wehub-resource-sync
80 lines
2.7 KiB
Python
80 lines
2.7 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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import math
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from typing import Union
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import torch
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from ...models.utils.list import val2list
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__all__ = ["CosineLRwithWarmup", "ConstantLRwithWarmup"]
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class CosineLRwithWarmup(torch.optim.lr_scheduler._LRScheduler):
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def __init__(
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self,
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optimizer: torch.optim.Optimizer,
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warmup_steps: int,
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warmup_lr: float,
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decay_steps: Union[int, list[int]],
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last_epoch: int = -1,
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) -> None:
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self.warmup_steps = warmup_steps
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self.warmup_lr = warmup_lr
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self.decay_steps = val2list(decay_steps)
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super().__init__(optimizer, last_epoch)
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def get_lr(self) -> list[float]:
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if self.last_epoch < self.warmup_steps:
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return [
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(base_lr - self.warmup_lr) * (self.last_epoch + 1) / self.warmup_steps + self.warmup_lr
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for base_lr in self.base_lrs
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]
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else:
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current_steps = self.last_epoch - self.warmup_steps
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decay_steps = [0] + self.decay_steps
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idx = len(decay_steps) - 2
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for i, decay_step in enumerate(decay_steps[:-1]):
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if decay_step <= current_steps < decay_steps[i + 1]:
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idx = i
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break
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current_steps -= decay_steps[idx]
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decay_step = decay_steps[idx + 1] - decay_steps[idx]
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return [0.5 * base_lr * (1 + math.cos(math.pi * current_steps / decay_step)) for base_lr in self.base_lrs]
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class ConstantLRwithWarmup(torch.optim.lr_scheduler._LRScheduler):
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def __init__(
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self,
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optimizer: torch.optim.Optimizer,
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warmup_steps: int,
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warmup_lr: float,
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last_epoch: int = -1,
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) -> None:
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self.warmup_steps = warmup_steps
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self.warmup_lr = warmup_lr
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super().__init__(optimizer, last_epoch)
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def get_lr(self) -> list[float]:
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if self.last_epoch < self.warmup_steps:
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return [
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(base_lr - self.warmup_lr) * (self.last_epoch + 1) / self.warmup_steps + self.warmup_lr
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for base_lr in self.base_lrs
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]
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else:
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return self.base_lrs
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