nnet-show-progress.cc

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// nnet2bin/nnet-show-progress.cc

// Copyright 2012-2013  Johns Hopkins University (author:  Daniel Povey)

// 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 "hmm/transition-model.h"
#include "nnet2/train-nnet.h"
#include "nnet2/am-nnet.h"


int main(int argc, char *argv[]) {
  try {
    using namespace kaldi;
    using namespace kaldi::nnet2;
    typedef kaldi::int32 int32;
    typedef kaldi::int64 int64;

    const char *usage =
        "Given an old and a new model and some training examples (possibly held-out),\n"
        "show the average objective function given the mean of the two models,\n"
        "and the breakdown by component of why this happened (computed from\n"
        "derivative information).  Also shows parameter differences per layer.\n"
        "If training examples not provided, only shows parameter differences per\n"
        "layer.\n"
        "\n"
        "Usage:  nnet-show-progress [options] <old-model-in> <new-model-in> [<training-examples-in>]\n"
        "e.g.: nnet-show-progress 1.nnet 2.nnet ark:valid.egs\n";
    
    ParseOptions po(usage);

    int32 num_segments = 1;
    int32 batch_size = 1024;
    std::string use_gpu = "optional";
    
    po.Register("num-segments", &num_segments,
                "Number of line segments used for computing derivatives");
    po.Register("use-gpu", &use_gpu,
                "yes|no|optional|wait, only has effect if compiled with CUDA");
    
    po.Read(argc, argv);
    
    if (po.NumArgs() < 2 || po.NumArgs() > 3) {
      po.PrintUsage();
      exit(1);
    }
    
#if HAVE_CUDA==1
    CuDevice::Instantiate().SelectGpuId(use_gpu);
#endif

    std::string nnet1_rxfilename = po.GetArg(1),
        nnet2_rxfilename = po.GetArg(2),
        examples_rspecifier = po.GetOptArg(3);

    TransitionModel trans_model;
    AmNnet am_nnet1, am_nnet2;
    {
      bool binary_read;
      Input ki(nnet1_rxfilename, &binary_read);
      trans_model.Read(ki.Stream(), binary_read);
      am_nnet1.Read(ki.Stream(), binary_read);
    }
    {
      bool binary_read;
      Input ki(nnet2_rxfilename, &binary_read);
      trans_model.Read(ki.Stream(), binary_read);
      am_nnet2.Read(ki.Stream(), binary_read);
    }    
    
    if (am_nnet1.GetNnet().GetParameterDim() !=
        am_nnet2.GetNnet().GetParameterDim()) {
      KALDI_WARN << "Parameter-dim mismatch, cannot show progress.";
      exit(0);
    }

    int32 ret = 0;
    
    if (!examples_rspecifier.empty()) { 
      Nnet nnet_gradient(am_nnet2.GetNnet());
      const bool treat_as_gradient = true;
      nnet_gradient.SetZero(treat_as_gradient);

      std::vector<NnetExample> examples;
      SequentialNnetExampleReader example_reader(examples_rspecifier);
      for (; !example_reader.Done(); example_reader.Next())
        examples.push_back(example_reader.Value());

      int32 num_examples = examples.size();
    
      int32 num_updatable = am_nnet1.GetNnet().NumUpdatableComponents();
      Vector<BaseFloat> diff(num_updatable);
    
      for (int32 s = 0; s < num_segments; s++) {
        // start and end segments of the line between 0 and 1
        BaseFloat start = (s + 0.0) / num_segments,
            end = (s + 1.0) / num_segments, middle = 0.5 * (start + end);
        Nnet interp_nnet(am_nnet2.GetNnet());
        interp_nnet.Scale(middle);
        interp_nnet.AddNnet(1.0 - middle, am_nnet1.GetNnet());
      
        Nnet nnet_gradient(am_nnet2.GetNnet());
        const bool treat_as_gradient = true;
        nnet_gradient.SetZero(treat_as_gradient);

        double objf_per_frame = ComputeNnetGradient(interp_nnet, examples,
                                                    batch_size, &nnet_gradient);
        KALDI_LOG << "At position " << middle << ", objf per frame is " << objf_per_frame;

        Vector<BaseFloat> old_dotprod(num_updatable), new_dotprod(num_updatable);
        nnet_gradient.ComponentDotProducts(am_nnet1.GetNnet(), &old_dotprod);
        nnet_gradient.ComponentDotProducts(am_nnet2.GetNnet(), &new_dotprod);
        old_dotprod.Scale(1.0 / num_examples);
        new_dotprod.Scale(1.0 / num_examples);
        diff.AddVec(1.0/ num_segments, new_dotprod);
        diff.AddVec(-1.0 / num_segments, old_dotprod);
        KALDI_VLOG(1) << "By segment " << s << ", objf change is " << diff;
      }
      KALDI_LOG << "Total objf change per component is " << diff;
      if (num_examples == 0) ret = 1;
    }
   
    { // Get info about magnitude of parameter change.
      Nnet diff_nnet(am_nnet1.GetNnet());
      diff_nnet.AddNnet(-1.0, am_nnet2.GetNnet());
      int32 num_updatable = diff_nnet.NumUpdatableComponents();
      Vector<BaseFloat> dot_prod(num_updatable);
      diff_nnet.ComponentDotProducts(diff_nnet, &dot_prod);
      dot_prod.ApplyPow(0.5); // take sqrt to get l2 norm of diff
      KALDI_LOG << "Parameter differences per layer are "
                << dot_prod;

      Vector<BaseFloat> baseline_prod(num_updatable);
      am_nnet1.GetNnet().ComponentDotProducts(am_nnet1.GetNnet(),
                                              &baseline_prod);
      baseline_prod.ApplyPow(0.5);
      dot_prod.DivElements(baseline_prod);
      KALDI_LOG << "Relative parameter differences per layer are "
                << dot_prod;
    }

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