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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES
# SPDX-License-Identifier: MIT
from abc import ABC, abstractmethod
import torch
import torch.distributed as dist
from torch import Tensor
class Metric(ABC):
""" Metric class with synchronization capabilities similar to TorchMetrics """
def __init__(self):
self.states = {}
def add_state(self, name: str, default: Tensor):
assert name not in self.states
self.states[name] = default.clone()
setattr(self, name, default)
def synchronize(self):
if dist.is_initialized():
for state in self.states:
dist.all_reduce(getattr(self, state), op=dist.ReduceOp.SUM, group=dist.group.WORLD)
def __call__(self, *args, **kwargs):
self.update(*args, **kwargs)
def reset(self):
for name, default in self.states.items():
setattr(self, name, default.clone())
def compute(self):
self.synchronize()
value = self._compute().item()
self.reset()
return value
@abstractmethod
def _compute(self):
pass
@abstractmethod
def update(self, preds: Tensor, targets: Tensor):
pass
class MeanAbsoluteError(Metric):
def __init__(self):
super().__init__()
self.add_state('error', torch.tensor(0, dtype=torch.float32, device='cuda'))
self.add_state('total', torch.tensor(0, dtype=torch.int32, device='cuda'))
def update(self, preds: Tensor, targets: Tensor):
preds = preds.detach()
n = preds.shape[0]
error = torch.abs(preds.view(n, -1) - targets.view(n, -1)).sum()
self.total += n
self.error += error
def _compute(self):
return self.error / self.total
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