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Configuration error
| # 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 | |