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Commit d7f69840 authored by liyinhao's avatar liyinhao
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finish test_indoor_eval

parent 2b3998cf
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Showing with 211 additions and 11 deletions
mmdet3d/core/evaluation/indoor_eval.py
View file @ d7f69840
......@@ -199,8 +199,9 @@ def eval_map_recall(det_infos, gt_infos, ovthresh=None):
for multiple classes.
Args:
det_infos (List): Label, bbox and score of the detection result.
gt_infos (List): Label, bbox of the groundtruth.
det_infos (List[ListList[[tuple]]]): Label, bbox and
score of the detection result.
gt_infos (List[List[List]]): Label, bbox of the groundtruth.
ovthresh (List[float]): iou threshold.
Default: None.
......@@ -253,9 +254,9 @@ def eval_map_recall(det_infos, gt_infos, ovthresh=None):
recall[iou_idx][label], precision[iou_idx][label], ap[iou_idx][
label] = ret_values[i][iou_idx]
else:
recall[iou_idx][label] = 0
precision[iou_idx][label] = 0
ap[iou_idx][label] = 0
recall[iou_idx][label] = [0]
precision[iou_idx][label] = [0]
ap[iou_idx][label] = [0]
return recall, precision, ap
......@@ -266,8 +267,8 @@ def indoor_eval(gt_annos, dt_annos, metric, label2cat):
Evaluate the result of the detection.
Args:
gt_annos (List): GT annotations.
dt_annos (List): Detection annotations.
gt_annos (List[List[dict]]): GT annotations.
dt_annos (List[List[List[tuple]]]): Detection annotations.
metric (List[float]): AP IoU thresholds.
label2cat (dict): {label: cat}.
......@@ -294,11 +295,14 @@ def indoor_eval(gt_annos, dt_annos, metric, label2cat):
rec, prec, ap = eval_map_recall(dt_annos, gt_infos, metric)
ret_dict = {}
for i, iou_thresh in enumerate(metric):
rec_list = []
for label in ap[i].keys():
ret_dict[f'{label2cat[label]}_AP_{iou_thresh:.2f}'] = ap[i][label]
ret_dict[f'mAP_{iou_thresh:.2f}'] = sum(ap[i].values()) / len(ap[i])
ret_dict[f'{label2cat[label]}_AP_{iou_thresh:.2f}'] = ap[i][label][
0]
ret_dict[f'mAP_{iou_thresh:.2f}'] = np.mean(list(ap[i].values()))
for label in rec[i].keys():
ret_dict[f'{label2cat[label]}_rec_{iou_thresh:.2f}'] = rec[i][
label]
ret_dict[f'mAR_{iou_thresh:.2f}'] = sum(rec[i].values()) / len(rec[i])
label][-1]
rec_list.append(rec[i][label][-1])
ret_dict[f'mAR_{iou_thresh:.2f}'] = np.mean(rec_list)
return ret_dict
tests/test_dataset/test_indoor_eval.py 0 → 100644
View file @ d7f69840
import numpy as np
import torch
from mmdet3d.core.evaluation.indoor_eval import indoor_eval
def test_indoor_eval():
det_infos = [[[[
17.0,
[
3.2112048e+00, 5.6918913e-01, -8.6143613e-04, 1.1942449e-01,
1.2988183e+00, 1.9952521e-01, 0.0000000e+00
],
torch.as_tensor(0.9965866)
],
[
17.0,
[
3.248133, 0.4324184, 0.20038621, 0.17225507,
1.2736976, 0.32598814, 0.
],
torch.as_tensor(0.99507546)
],
[
3.0,
[
-1.2793612, -2.3155289, 0.15598366, 1.2822601,
2.2253945, 0.8361754, 0.
],
torch.as_tensor(0.9916463)
],
[
4.0,
[
2.8716104, -0.26416883, -0.04933786, 0.8190681,
0.60294986, 0.5769499, 0.
],
torch.as_tensor(0.9702634)
],
[
17.0,
[
-2.2109854, 0.19445783, -0.01614259, 0.40659013,
0.35370222, 0.3290567, 0.
],
torch.as_tensor(0.95803124)
],
[
4.0,
[
0.18409574, -3.3322976, 0.13188198, 0.960528,
0.91082716, 0.59325826, 0.
],
torch.as_tensor(0.9483817)
],
[
17.0,
[
1.9499326, 2.0099056, 0.32836294, 0.98528206,
1.0611539, 1.2197046, 0.
],
torch.as_tensor(0.92196786)
],
[
2.0,
[
-1.6204697, 2.3374724, 0.06042781, 0.49681002,
0.44362187, 0.47277915, 0.
],
torch.as_tensor(0.87960094)
],
[
17.0,
[
2.1414487, -1.7601899, 0.17694443, 1.0071366,
2.211764, 1.4690719, 0.
],
torch.as_tensor(0.8586809)
],
[
17.0,
[
-0.0484907, -3.639972, 0.41367513, 3.948648,
1.3692774, 1.0810001, 0.
],
torch.as_tensor(0.80680436)
]]]]
label2cat = {
0: 'cabinet',
1: 'bed',
2: 'chair',
3: 'sofa',
4: 'table',
5: 'door',
6: 'window',
7: 'bookshelf',
8: 'picture',
9: 'counter',
10: 'desk',
11: 'curtain',
12: 'refrigerator',
13: 'showercurtrain',
14: 'toilet',
15: 'sink',
16: 'bathtub',
17: 'garbagebin'
}
gt_annos = [{
'gt_num':
12,
'name': [
'table', 'curtain', 'sofa', 'bookshelf', 'picture', 'chair',
'chair', 'garbagebin', 'table', 'chair', 'chair', 'garbagebin'
],
'gt_boxes_upright_depth':
np.array([[
3.48649406, 0.24238291, 0.48358256, 1.34014034, 0.72744983,
0.40819243
],
[
-0.50371504, 3.25293231, 1.25988698, 2.12330937,
0.27563906, 1.80230701
],
[
2.58820581, -0.99452347, 0.57732373, 2.94801593,
1.67463434, 0.88743341
],
[
-1.9116497, -2.88811016, 0.70502496, 1.62386703,
0.60732293, 1.5857985
],
[
-2.55324745, 0.6909315, 1.59045517, 0.07264495,
0.32018459, 0.3506999
],
[
-2.3436017, -2.1659112, 0.254318, 0.5333302, 0.56154585,
0.64904487
],
[
-2.32046795, -1.6880455, 0.26138437, 0.5586133,
0.59743834, 0.6378752
],
[
-0.46495372, 3.22126102, 0.03188983, 1.92557108,
0.15160203, 0.24680007
],
[
0.28087699, 2.88433838, 0.2495866, 0.57001019,
0.85177159, 0.5689255
],
[
-0.05292395, 2.90586925, 0.23064148, 0.39113954,
0.43746281, 0.52981442
],
[
0.25537968, 2.25156307, 0.24932587, 0.48192862,
0.51398182, 0.38040417
],
[
2.60432816, 1.62303996, 0.42025632, 1.23775268,
0.51761389, 0.66034317
]]),
'class': [4, 11, 3, 7, 8, 2, 2, 17, 4, 2, 2, 17]
}]
ret_value = indoor_eval(gt_annos, det_infos, [0.25, 0.5], label2cat)
garbagebin_AP_25 = ret_value['garbagebin_AP_0.25']
sofa_AP_25 = ret_value['sofa_AP_0.25']
table_AP_25 = ret_value['table_AP_0.25']
chair_AP_25 = ret_value['chair_AP_0.25']
mAP_25 = ret_value['mAP_0.25']
garbagebin_rec_25 = ret_value['garbagebin_rec_0.25']
sofa_rec_25 = ret_value['sofa_rec_0.25']
table_rec_25 = ret_value['table_rec_0.25']
chair_rec_25 = ret_value['chair_rec_0.25']
mAR_25 = ret_value['mAR_0.25']
table_AP_50 = ret_value['table_AP_0.50']
mAP_50 = ret_value['mAP_0.50']
table_rec_50 = ret_value['table_rec_0.50']
mAR_50 = ret_value['mAR_0.50']
assert garbagebin_AP_25 == 0.5
assert sofa_AP_25 == 1.0
assert table_AP_25 == 1.0
assert chair_AP_25 == 0.25
assert abs(mAP_25 - 0.392857) < 0.001
assert garbagebin_rec_25 == 0.5
assert sofa_rec_25 == 1.0
assert table_rec_25 == 1.0
assert chair_rec_25 == 0.25
assert abs(mAR_25 - 0.392857) < 0.001
assert table_AP_50 == 0.5
assert abs(mAP_50 - 0.0714) < 0.001
assert table_rec_50 == 0.5
assert abs(mAR_50 - 0.0714) < 0.001
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