beam=beam,
wt_ranker=wt_ranker)
for final, prob_gen, score_ranker, hyp in ret:
print('%.3f gen %.3f ranker %.3f\t%s' %
(final, prob_gen, score_ranker, hyp))
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--path_generator', '-pg', type=str)
parser.add_argument('--path_ranker', '-pr', type=str)
parser.add_argument('--cpu', action='store_true')
parser.add_argument('--topk', type=int, default=3)
parser.add_argument('--beam', type=int, default=3)
parser.add_argument('--wt_ranker', type=float, default=1.)
parser.add_argument('--topp', type=float, default=0.8)
args = parser.parse_args()
cuda = False if args.cpu else torch.cuda.is_available()
generator = GPT2Generator(args.path_generator, cuda)
if args.path_ranker is None:
generator.play(topk=args.topk, beam=args.beam, topp=args.topp)
else:
from score import get_model
ranker = get_model(args.path_ranker, cuda)
integrated = Integrated(generator, ranker)
integrated.play(topk=args.topk,
beam=args.beam,
topp=args.topp,
wt_ranker=args.wt_ranker)
import torch
from score import get_model
cuda = True if torch.cuda.is_available() else False
model = get_model('restore/updown.pth', cuda)
def rank(context, response):
model.eval()
score = model.predict(context, response, max_cxt_turn=None)
return str(score[0])
if __name__ == "__main__":
print(test)