File size: 2,121 Bytes
7decfe1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | # 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 logging
import numpy as np
import random
import torch
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
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