def bootstrap_config(config_id, seed=-1): """Method to generate the config (using config id) and set seeds""" config = get_config(config_id, experiment_id=0) if seed > 0: set_seed(seed=seed) else: set_seed(seed=config.general.seed) return config
def bootstrap(config_id): config_dict = get_config(config_id=config_id) print(config_dict.log) set_logger(config_dict) write_message_logs("Starting Experiment at {}".format( time.asctime(time.localtime(time.time())))) write_message_logs("torch version = {}".format(torch.__version__)) write_config_log(config_dict) set_seed(seed=config_dict.general.seed) return config_dict
def run(config_id): print("torch version = {}".format(torch.__version__)) config_dict = get_config(config_id=config_id) set_seed(seed=config_dict.general.seed) module_name = "codes.data.loader.loaders" datatset = importlib.import_module(module_name).RolloutSequenceDataset( config=config_dict, mode="train") datatset.load_next_buffer() for idx in range(1): a = datatset.__getitem__(idx)[0][0] show_tensor_as_image((a * 255).numpy().transpose(1, 2, 0))
def start(_config, _run): config = Dict(_config) set_seed(seed=config.seed) run_experiment(config, _run)
def resume(config, experiment): config = Dict(config) set_seed(seed=config.general.seed) run_experiment(config, experiment, resume=True)
def start(config, experiment): config = Dict(config) set_seed(seed=config.general.seed) run_experiment(config, experiment)
def bootstrap(config_dict): print(config_dict.log) set_seed(seed=config_dict.general.seed)