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Running
on
Zero
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"""
General utils
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# 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.
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from datetime import datetime
@torch.no_grad()
def offset2bincount(offset):
return torch.diff(
offset, prepend=torch.tensor([0], device=offset.device, dtype=torch.long)
)
@torch.no_grad()
def bincount2offset(bincount):
return torch.cumsum(bincount, dim=0)
@torch.no_grad()
def offset2batch(offset):
bincount = offset2bincount(offset)
return torch.arange(
len(bincount), device=offset.device, dtype=torch.long
).repeat_interleave(bincount)
@torch.no_grad()
def batch2offset(batch):
return torch.cumsum(batch.bincount(), dim=0).long()
def get_random_seed():
seed = (
os.getpid()
+ int(datetime.now().strftime("%S%f"))
+ int.from_bytes(os.urandom(2), "big")
)
return seed
def set_seed(seed=None):
if seed is None:
seed = get_random_seed()
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
cudnn.benchmark = False
cudnn.deterministic = True
os.environ["PYTHONHASHSEED"] = str(seed)
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