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import unittest
from io import StringIO
from unittest.mock import patch
import numpy as np
import torch
from mmdet3d.evaluation.metrics import IndoorMetric
from mmdet3d.structures import DepthInstance3DBoxes
class TestIndoorMetric(unittest.TestCase):
@patch('sys.stdout', new_callable=StringIO)
def test_process(self, stdout):
indoor_metric = IndoorMetric()
eval_ann_info = {
'gt_bboxes_3d':
DepthInstance3DBoxes(
torch.tensor([
[2.3578, 1.7841, -0.0987, 0.5532, 0.4948, 0.6474, 0.0000],
[-0.2773, -2.1403, 0.0615, 0.4786, 0.5170, 0.3842, 0.0000],
[0.0259, -2.7954, -0.0157, 0.3869, 0.4361, 0.5229, 0.0000],
[-2.3968, 1.1040, 0.0945, 2.5563, 1.5989, 0.9322, 0.0000],
[
-0.3173, -2.7770, -0.0134, 0.5473, 0.8569, 0.5577,
0.0000
],
[-2.4882, -1.4437, 0.0987, 1.2199, 0.4859, 0.6461, 0.0000],
[-3.4702, -0.1315, 0.2463, 1.3137, 0.8022, 0.4765, 0.0000],
[1.9786, 3.0196, -0.0934, 1.6129, 0.5834, 1.4662, 0.0000],
[2.3835, 2.2691, -0.1376, 0.5197, 0.5099, 0.6896, 0.0000],
[2.5986, -0.5313, 1.4269, 0.0696, 0.2933, 0.3104, 0.0000],
[0.4555, -3.1278, -0.0637, 2.0247, 0.1292, 0.2419, 0.0000],
[0.4655, -3.1941, 0.3769, 2.1132, 0.3536, 1.9803, 0.0000]
])),
'gt_labels_3d':
np.array([2, 2, 2, 3, 4, 17, 4, 7, 2, 8, 17, 11])
}
pred_instances_3d = dict()
pred_instances_3d['scores_3d'] = torch.ones(
len(eval_ann_info['gt_bboxes_3d']))
pred_instances_3d['bboxes_3d'] = eval_ann_info['gt_bboxes_3d']
pred_instances_3d['labels_3d'] = torch.Tensor(
eval_ann_info['gt_labels_3d'])
pred_dict = dict()
pred_dict['pred_instances_3d'] = pred_instances_3d
pred_dict['eval_ann_info'] = eval_ann_info
indoor_metric.dataset_meta = {
'classes': ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin'),
'box_type_3d':
'Depth',
}
indoor_metric.process({}, [pred_dict])
eval_results = indoor_metric.evaluate(1)
for v in eval_results.values():
# map == 1
self.assertEqual(1, v)
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