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# Copyright (c) OpenMMLab. All rights reserved.
import unittest
import numpy as np
import torch
from mmengine.testing import assert_allclose
from mmdet3d.datasets import S3DISDataset, S3DISSegDataset
from mmdet3d.structures import DepthInstance3DBoxes
from mmdet3d.utils import register_all_modules
def _generate_s3dis_seg_dataset_config():
data_root = './tests/data/s3dis/'
ann_file = 's3dis_infos.pkl'
classes = ('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)]
modality = dict(use_lidar=True, use_camera=False)
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'),
dict(
type='IndoorPatchPointSample',
num_points=5,
block_size=1.0,
ignore_index=len(classes),
use_normalized_coord=True,
enlarge_size=0.2,
min_unique_num=None),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
]
data_prefix = dict(
pts='points',
pts_instance_mask='instance_mask',
pts_semantic_mask='semantic_mask')
return (data_root, ann_file, classes, palette, scene_idxs, data_prefix,
pipeline, modality)
def _generate_s3dis_dataset_config():
data_root = 'tests/data/s3dis'
ann_file = 's3dis_infos.pkl'
classes = ('table', 'chair', 'sofa', 'bookcase', 'board')
modality = dict(use_lidar=True, use_camera=False)
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=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True),
dict(type='PointSegClassMapping'),
dict(type='PointSample', num_points=5),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=1.0,
flip_ratio_bev_vertical=1.0),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[1.0, 1.0]),
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='Pack3DDetInputs',
keys=[
'points', 'pts_semantic_mask', 'gt_bboxes_3d', 'gt_labels_3d',
'pts_instance_mask'
])
]
data_prefix = dict(
pts='points',
pts_instance_mask='instance_mask',
pts_semantic_mask='semantic_mask')
return data_root, ann_file, classes, data_prefix, pipeline, modality
class TestS3DISDataset(unittest.TestCase):
def test_s3dis(self):
np.random.seed(0)
data_root, ann_file, classes, data_prefix, \
pipeline, modality = _generate_s3dis_dataset_config()
register_all_modules()
s3dis_dataset = S3DISDataset(
data_root,
ann_file,
data_prefix=data_prefix,
pipeline=pipeline,
metainfo=dict(classes=classes),
modality=modality)
s3dis_dataset.prepare_data(0)
input_dict = s3dis_dataset.get_data_info(0)
s3dis_dataset[0]
# assert the path should contains data_prefix and data_root
self.assertIn(data_prefix['pts'],
input_dict['lidar_points']['lidar_path'])
self.assertIn(data_root, input_dict['lidar_points']['lidar_path'])
ann_info = s3dis_dataset.parse_ann_info(input_dict)
# assert the keys in ann_info and the type
except_label = np.array([1, 1, 3, 1, 2, 0, 0, 0, 3])
self.assertEqual(ann_info['gt_labels_3d'].dtype, np.int64)
assert_allclose(ann_info['gt_labels_3d'], except_label)
self.assertIsInstance(ann_info['gt_bboxes_3d'], DepthInstance3DBoxes)
assert len(ann_info['gt_bboxes_3d']) == 9
assert torch.allclose(ann_info['gt_bboxes_3d'].tensor.sum(),
torch.tensor([63.0455]))
no_class_s3dis_dataset = S3DISDataset(
data_root, ann_file, metainfo=dict(classes=['table']))
input_dict = no_class_s3dis_dataset.get_data_info(0)
ann_info = no_class_s3dis_dataset.parse_ann_info(input_dict)
# assert the keys in ann_info and the type
self.assertIn('gt_labels_3d', ann_info)
# assert mapping to -1 or 1
assert (ann_info['gt_labels_3d'] <= 0).all()
self.assertEqual(ann_info['gt_labels_3d'].dtype, np.int64)
# all instance have been filtered by classes
self.assertEqual(len(ann_info['gt_labels_3d']), 9)
self.assertEqual(len(no_class_s3dis_dataset.metainfo['classes']), 1)
def test_s3dis_seg(self):
data_root, ann_file, classes, palette, scene_idxs, data_prefix, \
pipeline, modality, = _generate_s3dis_seg_dataset_config()
register_all_modules()
np.random.seed(0)
s3dis_seg_dataset = S3DISSegDataset(
data_root,
ann_file,
metainfo=dict(classes=classes, palette=palette),
data_prefix=data_prefix,
pipeline=pipeline,
modality=modality,
scene_idxs=scene_idxs)
input_dict = s3dis_seg_dataset.prepare_data(0)
points = input_dict['inputs']['points']
data_sample = input_dict['data_samples']
pts_semantic_mask = data_sample.gt_pts_seg.pts_semantic_mask
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])
assert torch.allclose(points, expected_points, 1e-2)
self.assertTrue(
(pts_semantic_mask.numpy() == expected_pts_semantic_mask).all())
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