Square#

class deeptrack.elementwise.Square(feature: Feature | None = None, **kwargs: Any)#

Apply the square function elementwise.

This feature applies xp.square to each element in a NumPy array or a PyTorch tensor. It supports both direct input and pipeline composition.

This operation computes x ** 2 for each element.

Parameters#

feature: Feature | None, optional: The input feature to which the square function will be applied. If None, the function is applied directly to the input array or tensor.

>>> import deeptrack as dt
>>> from deeptrack.elementwise import Square

Use with NumPy directly:

>>> import numpy as np
>>> result = Square()(np.array([-2.0, 0.0, 3.0]))
>>> result
array([4., 0., 9.])

Use with PyTorch directly:

>>> import torch
>>> result = Square()(torch.tensor([-2.0, 0.0, 3.0]))
>>> result
tensor([4., 0., 9.])

Use in a pipeline with a NumPy value:

>>> value = dt.Value(value=np.array([-2.0, 0.0, 3.0]))
>>> pipeline = value >> Square()
>>> result = pipeline()
>>> result
array([4., 0., 9.])

Use in a pipeline with a PyTorch value:

>>> value = dt.Value(value=torch.tensor([-2.0, 0.0, 3.0]))
>>> pipeline = value >> Square()
>>> result = pipeline()
>>> result
tensor([4., 0., 9.])

These are equivalent to:

>>> pipeline = Square(value)