def load_default(): with open(abs_path("config_default.json"), 'r') as f: config = json.load(f) config = DD(config) config.net = DD(config.net) config.train = DD(config.train) config.train.static = DD(config.train.static) config.train.dynamic = DD(config.train.dynamic) config.data = DD(config.data) config.eval = DD(config.eval) config.train.dynamic.epoch = 0 return DD(config)
def get_parameters(opt, exp_type="model"): params = DD() params.net = DD() params.mle = 0 params.dataset = opt.dataset params.net = get_net_parameters(opt) params.train = get_training_parameters(opt) params.model = params.net.model params.exp = opt.exp params.data = get_data_parameters(opt, params.exp, params.dataset) params.eval = get_eval_parameters(opt, params.data.get("categories", None)) #params.n_per_node = opt.n_per_node #params.max_path_len = opt.max_path_len #params.n_train = opt.n_train #params.n_dev = opt.n_dev #params.n_test = opt.n_test meta = DD() params.trainer = opt.trainer meta.iterations = int(opt.iterations) meta.cycle = opt.cycle params.cycle = opt.cycle params.iters = int(opt.iterations) global toy toy = opt.toy global do_gen do_gen = opt.do_gen global save save = opt.save global test_save test_save = opt.test_save global save_strategy save_strategy = opt.save_strategy print(params) return params, meta