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