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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple
from mmengine.evaluator import BaseMetric
from mmaction.registry import METRICS
@METRICS.register_module()
class RecallatTopK(BaseMetric):
"""ActivityNet dataset evaluation metric."""
def __init__(self,
topK_list: Tuple[int] = (1, 5),
threshold: float = 0.5,
collect_device: str = 'cpu',
prefix: Optional[str] = None):
super().__init__(collect_device=collect_device, prefix=prefix)
self.topK_list = topK_list
self.threshold = threshold
def process(self, data_batch: Sequence[Tuple[Any, dict]],
predictions: Sequence[dict]) -> None:
"""Process one batch of data samples and predictions. The processed
results should be stored in ``self.results``, which will be used to
compute the metrics when all batches have been processed.
Args:
data_batch (Sequence[Tuple[Any, dict]]): A batch of data
from the dataloader.
predictions (Sequence[dict]): A batch of outputs from
the model.
"""
for pred in predictions:
self.results.append(pred)
def compute_metrics(self, results: list) -> dict:
"""Compute the metrics from processed results.
Args:
results (list): The processed results of each batch.
Returns:
dict: The computed metrics. The keys are the names of the metrics,
and the values are corresponding results.
"""
eval_results = dict()
for topK in self.topK_list:
total = len(results)
correct = 0.0
for result in results:
gt = result['gt']
predictions = result['predictions'][:topK]
for prediction in predictions:
IoU = self.calculate_IoU(gt, prediction)
if IoU > self.threshold:
correct += 1
break
acc = correct / total
eval_results[f'Recall@Top{topK}_IoU={self.threshold}'] = acc
return eval_results
def calculate_IoU(self, i0, i1):
union = (min(i0[0], i1[0]), max(i0[1], i1[1]))
inter = (max(i0[0], i1[0]), min(i0[1], i1[1]))
iou = (inter[1] - inter[0]) / (union[1] - union[0])
return iou
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