def test_seg_format_results():
import mmcv
from os import path as osp
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
test_mode=True)
results = []
pred_sem_mask = dict(semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0, 18,
6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7, 3, 19,
12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4, 17, 0, 14,
1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13, 13, 2, 3, 1,
0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results.append(pred_sem_mask)
result_files, tmp_dir = scannet_dataset.format_results(results)
expected_label = np.array([
16, 6, 2, 3, 7, 3, 16, 2, 24, 3, 1, 1, 6, 6, 4, 1, 2, 24, 1, 1, 1, 36,
7, 28, 16, 1, 3, 5, 1, 4, 33, 7, 16, 6, 16, 1, 1, 1, 1, 2, 8, 4, 39,
14, 9, 1, 12, 1, 1, 2, 3, 16, 34, 2, 2, 2, 7, 3, 16, 39, 5, 34, 1, 24,
2, 8, 3, 2, 8, 3, 1, 6, 34, 6, 1, 1, 4, 7, 6, 12, 2, 16, 16, 3, 4, 2,
1, 16, 39, 2, 24, 6, 4, 2, 16, 2, 3, 4, 3, 2
])
expected_txt_path = osp.join(tmp_dir.name, 'results', 'scene0000_00.txt')
assert np.all(result_files[0]['seg_mask'] == expected_label)
mmcv.check_file_exist(expected_txt_path)
def test_seg_show():
import mmcv
import tempfile
from os import path as osp
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(
data_root=root_path, ann_file=ann_file, scene_idxs=[0])
result = dict(
semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_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_seg_evaluate():
if not torch.cuda.is_available():
pytest.skip()
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(
data_root=root_path, ann_file=ann_file, test_mode=True)
results = []
pred_sem_mask = dict(
semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results.append(pred_sem_mask)
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39),
max_cat_id=40),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
ret_dict = scannet_dataset.evaluate(results, pipeline=eval_pipeline)
assert abs(ret_dict['miou'] - 0.5308) < 0.01
assert abs(ret_dict['acc'] - 0.8219) < 0.01
assert abs(ret_dict['acc_cls'] - 0.7649) < 0.01
def test_seg_evaluate():
if not torch.cuda.is_available():
pytest.skip()
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(
data_root=root_path, ann_file=ann_file, test_mode=True)
results = []
pred_sem_mask = dict(
semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results.append(pred_sem_mask)
ret_dict = scannet_dataset.evaluate(results)
assert abs(ret_dict['miou'] - 0.5308) < 0.01
assert abs(ret_dict['acc'] - 0.8219) < 0.01
assert abs(ret_dict['acc_cls'] - 0.7649) < 0.01
def test_seg_show():
import mmcv
import tempfile
from os import path as osp
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
scene_idxs=[0])
result = dict(semantic_mask=torch.tensor([
13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0, 18,
6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7, 3, 19,
12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4, 17, 0, 14,
1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13, 13, 2, 3, 1,
0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
]).long())
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_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()
# test show with pipeline
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
eval_pipeline = [
dict(type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39)),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
scannet_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_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_seg_getitem():
np.random.seed(0)
root_path = './tests/data/scannet/'
ann_file = './tests/data/scannet/scannet_infos.pkl'
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
palette = [
[174, 199, 232],
[152, 223, 138],
[31, 119, 180],
[255, 187, 120],
[188, 189, 34],
[140, 86, 75],
[255, 152, 150],
[214, 39, 40],
[197, 176, 213],
[148, 103, 189],
[196, 156, 148],
[23, 190, 207],
[247, 182, 210],
[219, 219, 141],
[255, 127, 14],
[158, 218, 229],
[44, 160, 44],
[112, 128, 144],
[227, 119, 194],
[82, 84, 163],
]
scene_idxs = [0 for _ in range(20)]
label_weight = [
2.389689, 2.7215734, 4.5944676, 4.8543367, 4.096086, 4.907941,
4.690836, 4.512031, 4.623311, 4.9242644, 5.358117, 5.360071, 5.019636,
4.967126, 5.3502126, 5.4023647, 5.4027233, 5.4169416, 5.3954206,
4.6971426
]
# test network inputs are (xyz, rgb, normalized_xyz)
pipelines = [
dict(type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39)),
dict(type='IndoorPatchPointSample',
num_points=5,
block_size=1.5,
sample_rate=1.0,
ignore_index=len(class_names),
use_normalized_coord=True),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D',
keys=['points', 'pts_semantic_mask'],
meta_keys=['file_name', 'sample_idx'])
]
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
pipeline=pipelines,
classes=None,
palette=None,
modality=None,
test_mode=False,
ignore_index=None,
scene_idxs=scene_idxs,
label_weight=label_weight)
data = scannet_dataset[0]
points = data['points']._data
pts_semantic_mask = data['pts_semantic_mask']._data
file_name = data['img_metas']._data['file_name']
sample_idx = data['img_metas']._data['sample_idx']
assert file_name == './tests/data/scannet/points/scene0000_00.bin'
assert sample_idx == 'scene0000_00'
expected_points = torch.tensor([[
0.0000, 0.0000, 1.2427, 0.6118, 0.5529, 0.4471, -0.6462, -1.0046,
0.4280
],
[
0.1553, -0.0074, 1.6077, 0.5882,
0.6157, 0.5569, -0.6001, -1.0068,
0.5537
],
[
0.1518, 0.6016, 0.6548, 0.1490, 0.1059,
0.0431, -0.6012, -0.8309, 0.2255
],
[
-0.7494, 0.1033, 0.6756, 0.5216,
0.4353, 0.3333, -0.8687, -0.9748,
0.2327
],
[
-0.6836, -0.0203, 0.5884, 0.5765,
0.5020, 0.4510, -0.8491, -1.0105,
0.2027
]])
expected_pts_semantic_mask = np.array([13, 13, 12, 2, 0])
original_classes = scannet_dataset.CLASSES
original_palette = scannet_dataset.PALETTE
assert scannet_dataset.CLASSES == class_names
assert scannet_dataset.ignore_index == 20
assert torch.allclose(points, expected_points, 1e-2)
assert np.all(pts_semantic_mask.numpy() == expected_pts_semantic_mask)
assert original_classes == class_names
assert original_palette == palette
assert scannet_dataset.scene_idxs.dtype == np.int32
assert np.all(scannet_dataset.scene_idxs == np.array(scene_idxs))
assert np.allclose(scannet_dataset.label_weight, np.array(label_weight),
1e-5)
# test network inputs are (xyz, rgb)
np.random.seed(0)
new_pipelines = copy.deepcopy(pipelines)
new_pipelines[3] = dict(type='IndoorPatchPointSample',
num_points=5,
block_size=1.5,
sample_rate=1.0,
ignore_index=len(class_names),
use_normalized_coord=False)
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
pipeline=new_pipelines,
scene_idxs=scene_idxs)
data = scannet_dataset[0]
points = data['points']._data
assert torch.allclose(points, expected_points[:, :6], 1e-2)
# test network inputs are (xyz, normalized_xyz)
np.random.seed(0)
new_pipelines = copy.deepcopy(pipelines)
new_pipelines[0] = dict(type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=False,
load_dim=6,
use_dim=[0, 1, 2])
new_pipelines.remove(new_pipelines[4])
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
pipeline=new_pipelines,
scene_idxs=scene_idxs)
data = scannet_dataset[0]
points = data['points']._data
assert torch.allclose(points, expected_points[:, [0, 1, 2, 6, 7, 8]], 1e-2)
# test network inputs are (xyz,)
np.random.seed(0)
new_pipelines = copy.deepcopy(pipelines)
new_pipelines[0] = dict(type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=False,
load_dim=6,
use_dim=[0, 1, 2])
new_pipelines[3] = dict(type='IndoorPatchPointSample',
num_points=5,
block_size=1.5,
sample_rate=1.0,
ignore_index=len(class_names),
use_normalized_coord=False)
new_pipelines.remove(new_pipelines[4])
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
pipeline=new_pipelines,
scene_idxs=scene_idxs)
data = scannet_dataset[0]
points = data['points']._data
assert torch.allclose(points, expected_points[:, :3], 1e-2)
# test dataset with selected classes
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
pipeline=None,
classes=['cabinet', 'chair'],
scene_idxs=scene_idxs)
label_map = {i: 20 for i in range(41)}
label_map.update({3: 0, 5: 1})
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ['cabinet', 'chair']
assert scannet_dataset.PALETTE == [palette[2], palette[4]]
assert scannet_dataset.VALID_CLASS_IDS == [3, 5]
assert scannet_dataset.label_map == label_map
assert scannet_dataset.label2cat == {0: 'cabinet', 1: 'chair'}
assert np.all(scannet_dataset.label_weight == np.ones(2))
# test load classes from file
import tempfile
tmp_file = tempfile.NamedTemporaryFile()
with open(tmp_file.name, 'w') as f:
f.write('cabinet\nchair\n')
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
pipeline=None,
classes=tmp_file.name,
scene_idxs=scene_idxs)
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ['cabinet', 'chair']
assert scannet_dataset.PALETTE == [palette[2], palette[4]]
assert scannet_dataset.VALID_CLASS_IDS == [3, 5]
assert scannet_dataset.label_map == label_map
assert scannet_dataset.label2cat == {0: 'cabinet', 1: 'chair'}
# test scene_idxs in dataset
# we should input scene_idxs in train mode
with pytest.raises(NotImplementedError):
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
pipeline=None,
scene_idxs=None)
# test mode
scannet_dataset = ScanNetSegDataset(data_root=root_path,
ann_file=ann_file,
pipeline=None,
test_mode=True,
scene_idxs=scene_idxs)
assert np.all(scannet_dataset.scene_idxs == np.array([0]))
assert np.all(scannet_dataset.label_weight == np.ones(len(class_names)))