nnet-compute-from-egs.cc
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// nnet2bin/nnet-compute-from-egs.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 =
"Does the neural net computation, taking as input the nnet-training examples\n"
"(typically an archive with the extension .egs), ignoring the labels; it\n"
"outputs as a matrix the result. Used mostly for debugging.\n"
"\n"
"Usage: nnet-compute-from-egs [options] <raw-nnet-in> <egs-rspecifier> "
"<feature-wspecifier>\n"
"e.g.: nnet-compute-from-egs 'nnet-to-raw-nnet final.mdl -|' egs.10.1.ark ark:-\n";
ParseOptions po(usage);
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string raw_nnet_rxfilename = po.GetArg(1),
examples_rspecifier = po.GetArg(2),
features_or_loglikes_wspecifier = po.GetArg(3);
Nnet nnet;
ReadKaldiObject(raw_nnet_rxfilename, &nnet);
int64 num_egs = 0;
SequentialNnetExampleReader example_reader(examples_rspecifier);
BaseFloatMatrixWriter writer(features_or_loglikes_wspecifier);
int32 left_context = nnet.LeftContext(),
context = nnet.LeftContext() + 1 + nnet.RightContext();
for (; !example_reader.Done(); example_reader.Next()) {
const NnetExample &eg = example_reader.Value();
int32 start_offset = eg.left_context - left_context;
int32 basic_dim = eg.input_frames.NumCols(),
spk_dim = eg.spk_info.Dim(), dim = basic_dim + spk_dim;
Matrix<BaseFloat> input_frames(eg.input_frames),
input_block(context, dim);
input_block.Range(0, context, 0, basic_dim).CopyFromMat(
input_frames.Range(start_offset, context, 0, basic_dim));
if (spk_dim != 0) {
input_block.Range(0, context, basic_dim, spk_dim).CopyRowsFromVec(
eg.spk_info);
}
CuMatrix<BaseFloat> gpu_input_block;
gpu_input_block.Swap(&input_block);
CuMatrix<BaseFloat> gpu_output_block(1, nnet.OutputDim());
bool pad_input = false;
NnetComputation(nnet, gpu_input_block, pad_input, &gpu_output_block);
writer.Write("global", Matrix<BaseFloat>(gpu_output_block));
num_egs++;
}
KALDI_LOG << "Processed " << num_egs << " examples.";
return (num_egs == 0 ? 1 : 0);
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}