LinearTransformation

class torchvision.transforms.v2.LinearTransformation(transformation_matrix: Tensor, mean_vector: Tensor)

Transform a tensor image or video with a square transformation matrix and a mean_vector computed offline.

This transform does not support PIL Image. 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.

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.

Parameters:

• transformation_matrix (Tensor) – tensor [D x D], D = C x H x W

• mean_vector (Tensor) – tensor [D], D = C x H x W

transform(inpt: Any, params: Dict[str, Any]) → Any

Method to override for custom transforms.

See How to write your own v2 transforms