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6d5047c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
"""Base metric class and batch/aggregate helpers."""
from __future__ import annotations
from collections import defaultdict
from typing import Dict, List
import torch
class Metric:
"""Base class for metrics that accumulate results over multiple __call__ and expose
aggregate()."""
def __init__(self, **kwargs):
self.clear()
def __call__(self, *args, **kwargs):
"""Compute metric for current batch, append to saved_metrics, and return the batch
result."""
metrics = self._compute(*args, **kwargs)
for key, val in metrics.items():
self.saved_metrics[key].append(val.detach().cpu().float())
return metrics
def _compute(self, **kwargs):
"""Subclasses implement this to compute metric dict from batch inputs."""
raise NotImplementedError()
def clear(self):
"""Reset all accumulated metric values."""
self.saved_metrics = defaultdict(list)
def aggregate(self):
"""Return a dict of concatenated/stacked tensors over all accumulated batches."""
output = {}
for key, lst in self.saved_metrics.items():
try:
output[key] = torch.cat(lst)
except RuntimeError:
output[key] = torch.stack(lst)
return output
def compute_metrics(metrics_list: List[Metric], metrics_in: Dict) -> Dict:
"""Run each metric on metrics_in and return the combined dict of batch results."""
metrics_out = {}
for metric in metrics_list:
metrics_out.update(metric(**metrics_in))
return metrics_out
def aggregate_metrics(metrics_list: List[Metric]) -> Dict:
"""Return combined aggregated results (concatenated over batches) for all metrics."""
metrics_out = {}
for metric in metrics_list:
metrics_out.update(metric.aggregate())
return metrics_out
def clear_metrics(metrics_list: List[Metric]) -> None:
"""Clear accumulated values for all metrics in the list."""
for metric in metrics_list:
metric.clear()
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