LinearTransformation¶
-
class
torchvision.transforms.LinearTransformation(transformation_matrix, mean_vector)[source]¶ Transform a tensor image 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
-
forward(tensor: torch.Tensor) → torch.Tensor[source]¶ - Parameters
tensor (Tensor) – Tensor image to be whitened.
- Returns
Transformed image.
- Return type
Tensor
