nnet-shuffle-egs-discriminative.cc File Reference

#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "hmm/transition-model.h"
#include "nnet2/nnet-example-functions.h"

Include dependency graph for nnet-shuffle-egs-discriminative.cc:

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Functions
int	main (int argc, char *argv[])

Function Documentation

main()

int main	(	int	argc,
		char *	argv[]
	)

Definition at line 25 of file nnet-shuffle-egs-discriminative.cc.

References SequentialTableReader< Holder >::Done(), ParseOptions::GetArg(), rnnlm::i, KALDI_ASSERT, KALDI_LOG, SequentialTableReader< Holder >::Next(), ParseOptions::NumArgs(), ParseOptions::PrintUsage(), kaldi::RandInt(), ParseOptions::Read(), ParseOptions::Register(), SequentialTableReader< Holder >::Value(), and TableWriter< Holder >::Write().

                                 {
  try {
    using namespace kaldi;
    using namespace kaldi::nnet2;
    typedef kaldi::int32 int32;
    typedef kaldi::int64 int64;

    const char *usage =
        "Copy examples (typically single frames) for neural network training,\n"
        "from the input to output, but randomly shuffle the order.  This program will keep\n"
        "all of the examples in memory at once, so don't give it too many.\n"
        "\n"
        "Usage:  nnet-shuffle-egs-discriminative [options] <egs-rspecifier> <egs-wspecifier>\n"
        "\n"
        "nnet-shuffle-egs-discriminative --srand=1 ark:train.degs ark:shuffled.degs\n";
    
    int32 srand_seed = 0;
    int32 buffer_size = 0;
    ParseOptions po(usage);
    po.Register("srand", &srand_seed, "Seed for random number generator ");
    po.Register("buffer-size", &buffer_size, "If >0, size of a buffer we use "
                "to do limited-memory partial randomization.  Otherwise, do "
                "full randomization.");
    
    po.Read(argc, argv);

    srand(srand_seed);
    
    if (po.NumArgs() != 2) {
      po.PrintUsage();
      exit(1);
    }

    std::string examples_rspecifier = po.GetArg(1),
        examples_wspecifier = po.GetArg(2);

    int64 num_done = 0;

    std::vector<DiscriminativeNnetExample*> egs;
    SequentialDiscriminativeNnetExampleReader example_reader(
        examples_rspecifier);
    DiscriminativeNnetExampleWriter example_writer(
        examples_wspecifier);
    if (buffer_size == 0) { // Do full randomization
      // Putting in an extra level of indirection here to avoid excessive
      // computation and memory demands when we have to resize the vector.
    
      for (; !example_reader.Done(); example_reader.Next())
        egs.push_back(new DiscriminativeNnetExample(
            example_reader.Value()));
      
      std::random_shuffle(egs.begin(), egs.end());
    } else {
      KALDI_ASSERT(buffer_size > 0);
      egs.resize(buffer_size, NULL);
      for (; !example_reader.Done(); example_reader.Next()) {
        int32 index = RandInt(0, buffer_size - 1);
        if (egs[index] == NULL) {
          egs[index] = new DiscriminativeNnetExample(example_reader.Value());
        } else {
          std::ostringstream ostr;
          ostr << num_done;
          example_writer.Write(ostr.str(), *(egs[index]));
          *(egs[index]) = example_reader.Value();
          num_done++;
        }
      }      
    }
    for (size_t i = 0; i < egs.size(); i++) {
      std::ostringstream ostr;
      ostr << num_done;
      if (egs[i] != NULL) {
        example_writer.Write(ostr.str(), *(egs[i]));
        delete egs[i];
      }
      num_done++;
    }

    KALDI_LOG << "Shuffled order of " << num_done
              << " neural-network training examples "
              << (buffer_size ? "using a buffer (partial randomization)" : "");
                  
    return (num_done == 0 ? 1 : 0);
  } catch(const std::exception &e) {
    std::cerr << e.what() << '\n';
    return -1;
  }
}