/
generate.py
69 lines (58 loc) · 2.27 KB
/
generate.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
import argparse
import skorch
import torch
from torch.autograd import Variable
import data
import model
import net
parser = argparse.ArgumentParser(description='PyTorch PennTreeBank RNN/LSTM Language Model')
parser.add_argument('--data', type=str, default='./data/penn',
help='location of the data corpus')
parser.add_argument('--bptt', type=int, default=35,
help='sequence length')
parser.add_argument('--seed', type=int, default=1111,
help='random seed')
parser.add_argument('--no-cuda', dest='cuda', action='store_false',
help='use CUDA')
parser.add_argument('--checkpoint', type=str, default='./model.pt',
help='model checkpoint to use')
parser.add_argument('--outf', type=str, default='generated.txt',
help='output file for generated text')
parser.add_argument('--temperature', type=float, default=1.0,
help='temperature - higher will increase diversity')
parser.add_argument('--words', type=int, default='1000',
help='number of words to generate')
parser.add_argument('--log-interval', type=int, default=100,
help='reporting interval')
args = parser.parse_args()
torch.manual_seed(args.seed)
corpus = data.Corpus(args.data)
ntokens = len(corpus.dictionary)
device = 'cuda' if args.cuda else 'cpu'
net = net.Net(
module=model.RNNModel,
batch_size=1,
device=device,
module__rnn_type='LSTM',
module__ntoken=ntokens,
module__ninp=200,
module__nhid=200,
module__nlayers=2)
net.initialize()
net.load_params(args.checkpoint)
hidden = None
input = skorch.utils.to_tensor(torch.rand(1, 1).mul(ntokens).long(),
device=device)
with open(args.outf, 'w') as outf:
for i in range(args.words):
word_idx, hidden = net.sample(
input=input,
temperature=args.temperature,
hidden=hidden)
input = skorch.utils.to_tensor(
torch.LongTensor([[word_idx]]),
device=device)
word = corpus.dictionary.idx2word[word_idx]
outf.write(word + ('\n' if i % 20 == 19 else ' '))
if i % args.log_interval == 0:
print('| Generated {}/{} words'.format(i, args.words))