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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmdet3d.evaluation import do_eval, eval_class, kitti_eval
def test_do_eval():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and CUDA')
gt_name = np.array(
['Pedestrian', 'Cyclist', 'Car', 'Car', 'Car', 'DontCare', 'DontCare'])
gt_truncated = np.array([0., 0., 0., -1., -1., -1., -1.])
gt_occluded = np.array([0, 0, 3, -1, -1, -1, -1])
gt_alpha = np.array([-1.57, 1.85, -1.65, -10., -10., -10., -10.])
gt_bbox = np.array([[674.9179, 165.48549, 693.23694, 193.42134],
[676.21954, 165.70988, 691.63745, 193.83748],
[389.4093, 182.48041, 421.49072, 202.13422],
[232.0577, 186.16724, 301.94623, 217.4024],
[758.6537, 172.98509, 816.32434, 212.76743],
[532.37, 176.35, 542.68, 185.27],
[559.62, 175.83, 575.4, 183.15]])
gt_dimensions = np.array([[12.34, 2.85, 2.63], [3.69, 1.67, 1.87],
[2.02, 1.86, 0.6], [-1., -1., -1.],
[-1., -1., -1.], [-1., -1., -1.],
[-1., -1., -1.]])
gt_location = np.array([[4.700e-01, 1.490e+00, 6.944e+01],
[-1.653e+01, 2.390e+00, 5.849e+01],
[4.590e+00, 1.320e+00, 4.584e+01],
[-1.000e+03, -1.000e+03, -1.000e+03],
[-1.000e+03, -1.000e+03, -1.000e+03],
[-1.000e+03, -1.000e+03, -1.000e+03],
[-1.000e+03, -1.000e+03, -1.000e+03]])
gt_rotation_y = [-1.56, 1.57, -1.55, -10., -10., -10., -10.]
gt_anno = dict(
name=gt_name,
truncated=gt_truncated,
occluded=gt_occluded,
alpha=gt_alpha,
bbox=gt_bbox,
dimensions=gt_dimensions,
location=gt_location,
rotation_y=gt_rotation_y)
dt_name = np.array(['Pedestrian', 'Cyclist', 'Car', 'Car', 'Car'])
dt_truncated = np.array([0., 0., 0., 0., 0.])
dt_occluded = np.array([0, 0, 0, 0, 0])
dt_alpha = np.array([1.0744612, 1.2775835, 1.82563, 2.1145396, -1.7676563])
dt_dimensions = np.array([[1.4441837, 1.7450154, 0.53160036],
[1.6501029, 1.7540325, 0.5162356],
[3.9313498, 1.4899347, 1.5655756],
[4.0111866, 1.5350999, 1.585221],
[3.7337692, 1.5117968, 1.5515774]])
dt_location = np.array([[4.6671643, 1.285098, 45.836895],
[4.658241, 1.3088846, 45.85148],
[-16.598526, 2.298814, 58.618088],
[-18.629122, 2.2990575, 39.305355],
[7.0964046, 1.5178275, 29.32426]])
dt_rotation_y = np.array(
[1.174933, 1.3778262, 1.550529, 1.6742425, -1.5330327])
dt_bbox = np.array([[674.9179, 165.48549, 693.23694, 193.42134],
[676.21954, 165.70988, 691.63745, 193.83748],
[389.4093, 182.48041, 421.49072, 202.13422],
[232.0577, 186.16724, 301.94623, 217.4024],
[758.6537, 172.98509, 816.32434, 212.76743]])
dt_score = np.array(
[0.18151495, 0.57920843, 0.27795696, 0.23100418, 0.21541929])
dt_anno = dict(
name=dt_name,
truncated=dt_truncated,
occluded=dt_occluded,
alpha=dt_alpha,
bbox=dt_bbox,
dimensions=dt_dimensions,
location=dt_location,
rotation_y=dt_rotation_y,
score=dt_score)
current_classes = [1, 2, 0]
min_overlaps = np.array([[[0.5, 0.5, 0.7], [0.5, 0.5, 0.7],
[0.5, 0.5, 0.7]],
[[0.5, 0.5, 0.7], [0.25, 0.25, 0.5],
[0.25, 0.25, 0.5]]])
eval_types = ['bbox', 'bev', '3d', 'aos']
mAP11_bbox, mAP11_bev, mAP11_3d, mAP11_aos, mAP40_bbox,\
mAP40_bev, mAP40_3d, mAP40_aos = do_eval([gt_anno], [dt_anno],
current_classes, min_overlaps,
eval_types)
expected_mAP11_bbox = np.array([[[0., 0.], [9.09090909, 9.09090909],
[9.09090909, 9.09090909]],
[[0., 0.], [9.09090909, 9.09090909],
[9.09090909, 9.09090909]],
[[0., 0.], [9.09090909, 9.09090909],
[9.09090909, 9.09090909]]])
expected_mAP40_bbox = np.array([[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [2.5, 2.5], [2.5, 2.5]]])
expected_mAP11_bev = np.array([[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.]]])
expected_mAP40_bev = np.array([[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.]]])
expected_mAP11_3d = np.array([[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.]]])
expected_mAP40_3d = np.array([[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.]]])
expected_mAP11_aos = np.array([[[0., 0.], [0.55020816, 0.55020816],
[0.55020816, 0.55020816]],
[[0., 0.], [8.36633862, 8.36633862],
[8.36633862, 8.36633862]],
[[0., 0.], [8.63476893, 8.63476893],
[8.63476893, 8.63476893]]])
expected_mAP40_aos = np.array([[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [1.58140643, 1.58140643],
[1.58140643, 1.58140643]]])
assert np.allclose(mAP11_bbox, expected_mAP11_bbox)
assert np.allclose(mAP11_bev, expected_mAP11_bev)
assert np.allclose(mAP11_3d, expected_mAP11_3d)
assert np.allclose(mAP11_aos, expected_mAP11_aos)
assert np.allclose(mAP40_bbox, expected_mAP40_bbox)
assert np.allclose(mAP40_bev, expected_mAP40_bev)
assert np.allclose(mAP40_3d, expected_mAP40_3d)
assert np.allclose(mAP40_aos, expected_mAP40_aos)
def test_kitti_eval():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and CUDA')
gt_name = np.array(
['Pedestrian', 'Cyclist', 'Car', 'Car', 'Car', 'DontCare', 'DontCare'])
gt_truncated = np.array([0., 0., 0., -1., -1., -1., -1.])
gt_occluded = np.array([0, 0, 3, -1, -1, -1, -1])
gt_alpha = np.array([-1.57, 1.85, -1.65, -10., -10., -10., -10.])
gt_bbox = np.array([[674.9179, 165.48549, 693.23694, 193.42134],
[676.21954, 165.70988, 691.63745, 193.83748],
[389.4093, 182.48041, 421.49072, 202.13422],
[232.0577, 186.16724, 301.94623, 217.4024],
[758.6537, 172.98509, 816.32434, 212.76743],
[532.37, 176.35, 542.68, 185.27],
[559.62, 175.83, 575.4, 183.15]])
gt_dimensions = np.array([[12.34, 2.85, 2.63], [3.69, 1.67, 1.87],
[2.02, 1.86, 0.6], [-1., -1., -1.],
[-1., -1., -1.], [-1., -1., -1.],
[-1., -1., -1.]])
gt_location = np.array([[4.700e-01, 1.490e+00, 6.944e+01],
[-1.653e+01, 2.390e+00, 5.849e+01],
[4.590e+00, 1.320e+00, 4.584e+01],
[-1.000e+03, -1.000e+03, -1.000e+03],
[-1.000e+03, -1.000e+03, -1.000e+03],
[-1.000e+03, -1.000e+03, -1.000e+03],
[-1.000e+03, -1.000e+03, -1.000e+03]])
gt_rotation_y = [-1.56, 1.57, -1.55, -10., -10., -10., -10.]
gt_anno = dict(
name=gt_name,
truncated=gt_truncated,
occluded=gt_occluded,
alpha=gt_alpha,
bbox=gt_bbox,
dimensions=gt_dimensions,
location=gt_location,
rotation_y=gt_rotation_y)
dt_name = np.array(['Pedestrian', 'Cyclist', 'Car', 'Car', 'Car'])
dt_truncated = np.array([0., 0., 0., 0., 0.])
dt_occluded = np.array([0, 0, 0, 0, 0])
dt_alpha = np.array([1.0744612, 1.2775835, 1.82563, 2.1145396, -1.7676563])
dt_dimensions = np.array([[1.4441837, 1.7450154, 0.53160036],
[1.6501029, 1.7540325, 0.5162356],
[3.9313498, 1.4899347, 1.5655756],
[4.0111866, 1.5350999, 1.585221],
[3.7337692, 1.5117968, 1.5515774]])
dt_location = np.array([[4.6671643, 1.285098, 45.836895],
[4.658241, 1.3088846, 45.85148],
[-16.598526, 2.298814, 58.618088],
[-18.629122, 2.2990575, 39.305355],
[7.0964046, 1.5178275, 29.32426]])
dt_rotation_y = np.array(
[1.174933, 1.3778262, 1.550529, 1.6742425, -1.5330327])
dt_bbox = np.array([[674.9179, 165.48549, 693.23694, 193.42134],
[676.21954, 165.70988, 691.63745, 193.83748],
[389.4093, 182.48041, 421.49072, 202.13422],
[232.0577, 186.16724, 301.94623, 217.4024],
[758.6537, 172.98509, 816.32434, 212.76743]])
dt_score = np.array(
[0.18151495, 0.57920843, 0.27795696, 0.23100418, 0.21541929])
dt_anno = dict(
name=dt_name,
truncated=dt_truncated,
occluded=dt_occluded,
alpha=dt_alpha,
bbox=dt_bbox,
dimensions=dt_dimensions,
location=dt_location,
rotation_y=dt_rotation_y,
score=dt_score)
current_classes = [1, 2, 0]
result, ret_dict = kitti_eval([gt_anno], [dt_anno], current_classes)
assert np.isclose(ret_dict['KITTI/Overall_2D_AP11_moderate'],
9.090909090909092)
assert np.isclose(ret_dict['KITTI/Overall_2D_AP11_hard'],
9.090909090909092)
assert np.isclose(ret_dict['KITTI/Overall_2D_AP40_moderate'],
0.8333333333333334)
assert np.isclose(ret_dict['KITTI/Overall_2D_AP40_hard'],
0.8333333333333334)
def test_eval_class():
gt_name = np.array(
['Pedestrian', 'Cyclist', 'Car', 'Car', 'Car', 'DontCare', 'DontCare'])
gt_truncated = np.array([0., 0., 0., -1., -1., -1., -1.])
gt_occluded = np.array([0, 0, 3, -1, -1, -1, -1])
gt_alpha = np.array([-1.57, 1.85, -1.65, -10., -10., -10., -10.])
gt_bbox = np.array([[674.9179, 165.48549, 693.23694, 193.42134],
[676.21954, 165.70988, 691.63745, 193.83748],
[389.4093, 182.48041, 421.49072, 202.13422],
[232.0577, 186.16724, 301.94623, 217.4024],
[758.6537, 172.98509, 816.32434, 212.76743],
[532.37, 176.35, 542.68, 185.27],
[559.62, 175.83, 575.4, 183.15]])
gt_anno = dict(
name=gt_name,
truncated=gt_truncated,
occluded=gt_occluded,
alpha=gt_alpha,
bbox=gt_bbox)
dt_name = np.array(['Pedestrian', 'Cyclist', 'Car', 'Car', 'Car'])
dt_truncated = np.array([0., 0., 0., 0., 0.])
dt_occluded = np.array([0, 0, 0, 0, 0])
dt_alpha = np.array([1.0744612, 1.2775835, 1.82563, 2.1145396, -1.7676563])
dt_bbox = np.array([[674.9179, 165.48549, 693.23694, 193.42134],
[676.21954, 165.70988, 691.63745, 193.83748],
[389.4093, 182.48041, 421.49072, 202.13422],
[232.0577, 186.16724, 301.94623, 217.4024],
[758.6537, 172.98509, 816.32434, 212.76743]])
dt_score = np.array(
[0.18151495, 0.57920843, 0.27795696, 0.23100418, 0.21541929])
dt_anno = dict(
name=dt_name,
truncated=dt_truncated,
occluded=dt_occluded,
alpha=dt_alpha,
bbox=dt_bbox,
score=dt_score)
current_classes = [1, 2, 0]
difficultys = [0, 1, 2]
metric = 0
min_overlaps = np.array([[[0.5, 0.5, 0.7], [0.5, 0.5, 0.7],
[0.5, 0.5, 0.7]],
[[0.5, 0.5, 0.7], [0.25, 0.25, 0.5],
[0.25, 0.25, 0.5]]])
ret_dict = eval_class([gt_anno], [dt_anno], current_classes, difficultys,
metric, min_overlaps, True, 1)
recall_sum = np.sum(ret_dict['recall'])
precision_sum = np.sum(ret_dict['precision'])
orientation_sum = np.sum(ret_dict['orientation'])
assert np.isclose(recall_sum, 16)
assert np.isclose(precision_sum, 16)
assert np.isclose(orientation_sum, 10.252829201850309)
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