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# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
# International Conference on Computer Vision (ICCV), 2023
import numpy as np
__all__ = ["rand_bbox"]
def rand_bbox(
h: int,
w: int,
lam: float,
rand_func: callable = np.random.uniform,
) -> tuple[int, int, int, int]:
"""randomly sample bbox, used in cutmix"""
cut_rat = np.sqrt(1.0 - lam)
cut_w = w * cut_rat
cut_h = h * cut_rat
# uniform
cx = rand_func(0, w)
cy = rand_func(0, h)
bbx1 = int(np.clip(cx - cut_w / 2, 0, w))
bby1 = int(np.clip(cy - cut_h / 2, 0, h))
bbx2 = int(np.clip(cx + cut_w / 2, 0, w))
bby2 = int(np.clip(cy + cut_h / 2, 0, h))
return bbx1, bby1, bbx2, bby2
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