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import numpy as np |
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import torch |
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from mmdet3d.evaluation import instance_seg_eval |
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def test_instance_seg_eval(): |
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valid_class_ids = (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, |
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36, 39) |
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class_labels = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', |
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'window', 'bookshelf', 'picture', 'counter', 'desk', |
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'curtain', 'refrigerator', 'showercurtrain', 'toilet', |
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'sink', 'bathtub', 'garbagebin') |
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n_points_list = [3300, 3000] |
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gt_labels_list = [[0, 0, 0, 0, 0, 0, 14, 14, 2, 1], |
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[13, 13, 2, 1, 3, 3, 0, 0, 0]] |
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gt_instance_masks = [] |
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gt_semantic_masks = [] |
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pred_instance_masks = [] |
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pred_instance_labels = [] |
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pred_instance_scores = [] |
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for n_points, gt_labels in zip(n_points_list, gt_labels_list): |
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gt_instance_mask = np.ones(n_points, dtype=np.int64) * -1 |
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gt_semantic_mask = np.ones(n_points, dtype=np.int64) * -1 |
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pred_instance_mask = np.ones(n_points, dtype=np.int64) * -1 |
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labels = [] |
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scores = [] |
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for i, gt_label in enumerate(gt_labels): |
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begin = i * 300 |
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end = begin + 300 |
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gt_instance_mask[begin:end] = i |
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gt_semantic_mask[begin:end] = gt_label |
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pred_instance_mask[begin:end] = i |
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labels.append(gt_label) |
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scores.append(.99) |
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gt_instance_masks.append(torch.tensor(gt_instance_mask)) |
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gt_semantic_masks.append(torch.tensor(gt_semantic_mask)) |
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pred_instance_masks.append(torch.tensor(pred_instance_mask)) |
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pred_instance_labels.append(torch.tensor(labels)) |
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pred_instance_scores.append(torch.tensor(scores)) |
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ret_value = instance_seg_eval( |
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gt_semantic_masks=gt_semantic_masks, |
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gt_instance_masks=gt_instance_masks, |
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pred_instance_masks=pred_instance_masks, |
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pred_instance_labels=pred_instance_labels, |
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pred_instance_scores=pred_instance_scores, |
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valid_class_ids=valid_class_ids, |
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class_labels=class_labels) |
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for label in [ |
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'cabinet', 'bed', 'chair', 'sofa', 'showercurtrain', 'toilet' |
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]: |
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metrics = ret_value['classes'][label] |
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assert metrics['ap'] == 1.0 |
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assert metrics['ap50%'] == 1.0 |
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assert metrics['ap25%'] == 1.0 |
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pred_instance_masks[1][2240:2700] = -1 |
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pred_instance_masks[0][2700:3000] = 8 |
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pred_instance_labels[0][9] = 2 |
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ret_value = instance_seg_eval( |
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gt_semantic_masks=gt_semantic_masks, |
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gt_instance_masks=gt_instance_masks, |
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pred_instance_masks=pred_instance_masks, |
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pred_instance_labels=pred_instance_labels, |
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pred_instance_scores=pred_instance_scores, |
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valid_class_ids=valid_class_ids, |
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class_labels=class_labels) |
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assert abs(ret_value['classes']['cabinet']['ap50%'] - 0.72916) < 0.01 |
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assert abs(ret_value['classes']['cabinet']['ap25%'] - 0.88888) < 0.01 |
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assert abs(ret_value['classes']['bed']['ap50%'] - 0.5) < 0.01 |
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assert abs(ret_value['classes']['bed']['ap25%'] - 0.5) < 0.01 |
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assert abs(ret_value['classes']['chair']['ap50%'] - 0.375) < 0.01 |
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assert abs(ret_value['classes']['chair']['ap25%'] - 1.0) < 0.01 |
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