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# Copyright 2024 MIT Han Lab
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
from typing import Union
import torch
from ...apps.utils.dist import sync_tensor
__all__ = ["AverageMeter"]
class AverageMeter:
"""Computes and stores the average and current value."""
def __init__(self, is_distributed=True):
self.is_distributed = is_distributed
self.sum = 0
self.count = 0
def _sync(self, val: Union[torch.Tensor, int, float]) -> Union[torch.Tensor, int, float]:
return sync_tensor(val, reduce="sum") if self.is_distributed else val
def update(self, val: Union[torch.Tensor, int, float], delta_n=1):
self.count += self._sync(delta_n)
self.sum += self._sync(val * delta_n)
def get_count(self) -> Union[torch.Tensor, int, float]:
return self.count.item() if isinstance(self.count, torch.Tensor) and self.count.numel() == 1 else self.count
@property
def avg(self):
avg = -1 if self.count == 0 else self.sum / self.count
return avg.item() if isinstance(avg, torch.Tensor) and avg.numel() == 1 else avg
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