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// nnetbin/nnet-forward.cc
// Copyright 2011-2013 Brno University of Technology (Author: Karel Vesely)
// 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.
extern "C"{
#include "lua.h"
#include "lauxlib.h"
#include "lualib.h"
#include "nerv/lib/matrix/matrix.h"
#include "nerv/lib/common.h"
#include "nerv/lib/luaT/luaT.h"
}
#include <limits>
#include "nnet/nnet-nnet.h"
#include "nnet/nnet-loss.h"
#include "nnet/nnet-pdf-prior.h"
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "base/timer.h"
typedef kaldi::BaseFloat BaseFloat;
typedef struct Matrix NervMatrix;
int main(int argc, char *argv[]) {
using namespace kaldi;
using namespace kaldi::nnet1;
try {
const char *usage =
"Perform forward pass through Neural Network.\n"
"\n"
"Usage: nnet-forward [options] <nerv-config> <feature-rspecifier> <feature-wspecifier> [nerv4decode.lua]\n"
"e.g.: \n"
" nnet-forward config.lua ark:features.ark ark:mlpoutput.ark\n";
ParseOptions po(usage);
PdfPriorOptions prior_opts;
prior_opts.Register(&po);
bool apply_log = false;
po.Register("apply-log", &apply_log, "Transform MLP output to logscale");
std::string use_gpu="no";
po.Register("use-gpu", &use_gpu, "yes|no|optional, only has effect if compiled with CUDA");
using namespace kaldi;
using namespace kaldi::nnet1;
typedef kaldi::int32 int32;
int32 time_shift = 0;
po.Register("time-shift", &time_shift, "LSTM : repeat last input frame N-times, discrad N initial output frames.");
po.Read(argc, argv);
if (po.NumArgs() < 3) {
po.PrintUsage();
exit(1);
}
std::string config = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
feature_wspecifier = po.GetArg(3),
nerv4decode = "src/nerv4decode.lua";
if(po.NumArgs() >= 4)
nerv4decode = po.GetArg(4);
//Select the GPU
#if HAVE_CUDA==1
CuDevice::Instantiate().SelectGpuId(use_gpu);
#endif
// we will subtract log-priors later,
PdfPrior pdf_prior(prior_opts);
kaldi::int64 tot_t = 0;
BaseFloatMatrixWriter feature_writer(feature_wspecifier);
CuMatrix<BaseFloat> nnet_out;
kaldi::Matrix<BaseFloat> nnet_out_host;
lua_State *L = lua_open();
luaL_openlibs(L);
if(luaL_loadfile(L, nerv4decode.c_str()))
KALDI_ERR << "luaL_loadfile() " << nerv4decode << " failed " << lua_tostring(L, -1);
if(lua_pcall(L, 0, 0, 0))
KALDI_ERR << "lua_pall failed " << lua_tostring(L, -1);
lua_settop(L, 0);
lua_getglobal(L, "init");
lua_pushstring(L, config.c_str());
lua_pushstring(L, feature_rspecifier.c_str());
if(lua_pcall(L, 2, 0, 0))
KALDI_ERR << "lua_pcall failed " << lua_tostring(L, -1);
Timer time;
double time_now = 0;
int32 num_done = 0;
// iterate over all feature files
for(;;){
lua_settop(L, 0);
lua_getglobal(L, "feed");
if(lua_pcall(L, 0, 2, 0))
KALDI_ERR << "lua_pcall failed " << lua_tostring(L, -1);
std::string utt = std::string(lua_tostring(L, -2));
if(utt == "")
break;
NervMatrix *mat = *(NervMatrix **)lua_touserdata(L, -1);
nnet_out_host.Resize(mat->nrow, mat->ncol, kUndefined);
size_t stride = mat->stride;
for(int i = 0; i < mat->nrow; i++){
const BaseFloat *nerv_row = (BaseFloat *)((char *)mat->data.f + i * stride);
BaseFloat *row = nnet_out_host.RowData(i);
memmove(row, nerv_row, sizeof(BaseFloat) * mat->ncol);
}
KALDI_VLOG(2) << "Processing utterance " << num_done+1
<< ", " << utt
<< ", " << nnet_out_host.NumRows() << "frm";
nnet_out.Resize(nnet_out_host.NumRows(), nnet_out_host.NumCols(), kUndefined);
nnet_out.CopyFromMat(nnet_out_host);
if (!KALDI_ISFINITE(nnet_out.Sum())) { // check there's no nan/inf,
KALDI_ERR << "NaN or inf found in nn-output for " << utt;
}
// convert posteriors to log-posteriors,
if (apply_log) {
if (!(nnet_out.Min() >= 0.0 && nnet_out.Max() <= 1.0)) {
KALDI_WARN << utt << " "
<< "Applying 'log' to data which don't seem to be probabilities "
<< "(is there a softmax somwhere?)";
}
nnet_out.Add(1e-20); // avoid log(0),
nnet_out.ApplyLog();
}
// subtract log-priors from log-posteriors or pre-softmax,
if (prior_opts.class_frame_counts != "") {
if (nnet_out.Min() >= 0.0 && nnet_out.Max() <= 1.0) {
KALDI_WARN << utt << " "
<< "Subtracting log-prior on 'probability-like' data in range [0..1] "
<< "(Did you forget --no-softmax=true or --apply-log=true ?)";
}
pdf_prior.SubtractOnLogpost(&nnet_out);
}
// download from GPU,
nnet_out_host.Resize(nnet_out.NumRows(), nnet_out.NumCols());
nnet_out.CopyToMat(&nnet_out_host);
// time-shift, remove N first frames of LSTM output,
if (time_shift > 0) {
kaldi::Matrix<BaseFloat> tmp(nnet_out_host);
nnet_out_host = tmp.RowRange(time_shift, tmp.NumRows() - time_shift);
}
// write,
if (!KALDI_ISFINITE(nnet_out_host.Sum())) { // check there's no nan/inf,
KALDI_ERR << "NaN or inf found in final output nn-output for " << utt;
}
feature_writer.Write(utt, nnet_out_host);
// progress log
if (num_done % 100 == 0) {
time_now = time.Elapsed();
KALDI_VLOG(1) << "After " << num_done << " utterances: time elapsed = "
<< time_now/60 << " min; processed " << tot_t/time_now
<< " frames per second.";
}
num_done++;
tot_t += nnet_out_host.NumRows();
}
// final message
KALDI_LOG << "Done " << num_done << " files"
<< " in " << time.Elapsed()/60 << "min,"
<< " (fps " << tot_t/time.Elapsed() << ")";
#if HAVE_CUDA==1
if (kaldi::g_kaldi_verbose_level >= 1) {
CuDevice::Instantiate().PrintProfile();
}
#endif
lua_close(L);
if (num_done == 0) return -1;
return 0;
} catch(const std::exception &e) {
KALDI_ERR << e.what();
return -1;
}
}
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