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import copy |
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from collections import defaultdict |
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from pathlib import Path |
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import torch |
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import torch.utils.data |
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import util.misc as utils |
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from util.box_ops import generalized_box_iou |
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class RefExpEvaluator(object): |
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def __init__(self, refexp_gt, iou_types, k=(1, 5, 10), thresh_iou=0.5): |
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assert isinstance(k, (list, tuple)) |
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refexp_gt = copy.deepcopy(refexp_gt) |
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self.refexp_gt = refexp_gt |
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self.iou_types = iou_types |
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self.img_ids = self.refexp_gt.imgs.keys() |
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self.predictions = {} |
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self.k = k |
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self.thresh_iou = thresh_iou |
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def accumulate(self): |
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pass |
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def update(self, predictions): |
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self.predictions.update(predictions) |
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def synchronize_between_processes(self): |
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all_predictions = utils.all_gather(self.predictions) |
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merged_predictions = {} |
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for p in all_predictions: |
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merged_predictions.update(p) |
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self.predictions = merged_predictions |
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def summarize(self): |
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if utils.is_main_process(): |
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dataset2score = { |
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"refcoco": {k: 0.0 for k in self.k}, |
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"refcoco+": {k: 0.0 for k in self.k}, |
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"refcocog": {k: 0.0 for k in self.k}, |
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} |
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dataset2count = {"refcoco": 0.0, "refcoco+": 0.0, "refcocog": 0.0} |
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for image_id in self.img_ids: |
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ann_ids = self.refexp_gt.getAnnIds(imgIds=image_id) |
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assert len(ann_ids) == 1 |
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img_info = self.refexp_gt.loadImgs(image_id)[0] |
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target = self.refexp_gt.loadAnns(ann_ids[0]) |
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prediction = self.predictions[image_id] |
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assert prediction is not None |
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sorted_scores_boxes = sorted( |
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zip(prediction["scores"].tolist(), prediction["boxes"].tolist()), reverse=True |
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) |
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sorted_scores, sorted_boxes = zip(*sorted_scores_boxes) |
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sorted_boxes = torch.cat([torch.as_tensor(x).view(1, 4) for x in sorted_boxes]) |
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target_bbox = target[0]["bbox"] |
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converted_bbox = [ |
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target_bbox[0], |
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target_bbox[1], |
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target_bbox[2] + target_bbox[0], |
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target_bbox[3] + target_bbox[1], |
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] |
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giou = generalized_box_iou(sorted_boxes, torch.as_tensor(converted_bbox).view(-1, 4)) |
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for k in self.k: |
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if max(giou[:k]) >= self.thresh_iou: |
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dataset2score[img_info["dataset_name"]][k] += 1.0 |
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dataset2count[img_info["dataset_name"]] += 1.0 |
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for key, value in dataset2score.items(): |
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for k in self.k: |
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try: |
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value[k] /= dataset2count[key] |
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except: |
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pass |
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results = {} |
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for key, value in dataset2score.items(): |
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results[key] = sorted([v for k, v in value.items()]) |
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print(f" Dataset: {key} - Precision @ 1, 5, 10: {results[key]} \n") |
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return results |
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return None |
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