/
translate.py
171 lines (137 loc) · 6.04 KB
/
translate.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
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
from __future__ import division
from builtins import bytes
import onmt
import onmt.Markdown
import onmt.IO
import torch
import argparse
import math
import codecs
import os
parser = argparse.ArgumentParser(description='translate.py')
onmt.Markdown.add_md_help_argument(parser)
parser.add_argument('-model', required=True,
help='Path to model .pt file')
parser.add_argument('-src', required=True,
help='Source sequence to decode (one line per sequence)')
parser.add_argument('-src_img_dir', default="",
help='Source image directory')
parser.add_argument('-tgt',
help='True target sequence (optional)')
parser.add_argument('-output', default='pred.txt',
help="""Path to output the predictions (each line will
be the decoded sequence""")
parser.add_argument('-beam_size', type=int, default=5,
help='Beam size')
parser.add_argument('-batch_size', type=int, default=30,
help='Batch size')
parser.add_argument('-max_sent_length', type=int, default=100,
help='Maximum sentence length.')
parser.add_argument('-replace_unk', action="store_true",
help="""Replace the generated UNK tokens with the source
token that had highest attention weight. If phrase_table
is provided, it will lookup the identified source token and
give the corresponding target token. If it is not provided
(or the identified source token does not exist in the
table) then it will copy the source token""")
# parser.add_argument('-phrase_table',
# help="""Path to source-target dictionary to replace UNK
# tokens. See README.md for the format of this file.""")
parser.add_argument('-verbose', action="store_true",
help='Print scores and predictions for each sentence')
parser.add_argument('-attn_debug', action="store_true",
help='Print best attn for each word')
parser.add_argument('-dump_beam', type=str, default="",
help='File to dump beam information to.')
parser.add_argument('-n_best', type=int, default=1,
help="""If verbose is set, will output the n_best
decoded sentences""")
parser.add_argument('-gpu', type=int, default=-1,
help="Device to run on")
def reportScore(name, scoreTotal, wordsTotal):
print("%s AVG SCORE: %.4f, %s PPL: %.4f" % (
name, scoreTotal / wordsTotal,
name, math.exp(-scoreTotal/wordsTotal)))
def addone(f):
for line in f:
yield line
yield None
def main():
opt = parser.parse_args()
opt.cuda = opt.gpu > -1
if opt.cuda:
torch.cuda.set_device(opt.gpu)
translator = onmt.Translator(opt)
outF = codecs.open(opt.output, 'w', 'utf-8')
predScoreTotal, predWordsTotal, goldScoreTotal, goldWordsTotal = 0, 0, 0, 0
srcBatch, tgtBatch = [], []
count = 0
tgtF = codecs.open(opt.tgt, 'r', 'utf-8') if opt.tgt else None
if opt.dump_beam != "":
import json
translator.initBeamAccum()
for line in addone(codecs.open(opt.src, 'r', 'utf-8')):
if line is not None:
srcTokens = line.split()
srcBatch += [srcTokens]
if tgtF:
tgtTokens = tgtF.readline().split() if tgtF else None
tgtBatch += [tgtTokens]
if len(srcBatch) < opt.batch_size:
continue
else:
# at the end of file, check last batch
if len(srcBatch) == 0:
break
predBatch, predScore, goldScore, attn, src \
= translator.translate(srcBatch, tgtBatch)
predScoreTotal += sum(score[0] for score in predScore)
predWordsTotal += sum(len(x[0]) for x in predBatch)
if tgtF is not None:
goldScoreTotal += sum(goldScore)
goldWordsTotal += sum(len(x) for x in tgtBatch)
for b in range(len(predBatch)):
count += 1
outF.write(" ".join([i
for i in predBatch[b][0]]) + '\n')
outF.flush()
if opt.verbose:
srcSent = ' '.join(srcBatch[b])
if translator.tgt_dict.lower:
srcSent = srcSent.lower()
os.write(1, bytes('SENT %d: %s\n' % (count, srcSent), 'UTF-8'))
os.write(1, bytes('PRED %d: %s\n' %
(count, " ".join(predBatch[b][0])), 'UTF-8'))
print("PRED SCORE: %.4f" % predScore[b][0])
if tgtF is not None:
tgtSent = ' '.join(tgtBatch[b])
if translator.tgt_dict.lower:
tgtSent = tgtSent.lower()
os.write(1, bytes('GOLD %d: %s\n' %
(count, tgtSent), 'UTF-8'))
print("GOLD SCORE: %.4f" % goldScore[b])
if opt.n_best > 1:
print('\nBEST HYP:')
for n in range(opt.n_best):
os.write(1, bytes("[%.4f] %s\n" % (predScore[b][n],
" ".join(predBatch[b][n])),
'UTF-8'))
if opt.attn_debug:
print('')
for i, w in enumerate(predBatch[b][0]):
print(w)
_, ids = attn[b][0][i].sort(0, descending=True)
for j in ids[:5].tolist():
print("\t%s\t%d\t%3f" % (srcTokens[j], j,
attn[b][0][i][j]))
srcBatch, tgtBatch = [], []
reportScore('PRED', predScoreTotal, predWordsTotal)
if tgtF:
reportScore('GOLD', goldScoreTotal, goldWordsTotal)
if tgtF:
tgtF.close()
if opt.dump_beam:
json.dump(translator.beam_accum,
codecs.open(opt.dump_beam, 'w', 'utf-8'))
if __name__ == "__main__":
main()