mixup_batch#

composer.functional.mixup_batch(input, target, mixing=None, alpha=0.2, indices=None)[source]#

Create new samples using convex combinations of pairs of samples.

This is done by taking a convex combination of input with a randomly permuted copy of input. The permutation takes place along the sample axis (dim=0).

The relative weight of the original input versus the permuted copy is defined by the mixing parameter. This parameter should be chosen from a Beta(alpha, alpha) distribution for some parameter alpha > 0. Note that the same mixing is used for the whole batch.

Parameters

  • input (Tensor) โ€“ input tensor of shape (minibatch, ...), where ... indicates zero or more dimensions.

  • target (Tensor) โ€“ target tensor of shape (minibatch, ...), where ... indicates zero or more dimensions.

  • mixing (float, optional) โ€“ coefficient used to interpolate between the two examples. If provided, must be in \([0, 1]\). If None, value is drawn from a Beta(alpha, alpha) distribution. Default: None.

  • alpha (float, optional) โ€“ parameter for the Beta distribution over mixing. Ignored if mixing is provided. Default: 0.2.

  • indices (Tensor, optional) โ€“ Permutation of the samples to use. Default: None.

Returns

  • input_mixed (torch.Tensor) โ€“ batch of inputs after mixup has been applied

  • target_perm (torch.Tensor) โ€“ The labels of the mixed-in examples

  • mixing (torch.Tensor) โ€“ the amount of mixing used

Example

import torch
from composer.functional import mixup_batch

N, C, H, W = 2, 3, 4, 5
X = torch.randn(N, C, H, W)
y = torch.randint(num_classes, size=(N,))
X_mixed, y_perm, mixing = mixup_batch(
    X,
    y,
    alpha=0.2,
)