Skip to content

GitLab

"git@developer.sourcefind.cn:change/sglang.git" did not exist on "1998ce40466577a9563149e1449f997dcd162cee"
Commit 7d68f829 authored by liyinhao's avatar liyinhao
Browse files

add two pipelines to the uninttest, change label shape and mask shape, delete init file

parent 2d3eac30
Hide whitespace changes
Inline Side-by-side
Showing with 60 additions and 34 deletions
mmdet3d/datasets/pipelines/formating.py
View file @ 7d68f829
...@@ -127,8 +127,9 @@ class DefaultFormatBundle3D(DefaultFormatBundle): ...@@ -127,8 +127,9 @@ class DefaultFormatBundle3D(DefaultFormatBundle):
gt_bboxes_3d_mask = results['gt_bboxes_3d_mask'] gt_bboxes_3d_mask = results['gt_bboxes_3d_mask']
results['gt_bboxes_3d'] = results['gt_bboxes_3d'][ results['gt_bboxes_3d'] = results['gt_bboxes_3d'][
gt_bboxes_3d_mask] gt_bboxes_3d_mask]
results['gt_names_3d'] = results['gt_names_3d'][ if 'gt_names_3d' in results:
gt_bboxes_3d_mask] results['gt_names_3d'] = results['gt_names_3d'][
gt_bboxes_3d_mask]
if 'gt_bboxes_mask' in results: if 'gt_bboxes_mask' in results:
gt_bboxes_mask = results['gt_bboxes_mask'] gt_bboxes_mask = results['gt_bboxes_mask']
if 'gt_bboxes' in results: if 'gt_bboxes' in results:
...@@ -151,10 +152,12 @@ class DefaultFormatBundle3D(DefaultFormatBundle): ...@@ -151,10 +152,12 @@ class DefaultFormatBundle3D(DefaultFormatBundle):
dtype=np.int64) dtype=np.int64)
# we still assume one pipeline for one frame LiDAR # we still assume one pipeline for one frame LiDAR
# thus, the 3D name is list[string] # thus, the 3D name is list[string]
results['gt_labels_3d'] = np.array([ if 'gt_names_3d' in results:
self.class_names.index(n) for n in results['gt_names_3d'] results['gt_labels_3d'] = np.array([
], self.class_names.index(n)
dtype=np.int64) for n in results['gt_names_3d']
],
dtype=np.int64)
results = super(DefaultFormatBundle3D, self).__call__(results) results = super(DefaultFormatBundle3D, self).__call__(results)
return results return results
......
tests/test_pipeline/test_indoor_loading.py
View file @ 7d68f829
...@@ -39,15 +39,16 @@ def test_load_annotations3D(): ...@@ -39,15 +39,16 @@ def test_load_annotations3D():
sunrgbd_info = mmcv.load('./tests/data/sunrgbd/sunrgbd_infos.pkl')[0] sunrgbd_info = mmcv.load('./tests/data/sunrgbd/sunrgbd_infos.pkl')[0]
if sunrgbd_info['annos']['gt_num'] != 0: if sunrgbd_info['annos']['gt_num'] != 0:
sunrgbd_gt_bboxes_3d = sunrgbd_info['annos']['gt_boxes_upright_depth'] sunrgbd_gt_bboxes_3d = sunrgbd_info['annos']['gt_boxes_upright_depth']
sunrgbd_gt_labels = sunrgbd_info['annos']['class'].reshape(-1, 1) sunrgbd_gt_labels = sunrgbd_info['annos']['class']
sunrgbd_gt_bboxes_3d_mask = np.ones_like(sunrgbd_gt_labels) sunrgbd_gt_bboxes_3d_mask = np.ones_like(sunrgbd_gt_labels).astype(
np.bool)
else: else:
sunrgbd_gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32) sunrgbd_gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32)
sunrgbd_gt_labels = np.zeros((1, 1)) sunrgbd_gt_labels = np.zeros((1, ))
sunrgbd_gt_bboxes_3d_mask = np.zeros((1, 1)) sunrgbd_gt_bboxes_3d_mask = np.zeros((1, ))
assert sunrgbd_gt_bboxes_3d.shape == (3, 7) assert sunrgbd_gt_bboxes_3d.shape == (3, 7)
assert sunrgbd_gt_labels.shape == (3, 1) assert sunrgbd_gt_labels.shape == (3, )
assert sunrgbd_gt_bboxes_3d_mask.shape == (3, 1) assert sunrgbd_gt_bboxes_3d_mask.shape == (3, )
scannet_info = mmcv.load('./tests/data/scannet/scannet_infos.pkl')[0] scannet_info = mmcv.load('./tests/data/scannet/scannet_infos.pkl')[0]
scannet_load_annotations3D = IndoorLoadAnnotations3D() scannet_load_annotations3D = IndoorLoadAnnotations3D()
...@@ -55,12 +56,13 @@ def test_load_annotations3D(): ...@@ -55,12 +56,13 @@ def test_load_annotations3D():
data_path = './tests/data/scannet/scannet_train_instance_data' data_path = './tests/data/scannet/scannet_train_instance_data'
if scannet_info['annos']['gt_num'] != 0: if scannet_info['annos']['gt_num'] != 0:
scannet_gt_bboxes_3d = scannet_info['annos']['gt_boxes_upright_depth'] scannet_gt_bboxes_3d = scannet_info['annos']['gt_boxes_upright_depth']
scannet_gt_labels = scannet_info['annos']['class'].reshape(-1, 1) scannet_gt_labels = scannet_info['annos']['class']
scannet_gt_bboxes_3d_mask = np.ones_like(scannet_gt_labels) scannet_gt_bboxes_3d_mask = np.ones_like(scannet_gt_labels).astype(
np.bool)
else: else:
scannet_gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32) scannet_gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32)
scannet_gt_labels = np.zeros((1, 1)) scannet_gt_labels = np.zeros((1, ))
scannet_gt_bboxes_3d_mask = np.zeros((1, 1)) scannet_gt_bboxes_3d_mask = np.zeros((1, )).astype(np.bool)
scan_name = scannet_info['point_cloud']['lidar_idx'] scan_name = scannet_info['point_cloud']['lidar_idx']
scannet_results['pts_instance_mask_path'] = osp.join( scannet_results['pts_instance_mask_path'] = osp.join(
data_path, f'{scan_name}_ins_label.npy') data_path, f'{scan_name}_ins_label.npy')
...@@ -76,7 +78,7 @@ def test_load_annotations3D(): ...@@ -76,7 +78,7 @@ def test_load_annotations3D():
scannet_pts_instance_mask = scannet_results['pts_instance_mask'] scannet_pts_instance_mask = scannet_results['pts_instance_mask']
scannet_pts_semantic_mask = scannet_results['pts_semantic_mask'] scannet_pts_semantic_mask = scannet_results['pts_semantic_mask']
assert scannet_gt_boxes.shape == (27, 6) assert scannet_gt_boxes.shape == (27, 6)
assert scannet_gt_lbaels.shape == (27, 1) assert scannet_gt_lbaels.shape == (27, )
assert scannet_gt_boxes_mask.shape == (27, 1) assert scannet_gt_boxes_mask.shape == (27, )
assert scannet_pts_instance_mask.shape == (100, ) assert scannet_pts_instance_mask.shape == (100, )
assert scannet_pts_semantic_mask.shape == (100, ) assert scannet_pts_semantic_mask.shape == (100, )
tests/test_pipeline/test_indoor_pipeline.py
View file @ 7d68f829
...@@ -7,6 +7,11 @@ from mmdet3d.datasets.pipelines import Compose ...@@ -7,6 +7,11 @@ from mmdet3d.datasets.pipelines import Compose
def test_scannet_pipeline(): def test_scannet_pipeline():
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
np.random.seed(0) np.random.seed(0)
pipelines = [ pipelines = [
dict( dict(
...@@ -21,7 +26,14 @@ def test_scannet_pipeline(): ...@@ -21,7 +26,14 @@ def test_scannet_pipeline():
type='IndoorGlobalRotScale', type='IndoorGlobalRotScale',
use_height=True, use_height=True,
rot_range=[-np.pi * 1 / 36, np.pi * 1 / 36], rot_range=[-np.pi * 1 / 36, np.pi * 1 / 36],
scale_range=None) scale_range=None),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels', 'pts_semantic_mask',
'pts_instance_mask'
]),
] ]
pipeline = Compose(pipelines) pipeline = Compose(pipelines)
info = mmcv.load('./tests/data/scannet/scannet_infos.pkl')[0] info = mmcv.load('./tests/data/scannet/scannet_infos.pkl')[0]
...@@ -32,13 +44,15 @@ def test_scannet_pipeline(): ...@@ -32,13 +44,15 @@ def test_scannet_pipeline():
results['pts_filename'] = osp.join(data_path, f'{scan_name}_vert.npy') results['pts_filename'] = osp.join(data_path, f'{scan_name}_vert.npy')
if info['annos']['gt_num'] != 0: if info['annos']['gt_num'] != 0:
scannet_gt_bboxes_3d = info['annos']['gt_boxes_upright_depth'] scannet_gt_bboxes_3d = info['annos']['gt_boxes_upright_depth']
scannet_gt_labels = info['annos']['class'].reshape(-1, 1) scannet_gt_labels = info['annos']['class']
scannet_gt_bboxes_3d_mask = np.ones_like(scannet_gt_labels) scannet_gt_bboxes_3d_mask = np.ones_like(scannet_gt_labels).astype(
np.bool)
else: else:
scannet_gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32) scannet_gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32)
scannet_gt_labels = np.zeros((1, 1)) scannet_gt_labels = np.zeros((1, ))
scannet_gt_bboxes_3d_mask = np.zeros((1, 1)) scannet_gt_bboxes_3d_mask = np.zeros((1, )).astype(np.bool)
scan_name = info['point_cloud']['lidar_idx'] scan_name = info['point_cloud']['lidar_idx']
results['pts_instance_mask_path'] = osp.join(data_path, results['pts_instance_mask_path'] = osp.join(data_path,
f'{scan_name}_ins_label.npy') f'{scan_name}_ins_label.npy')
results['pts_semantic_mask_path'] = osp.join(data_path, results['pts_semantic_mask_path'] = osp.join(data_path,
...@@ -46,10 +60,12 @@ def test_scannet_pipeline(): ...@@ -46,10 +60,12 @@ def test_scannet_pipeline():
results['gt_bboxes_3d'] = scannet_gt_bboxes_3d results['gt_bboxes_3d'] = scannet_gt_bboxes_3d
results['gt_labels'] = scannet_gt_labels results['gt_labels'] = scannet_gt_labels
results['gt_bboxes_3d_mask'] = scannet_gt_bboxes_3d_mask results['gt_bboxes_3d_mask'] = scannet_gt_bboxes_3d_mask
results = pipeline(results) results = pipeline(results)
points = results['points']
gt_bboxes_3d = results['gt_bboxes_3d'] points = results['points']._data
gt_labels = results['gt_labels'] gt_bboxes_3d = results['gt_bboxes_3d']._data
gt_labels = results['gt_labels']._data
pts_semantic_mask = results['pts_semantic_mask'] pts_semantic_mask = results['pts_semantic_mask']
pts_instance_mask = results['pts_instance_mask'] pts_instance_mask = results['pts_instance_mask']
expected_points = np.array( expected_points = np.array(
...@@ -73,12 +89,14 @@ def test_scannet_pipeline(): ...@@ -73,12 +89,14 @@ def test_scannet_pipeline():
expected_pts_instance_mask = np.array([44, 22, 10, 10, 57]) expected_pts_instance_mask = np.array([44, 22, 10, 10, 57])
assert np.allclose(points, expected_points) assert np.allclose(points, expected_points)
assert np.allclose(gt_bboxes_3d[:5, :], expected_gt_bboxes_3d) assert np.allclose(gt_bboxes_3d[:5, :], expected_gt_bboxes_3d)
assert np.all(gt_labels.flatten() == expected_gt_labels) assert np.all(gt_labels.numpy() == expected_gt_labels)
assert np.all(pts_semantic_mask == expected_pts_semantic_mask) assert np.all(pts_semantic_mask == expected_pts_semantic_mask)
assert np.all(pts_instance_mask == expected_pts_instance_mask) assert np.all(pts_instance_mask == expected_pts_instance_mask)
def test_sunrgbd_pipeline(): def test_sunrgbd_pipeline():
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk',
'dresser', 'night_stand', 'bookshelf', 'bathtub')
np.random.seed(0) np.random.seed(0)
pipelines = [ pipelines = [
dict( dict(
...@@ -93,6 +111,8 @@ def test_sunrgbd_pipeline(): ...@@ -93,6 +111,8 @@ def test_sunrgbd_pipeline():
rot_range=[-np.pi / 6, np.pi / 6], rot_range=[-np.pi / 6, np.pi / 6],
scale_range=[0.85, 1.15]), scale_range=[0.85, 1.15]),
dict(type='IndoorPointSample', num_points=5), dict(type='IndoorPointSample', num_points=5),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels']),
] ]
pipeline = Compose(pipelines) pipeline = Compose(pipelines)
results = dict() results = dict()
...@@ -104,19 +124,20 @@ def test_sunrgbd_pipeline(): ...@@ -104,19 +124,20 @@ def test_sunrgbd_pipeline():
if info['annos']['gt_num'] != 0: if info['annos']['gt_num'] != 0:
gt_bboxes_3d = info['annos']['gt_boxes_upright_depth'] gt_bboxes_3d = info['annos']['gt_boxes_upright_depth']
gt_labels = info['annos']['class'].reshape(-1, 1) gt_labels = info['annos']['class']
gt_bboxes_3d_mask = np.ones_like(gt_labels) gt_bboxes_3d_mask = np.ones_like(gt_labels).astype(np.bool)
else: else:
gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32) gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32)
gt_labels = np.zeros((1, 1)) gt_labels = np.zeros((1, ))
gt_bboxes_3d_mask = np.zeros((1, 1)) gt_bboxes_3d_mask = np.zeros((1, )).astype(np.bool)
results['gt_bboxes_3d'] = gt_bboxes_3d results['gt_bboxes_3d'] = gt_bboxes_3d
results['gt_labels'] = gt_labels results['gt_labels'] = gt_labels
results['gt_bboxes_3d_mask'] = gt_bboxes_3d_mask results['gt_bboxes_3d_mask'] = gt_bboxes_3d_mask
results = pipeline(results) results = pipeline(results)
points = results['points'] points = results['points']._data
gt_bboxes_3d = results['gt_bboxes_3d'] gt_bboxes_3d = results['gt_bboxes_3d']._data
gt_labels = results['gt_labels'] gt_labels = results['gt_labels']._data
expected_points = np.array( expected_points = np.array(
[[0.6570105, 1.5538014, 0.24514851, 1.0165423], [[0.6570105, 1.5538014, 0.24514851, 1.0165423],
[0.656101, 1.558591, 0.21755838, 0.98895216], [0.656101, 1.558591, 0.21755838, 0.98895216],
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment