Spaces:
Runtime error
Runtime error
| # 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 | |
| def _compute(self): | |
| pass | |
| 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 | |