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import logging |
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import torch |
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from torchvision.ops import box_iou |
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from detectron2.structures import BoxMode |
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from detectron2.data import MetadataCatalog |
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from detectron2.utils.comm import all_gather, is_main_process, synchronize |
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from detectron2.evaluation.evaluator import DatasetEvaluator |
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class GroundingEvaluator(DatasetEvaluator): |
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""" |
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Evaluate grounding segmentation metrics. |
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""" |
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def __init__( |
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self, |
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dataset_name, |
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compute_box=False, |
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distributed=True, |
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): |
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self._logger = logging.getLogger(__name__) |
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self._dataset_name = dataset_name |
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self._distributed = distributed |
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self._cpu_device = torch.device("cpu") |
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self._compute_box = compute_box |
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meta = MetadataCatalog.get(dataset_name) |
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def reset(self): |
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self.cum_I = 0 |
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self.cum_U = 0 |
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self.mIoU = 0 |
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self.eval_seg_iou_list = [.5, .6, .7, .8, .9] |
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self.seg_correct = torch.zeros(len(self.eval_seg_iou_list), device=self._cpu_device) |
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self.seg_total = 0 |
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if self._compute_box: |
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self.mIoU_box = 0 |
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self.seg_correct_box = torch.zeros(len(self.eval_seg_iou_list), device=self._cpu_device) |
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@staticmethod |
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def computeIoU(pred_seg, gd_seg): |
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I = (pred_seg & gd_seg) |
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U = (pred_seg | gd_seg) |
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return I, U |
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def process(self, inputs, outputs): |
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for input, output in zip(inputs, outputs): |
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pred = output['grounding_mask'].sigmoid() > 0.5 |
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gt = input['groundings']['masks'].bool() |
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bsi = len(pred) |
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I, U = self.computeIoU(pred, gt) |
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self.cum_I += I.sum().cpu() |
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self.cum_U += U.sum().cpu() |
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IoU = I.reshape(bsi,-1).sum(-1)*1.0 / (U.reshape(bsi,-1).sum(-1) + 1e-6) |
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self.mIoU += IoU.sum().cpu() |
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if self._compute_box: |
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pred_box = BoxMode.convert(output['grounding_box'], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) |
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gt_box = BoxMode.convert(input['groundings']['boxes'], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS).cpu() |
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IoU_box = box_iou(pred_box, gt_box).diagonal() |
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self.mIoU_box += IoU_box.sum() |
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for idx in range(len(self.eval_seg_iou_list)): |
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eval_seg_iou = self.eval_seg_iou_list[idx] |
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self.seg_correct[idx] += (IoU >= eval_seg_iou).sum().cpu() |
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if self._compute_box: |
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self.seg_correct_box[idx] += (IoU_box >= eval_seg_iou).sum().cpu() |
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self.seg_total += bsi |
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def evaluate(self): |
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if self._distributed: |
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synchronize() |
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self.cum_I = torch.stack(all_gather(self.cum_I)).sum() |
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self.cum_U = torch.stack(all_gather(self.cum_U)).sum() |
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self.mIoU = torch.stack(all_gather(self.mIoU)).sum() |
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self.seg_correct = torch.stack(all_gather(self.seg_correct)).sum(0) |
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self.seg_total = sum(all_gather(self.seg_total)) |
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if self._compute_box: |
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self.mIoU_box = torch.stack(all_gather(self.mIoU_box)).sum() |
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self.seg_correct_box = torch.stack(all_gather(self.seg_correct_box)).sum(0) |
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if not is_main_process(): |
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return |
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results = {} |
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for idx in range(len(self.eval_seg_iou_list)): |
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result_str = 'precision@{}'.format(self.eval_seg_iou_list[idx]) |
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results[result_str] = (self.seg_correct[idx]*100 / self.seg_total).item() |
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results['cIoU'] = (self.cum_I*100./self.cum_U).item() |
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results['mIoU'] = (self.mIoU*100./self.seg_total).item() |
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if self._compute_box: |
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for idx in range(len(self.eval_seg_iou_list)): |
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result_str = 'precisionB@{}'.format(self.eval_seg_iou_list[idx]) |
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results[result_str] = (self.seg_correct_box[idx]*100 / self.seg_total).item() |
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results['mBIoU'] = (self.mIoU_box*100./self.seg_total).item() |
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self._logger.info(results) |
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return {'grounding': results} |
