How can I create custom transforms in PyTorch?

How can I create custom transforms in PyTorch?

You can create custom transforms in PyTorch by subclassing the torchvision.transforms.Transform class and implementing the __call__ method.

Can you give me an example of a custom transform?

Sure! Here's a simple example that converts an image to grayscale:

from PIL import Image
import torchvision.transforms as transforms

class ToGrayscale:
    def __call__(self, img):
        return img.convert('L')

transform = ToGrayscale()
image = Image.open('path_to_image.jpg')
gray_image = transform(image)

How do I apply multiple transforms to an image?

You can use transforms.Compose to chain multiple transforms together. For example:

transform = transforms.Compose([
    ToGrayscale(),
    transforms.Resize((256, 256)),
    transforms.ToTensor()
])

What if I want to apply a transform conditionally?

You can implement conditional logic within your custom transform's __call__ method to apply transforms based on certain criteria.