| # Copyright 2023-2025 Marigold Team, ETH Zürich. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # -------------------------------------------------------------------------- | |
| # More information about Marigold: | |
| # https://marigoldmonodepth.github.io | |
| # https://marigoldcomputervision.github.io | |
| # Efficient inference pipelines are now part of diffusers: | |
| # https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage | |
| # https://huggingface.co/docs/diffusers/api/pipelines/marigold | |
| # Examples of trained models and live demos: | |
| # https://huggingface.co/prs-eth | |
| # Related projects: | |
| # https://rollingdepth.github.io/ | |
| # https://marigolddepthcompletion.github.io/ | |
| # Citation (BibTeX): | |
| # https://github.com/prs-eth/Marigold#-citation | |
| # If you find Marigold useful, we kindly ask you to cite our papers. | |
| # -------------------------------------------------------------------------- | |
| import numpy as np | |
| class IterExponential: | |
| def __init__(self, total_iter_length, final_ratio, warmup_steps=0) -> None: | |
| """ | |
| Customized iteration-wise exponential scheduler. | |
| Re-calculate for every step, to reduce error accumulation | |
| Args: | |
| total_iter_length (int): Expected total iteration number | |
| final_ratio (float): Expected LR ratio at n_iter = total_iter_length | |
| """ | |
| self.total_length = total_iter_length | |
| self.effective_length = total_iter_length - warmup_steps | |
| self.final_ratio = final_ratio | |
| self.warmup_steps = warmup_steps | |
| def __call__(self, n_iter) -> float: | |
| if n_iter < self.warmup_steps: | |
| alpha = 1.0 * n_iter / self.warmup_steps | |
| elif n_iter >= self.total_length: | |
| alpha = self.final_ratio | |
| else: | |
| actual_iter = n_iter - self.warmup_steps | |
| alpha = np.exp( | |
| actual_iter / self.effective_length * np.log(self.final_ratio) | |
| ) | |
| return alpha | |
| if "__main__" == __name__: | |
| lr_scheduler = IterExponential( | |
| total_iter_length=50000, final_ratio=0.01, warmup_steps=200 | |
| ) | |
| # lr_scheduler = IterExponential( | |
| # total_iter_length=50000, final_ratio=0.01, warmup_steps=0 | |
| # ) | |
| x = np.arange(100000) | |
| alphas = [lr_scheduler(i) for i in x] | |
| import matplotlib.pyplot as plt | |
| plt.plot(alphas) | |
| plt.savefig("lr_scheduler.png") | |