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52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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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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"""Scheduler Classes"""
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import numpy as np
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from torch.optim.lr_scheduler import LambdaLR
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class ExponentialDecaySchedule(LambdaLR):
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"""Exponential learning rate decay function.
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See https://github.com/google-research/google-research/blob/
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fd2cea8cdd86b3ed2c640cbe5561707639e682f3/jaxnerf/nerf/utils.py#L360
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for details.
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Args:
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optimizer: The optimizer to update.
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lr_init: The initial learning rate.
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lr_final: The final learning rate.
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max_steps: The maximum number of steps.
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lr_delay_steps: The number of steps to delay the learning rate.
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lr_delay_mult: The multiplier for the learning rate after the delay.
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"""
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def __init__(self, optimizer, lr_init, lr_final, max_steps, lr_delay_steps=0, lr_delay_mult=1.0) -> None:
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def func(step):
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if lr_delay_steps > 0:
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delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin(
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0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1)
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)
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else:
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delay_rate = 1.0
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t = np.clip(step / max_steps, 0, 1)
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log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t)
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multiplier = (
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log_lerp / lr_init
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) # divided by lr_init because the multiplier is with the initial learning rate
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return delay_rate * multiplier
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super().__init__(optimizer, lr_lambda=func)
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