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