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PyTorch Articles
Page 11 of 12
PyTorch – How to convert an image to grayscale?
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 MorePyTorch – FiveCrop Transformation
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 MorePyTorch – Randomly change the brightness, contrast, saturation and hue of an image
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 MoreHow to crop an image at center in PyTorch?
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 MoreHow to convert a PyTorch tensor with gradient to a numpy array?
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 MoreHow to check if an object is a PyTorch Tensor?
To check if an object is a tensor or not, we can use the torch.is_tensor() method. It returns True if the input is a tensor; False otherwise.Syntaxtorch.is_tensor(input)Parametersinput – The object to be checked, if it is a tensor or not .OutputIt returns True if the input is a tensor; else False.StepsImport the required library. The required library is torch.Define a tensor or other object.Check if the created object is a tensor or not using torch.is_tensor(input).Display the result.Example 1# import the required library import torch # create an object x x = torch.rand(4) print(x) # check if the above ...
Read MoreWhat does "with torch no_grad" do in PyTorch?
The use of "with torch.no_grad()" is like a loop where every tensor inside the loop will have requires_grad set to False. It means any tensor with gradient currently attached with the current computational graph is now detached from the current graph. We no longer be able to compute the gradients with respect to this tensor.A tensor is detached from the current graph until it is within the loop. As soon as it is out of the loop, it is again attached to the current graph if the tensor was defined with gradient.Let's take a couple of examples for a better ...
Read MoreWhat does backward() do in PyTorch?
The backward() method is used to compute the gradient during the backward pass in a neural network.The gradients are computed when this method is executed.These gradients are stored in the respective variables.The gradients are computed with respect to these variables, and the gradients are accessed using .grad.If we do not call the backward() method for computing the gradient, the gradients are not computed.And, if we access the gradients using .grad, the result is None.Let's have a couple of examples to demonstrate how it works.Example 1In this example, we attempt to access the gradients without calling the backward() method. We notice ...
Read MorePyTorch – How to check if a tensor is contiguous or not?
A contiguous tensor is a tensor whose elements are stored in a contiguous order without leaving any empty space between them. A tensor created originally is always a contiguous tensor. A tensor can be viewed with different dimensions in contiguous manner.A transpose of a tensor creates a view of the original tensor which follows non-contiguous order. The transpose of a tensor is non-contiguous.SyntaxTensor.is_contiguous()It returns True if the Tensor is contiguous; False otherwise.Let's take a couple of example to demonstrate how to use this function to check if a tensor is contiguous or non-contiguous.Example 1# import torch library import torch ...
Read MoreHow to find the transpose of a tensor in PyTorch?
To transpose a tensor, we need two dimensions to be transposed. If a tensor is 0-D or 1-D tensor, the transpose of the tensor is same as is. For a 2-D tensor, the transpose is computed using the two dimensions 0 and 1 as transpose(input, 0, 1).SyntaxTo find the transpose of a scalar, a vector or a matrix, we can apply the first syntax defined below.And for any dimensional tensor, we can apply the second syntax.For
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