def __init__(self, mode, test_id=None):
self.name = 'SemanticKITTI'
self.dataset_path = '/data/WQ/DataSet/semantic-kitti/dataset/sequences_0.06'
self.label_to_names = {0: 'unlabeled',
1: 'car',
2: 'bicycle',
3: 'motorcycle',
4: 'truck',
5: 'other-vehicle',
6: 'person',
7: 'bicyclist',
8: 'motorcyclist',
9: 'road',
10: 'parking',
11: 'sidewalk',
12: 'other-ground',
13: 'building',
14: 'fence',
15: 'vegetation',
16: 'trunk',
17: 'terrain',
18: 'pole',
19: 'traffic-sign'}
self.num_classes = len(self.label_to_names)
self.label_values = np.sort([k for k, v in self.label_to_names.items()])
self.label_to_idx = {l: i for i, l in enumerate(self.label_values)}
self.ignored_labels = np.sort([0])
self.seq_list = np.sort(os.listdir(self.dataset_path))
if mode == 'test':
self.test_scan_number = str(test_id)
self.mode = mode
train_list, val_list, test_list = DP.get_file_list(self.dataset_path, str(test_id))
if mode == 'training':
self.data_list = train_list
elif mode == 'validation':
self.data_list = val_list
elif mode == 'test':
self.data_list = test_list
# self.data_list = self.data_list[0:1]
self.data_list = DP.shuffle_list(self.data_list)
self.possibility = []
self.min_possibility = []
if mode == 'test':
path_list = self.data_list
for test_file_name in path_list:
points = np.load(test_file_name)
self.possibility += [np.random.rand(points.shape[0]) * 1e-3]
self.min_possibility += [float(np.min(self.possibility[-1]))]
cfg.ignored_label_inds = [self.label_to_idx[ign_label] for ign_label in self.ignored_labels]
cfg.class_weights = DP.get_class_weights('SemanticKITTI')
def __init__(self, test_id):
self.name = 'SemanticKITTI'
self.dataset_path = '/data/semantic_kitti/dataset/sequences_0.06'
self.label_to_names = {
0: 'unlabeled',
1: 'car',
2: 'bicycle',
3: 'motorcycle',
4: 'truck',
5: 'other-vehicle',
6: 'person',
7: 'bicyclist',
8: 'motorcyclist',
9: 'road',
10: 'parking',
11: 'sidewalk',
12: 'other-ground',
13: 'building',
14: 'fence',
15: 'vegetation',
16: 'trunk',
17: 'terrain',
18: 'pole',
19: 'traffic-sign'
}
self.num_classes = len(self.label_to_names)
self.label_values = np.sort(
[k for k, v in self.label_to_names.items()])
self.label_to_idx = {l: i for i, l in enumerate(self.label_values)}
self.ignored_labels = np.sort([0])
self.val_split = '08'
self.seq_list = np.sort(os.listdir(self.dataset_path))
self.test_scan_number = str(test_id)
self.train_list, self.val_list, self.test_list = DP.get_file_list(
self.dataset_path, self.test_scan_number)
self.train_list = DP.shuffle_list(self.train_list)
self.val_list = DP.shuffle_list(self.val_list)
self.possibility = []
self.min_possibility = []
