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MonoCon/mmdetection3d-0.14.0/tests/test_data/test_datasets/test_s3dis_dataset.py

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import numpy as np
import pytest
import torch
from mmdet3d.datasets import S3DISSegDataset
def test_seg_getitem():
np.random.seed(0)
root_path = './tests/data/s3dis/'
ann_file = './tests/data/s3dis/s3dis_infos.pkl'
class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window',
'door', 'table', 'chair', 'sofa', 'bookcase', 'board',
'clutter')
palette = [[0, 255, 0], [0, 0, 255], [0, 255, 255], [255, 255, 0],
[255, 0, 255], [100, 100, 255], [200, 200, 100],
[170, 120, 200], [255, 0, 0], [200, 100, 100], [10, 200, 100],
[200, 200, 200], [50, 50, 50]]
scene_idxs = [0 for _ in range(20)]
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=tuple(range(len(class_names))),
max_cat_id=13),
dict(
type='IndoorPatchPointSample',
num_points=5,
block_size=1.0,
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'])
]
s3dis_dataset = S3DISSegDataset(
data_root=root_path,
ann_files=ann_file,
pipeline=pipelines,
classes=None,
palette=None,
modality=None,
test_mode=False,
ignore_index=None,
scene_idxs=scene_idxs)
data = s3dis_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/s3dis/points/Area_1_office_2.bin'
assert sample_idx == 'Area_1_office_2'
expected_points = torch.tensor([[
0.0000, 0.0000, 3.1720, 0.4706, 0.4431, 0.3725, 0.4624, 0.7502, 0.9543
], [
0.2880, -0.5900, 0.0650, 0.3451, 0.3373, 0.3490, 0.5119, 0.5518, 0.0196
], [
0.1570, 0.6000, 3.1700, 0.4941, 0.4667, 0.3569, 0.4893, 0.9519, 0.9537
], [
-0.1320, 0.3950, 0.2720, 0.3216, 0.2863, 0.2275, 0.4397, 0.8830, 0.0818
],
[
-0.4860, -0.0640, 3.1710, 0.3843,
0.3725, 0.3059, 0.3789, 0.7286, 0.9540
]])
expected_pts_semantic_mask = np.array([0, 1, 0, 8, 0])
original_classes = s3dis_dataset.CLASSES
original_palette = s3dis_dataset.PALETTE
assert s3dis_dataset.CLASSES == class_names
assert s3dis_dataset.ignore_index == 13
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 s3dis_dataset.scene_idxs.dtype == np.int32
assert np.all(s3dis_dataset.scene_idxs == np.array(scene_idxs))
# test dataset with selected classes
s3dis_dataset = S3DISSegDataset(
data_root=root_path,
ann_files=ann_file,
pipeline=None,
classes=['beam', 'window'],
scene_idxs=scene_idxs)
label_map = {i: 13 for i in range(14)}
label_map.update({3: 0, 5: 1})
assert s3dis_dataset.CLASSES != original_classes
assert s3dis_dataset.CLASSES == ['beam', 'window']
assert s3dis_dataset.PALETTE == [palette[3], palette[5]]
assert s3dis_dataset.VALID_CLASS_IDS == [3, 5]
assert s3dis_dataset.label_map == label_map
assert s3dis_dataset.label2cat == {0: 'beam', 1: 'window'}
# test load classes from file
import tempfile
tmp_file = tempfile.NamedTemporaryFile()
with open(tmp_file.name, 'w') as f:
f.write('beam\nwindow\n')
s3dis_dataset = S3DISSegDataset(
data_root=root_path,
ann_files=ann_file,
pipeline=None,
classes=tmp_file.name,
scene_idxs=scene_idxs)
assert s3dis_dataset.CLASSES != original_classes
assert s3dis_dataset.CLASSES == ['beam', 'window']
assert s3dis_dataset.PALETTE == [palette[3], palette[5]]
assert s3dis_dataset.VALID_CLASS_IDS == [3, 5]
assert s3dis_dataset.label_map == label_map
assert s3dis_dataset.label2cat == {0: 'beam', 1: 'window'}
# test scene_idxs in dataset
# we should input scene_idxs in train mode
with pytest.raises(NotImplementedError):
s3dis_dataset = S3DISSegDataset(
data_root=root_path,
ann_files=ann_file,
pipeline=None,
scene_idxs=None)
# test mode
s3dis_dataset = S3DISSegDataset(
data_root=root_path,
ann_files=ann_file,
pipeline=None,
test_mode=True,
scene_idxs=scene_idxs)
assert np.all(s3dis_dataset.scene_idxs == np.array([0]))
def test_seg_evaluate():
if not torch.cuda.is_available():
pytest.skip()
root_path = './tests/data/s3dis'
ann_file = './tests/data/s3dis/s3dis_infos.pkl'
s3dis_dataset = S3DISSegDataset(
data_root=root_path, ann_files=ann_file, test_mode=True)
results = []
pred_sem_mask = dict(
semantic_mask=torch.tensor([
2, 3, 1, 2, 2, 6, 1, 0, 1, 1, 9, 12, 3, 0, 2, 0, 2, 0, 8, 3, 1, 2,
0, 2, 1, 7, 2, 10, 2, 0, 0, 0, 2, 3, 2, 2, 2, 2, 2, 3, 0, 0, 4, 6,
7, 2, 1, 2, 0, 1, 7, 0, 2, 2, 2, 0, 2, 2, 1, 12, 0, 2, 2, 2, 2, 7,
2, 2, 0, 2, 6, 2, 12, 6, 3, 12, 2, 1, 6, 1, 2, 6, 8, 2, 10, 1, 11,
0, 6, 9, 4, 3, 0, 0, 12, 1, 1, 5, 3, 2
]).long())
results.append(pred_sem_mask)
ret_dict = s3dis_dataset.evaluate(results)
assert abs(ret_dict['miou'] - 0.7625) < 0.01
assert abs(ret_dict['acc'] - 0.9) < 0.01
assert abs(ret_dict['acc_cls'] - 0.9074) < 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/s3dis'
ann_file = './tests/data/s3dis/s3dis_infos.pkl'
s3dis_dataset = S3DISSegDataset(
data_root=root_path, ann_files=ann_file, scene_idxs=[0])
result = dict(
semantic_mask=torch.tensor([
2, 2, 1, 2, 2, 5, 1, 0, 1, 1, 9, 12, 3, 0, 2, 0, 2, 0, 8, 2, 0, 2,
0, 2, 1, 7, 2, 10, 2, 0, 0, 0, 2, 2, 2, 2, 2, 1, 2, 2, 0, 0, 4, 6,
7, 2, 1, 2, 0, 1, 7, 0, 2, 2, 2, 0, 2, 2, 1, 12, 0, 2, 2, 2, 2, 7,
2, 2, 0, 2, 6, 2, 12, 6, 2, 12, 2, 1, 6, 1, 2, 6, 8, 2, 10, 1, 10,
0, 6, 9, 4, 3, 0, 0, 12, 1, 1, 5, 2, 2
]).long())
results = [result]
s3dis_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'Area_1_office_2',
'Area_1_office_2_points.obj')
gt_file_path = osp.join(temp_dir, 'Area_1_office_2',
'Area_1_office_2_gt.obj')
pred_file_path = osp.join(temp_dir, 'Area_1_office_2',
'Area_1_office_2_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_multi_areas():
# S3DIS dataset has 6 areas, we often train on several of them
# need to verify the concat function of S3DISSegDataset
root_path = './tests/data/s3dis'
ann_file = './tests/data/s3dis/s3dis_infos.pkl'
class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window',
'door', 'table', 'chair', 'sofa', 'bookcase', 'board',
'clutter')
palette = [[0, 255, 0], [0, 0, 255], [0, 255, 255], [255, 255, 0],
[255, 0, 255], [100, 100, 255], [200, 200, 100],
[170, 120, 200], [255, 0, 0], [200, 100, 100], [10, 200, 100],
[200, 200, 200], [50, 50, 50]]
scene_idxs = [0 for _ in range(20)]
# repeat
repeat_num = 3
s3dis_dataset = S3DISSegDataset(
data_root=root_path,
ann_files=[ann_file for _ in range(repeat_num)],
scene_idxs=scene_idxs)
assert s3dis_dataset.CLASSES == class_names
assert s3dis_dataset.PALETTE == palette
assert len(s3dis_dataset.data_infos) == repeat_num
assert np.all(s3dis_dataset.scene_idxs == np.concatenate(
[np.array(scene_idxs) + i for i in range(repeat_num)]))
# different scene_idxs input
s3dis_dataset = S3DISSegDataset(
data_root=root_path,
ann_files=[ann_file for _ in range(repeat_num)],
scene_idxs=[[0, 0, 1, 2, 2], [0, 1, 2, 3, 3, 4], [0, 1, 1, 2, 2, 2]])
assert np.all(s3dis_dataset.scene_idxs == np.array(
[0, 0, 1, 2, 2, 3, 4, 5, 6, 6, 7, 8, 9, 9, 10, 10, 10]))
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