wehub-resource-sync
80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
# Copyright 2024 MIT Han Lab
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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from typing import Any, Optional, Union
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import numpy as np
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import torch
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__all__ = [
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"torch_randint",
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"torch_random",
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"torch_shuffle",
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"torch_uniform",
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"torch_random_choices",
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]
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def torch_randint(low: int, high: int, generator: Optional[torch.Generator] = None) -> int:
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"""uniform: [low, high)"""
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if low == high:
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return low
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else:
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assert low < high
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return int(torch.randint(low=low, high=high, generator=generator, size=(1,)))
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def torch_random(generator: Optional[torch.Generator] = None) -> float:
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"""uniform distribution on the interval [0, 1)"""
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return float(torch.rand(1, generator=generator))
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def torch_shuffle(src_list: list[Any], generator: Optional[torch.Generator] = None) -> list[Any]:
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rand_indexes = torch.randperm(len(src_list), generator=generator).tolist()
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return [src_list[i] for i in rand_indexes]
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def torch_uniform(low: float, high: float, generator: Optional[torch.Generator] = None) -> float:
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"""uniform distribution on the interval [low, high)"""
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rand_val = torch_random(generator)
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return (high - low) * rand_val + low
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def torch_random_choices(
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src_list: list[Any],
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generator: Optional[torch.Generator] = None,
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k=1,
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weight_list: Optional[list[float]] = None,
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) -> Union[Any, list]:
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if weight_list is None:
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rand_idx = torch.randint(low=0, high=len(src_list), generator=generator, size=(k,))
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out_list = [src_list[i] for i in rand_idx]
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else:
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assert len(weight_list) == len(src_list)
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accumulate_weight_list = np.cumsum(weight_list)
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out_list = []
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for _ in range(k):
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val = torch_uniform(0, accumulate_weight_list[-1], generator)
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active_id = 0
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for i, weight_val in enumerate(accumulate_weight_list):
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active_id = i
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if weight_val > val:
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break
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out_list.append(src_list[active_id])
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return out_list[0] if k == 1 else out_list
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