holocron.utils.data¶

Batch collate¶

Mixup ¶

Mixup(num_classes: int, alpha: float = 0.2)

Bases: Module

Implements a batch collate function with MixUp strategy from "mixup: Beyond Empirical Risk Minimization".

import torch from torch.utils.data._utils.collate import default_collate from holocron.utils.data import Mixup mix = Mixup(num_classes=10, alpha=0.4) loader = torch.utils.data.DataLoader(dataset, batch_size, collate_fn=lambda b: mix(*default_collate(b)))

PARAMETER	DESCRIPTION
`num_classes`	number of expected classes TYPE: `int`
`alpha`	mixup factor TYPE: `float` DEFAULT: `0.2`

Source code in holocron/utils/data/collate.py

def __init__(self, num_classes: int, alpha: float = 0.2) -> None:
    super().__init__()
    self.num_classes: int = num_classes
    if alpha < 0:
        raise ValueError("`alpha` only takes positive values")
    self.alpha: float = alpha