spyrit.core.meas.HadamSplit.forward

HadamSplit.forward(x: tensor) → tensor

Applies linear transform to incoming images: \(y = Px\).

This is equivalent to computing \(x \cdot P^T\).

Warning

This method uses the splitted measurement matrix \(P\) to compute the linear measurements from incoming images. If you want to apply the operator \(H\) directly, use the method forward_H().

Args:

\(x\) (torch.tensor): Batch of vectorized (flattened) images. If x has more than 1 dimension, the linear measurement is applied to each image in the batch.

Shape:

\(x\): \((*, N)\) where * denotes the batch size and N the total number of pixels in the image.

Output: \((*, 2M)\) where * denotes the batch size and M the number of measurements.

Example:

>>> H = torch.randn(400, 1600)
>>> meas_op = LinearSplit(H)
>>> x = torch.randn(10, 1600)
>>> y = meas_op(x)
>>> print(y.shape)
torch.Size([10, 800])