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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
import torch
__all__ = [
"torch_randint",
"torch_random",
"torch_shuffle",
"torch_uniform",
"torch_random_choices",
]
def torch_randint(
low: int, high: int, generator: torch.Generator or None = None
) -> int:
"""uniform: [low, high)"""
if low == high:
return low
else:
assert low < high
return int(torch.randint(low=low, high=high, generator=generator, size=(1,)))
def torch_random(generator: torch.Generator or None = None) -> float:
"""uniform distribution on the interval [0, 1)"""
return float(torch.rand(1, generator=generator))
def torch_shuffle(
src_list: list[any], generator: torch.Generator or None = None
) -> list[any]:
rand_indexes = torch.randperm(len(src_list), generator=generator).tolist()
return [src_list[i] for i in rand_indexes]
def torch_uniform(
low: float, high: float, generator: torch.Generator or None = None
) -> float:
"""uniform distribution on the interval [low, high)"""
rand_val = torch_random(generator)
return (high - low) * rand_val + low
def torch_random_choices(
src_list: list[any],
generator: torch.Generator or None = None,
k=1,
weight_list: list[float] or None = None,
) -> any or list:
if weight_list is None:
rand_idx = torch.randint(
low=0, high=len(src_list), generator=generator, size=(k,)
)
out_list = [src_list[i] for i in rand_idx]
else:
assert len(weight_list) == len(src_list)
accumulate_weight_list = np.cumsum(weight_list)
out_list = []
for _ in range(k):
val = torch_uniform(0, accumulate_weight_list[-1], generator)
active_id = 0
for i, weight_val in enumerate(accumulate_weight_list):
active_id = i
if weight_val > val:
break
out_list.append(src_list[active_id])
return out_list[0] if k == 1 else out_list
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