Given transformation_matrix and mean_vector, will flatten the torch_tensor and subtract mean_vector from it which is then followed by computing the dot product with the transformation matrix and then reshaping the tensor to its original shape.

transform_linear_transformation(img, transformation_matrix, mean_vector)

Arguments

img

A magick-image, array or torch_tensor.

transformation_matrix

(Tensor): tensor [D x D], D = C x H x W.

mean_vector

(Tensor): tensor D, D = C x H x W.

Applications

whitening transformation: Suppose X is a column vector zero-centered data. Then compute the data covariance matrix [D x D] with torch.mm(X.t(), X), perform SVD on this matrix and pass it as transformation_matrix.

See also