type=int,
default=50,
help='the number of sampled points')
parser.add_argument('--order', type=int, default=2)
parser.add_argument('--norm',
action='store_true',
default=False,
help='normalize coordinates')
args = parser.parse_args()
root = root_map[args.dataset]
image_size = size_map[args.dataset]
bezier_threshold = 5
image_set = 'test' if args.state == 1 else 'val'
if args.dataset == 'llamas' and image_set != 'val':
warnings.warn(
'LLAMAS test labels not available! Switching to validation set!')
image_set = 'val'
order = args.order
lkp = SimpleKPLoader(root=root,
image_set=image_set,
data_set=args.dataset,
image_size=image_size,
norm=args.norm)
lane_interpolate = True if args.dataset == 'curvelanes' else False
keypoints = lkp.load_annotations()
all_lanes = []
for kps in tqdm(keypoints.keys()):
coordinates = []
for kp in keypoints[kps]:
if args.fit_function == 'bezier':
type=str,
help='Continue/Load from a previous checkpoint')
retain_args = [
'mixed_precision', 'pred', 'metric', 'image_path', 'save_path',
'mask_path', 'keypoint_path', 'gt_keypoint_path', 'image_suffix',
'keypoint_suffix', 'gt_keypoint_suffix', 'mask_suffix',
'use_color_pool', 'style'
]
args = parser.parse_args()
# Parse configs and build model
if args.mixed_precision and torch.__version__ < '1.6.0':
warnings.warn(
'PyTorch version too low, mixed precision training is not available.'
)
if args.image_path is not None and args.save_path is not None:
assert args.image_path != args.save_path, "Try not to overwrite your dataset!"
cfg = read_config(args.config)
args, cfg = parse_arg_cfg(args, cfg)
cfg_runner_key = 'vis' if 'vis' in cfg.keys() else 'test'
for k in retain_args:
cfg[cfg_runner_key][k] = vars(args)[k]
if not cfg[cfg_runner_key]['pred']:
assert cfg[cfg_runner_key][
'style'] != 'bezier', 'Must use --pred for style bezier!'
cfg['model'] = None
runner = LaneDetDir(cfg=cfg)
runner.run()
parser.add_argument('--mixed-precision',
action='store_true',
help='Enable mixed precision training')
parser.add_argument(
'--cfg-options',
type=cmd_dict,
help='Override config options with \"x1=y1 x2=y2 xn=yn\"')
states = ['train', 'fastval', 'test', 'val']
retain_args = ['state', 'mixed_precision']
args = parser.parse_args()
if args.state is not None:
warnings.warn(
'--state={} is deprecated, it is recommended to specify with --{}'.
format(args.state, states[args.state]))
args.state = map_states(args, states)
if args.mixed_precision and torch.__version__ < '1.6.0':
warnings.warn(
'PyTorch version too low, mixed precision training is not available.'
)
# Parse configs and execute runner
cfg = read_config(args.config)
cfg_runner_key = 'train' if args.state == 0 else 'test'
Runner = LaneDetTrainer if args.state == 0 else LaneDetTester
args, cfg = parse_arg_cfg(args, cfg)
for k in retain_args:
cfg[cfg_runner_key][k] = vars(args)[k]
runner = Runner(cfg=cfg)