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Advanced augmentations: MixUp, CutMix at batch level.
These are applied after the dataloader returns a batch,
not as part of the per-image transform pipeline.
"""
import torch
import numpy as np
def mixup_data(x: torch.Tensor, y: torch.Tensor, alpha: float = 0.2) -> tuple:
"""MixUp: convex combination of pairs of examples."""
if alpha <= 0:
return x, y, y, 1.0
lam = np.random.beta(alpha, alpha)
batch_size = x.size(0)
index = torch.randperm(batch_size, device=x.device)
mixed_x = lam * x + (1 - lam) * x[index]
return mixed_x, y, y[index], lam
def cutmix_data(x: torch.Tensor, y: torch.Tensor, alpha: float = 1.0) -> tuple:
"""CutMix: cut and paste patches between training images."""
if alpha <= 0:
return x, y, y, 1.0
lam = np.random.beta(alpha, alpha)
batch_size = x.size(0)
index = torch.randperm(batch_size, device=x.device)
_, _, h, w = x.shape
cut_ratio = np.sqrt(1.0 - lam)
cut_h = int(h * cut_ratio)
cut_w = int(w * cut_ratio)
cy = np.random.randint(h)
cx = np.random.randint(w)
y1 = np.clip(cy - cut_h // 2, 0, h)
y2 = np.clip(cy + cut_h // 2, 0, h)
x1 = np.clip(cx - cut_w // 2, 0, w)
x2 = np.clip(cx + cut_w // 2, 0, w)
x_cut = x.clone()
x_cut[:, :, y1:y2, x1:x2] = x[index, :, y1:y2, x1:x2]
# Adjust lambda to the actual area ratio
lam = 1 - (y2 - y1) * (x2 - x1) / (h * w)
return x_cut, y, y[index], lam
def mixup_criterion(criterion, pred, y_a, y_b, lam):
"""Loss for MixUp/CutMix: weighted sum of losses."""
return lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)
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