def main():
# Get args from command line
args = get_args_from_command_line()
if args.gpu_id is not None:
cfg.CONST.DEVICE = args.gpu_id
if args.weights is not None:
cfg.CONST.WEIGHTS = args.weights
# Print config
print('Use config:')
pprint(cfg)
# Set GPU to use
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.CONST.DEVICE
# Start train/test process
if not args.test and not args.inference:
train_net_new(cfg)
else:
if 'WEIGHTS' not in cfg.CONST or not os.path.exists(cfg.CONST.WEIGHTS):
logging.error('Please specify the file path of checkpoint.')
sys.exit(2)
if args.test:
test_net_new(cfg)
else:
inference_net(cfg)
def inference(self):
self.makeCurrent()
pc_points = inference_net(cfg, upload_image=False)
GM.base_point_cloud = PointCloud(pc_points)
GM.base_point_cloud.set_color_according_camera_pos(
camera_pos=[1.5, 1.5, 0.0])
self.update()
def main():
# Get args from command line
args = get_args_from_command_line()
if args.gpu_id is not None:
cfg.CONST.DEVICE = args.gpu_id
if args.weights is not None:
cfg.CONST.WEIGHTS = args.weights
# Print config
print('Use config:')
pprint(cfg)
# f_runner.write(str(cfg))
# Set GPU to use
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.CONST.DEVICE
# Start train/test process
if not args.test and not args.inference:
train_net(cfg)
else:
'''
if 'WEIGHTS' not in cfg.CONST or not os.path.exists(cfg.CONST.WEIGHTS):
logging.error('Please specify the file path of checkpoint.')
sys.exit(2)
'''
if 'WEIGHTS' not in cfg.CONST:
logging.error('Please specify the file path of checkpoint.1')
sys.exit(2)
if not os.path.exists(cfg.CONST.WEIGHTS):
logging.error('Please specify the file path of checkpoint.2')
sys.exit(2)
if args.test:
# test_net(cfg)
path = '/raid/wuruihai/GRNet_FILES/tb_log'
test_writer = SummaryWriter(path)
test_net(cfg, test_writer=test_writer)
if args.test_KITTI:
path = '/raid/wuruihai/GRNet_FILES/tb_log'
test_writer = SummaryWriter(path)
test_net_KITTI(cfg, test_writer=test_writer)
else:
inference_net(cfg)
def main():
# Get args from command line
args = get_args_from_command_line()
# Read the experimental config
exec(compile(open(args.cfg_file, "rb").read(), args.cfg_file, 'exec'))
cfg = locals()['__C']
pprint(cfg)
# Parse runtime arguments
if args.gpu_id is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
if not args.randomize:
random.seed(cfg.CONST.RNG_SEED)
np.random.seed(cfg.CONST.RNG_SEED)
torch.manual_seed(cfg.CONST.RNG_SEED)
torch.cuda.manual_seed(cfg.CONST.RNG_SEED)
torch.cuda.manual_seed_all(cfg.CONST.RNG_SEED)
# References: https://pytorch.org/docs/stable/notes/randomness.html
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if args.exp_name is not None:
cfg.CONST.EXP_NAME = args.exp_name
if args.weights is not None:
cfg.CONST.WEIGHTS = args.weights
# Start train/test process
if not args.test and not args.inference:
# Make sure cfg.TRAIN.NETWORK in ['RMNet', 'TinyFlowNet']
if cfg.TRAIN.NETWORK not in ['RMNet', 'TinyFlowNet']:
logging.error(
'Please make sure cfg.TRAIN.NETWORK in ["RMNet", "TinyFlowNet"].'
)
sys.exit(1)
train_net(cfg)
else:
if 'WEIGHTS' not in cfg.CONST or not os.path.exists(cfg.CONST.WEIGHTS):
logging.error('Please specify the file path of checkpoint.')
sys.exit(2)
if args.test:
test_net(cfg)
else:
inference_net(cfg)