transforms.RandomVerticalFlip¶

class lucid.transforms.RandomVerticalFlip(p: float = 0.5)¶

The RandomVerticalFlip class is a transformation that randomly flips an input tensor vertically with a given probability. This transformation is commonly used in data augmentation to increase the diversity of training samples for image-based neural networks.

Class Signature¶

class lucid.transforms.RandomVerticalFlip(p: float = 0.5)

Parameters¶

p (float, optional): The probability of flipping the input tensor vertically. Must be a value between 0 and 1. Default is 0.5.

Attributes¶

p (float): The probability with which the input tensor will be flipped vertically, stored as an internal attribute.

Forward Calculation¶

The vertical flip of an input tensor \(\mathbf{x}\) is applied with a probability \(p\) as follows:

Generate a random number \(r\) from a uniform distribution over [0, 1].
If \(r < p\), flip the tensor along its vertical axis.

This can be mathematically represented as:

\[\mathbf{x}_{\text{flipped}} = \mathbf{x}[..., ::-1, :]\]

where the second-to-last axis of the tensor is reversed to achieve the vertical flip.

Examples¶

Example 1: Basic Usage

>>> import lucid.transforms as T
>>> input_tensor = lucid.Tensor([[[1.0, 2.0], [3.0, 4.0]]])  # Shape: (1, 2, 2)
>>> flip = T.RandomVerticalFlip(p=1.0)  # Always flip
>>> output = flip(input_tensor)
>>> print(output)
Tensor([[[3.0, 4.0],
         [1.0, 2.0]]], grad=None)

Example 2: Randomized Flip

>>> import lucid.transforms as T
>>> input_tensor = lucid.Tensor([[[1.0, 2.0], [3.0, 4.0]]])  # Shape: (1, 2, 2)
>>> flip = T.RandomVerticalFlip(p=0.5)  # 50% chance to flip
>>> output = flip(input_tensor)  # Random outcome