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Shahid Akhtar Khan has Published 216 Articles

Shahid Akhtar Khan
7K+ Views
The torchvision.utils package provides the draw_bounding_boxes() function to draw bounding boxes on an image. It supports images of type torch Tensor with shape (C x H x W) where C is the number of channels, and W and H are the width and height of the image, respectively.If we read ... Read More

Shahid Akhtar Khan
7K+ Views
Reading the images is a very important part in image processing or computer vision related tasks. The torchvision.io package provides functions to perform different IO operations. To read an image, torchvision.io package provides the image_read() function. This function reads JPEG and PNG images. It returns a 3D RGB or Grayscale Tensor.The ... Read More

Shahid Akhtar Khan
301 Views
To compute the condition number of a matrix with respect to a matrix norm, we could apply torch.linalg.cond() method. It returns a new tensor with computed condition number. It accepts a matrix, a batch of matrices and also batches of matrices. A matrix is a 2D torch Tensor. It supports ... Read More

Shahid Akhtar Khan
702 Views
To compute the pseudoinverse of a square matrix, we could apply torch.linalg.pinv() method. It returns a new tensor with pseudoinverse of the given matrix. It accepts a matrix, a batch of matrices and also batches of matrices. A matrix is a 2D torch Tensor. It supports input of float, double, ... Read More

Shahid Akhtar Khan
1K+ Views
To compute the inverse of a square matrix, we could apply torch.linalg.inv() method. It returns a new tensor with inverse of the given matrix. It accepts a square matrix, a batch of square matrices, and also batches of square matrices.A matrix is a 2D torch Tensor. It supports input of ... Read More

Shahid Akhtar Khan
1K+ Views
To compute the norm of a vector or a matrix, we could apply torch.linalg.norm() method. It returns a new tensor with computed norm. It accepts a vector, matrix, a batch of matrices and also batches of matrices.A vector is a 1D torch Tensor where a matrix is a 2D torch ... Read More

Shahid Akhtar Khan
811 Views
To solve a square system of linear equations with unique solution, we could apply the torch.linalg.solve() method. This method takes two parameters −first, the coefficient matrix A, andsecond, the right-hand tensor b.Where A is a square matrix and b is a vector. The solution is unique if A invertible. We ... Read More

Shahid Akhtar Khan
985 Views
To compute the determinant of a square matrix, we could apply torch.linalg.det() method. It returns a new tensor with computed determinant. It accepts a square matrix, a batch of square matrices and also batches of square matrices. It supports matrix of float, double, cfloat, and cdouble data types.We could also ... Read More

Shahid Akhtar Khan
1K+ Views
To compute the logistic function of elements of a tensor, we use torch.special.expit() method. It returns a new tensor with computed logistic function element-wise. It accepts torch tensor of any dimension. We could also apply torch.sigmoid() method to compute the logistic function of elements of the tensor. It is an ... Read More

Shahid Akhtar Khan
337 Views
torch.linalg.qr() computes the QR decomposition of a matrix or a batch of matrices. It accepts matrix and batch of matrices of float, double, cfloat and cdouble data types.It returns a named tuple (Q, R). Q is orthogonal when the matrix is real valued and unitary when matrix is complex valued. ... Read More