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https://github.com/NVIDIA/Model-Optimizer.git
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55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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"""Utils for operating on lists."""
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from typing import Any
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import numpy as np
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__all__ = ["list_closest_to_median", "stats", "val2list", "val2tuple"]
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def list_closest_to_median(x: list) -> Any:
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"""Return element from list that's closest to list mean."""
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median = np.median(x)
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diff = [abs(elem - median) for elem in x]
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return x[diff.index(min(diff))]
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def val2list(val: list | tuple | Any, repeat_time: int = 1) -> list:
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"""Repeat `val` for `repeat_time` times and return the list or val if list/tuple."""
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if isinstance(val, (list, tuple)):
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return list(val)
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return [val for _ in range(repeat_time)]
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def val2tuple(val: list | tuple | Any, min_len: int = 1, idx_repeat: int = -1) -> tuple:
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"""Return tuple with min_len by repeating element at idx_repeat."""
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# convert to list first
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val = val2list(val)
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# repeat elements if necessary
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if len(val) > 0:
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val[idx_repeat:idx_repeat] = [val[idx_repeat] for _ in range(min_len - len(val))]
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return tuple(val)
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def stats(vals: list[float]) -> dict[str, float]:
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"""Compute min, max, avg, std of vals."""
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stats = {"min": np.min, "max": np.max, "avg": np.mean, "std": np.std}
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return {name: fn(vals) for name, fn in stats.items()} if vals else {}
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