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The Normalize() transform normalizes an image with mean and standard deviation. The torchvision.transforms module provides many important transforms that can be used to perform different types of manipulations on the image data.Normalize() accepts only tensor images of any size. A tensor image is a torch tensor. A tensor image may have n number of channels. The Normalize() transform normalizes the tensor image for each channel.As this transform supports only tensor image, the PIL images should be first converted to a torch tensor. And after applying Normalize() transform, we convert the normalized torch tensor to a PIL image.StepsWe could use the ... Read More
The torchvision.transforms module provides many important transformations that can be used to perform different types of manipulations on the image data. GaussianBlur() transformation is used to blur an image with randomly chosen Gaussian blur.The GaussianBlur() transformation accepts both PIL and tensor images or a batch of tensor images. A tensor image is a PyTorch Tensor with shape [3, H, W], where H is the image height and W is the image width. A batch of tensor images is also a torch tensor with [B, 3, H, W] where B is the number of images in the batch.Syntaxtorchvision.transforms.GaussianBlur(kernel_size, sigma=(0.1, .2))(img)kernel_size – ... Read More
The Resize() transform resizes the input image to a given size. It's one of the transforms provided by the torchvision.transforms module. Resize() accepts both PIL and tensor images. A tensor image is a torch tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width.This transform also accepts a batch of tensor images, which is a tensor with [B, C, H, W] where B is the number of images in the batch. If the image is neither a PIL image nor a tensor image, then we first convert ... Read More
We apply RandomVerticalFlip() transform to flip an image vertically at a random angle with a given probability. It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different types of manipulations on the image data.RandomVerticalFlip() accepts both PIL and tensor images. A tensor image is a torch Tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width.Syntaxtorchvision.transforms.RandomVerticalFlip(p)(img)If p = 1, it returns the vertically flipped image.If p = 0, It returns the original image.If ... Read More
RandomResizedCrop() transform crops a random area of the original input image. This crop size is randomly selected and finally the cropped image is resized to the given size. RandomResizedCrop() transform is one of the transforms provided by the torchvision.transforms module. This module contains many important transforms that can be used to perform different types of manipulations on the image data.RandomResizedCrop() accepts both PIL and tensor images. A tensor image is a PyTorch tensor with shape [..., H, W], where ... means a number of dimensions, H is the image height, and W is the image width. If the image is ... Read More
To flip an image horizontally in a random fashion with a given probability, we apply RandomHorizontalFlip() transform. It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different types of manipulations on the image data.RandomHorizontalFlip() accepts both PIL and tensor images. A tensor image is a PyTorch Tensor with shape [C, H, W], where C is the number channels, H is the image height, and W is the image width.Syntaxtorchvision.transforms.RandomHorizontalFlip(p)(img)If p = 1, it returns a horizontally flipped image.If p = 0, It returns the original image.If p ... Read More
To randomly convert an image to grayscale with a probability, we apply RandomGrayscale() transformation. It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different manipulations on the image data.RandomGrayscale() accepts both PIL and tensor images or a batch of tensor images. A tensor image is a PyTorch Tensor with shape [3, H, W], where H is the image height and W is the image width. A batch of tensor images is also a torch tensor with [B, 3, H, W]. B is the number of images in the batch.Syntaxtorchvision.transforms.RandomGrayscale(p)(img)If ... Read More
To crop an image at a random location, we apply RandomCrop() transformation. It's one of the many important transforms provided by the torchvision.transforms module.The RandomCrop() transformation accepts both PIL and tensor images. A tensor image is a torch tensor with shape [C, H, W], where C is the number of channels, H is the image height and W is the image width.If the image is neither a PIL image nor tensor image, then we first convert it to a tensor image and then apply RandomCrop().Syntaxtorchvision.transforms.RandomCrop(size)(img)where size is the desired crop size. size is a sequence like (h, w), where h ... Read More
To convert an image to grayscale, we apply Grayscale() transformation. It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different types manipulations on the image data.Grayscale() transformation accepts both PIL and tensor images or a batch of tensor images. A tensor image is a PyTorch Tensor with shape [3, H, W], where H is the image height and W is the image width. A batch of tensor images is also a torch tensor with [B, 3, H, W]. B is the number of images in the batch.Syntaxtorchvision.transforms.Grayscale()(img)It ... Read More
To crop a given image into four corners and the central crop, we apply FiveCrop() transformation. It's one of the transformations provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different types of manipulations on the image data.FiveCrop() transformation accepts both PIL and tensor images. A tensor image is a torch Tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width. If the image is neither a PIL image nor a tensor image, then we first convert it ... Read More