def main():
args_parser = argparse.ArgumentParser()
args_parser.add_argument('--config_path', required=True)
args = args_parser.parse_args()
config = Config(None)
config.load_config(args.config_path)
logger = get_logger("RSTParser (Top-Down) RUN", config.use_dynamic_oracle,
config.model_path)
word_alpha, tag_alpha, gold_action_alpha, action_label_alpha, relation_alpha, nuclear_alpha, nuclear_relation_alpha, etype_alpha = create_alphabet(
None, config.alphabet_path, logger)
vocab = Vocab(word_alpha, tag_alpha, etype_alpha, gold_action_alpha,
action_label_alpha, relation_alpha, nuclear_alpha,
nuclear_relation_alpha)
network = MainArchitecture(vocab, config)
if config.use_gpu:
network.load_state_dict(torch.load(config.model_name))
network = network.cuda()
else:
network.load_state_dict(
torch.load(config.model_name, map_location=torch.device('cpu')))
network.eval()
logger.info('Reading dev instance, and predict...')
reader = Reader(config.dev_path, config.dev_syn_feat_path)
dev_instances = reader.read_data()
predict(network, dev_instances, vocab, config, logger)
logger.info('Reading test instance, and predict...')
reader = Reader(config.test_path, config.test_syn_feat_path)
test_instances = reader.read_data()
predict(network, test_instances, vocab, config, logger)
def main():
args_parser = argparse.ArgumentParser()
args_parser.add_argument('--config_path', required=True)
args = args_parser.parse_args()
config = Config(None)
config.load_config(args.config_path)
logger = get_logger("RSTParser RUN", config.use_dynamic_oracle,
config.model_path)
word_alpha, tag_alpha, gold_action_alpha, action_label_alpha, etype_alpha = create_alphabet(
None, config.alphabet_path, logger)
vocab = Vocab(word_alpha, tag_alpha, etype_alpha, gold_action_alpha,
action_label_alpha)
network = MainArchitecture(vocab, config)
network.load_state_dict(torch.load(config.model_name))
if config.use_gpu:
network = network.cuda()
network.eval()
logger.info('Reading test instance')
reader = Reader(config.test_path, config.test_syn_feat_path)
test_instances = reader.read_data()
time_start = datetime.now()
batch_size = config.batch_size
span = Metric()
nuclear = Metric()
relation = Metric()
full = Metric()
predictions = []
total_data_test = len(test_instances)
for i in range(0, total_data_test, batch_size):
end_index = i + batch_size
if end_index > total_data_test:
end_index = total_data_test
indices = np.array(range(i, end_index))
subset_data_test = batch_data_variable(test_instances, indices, vocab,
config)
prediction_of_subtrees = network.loss(subset_data_test, None)
predictions += prediction_of_subtrees
for i in range(total_data_test):
span, nuclear, relation, full = test_instances[i].evaluate(
predictions[i], span, nuclear, relation, full)
time_elapsed = datetime.now() - time_start
m, s = divmod(time_elapsed.seconds, 60)
logger.info('TEST is finished in {} mins {} secs'.format(m, s))
logger.info("S: " + span.print_metric())
logger.info("N: " + nuclear.print_metric())
logger.info("R: " + relation.print_metric())
logger.info("F: " + full.print_metric())
import ipdb
ipdb.set_trace()
