def main():
args = parse_args()
num_gpus = torch.cuda.device_count()
cfg = importlib.import_module("shaper.config.{:s}".format(args.task)).cfg
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
purge_cfg(cfg)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir:
config_path = osp.splitext(args.config_file)[0]
config_path = config_path.replace("configs", "outputs")
output_dir = output_dir.replace('@', config_path)
mkdir(output_dir)
logger = setup_logger("shaper", output_dir, prefix="test_part_seg")
logger.info("Using {} GPUs".format(num_gpus))
logger.info(args)
logger.info("Loaded configuration file {}".format(args.config_file))
logger.info("Running with config:\n{}".format(cfg))
test(cfg, output_dir)
def main():
args = parse_args()
# Load the configuration
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
purge_cfg(cfg)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
# Replace '@' with config path
if output_dir:
config_path = osp.splitext(args.config_file)[0]
config_path = config_path.replace("configs", "outputs")
output_dir = output_dir.replace('@', config_path)
mkdir(output_dir)
logger = setup_logger("shaper", output_dir, prefix="train")
logger.info("Using {} GPUs".format(torch.cuda.device_count()))
logger.info(args)
logger.info("Loaded configuration file {}".format(args.config_file))
logger.info("Running with config:\n{}".format(cfg))
assert cfg.TASK == "part_segmentation"
train(cfg, output_dir)
def main():
args = parse_args()
# Load the configuration
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
purge_cfg(cfg)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
# Replace '@' with config path
if output_dir:
config_path = osp.splitext(args.config_file)[0]
config_path = config_path.replace("configs", "outputs")
output_dir = output_dir.replace('@', config_path)
mkdir(output_dir)
logger = setup_logger("shaper", output_dir, prefix="train")
logger.info("Using {} GPUs".format(torch.cuda.device_count()))
logger.info(args)
# logger.info("Collecting env info (might take some time)")
# logger.info("\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
# with open(args.config_file, "r") as fid:
# config_str = "\n" + fid.read()
# logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
assert cfg.TASK == "classification"
train(cfg, output_dir)