part of this process is the final return statement:

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

This line ensures that all cropped and transformed images are stacked together into a single tensor, maintaining the shape torch.Size([bs, 5, c, 128, 128]).

Conclusion

By restructuring the way you apply transformations, you can easily maintain the batch dimensions throughout your processing. This approach not only makes your code cleaner but also ensures that both original and transformed images can be fed into separate networks as required.

With this method, transforming images with transforms.FiveCrop() becomes straightforward and efficient, allowing you to focus on what really matters—building and optimizing your neural networks.

Happy coding!