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
|
#include "data/text_input.h"
#include "common/utils.h"
namespace marian {
namespace data {
TextIterator::TextIterator() : pos_(-1), tup_(0) {}
TextIterator::TextIterator(TextInput& corpus) : corpus_(&corpus), pos_(0), tup_(corpus_->next()) {}
void TextIterator::increment() {
tup_ = corpus_->next();
pos_++;
}
bool TextIterator::equal(TextIterator const& other) const {
return this->pos_ == other.pos_ || (this->tup_.empty() && other.tup_.empty());
}
const SentenceTuple& TextIterator::dereference() const {
return tup_;
}
TextInput::TextInput(std::vector<std::string> inputs,
std::vector<Ptr<Vocab>> vocabs,
Ptr<Options> options)
: DatasetBase(inputs, options), vocabs_(vocabs) {
// note: inputs are automatically stored in the inherited variable named paths_, but these are
// texts not paths!
for(const auto& text : paths_)
files_.emplace_back(new std::istringstream(text));
}
// TextInput is mainly used for inference in the server mode, not for training, so skipping too long
// or ill-formed inputs is not necessary here
SentenceTuple TextInput::next() {
// get index of the current sentence
size_t curId = pos_++;
// fill up the sentence tuple with source and/or target sentences
SentenceTuple tup(curId);
for(size_t i = 0; i < files_.size(); ++i) {
std::string line;
if(io::getline(*files_[i], line)) {
Words words = vocabs_[i]->encode(line, /*addEOS =*/ true, /*inference =*/ inference_);
if(words.empty())
words.push_back(Word::ZERO); // @TODO: What is this for? @BUGBUG: addEOS=true, so this can never happen, right?
tup.push_back(words);
}
}
// check if each input file provided an example
if(tup.size() == files_.size())
return tup;
return SentenceTuple(0);
}
} // namespace data
} // namespace marian
|
