// nnet3bin/nnet3-compute-batch.cc
  
  // Copyright 2012-2018   Johns Hopkins University (author: Daniel Povey)
  //           2018        Hang Lyu
  
  // See ../../COPYING for clarification regarding multiple authors
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //  http://www.apache.org/licenses/LICENSE-2.0
  //
  // THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  // KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
  // WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
  // MERCHANTABLITY OR NON-INFRINGEMENT.
  // See the Apache 2 License for the specific language governing permissions and
  // limitations under the License.
  
  
  #include "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "nnet3/nnet-batch-compute.h"
  #include "base/timer.h"
  #include "nnet3/nnet-utils.h"
  
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet3;
      typedef kaldi::int32 int32;
      typedef kaldi::int64 int64;
  
      const char *usage =
          "Propagate the features through raw neural network model "
          "and write the output.  This version is optimized for GPU use. "
          "If --apply-exp=true, apply the Exp() function to the output "
          "before writing it out.
  "
          "
  "
          "Usage: nnet3-compute-batch [options] <nnet-in> <features-rspecifier> "
          "<matrix-wspecifier>
  "
          " e.g.: nnet3-compute-batch final.raw scp:feats.scp "
          "ark:nnet_prediction.ark
  ";
  
      ParseOptions po(usage);
      Timer timer;
  
      NnetBatchComputerOptions opts;
      opts.acoustic_scale = 1.0;  // by default do no scaling
  
      bool apply_exp = false, use_priors = false;
      std::string use_gpu = "yes";
  
      std::string word_syms_filename;
      std::string ivector_rspecifier,
                  online_ivector_rspecifier,
                  utt2spk_rspecifier;
      int32 online_ivector_period = 0;
      opts.Register(&po);
  
      po.Register("ivectors", &ivector_rspecifier, "Rspecifier for "
                  "iVectors as vectors (i.e. not estimated online); per "
                  "utterance by default, or per speaker if you provide the "
                  "--utt2spk option.");
      po.Register("utt2spk", &utt2spk_rspecifier, "Rspecifier for "
                  "utt2spk option used to get ivectors per speaker");
      po.Register("online-ivectors", &online_ivector_rspecifier, "Rspecifier for "
                  "iVectors estimated online, as matrices.  If you supply this,"
                  " you must set the --online-ivector-period option.");
      po.Register("online-ivector-period", &online_ivector_period, "Number of "
                  "frames between iVectors in matrices supplied to the "
                  "--online-ivectors option");
      po.Register("apply-exp", &apply_exp, "If true, apply exp function to "
                  "output");
      po.Register("use-gpu", &use_gpu,
                  "yes|no|optional|wait, only has effect if compiled with CUDA");
      po.Register("use-priors", &use_priors, "If true, subtract the logs of the "
                  "priors stored with the model (in this case, "
                  "a .mdl file is expected as input).");
  
  #if HAVE_CUDA==1
      CuDevice::RegisterDeviceOptions(&po);
  #endif
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 3) {
        po.PrintUsage();
        exit(1);
      }
  
  #if HAVE_CUDA==1
      CuDevice::Instantiate().AllowMultithreading();
      CuDevice::Instantiate().SelectGpuId(use_gpu);
  #endif
  
      std::string nnet_rxfilename = po.GetArg(1),
                  feature_rspecifier = po.GetArg(2),
                  matrix_wspecifier = po.GetArg(3);
  
      Nnet raw_nnet;
      AmNnetSimple am_nnet;
      if (use_priors) {
        bool binary;
        TransitionModel trans_model;
        Input ki(nnet_rxfilename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_nnet.Read(ki.Stream(), binary);
      } else {
        ReadKaldiObject(nnet_rxfilename, &raw_nnet);
      }
      Nnet &nnet = (use_priors ? am_nnet.GetNnet() : raw_nnet);
      SetBatchnormTestMode(true, &nnet);
      SetDropoutTestMode(true, &nnet);
      CollapseModel(CollapseModelConfig(), &nnet);
  
      Vector<BaseFloat> priors;
      if (use_priors)
        priors = am_nnet.Priors();
  
      RandomAccessBaseFloatMatrixReader online_ivector_reader(
          online_ivector_rspecifier);
      RandomAccessBaseFloatVectorReaderMapped ivector_reader(
          ivector_rspecifier, utt2spk_rspecifier);
  
      BaseFloatMatrixWriter matrix_writer(matrix_wspecifier);
  
      int32 num_success = 0, num_fail = 0;
      std::string output_uttid;
      Matrix<BaseFloat> output_matrix;
  
  
      NnetBatchInference inference(opts, nnet, priors);
  
      SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
  
      for (; !feature_reader.Done(); feature_reader.Next()) {
        std::string utt = feature_reader.Key();
        const Matrix<BaseFloat> &features = feature_reader.Value();
        if (features.NumRows() == 0) {
          KALDI_WARN << "Zero-length utterance: " << utt;
          num_fail++;
          continue;
        }
        const Matrix<BaseFloat> *online_ivectors = NULL;
        const Vector<BaseFloat> *ivector = NULL;
        if (!ivector_rspecifier.empty()) {
          if (!ivector_reader.HasKey(utt)) {
            KALDI_WARN << "No iVector available for utterance " << utt;
            num_fail++;
            continue;
          } else {
            ivector = new Vector<BaseFloat>(ivector_reader.Value(utt));
          }
        }
        if (!online_ivector_rspecifier.empty()) {
          if (!online_ivector_reader.HasKey(utt)) {
            KALDI_WARN << "No online iVector available for utterance " << utt;
            num_fail++;
            continue;
          } else {
            online_ivectors = new Matrix<BaseFloat>(
                online_ivector_reader.Value(utt));
          }
        }
  
        inference.AcceptInput(utt, features, ivector, online_ivectors,
                              online_ivector_period);
  
        std::string output_key;
        Matrix<BaseFloat> output;
        while (inference.GetOutput(&output_key, &output)) {
          if (apply_exp)
            output.ApplyExp();
          matrix_writer.Write(output_key, output);
          num_success++;
        }
      }
  
      inference.Finished();
      std::string output_key;
      Matrix<BaseFloat> output;
      while (inference.GetOutput(&output_key, &output)) {
        if (apply_exp)
          output.ApplyExp();
        matrix_writer.Write(output_key, output);
        num_success++;
      }
  #if HAVE_CUDA==1
      CuDevice::Instantiate().PrintProfile();
  #endif
      double elapsed = timer.Elapsed();
      KALDI_LOG << "Time taken "<< elapsed << "s";
      KALDI_LOG << "Done " << num_success << " utterances, failed for "
                << num_fail;
  
      if (num_success != 0) {
        return 0;
      } else {
        return 1;
      }
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }