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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmdet3d.evaluation.functional.panoptic_seg_eval import panoptic_seg_eval
def test_panoptic_seg_eval():
if not torch.cuda.is_available():
pytest.skip()
classes = ['unlabeled', 'person', 'dog', 'grass', 'sky']
label2cat = {
0: 'unlabeled',
1: 'person',
2: 'dog',
3: 'grass',
4: 'sky',
}
thing_classes = ['person', 'dog']
stuff_classes = ['grass', 'sky']
ignore_index = [0] # only ignore ignore class
min_points = 1 # for this example we care about all points
offset = 2**16
# generate ground truth and prediction
semantic_preds = []
instance_preds = []
gt_semantic = []
gt_instance = []
# some ignore stuff
num_ignore = 50
semantic_preds.extend([0 for i in range(num_ignore)])
instance_preds.extend([0 for i in range(num_ignore)])
gt_semantic.extend([0 for i in range(num_ignore)])
gt_instance.extend([0 for i in range(num_ignore)])
# grass segment
num_grass = 50
num_grass_pred = 40 # rest is sky
semantic_preds.extend([1 for i in range(num_grass_pred)]) # grass
semantic_preds.extend([2
for i in range(num_grass - num_grass_pred)]) # sky
instance_preds.extend([0 for i in range(num_grass)])
gt_semantic.extend([1 for i in range(num_grass)]) # grass
gt_instance.extend([0 for i in range(num_grass)])
# sky segment
num_sky = 50
num_sky_pred = 40 # rest is grass
semantic_preds.extend([2 for i in range(num_sky_pred)]) # sky
semantic_preds.extend([1 for i in range(num_sky - num_sky_pred)]) # grass
instance_preds.extend([0 for i in range(num_sky)]) # first instance
gt_semantic.extend([2 for i in range(num_sky)]) # sky
gt_instance.extend([0 for i in range(num_sky)]) # first instance
# wrong dog as person prediction
num_dog = 50
num_person = num_dog
semantic_preds.extend([3 for i in range(num_person)])
instance_preds.extend([35 for i in range(num_person)])
gt_semantic.extend([4 for i in range(num_dog)])
gt_instance.extend([22 for i in range(num_dog)])
# two persons in prediction, but three in gt
num_person = 50
semantic_preds.extend([3 for i in range(6 * num_person)])
instance_preds.extend([8 for i in range(4 * num_person)])
instance_preds.extend([95 for i in range(2 * num_person)])
gt_semantic.extend([3 for i in range(6 * num_person)])
gt_instance.extend([33 for i in range(3 * num_person)])
gt_instance.extend([42 for i in range(num_person)])
gt_instance.extend([11 for i in range(2 * num_person)])
# gt and pred to numpy
semantic_preds = np.array(semantic_preds, dtype=int).reshape(1, -1)
instance_preds = np.array(instance_preds, dtype=int).reshape(1, -1)
gt_semantic = np.array(gt_semantic, dtype=int).reshape(1, -1)
gt_instance = np.array(gt_instance, dtype=int).reshape(1, -1)
gt_labels = [{
'pts_semantic_mask': gt_semantic,
'pts_instance_mask': gt_instance
}]
seg_preds = [{
'pts_semantic_mask': semantic_preds,
'pts_instance_mask': instance_preds
}]
ret_value = panoptic_seg_eval(gt_labels, seg_preds, classes, thing_classes,
stuff_classes, min_points, offset, label2cat,
ignore_index)
assert np.isclose(ret_value['pq'], 0.47916666666666663)
assert np.isclose(ret_value['rq_mean'], 0.6666666666666666)
assert np.isclose(ret_value['sq_mean'], 0.5520833333333333)
assert np.isclose(ret_value['miou'], 0.5476190476190476)
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