GaussianBlur in PyTorch (3)

The code provided is used to demonstrate the effect of the GaussianBlur function from the torchvision library on images from the OxfordIIITPet dataset. The GaussianBlur function takes two main arguments: kernel_size and sigma. The kernel_size argument determines the size of the blur kernel, while the sigma argument determines the standard deviation of the blur. The code creates multiple instances of the OxfordIIITPet dataset with different kernel_size and sigma values for the GaussianBlur function. It then displays the original images and the blurred images side by side for comparison. The code also defines two functions, show_images1 and show_images2, which are used to display the images. The show_images1 function displays the images directly from the dataset, while the show_images2 function applies the GaussianBlur function to the images before displaying them. The code demonstrates how different kernel_size and sigma values affect the blurring of the images. It shows that larger kernel_size values result in more severe blurring, while larger sigma values result in more subtle blurring. The code also shows that the kernel_size and sigma values can be adjusted independently to achieve different blurring effects. For example, a large kernel_size value with a small sigma value can result in a more severe blurring effect, while a small kernel_size value with a large sigma value can result in a more subtle blurring effect. Overall, the code provides a useful demonstration of the GaussianBlur function and its effects on images.