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
else:
data_params[case.split("_")[0]] = case.split("_")[1]
return data_params
gens_file = args.gens_file
split = gens_file.split("/")[-1].split(".")[0]
n = args.n
def flatten(outer):
return [el for key in outer for el in key]
opt = DD()
opt.data = DD()
opt.dataset = "atomic"
opt.exp = "generation"
data_params = get_data_params(gens_file)
categories = data_params[
"categories"] #sorted(["oReact", "oEffect", "oWant", "xAttr", "xEffect", "xIntent", "xNeed", "xReact", "xWant"])
opt.data.categories = data_params["categories"]
if "maxe1" in data_params:
opt.data.maxe1 = data_params["maxe1"]
opt.data.maxe2 = data_params["maxe2"]
opt.data.maxr = data_params["maxr"]