def coco_eval()

in models/vision/detection/awsdet/core/evaluation/coco_utils.py [0:0]

• 58 lines of code
• 15 McCabe index (conditional complexity)

def coco_eval(result_files,
              result_types,
              coco,
              max_dets=(100, 300, 1000),
              classwise=False):
    for res_type in result_types:
        assert res_type in [
            'proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'
        ]

    if isinstance(coco, str):
        coco = COCO(coco)
    assert isinstance(coco, COCO)

    if result_types == ['proposal_fast']:
        ar = fast_eval_recall(result_files, coco, np.array(max_dets))
        for i, num in enumerate(max_dets):
            print('AR@{}\t= {:.4f}'.format(num, ar[i]))
        return

    for res_type in result_types:
        if isinstance(result_files, str):
            result_file = result_files
        elif isinstance(result_files, dict):
            result_file = result_files[res_type]
        else:
            assert TypeError('result_files must be a str or dict')
        assert result_file.endswith('.json')

        coco_dets = coco.loadRes(result_file)
        img_ids = coco.getImgIds()
        iou_type = 'bbox' if res_type == 'proposal' else res_type
        cocoEval = COCOeval(coco, coco_dets, iou_type)
        cocoEval.params.imgIds = img_ids
        if res_type == 'proposal':
            cocoEval.params.useCats = 0
            cocoEval.params.maxDets = list(max_dets)
        cocoEval.evaluate()
        cocoEval.accumulate()
        cocoEval.summarize()

        if classwise:
            # Compute per-category AP
            # from https://github.com/facebookresearch/detectron2/blob/03064eb5bafe4a3e5750cc7a16672daf5afe8435/detectron2/evaluation/coco_evaluation.py#L259-L283 # noqa
            precisions = cocoEval.eval['precision']
            catIds = coco.getCatIds()
            # precision has dims (iou, recall, cls, area range, max dets)
            assert len(catIds) == precisions.shape[2]

            results_per_category = []
            for idx, catId in enumerate(catIds):
                # area range index 0: all area ranges
                # max dets index -1: typically 100 per image
                nm = coco.loadCats(catId)[0]
                precision = precisions[:, :, idx, 0, -1]
                precision = precision[precision > -1]
                ap = np.mean(precision) if precision.size else float('nan')
                results_per_category.append(
                    ('{}'.format(nm['name']),
                     '{:0.3f}'.format(float(ap * 100))))

            N_COLS = min(6, len(results_per_category) * 2)
            results_flatten = list(itertools.chain(*results_per_category))
            headers = ['category', 'AP'] * (N_COLS // 2)
            results_2d = itertools.zip_longest(
                *[results_flatten[i::N_COLS] for i in range(N_COLS)])
            table_data = [headers]
            table_data += [result for result in results_2d]
            table = AsciiTable(table_data)
            print(table.table)