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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmdet3d.evaluation.functional.seg_eval import seg_eval
def test_indoor_eval():
if not torch.cuda.is_available():
pytest.skip()
seg_preds = [
np.array([
0, 0, 1, 0, 0, 2, 1, 3, 1, 2, 1, 0, 2, 2, 2, 2, 1, 3, 0, 3, 3, 4, 0
])
]
gt_labels = [
np.array([
0, 0, 0, 4, 0, 0, 1, 1, 1, 4, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4
])
]
label2cat = {
0: 'car',
1: 'bicycle',
2: 'motorcycle',
3: 'truck',
4: 'unlabeled'
}
ret_value = seg_eval(gt_labels, seg_preds, label2cat, ignore_index=4)
assert np.isclose(ret_value['car'], 0.428571429)
assert np.isclose(ret_value['bicycle'], 0.428571429)
assert np.isclose(ret_value['motorcycle'], 0.6666667)
assert np.isclose(ret_value['truck'], 0.5)
assert np.isclose(ret_value['acc'], 0.65)
assert np.isclose(ret_value['acc_cls'], 0.65)
assert np.isclose(ret_value['miou'], 0.50595238)
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