tfm.vision.augment.MixupAndCutmix

Applies Mixup and/or Cutmix to a batch of images.

tfm.vision.augment.MixupAndCutmix(
    num_classes: int,
    mixup_alpha: float = 0.8,
    cutmix_alpha: float = 1.0,
    prob: float = 1.0,
    switch_prob: float = 0.5,
    label_smoothing: float = 0.1
)

Mixup: https://arxiv.org/abs/1710.09412
Cutmix: https://arxiv.org/abs/1905.04899

Implementaion is inspired by https://github.com/rwightman/pytorch-image-models

Args
`num_classes`	`int` Number of classes.
`mixup_alpha`	`float, optional` For drawing a random lambda (`lam`) from a beta distribution (for each image). If zero Mixup is deactivated. Defaults to .8.
`cutmix_alpha`	`float, optional` For drawing a random lambda (`lam`) from a beta distribution (for each image). If zero Cutmix is deactivated. Defaults to 1..
`prob`	`float, optional` Of augmenting the batch. Defaults to 1.0.
`switch_prob`	`float, optional` Probability of applying Cutmix for the batch. Defaults to 0.5.
`label_smoothing`	`float, optional` Constant for label smoothing. Defaults to 0.1.

Methods

`distort`

View source

distort(
    images: tf.Tensor, labels: tf.Tensor
) -> Tuple[tf.Tensor, tf.Tensor]

Applies Mixup and/or Cutmix to batch of images and transforms labels.

Args

Args
`images`	`tf.Tensor` Of shape [batch_size, height, width, 3] representing a batch of image, or [batch_size, time, height, width, 3] representing a batch of video.
`labels`	`tf.Tensor` Of shape [batch_size, ] representing the class id for each image of the batch.

images

tf.Tensor

Of shape [batch_size, height, width, 3] representing a batch of image, or [batch_size, time, height, width, 3] representing a batch of video.

labels

tf.Tensor

Of shape [batch_size, ] representing the class id for each image of the batch.

Returns
Tuple[tf.Tensor, tf.Tensor]: The augmented version of `image` and `labels`.

`call`

View source

__call__(
    images: tf.Tensor, labels: tf.Tensor
) -> Tuple[tf.Tensor, tf.Tensor]

Call self as a function.