75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
###############################################################################
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# Language Modeling on Penn Tree Bank
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#
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# This file generates new sentences sampled from the language model
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#
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###############################################################################
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import argparse
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import torch
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from torch.autograd import Variable
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import data
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parser = argparse.ArgumentParser(description='PyTorch Wikitext-2 Language Model')
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# Model parameters.
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parser.add_argument('--data', type=str, default='./data/wikitext-2',
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help='location of the data corpus')
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parser.add_argument('--checkpoint', type=str, default='./model.pt',
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help='model checkpoint to use')
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parser.add_argument('--outf', type=str, default='generated.txt',
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help='output file for generated text')
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parser.add_argument('--words', type=int, default='1000',
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help='number of words to generate')
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parser.add_argument('--seed', type=int, default=1111,
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help='random seed')
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parser.add_argument('--cuda', action='store_true',
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help='use CUDA')
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parser.add_argument('--temperature', type=float, default=1.0,
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help='temperature - higher will increase diversity')
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parser.add_argument('--log-interval', type=int, default=100,
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help='reporting interval')
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args = parser.parse_args()
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# Set the random seed manually for reproducibility.
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torch.manual_seed(args.seed)
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if torch.cuda.is_available():
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if not args.cuda:
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print("WARNING: You have a CUDA device, so you should probably run with --cuda")
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else:
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torch.cuda.manual_seed(args.seed)
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if args.temperature < 1e-3:
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parser.error("--temperature has to be greater or equal 1e-3")
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with open(args.checkpoint, 'rb') as f:
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model = torch.load(f)
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model.eval()
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if args.cuda:
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model.cuda()
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else:
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model.cpu()
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corpus = data.Corpus(args.data)
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ntokens = len(corpus.dictionary)
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hidden = model.init_hidden(1)
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input = Variable(torch.rand(1, 1).mul(ntokens).long(), volatile=True)
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if args.cuda:
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input.data = input.data.cuda()
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with open(args.outf, 'w') as outf:
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for i in range(args.words):
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output, hidden = model(input, hidden)
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word_weights = output.squeeze().data.div(args.temperature).exp().cpu()
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word_idx = torch.multinomial(word_weights, 1)[0]
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input.data.fill_(word_idx)
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word = corpus.dictionary.idx2word[word_idx]
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outf.write(word + ('\n' if i % 20 == 19 else ' '))
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if i % args.log_interval == 0:
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print('| Generated {}/{} words'.format(i, args.words))
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