def test_evaluate():
root_path = './tests/data/lyft'
ann_file = './tests/data/lyft/lyft_infos_val.pkl'
lyft_dataset = LyftDataset(ann_file, None, root_path)
results = mmcv.load('./tests/data/lyft/sample_results.pkl')
ap_dict = lyft_dataset.evaluate(results, 'bbox')
car_precision = ap_dict['pts_bbox_Lyft/car_AP']
assert car_precision == 0.6
def test_show():
import mmcv
from os import path as osp
from mmdet3d.core.bbox import LiDARInstance3DBoxes
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/lyft'
ann_file = './tests/data/lyft/lyft_infos.pkl'
class_names = ('car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle',
'motorcycle', 'bicycle', 'pedestrian', 'animal')
eval_pipeline = [
dict(type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=dict(backend='disk')),
dict(type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
kitti_dataset = LyftDataset(ann_file, None, root_path)
boxes_3d = LiDARInstance3DBoxes(
torch.tensor(
[[46.1218, -4.6496, -0.9275, 0.5316, 1.4442, 1.7450, 1.1749],
[33.3189, 0.1981, 0.3136, 0.5656, 1.2301, 1.7985, 1.5723],
[46.1366, -4.6404, -0.9510, 0.5162, 1.6501, 1.7540, 1.3778],
[33.2646, 0.2297, 0.3446, 0.5746, 1.3365, 1.7947, 1.5430],
[58.9079, 16.6272, -1.5829, 1.5656, 3.9313, 1.4899, 1.5505]]))
scores_3d = torch.tensor([0.1815, 0.1663, 0.5792, 0.2194, 0.2780])
labels_3d = torch.tensor([0, 0, 1, 1, 2])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [dict(pts_bbox=result)]
kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
file_name = 'host-a017_lidar1_1236118886901125926'
pts_file_path = osp.join(temp_dir, file_name, f'{file_name}_points.obj')
gt_file_path = osp.join(temp_dir, file_name, f'{file_name}_gt.obj')
pred_file_path = osp.join(temp_dir, file_name, f'{file_name}_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
def test_getitem():
np.random.seed(0)
torch.manual_seed(0)
root_path = './tests/data/lyft'
ann_file = './tests/data/lyft/lyft_infos.pkl'
class_names = ('car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle',
'motorcycle', 'bicycle', 'pedestrian', 'animal')
point_cloud_range = [-80, -80, -10, 80, 80, 10]
pipelines = [
dict(type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(type='LoadPointsFromMultiSweeps',
sweeps_num=2,
file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='GlobalRotScaleTrans',
rot_range=[-0.523599, 0.523599],
scale_ratio_range=[0.85, 1.15],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
lyft_dataset = LyftDataset(ann_file, pipelines, root_path)
data = lyft_dataset[0]
points = data['points']._data
gt_bboxes_3d = data['gt_bboxes_3d']._data
gt_labels_3d = data['gt_labels_3d']._data
pts_filename = data['img_metas']._data['pts_filename']
pcd_horizontal_flip = data['img_metas']._data['pcd_horizontal_flip']
pcd_scale_factor = data['img_metas']._data['pcd_scale_factor']
pcd_rotation = data['img_metas']._data['pcd_rotation']
sample_idx = data['img_metas']._data['sample_idx']
pcd_rotation_expected = np.array([[0.99869376, -0.05109515, 0.],
[0.05109515, 0.99869376, 0.],
[0., 0., 1.]])
assert pts_filename == \
'tests/data/lyft/lidar/host-a017_lidar1_1236118886901125926.bin'
assert pcd_horizontal_flip is True
assert abs(pcd_scale_factor - 1.0645568099117257) < 1e-5
assert np.allclose(pcd_rotation, pcd_rotation_expected, 1e-3)
assert sample_idx == \
'b98a05255ba2632e957884758cb31f0e6fcc8d3cd6ee76b6d0ba55b72f08fc54'
expected_points = torch.tensor([[61.4785, -3.7393, 6.7699, 0.4001],
[47.7904, -3.9887, 6.0926, 0.0000],
[52.5683, -4.2178, 6.7179, 0.0000],
[52.4867, -4.0315, 6.7057, 0.0000],
[59.8372, -1.7366, 6.5864, 0.4001],
[53.0842, -3.7064, 6.7811, 0.0000],
[60.5549, -3.4978, 6.6578, 0.4001],
[59.1695, -1.2910, 7.0296, 0.2000],
[53.0702, -3.8868, 6.7807, 0.0000],
[47.9579, -4.1648, 5.6219, 0.2000],
[59.8226, -1.5522, 6.5867, 0.4001],
[61.2858, -4.2254, 7.3089, 0.2000],
[49.9896, -4.5202, 5.8823, 0.2000],
[61.4597, -4.6402, 7.3340, 0.2000],
[59.8244, -1.3499, 6.5895, 0.4001]])
expected_gt_bboxes_3d = torch.tensor(
[[63.2257, 17.5206, -0.6307, 2.0109, 5.1652, 1.9471, -1.5868],
[-25.3804, 27.4598, -2.3297, 2.7412, 8.4792, 3.4343, -1.5939],
[-15.2098, -7.0109, -2.2566, 0.7931, 0.8410, 1.7916, 1.5090]])
expected_gt_labels = np.array([0, 4, 7])
original_classes = lyft_dataset.CLASSES
assert torch.allclose(points, expected_points, 1e-2)
assert torch.allclose(gt_bboxes_3d.tensor, expected_gt_bboxes_3d, 1e-3)
assert np.all(gt_labels_3d.numpy() == expected_gt_labels)
assert original_classes == class_names
lyft_dataset = LyftDataset(ann_file,
None,
root_path,
classes=['car', 'pedestrian'])
assert lyft_dataset.CLASSES != original_classes
assert lyft_dataset.CLASSES == ['car', 'pedestrian']
lyft_dataset = LyftDataset(ann_file,
None,
root_path,
classes=('car', 'pedestrian'))
assert lyft_dataset.CLASSES != original_classes
assert lyft_dataset.CLASSES == ('car', 'pedestrian')
import tempfile
tmp_file = tempfile.NamedTemporaryFile()
with open(tmp_file.name, 'w') as f:
f.write('car\npedestrian\n')
lyft_dataset = LyftDataset(ann_file,
None,
root_path,
classes=tmp_file.name)
assert lyft_dataset.CLASSES != original_classes
assert lyft_dataset.CLASSES == ['car', 'pedestrian']