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