nnet3-discriminative-compute-objf.cc File Reference

#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "nnet3/nnet-discriminative-diagnostics.h"
#include "nnet3/am-nnet-simple.h"
#include "nnet3/nnet-utils.h"

Include dependency graph for nnet3-discriminative-compute-objf.cc:

Go to the source code of this file.

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

Function Documentation

main()

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

Definition at line 27 of file nnet3-discriminative-compute-objf.cc.

References ParseOptions::GetArg(), AmNnetSimple::GetNnet(), ParseOptions::NumArgs(), ParseOptions::PrintUsage(), AmNnetSimple::Priors(), AmNnetSimple::Read(), ParseOptions::Read(), TransitionModel::Read(), ParseOptions::Register(), NnetComputeProbOptions::Register(), DiscriminativeOptions::Register(), kaldi::nnet3::SetBatchnormTestMode(), kaldi::nnet3::SetDropoutTestMode(), and Input::Stream().

                                 {
  try {
    using namespace kaldi;
    using namespace kaldi::nnet3;
    typedef kaldi::int32 int32;
    typedef kaldi::int64 int64;

    const char *usage =
        "Computes and prints to in logging messages the objective function per frame of\n"
        "the given data with an nnet3 neural net.  The input of this is the output of\n"
        "e.g. nnet3-discriminative-get-egs | nnet3-discriminative-merge-egs.\n"
        "\n"
        "Usage:  nnet3-discrminative-compute-objf [options] <nnet3-model-in> <training-examples-in>\n"
        "e.g.: nnet3-discriminative-compute-objf 0.mdl ark:valid.degs\n";

    bool batchnorm_test_mode = true, dropout_test_mode = true;

    // This program doesn't support using a GPU, because these probabilities are
    // used for diagnostics, and you can just compute them with a small enough
    // amount of data that a CPU can do it within reasonable time.
    // It wouldn't be hard to make it support GPU, though.

    NnetComputeProbOptions nnet_opts;
    discriminative::DiscriminativeOptions discriminative_opts;

    ParseOptions po(usage);

    po.Register("batchnorm-test-mode", &batchnorm_test_mode,
                "If true, set test-mode to true on any BatchNormComponents.");
    po.Register("dropout-test-mode", &dropout_test_mode,
                "If true, set test-mode to true on any DropoutComponents and "
                "DropoutMaskComponents.");

    nnet_opts.Register(&po);
    discriminative_opts.Register(&po);

    po.Read(argc, argv);

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

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

    TransitionModel tmodel;
    AmNnetSimple am_nnet;

    {
      bool binary;
      Input ki(model_rxfilename, &binary);
      tmodel.Read(ki.Stream(), binary);
      am_nnet.Read(ki.Stream(), binary);
    }
    
    Nnet* nnet = &(am_nnet.GetNnet());

    if (batchnorm_test_mode)
      SetBatchnormTestMode(true, nnet);

    if (dropout_test_mode)
      SetDropoutTestMode(true, nnet);

    NnetDiscriminativeComputeObjf discriminative_objf_computer(nnet_opts, 
                                              discriminative_opts, 
                                              tmodel, am_nnet.Priors(), 
                                              *nnet);

    SequentialNnetDiscriminativeExampleReader example_reader(examples_rspecifier);

    for (; !example_reader.Done(); example_reader.Next())
      discriminative_objf_computer.Compute(example_reader.Value());

    bool ok = discriminative_objf_computer.PrintTotalStats();

    return (ok ? 0 : 1);
  } catch(const std::exception &e) {
    std::cerr << e.what() << '\n';
    return -1;
  }
}