def main(args, unknown_args):
args, config = parse_args_uargs(args, unknown_args)
set_global_seeds(args.seed)
modules = prepare_modules(expdir=args.expdir)
model = Registry.get_model(**config["model_params"])
datasource = modules["data"].DataSource()
data_params = config.get("data_params", {}) or {}
loaders = datasource.prepare_loaders(mode="infer",
n_workers=args.workers,
batch_size=args.batch_size,
**data_params)
runner = modules["model"].ModelRunner(model=model)
callbacks_params = config.get("callbacks_params", {}) or {}
callbacks = runner.prepare_callbacks(mode="infer",
resume=args.resume,
out_prefix=args.out_prefix,
**callbacks_params)
runner.infer(loaders=loaders, callbacks=callbacks, verbose=args.verbose)
def main(args, unknown_args):
args, config = parse_args_uargs(args, unknown_args, dump_config=True)
set_global_seeds(args.seed)
assert args.baselogdir is not None or args.logdir is not None
if args.logdir is None:
modules_ = prepare_modules(expdir=args.expdir)
logdir = modules_["model"].prepare_logdir(config=config)
args.logdir = str(pathlib.Path(args.baselogdir).joinpath(logdir))
os.makedirs(args.logdir, exist_ok=True)
save_config(config=config, logdir=args.logdir)
modules = prepare_modules(expdir=args.expdir, dump_dir=args.logdir)
model = Registry.get_model(**config["model_params"])
datasource = modules["data"].DataSource()
runner = modules["model"].ModelRunner(model=model)
runner.train_stages(datasource=datasource,
args=args,
stages_config=config["stages"],
verbose=args.verbose)
def get_model(self, stage: str) -> _Model:
model = Registry.get_model(**self._config["model_params"])
model = self._preprocess_model_for_stage(stage, model)
model = self._postprocess_model_for_stage(stage, model)
return model
