File size: 2,632 Bytes
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#
# Copyright 2025 Ziyang Song, USTC. All rights reserved.
#
# This file has been modified from the original version.
# Original copyright (c) 2023 Bingxin Ke, ETH Zurich. 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.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/indu1ge/DepthMaster#-citation
# More information about the method can be found at https://indu1ge.github.io/DepthMaster_page
# --------------------------------------------------------------------------
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")
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