| # Copyright 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/prs-eth/Marigold#-citation | |
| # More information about the method can be found at https://marigoldmonodepth.github.io | |
| # -------------------------------------------------------------------------- | |
| import numpy as np | |
| import random | |
| import torch | |
| import logging | |
| def seed_all(seed: int = 0): | |
| """ | |
| Set random seeds of all components. | |
| """ | |
| random.seed(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| def generate_seed_sequence( | |
| initial_seed: int, | |
| length: int, | |
| min_val=-0x8000_0000_0000_0000, | |
| max_val=0xFFFF_FFFF_FFFF_FFFF, | |
| ): | |
| if initial_seed is None: | |
| logging.warning("initial_seed is None, reproducibility is not guaranteed") | |
| random.seed(initial_seed) | |
| seed_sequence = [] | |
| for _ in range(length): | |
| seed = random.randint(min_val, max_val) | |
| seed_sequence.append(seed) | |
| return seed_sequence | |