model/se3_transformer/runtime/metrics.py

# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# 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