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https://github.com/open-mmlab/mmclassification.git
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86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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from typing import List
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import torch
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import torchvision.ops.boxes as boxes
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from mmengine.evaluator import BaseMetric
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from mmpretrain.registry import METRICS
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def aligned_box_iou(boxes1: torch.Tensor, boxes2: torch.Tensor):
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area1 = boxes.box_area(boxes1)
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area2 = boxes.box_area(boxes2)
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lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # (B, 2)
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rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # (B, 2)
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wh = boxes._upcast(rb - lt).clamp(min=0) # (B, 2)
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inter = wh[:, 0] * wh[:, 1] # (B, )
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union = area1 + area2 - inter
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iou = inter / union
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return iou
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@METRICS.register_module()
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class VisualGroundingMetric(BaseMetric):
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"""Visual Grounding evaluator.
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Calculate the box mIOU and box grounding accuracy for visual grounding
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model.
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Args:
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collect_device (str): Device name used for collecting results from
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different ranks during distributed training. Must be 'cpu' or
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'gpu'. Defaults to 'cpu'.
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prefix (str, optional): The prefix that will be added in the metric
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names to disambiguate homonymous metrics of different evaluators.
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If prefix is not provided in the argument, self.default_prefix
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will be used instead. Should be modified according to the
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`retrieval_type` for unambiguous results. Defaults to TR.
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"""
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default_prefix = 'visual-grounding'
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def process(self, data_batch, data_samples):
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"""Process one batch of data samples.
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The processed results should be stored in ``self.results``, which will
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be used to computed the metrics when all batches have been processed.
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Args:
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data_batch: A batch of data from the dataloader.
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data_samples (Sequence[dict]): A batch of outputs from the model.
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"""
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for preds in data_samples:
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pred_box = preds['pred_bboxes'].squeeze()
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box_gt = torch.Tensor(preds['gt_bboxes']).squeeze()
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result = {
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'box': pred_box.to('cpu').squeeze(),
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'box_target': box_gt.squeeze(),
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}
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self.results.append(result)
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def compute_metrics(self, results: List):
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"""Compute the metrics from processed results.
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Args:
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results (dict): 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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pred_boxes = torch.stack([each['box'] for each in results])
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gt_boxes = torch.stack([each['box_target'] for each in results])
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iou = aligned_box_iou(pred_boxes, gt_boxes)
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accu_num = torch.sum(iou >= 0.5)
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miou = torch.mean(iou)
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acc = accu_num / len(gt_boxes)
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coco_val = {'miou': miou, 'acc': acc}
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return coco_val
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