parser.add_argument('train_config_path', type=str, help='Path to train_config')
parser.add_argument('config_path', type=str, help='Path to config')
parser.add_argument(
'--dataset_root_dir',
type=str,
default='/home/wrjs/pc/kitti/',
help='Path to KITTI dataset. Default="/home/wrjs/pc/kitti/"')
parser.add_argument('--dataset_split_file',
type=str,
default='',
help='Path to KITTI dataset split file.'
'Default="DATASET_ROOT_DIR/3DOP_splits'
'/train_config["train_dataset"]"')
args = parser.parse_args()
train_config = load_train_config(args.train_config_path)
DATASET_DIR = args.dataset_root_dir
if args.dataset_split_file == '':
DATASET_SPLIT_FILE = os.path.join(
DATASET_DIR, '3DOP_splits/' + train_config['train_dataset'])
else:
DATASET_SPLIT_FILE = args.dataset_split_file
config_complete = load_config(args.config_path)
if 'train' in config_complete:
config = config_complete['train']
else:
config = config_complete
# input function ==============================================================
dataset = KittiDataset(os.path.join(DATASET_DIR, 'image/training/image_2'),
os.path.join(DATASET_DIR,
'velodyne/training/velodyne/'),
from models.models import get_model
from models.box_encoding import get_box_decoding_fn, get_box_encoding_fn, \
get_encoding_len
from models import preprocess
from util.config_util import load_config, load_train_config
from util.summary_util import write_summary_scale
parser = argparse.ArgumentParser(
description='Repeated evaluation of PointGNN.')
parser.add_argument('eval_config_path', type=str, help='Path to train_config')
parser.add_argument('--dataset_root_dir',
type=str,
default='../dataset/kitti/',
help='Path to KITTI dataset. Default="../dataset/kitti/"')
args = parser.parse_args()
eval_config = load_train_config(args.eval_config_path)
DATASET_DIR = args.dataset_root_dir
config_path = os.path.join(eval_config['train_dir'],
eval_config['config_path'])
while not os.path.isfile(config_path):
print('No config file found in %s, waiting' % config_path)
time.sleep(eval_config['eval_every_second'])
config = load_config(config_path)
if 'eval' in config:
config = config['eval']
dataset = KittiDataset(os.path.join(DATASET_DIR, 'velodyne/val/'),
os.path.join(DATASET_DIR, 'labels/val/'),
num_classes=config['num_classes'])
NUM_CLASSES = dataset.num_classes
print(dataset)