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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
__all__ = [
"list_sum",
"list_mean",
"weighted_list_sum",
"list_join",
"val2list",
"val2tuple",
"squeeze_list",
]
def list_sum(x: list) -> any:
return x[0] if len(x) == 1 else x[0] + list_sum(x[1:])
def list_mean(x: list) -> any:
return list_sum(x) / len(x)
def weighted_list_sum(x: list, weights: list) -> any:
assert len(x) == len(weights)
return (
x[0] * weights[0]
if len(x) == 1
else x[0] * weights[0] + weighted_list_sum(x[1:], weights[1:])
)
def list_join(x: list, sep="\t", format_str="%s") -> str:
return sep.join([format_str % val for val in x])
def val2list(x: list or tuple or any, repeat_time=1) -> list:
if isinstance(x, (list, tuple)):
return list(x)
return [x for _ in range(repeat_time)]
def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple:
x = val2list(x)
# repeat elements if necessary
if len(x) > 0:
x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))]
return tuple(x)
def squeeze_list(x: list or None) -> list or any:
if x is not None and len(x) == 1:
return x[0]
else:
return x
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