Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
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
To randomly change the brightness, contrast, saturation and hue of an image, we apply ColorJitter(). It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to manipulate the image data.ColorJitter() transformation accepts both PIL and tensor images. A tensor image is a PyTorch 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. A batch of tensor images is a tensor with [B, C, H, W]. B is ... Read More
To crop an image at its center, we apply CenterCrop(). It's one of the transforms provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform manipulation on the image data.CenterCrop() transformation accepts both PIL and tensor images. A tensor image is a PyTorch 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. A batch of tensor images is a tensor with [B, C, H, W]. B is the number of ... Read More
To convert a Torch tensor with gradient to a Numpy array, first we have to detach the tensor from the current computing graph. To do it, we use the Tensor.detach() operation. This operation detaches the tensor from the current computational graph. Now we cannot compute the gradient with respect to this tensor. After the detach() operation, we use the .numpy() method to convert it to a Numpy array.If a tensor with requires_grad=True is defined on GPU, then to convert this tensor to a Numpy array, we have to perform one more step. First we have to move the tensor to ... Read More
Data ReconstructionThe data reconstruction is defined as the process of obtaining the analog signal $\mathrm{\mathit{x\left ( t \right )}}$ from the sampled signal $\mathrm{\mathit{x_{s}\left ( t \right )}}$. The data reconstruction is also known as interpolation.The sampled signal is given by, $$\mathrm{\mathit{x_{s}\left ( t \right )\mathrm{=}x\left ( t \right )\sum_{n\mathrm{=}-\infty }^{\infty }\delta \left ( t-nT \right )}}$$$$\mathrm{\Rightarrow \mathit{x_{s}\left ( t \right )\mathrm{=}\sum_{n\mathrm{=}-\infty }^{\infty }x\left ( nT \right )\delta \left ( t-nT \right )}}$$Where, $\mathrm{\mathit{\delta \left ( t-nT \right )}}$ is zero except at the instants $\mathrm{\mathit{t\mathrm{=}nT}}$. A reconstruction filter which is assumed to be linear and time invariant has unit ... Read More