import torch
log = logging.getLogger("data_test")
if __name__ == "__main__":
logging.basicConfig(format="%(asctime)-15s %(levelname)s %(message)s", level=logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument("--data", required=True,
help="Category to use for training. Empty string to train on full dataset")
parser.add_argument("-m", "--model", required=True, help="Model name to load")
args = parser.parse_args()
phrase_pairs, emb_dict = data.load_data(args.data)
log.info("Obtained %d phrase pairs with %d uniq words", len(phrase_pairs), len(emb_dict))
train_data = data.encode_phrase_pairs(phrase_pairs, emb_dict)
train_data = data.group_train_data(train_data)
rev_emb_dict = {idx: word for word, idx in emb_dict.items()}
net = model.PhraseModel(emb_size=model.EMBEDDING_DIM, dict_size=len(emb_dict), hid_size=model.HIDDEN_STATE_SIZE)
net.load_state_dict(torch.load(args.model))
end_token = emb_dict[data.END_TOKEN]
seq_count = 0
sum_bleu = 0.0
for seq_1, targets in train_data:
input_seq = model.pack_input(seq_1, net.emb)
enc = net.encode(input_seq)
_, tokens = net.decode_chain_argmax(enc, input_seq.data[0:1],
"Open: %s", '../data/auto_QA_data/nomask_test/' +
str(args.pred).upper() + '_test.question')
TEST_ACTION_PATH = '../data/auto_QA_data/nomask_test/' + str(
args.pred).upper() + '_test.action'
log.info(
"Open: %s", '../data/auto_QA_data/nomask_test/' +
str(args.pred).upper() + '_test.action')
if args.pred == 'pt' or 'final' in args.pred:
phrase_pairs, emb_dict = data.load_data_from_existing_data(
TEST_QUESTION_PATH, TEST_ACTION_PATH, DIC_PATH)
elif args.pred == 'rl':
phrase_pairs, emb_dict = data.load_RL_data(TEST_QUESTION_PATH,
TEST_ACTION_PATH, DIC_PATH)
log.info("Obtained %d phrase pairs with %d uniq words", len(phrase_pairs),
len(emb_dict))
train_data = data.encode_phrase_pairs(phrase_pairs, emb_dict)
if args.pred == 'rl':
train_data = data.group_train_data(train_data)
else:
train_data = data.group_train_data_one_to_one(train_data)
rev_emb_dict = {idx: word for word, idx in emb_dict.items()}
net = model.PhraseModel(emb_size=model.EMBEDDING_DIM,
dict_size=len(emb_dict),
hid_size=model.HIDDEN_STATE_SIZE)
net = net.cuda()
model_path = '../data/saves/' + str(args.name) + '/' + str(args.model)
net.load_state_dict((torch.load(model_path)))
end_token = emb_dict[data.END_TOKEN]
seq_count = 0
