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src/bin/matrix-sum.cc
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// bin/matrix-sum.cc
// Copyright 2012-2014 Johns Hopkins University (author: Daniel Povey)
// 2014 Vimal Manohar
// 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 "matrix/kaldi-matrix.h"
namespace kaldi {
// sums a bunch of archives to produce one archive
// for back-compatibility with an older form, we support scaling
// of the first two input archives.
int32 TypeOneUsage(const ParseOptions &po,
BaseFloat scale1,
BaseFloat scale2) {
int32 num_args = po.NumArgs();
std::string matrix_in_fn1 = po.GetArg(1),
matrix_out_fn = po.GetArg(num_args);
// Output matrix
BaseFloatMatrixWriter matrix_writer(matrix_out_fn);
// Input matrices
SequentialBaseFloatMatrixReader matrix_reader1(matrix_in_fn1);
std::vector<RandomAccessBaseFloatMatrixReader*>
matrix_readers(num_args-2,
static_cast<RandomAccessBaseFloatMatrixReader*>(NULL));
std::vector<std::string> matrix_in_fns(num_args-2);
for (int32 i = 2; i < num_args; ++i) {
matrix_readers[i-2] = new RandomAccessBaseFloatMatrixReader(po.GetArg(i));
matrix_in_fns[i-2] = po.GetArg(i);
}
int32 n_utts = 0, n_total_matrices = 0,
n_success = 0, n_missing = 0, n_other_errors = 0;
for (; !matrix_reader1.Done(); matrix_reader1.Next()) {
std::string key = matrix_reader1.Key();
Matrix<BaseFloat> matrix1 = matrix_reader1.Value();
matrix_reader1.FreeCurrent();
n_utts++;
n_total_matrices++;
matrix1.Scale(scale1);
Matrix<BaseFloat> matrix_out(matrix1);
for (int32 i = 0; i < num_args-2; ++i) {
if (matrix_readers[i]->HasKey(key)) {
Matrix<BaseFloat> matrix2 = matrix_readers[i]->Value(key);
n_total_matrices++;
if (SameDim(matrix2, matrix_out)) {
BaseFloat scale = (i == 0 ? scale2 : 1.0);
// note: i == 0 corresponds to the 2nd input archive.
matrix_out.AddMat(scale, matrix2, kNoTrans);
} else {
KALDI_WARN << "Dimension mismatch for utterance " << key
<< " : " << matrix2.NumRows() << " by "
<< matrix2.NumCols() << " for "
<< "system " << (i + 2) << ", rspecifier: "
<< matrix_in_fns[i] << " vs " << matrix_out.NumRows()
<< " by " << matrix_out.NumCols()
<< " primary matrix, rspecifier:" << matrix_in_fn1;
n_other_errors++;
}
} else {
KALDI_WARN << "No matrix found for utterance " << key << " for "
<< "system " << (i + 2) << ", rspecifier: "
<< matrix_in_fns[i];
n_missing++;
}
}
matrix_writer.Write(key, matrix_out);
n_success++;
}
KALDI_LOG << "Processed " << n_utts << " utterances: with a total of "
<< n_total_matrices << " matrices across " << (num_args-1)
<< " different systems";
KALDI_LOG << "Produced output for " << n_success << " utterances; "
<< n_missing << " total missing matrices";
DeletePointers(&matrix_readers);
return (n_success != 0 && n_missing < (n_success - n_missing)) ? 0 : 1;
}
int32 TypeOneUsageAverage(const ParseOptions &po) {
int32 num_args = po.NumArgs();
std::string matrix_in_fn1 = po.GetArg(1),
matrix_out_fn = po.GetArg(num_args);
BaseFloat scale = 1.0 / (num_args - 1);
// Output matrix
BaseFloatMatrixWriter matrix_writer(matrix_out_fn);
// Input matrices
SequentialBaseFloatMatrixReader matrix_reader1(matrix_in_fn1);
std::vector<RandomAccessBaseFloatMatrixReader*>
matrix_readers(num_args-2,
static_cast<RandomAccessBaseFloatMatrixReader*>(NULL));
std::vector<std::string> matrix_in_fns(num_args-2);
for (int32 i = 2; i < num_args; ++i) {
matrix_readers[i-2] = new RandomAccessBaseFloatMatrixReader(po.GetArg(i));
matrix_in_fns[i-2] = po.GetArg(i);
}
int32 n_utts = 0, n_total_matrices = 0,
n_success = 0, n_missing = 0, n_other_errors = 0;
for (; !matrix_reader1.Done(); matrix_reader1.Next()) {
std::string key = matrix_reader1.Key();
Matrix<BaseFloat> matrix1 = matrix_reader1.Value();
matrix_reader1.FreeCurrent();
n_utts++;
n_total_matrices++;
matrix1.Scale(scale);
Matrix<BaseFloat> matrix_out(matrix1);
for (int32 i = 0; i < num_args-2; ++i) {
if (matrix_readers[i]->HasKey(key)) {
Matrix<BaseFloat> matrix2 = matrix_readers[i]->Value(key);
n_total_matrices++;
if (SameDim(matrix2, matrix_out)) {
matrix_out.AddMat(scale, matrix2, kNoTrans);
} else {
KALDI_WARN << "Dimension mismatch for utterance " << key
<< " : " << matrix2.NumRows() << " by "
<< matrix2.NumCols() << " for "
<< "system " << (i + 2) << ", rspecifier: "
<< matrix_in_fns[i] << " vs " << matrix_out.NumRows()
<< " by " << matrix_out.NumCols()
<< " primary matrix, rspecifier:" << matrix_in_fn1;
n_other_errors++;
}
} else {
KALDI_WARN << "No matrix found for utterance " << key << " for "
<< "system " << (i + 2) << ", rspecifier: "
<< matrix_in_fns[i];
n_missing++;
}
}
matrix_writer.Write(key, matrix_out);
n_success++;
}
KALDI_LOG << "Processed " << n_utts << " utterances: with a total of "
<< n_total_matrices << " matrices across " << (num_args-1)
<< " different systems";
KALDI_LOG << "Produced output for " << n_success << " utterances; "
<< n_missing << " total missing matrices";
DeletePointers(&matrix_readers);
return (n_success != 0 && n_missing < (n_success - n_missing)) ? 0 : 1;
}
int32 TypeTwoUsage(const ParseOptions &po,
bool binary) {
KALDI_ASSERT(po.NumArgs() == 2);
KALDI_ASSERT(ClassifyRspecifier(po.GetArg(1), NULL, NULL) != kNoRspecifier &&
"matrix-sum: first argument must be an rspecifier");
// if next assert fails it would be bug in the code as otherwise we shouldn't
// be called.
KALDI_ASSERT(ClassifyWspecifier(po.GetArg(2), NULL, NULL, NULL) ==
kNoWspecifier);
SequentialBaseFloatMatrixReader mat_reader(po.GetArg(1));
Matrix<double> sum;
int32 num_done = 0, num_err = 0;
for (; !mat_reader.Done(); mat_reader.Next()) {
const Matrix<BaseFloat> &mat = mat_reader.Value();
if (mat.NumRows() == 0) {
KALDI_WARN << "Zero matrix input for key " << mat_reader.Key();
num_err++;
} else {
if (sum.NumRows() == 0) sum.Resize(mat.NumRows(), mat.NumCols());
if (sum.NumRows() != mat.NumRows() || sum.NumCols() != mat.NumCols()) {
KALDI_WARN << "Dimension mismatch for key " << mat_reader.Key()
<< ": " << mat.NumRows() << " by " << mat.NumCols() << " vs. "
<< sum.NumRows() << " by " << sum.NumCols();
num_err++;
} else {
Matrix<double> dmat(mat);
sum.AddMat(1.0, dmat, kNoTrans);
num_done++;
}
}
}
Matrix<BaseFloat> sum_float(sum);
WriteKaldiObject(sum_float, po.GetArg(2), binary);
KALDI_LOG << "Summed " << num_done << " matrices, "
<< num_err << " with errors; wrote sum to "
<< PrintableWxfilename(po.GetArg(2));
return (num_done > 0 && num_err < num_done) ? 0 : 1;
}
// sum a bunch of single files to produce a single file [including
// extended filenames, of course]
int32 TypeThreeUsage(const ParseOptions &po,
bool binary, bool average) {
KALDI_ASSERT(po.NumArgs() >= 2);
for (int32 i = 1; i < po.NumArgs(); i++) {
if (ClassifyRspecifier(po.GetArg(i), NULL, NULL) != kNoRspecifier) {
KALDI_ERR << "Wrong usage (type 3): if first and last arguments are not "
<< "tables, the intermediate arguments must not be tables.";
}
}
if (ClassifyWspecifier(po.GetArg(po.NumArgs()), NULL, NULL, NULL) !=
kNoWspecifier) {
KALDI_ERR << "Wrong usage (type 3): if first and last arguments are not "
<< "tables, the intermediate arguments must not be tables.";
}
Matrix<BaseFloat> sum;
for (int32 i = 1; i < po.NumArgs(); i++) {
Matrix<BaseFloat> this_mat;
ReadKaldiObject(po.GetArg(i), &this_mat);
if (sum.NumRows() < this_mat.NumRows() ||
sum.NumCols() < this_mat.NumCols())
sum.Resize(std::max(sum.NumRows(), this_mat.NumRows()),
std::max(sum.NumCols(), this_mat.NumCols()),
kCopyData);
sum.AddMat(1.0, this_mat);
}
if (average)
sum.Scale(1.0 / (po.NumArgs() - 1));
WriteKaldiObject(sum, po.GetArg(po.NumArgs()), binary);
KALDI_LOG << "Summed " << (po.NumArgs() - 1) << " matrices; "
<< "wrote sum to " << PrintableWxfilename(po.GetArg(po.NumArgs()));
return 0;
}
} // namespace kaldi
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
const char *usage =
"Add matrices (supports various forms)
"
"
"
"Type one usage:
"
" matrix-sum [options] <matrix-in-rspecifier1> [<matrix-in-rspecifier2>"
" <matrix-in-rspecifier3> ...] <matrix-out-wspecifier>
"
" e.g.: matrix-sum ark:1.weights ark:2.weights ark:combine.weights
"
" This usage supports the --scale1 and --scale2 options to scale the
"
" first two input tables.
"
"Type two usage (sums a single table input to produce a single output):
"
" matrix-sum [options] <matrix-in-rspecifier> <matrix-out-wxfilename>
"
" e.g.: matrix-sum --binary=false mats.ark sum.mat
"
"Type three usage (sums or averages single-file inputs to produce
"
"a single output):
"
" matrix-sum [options] <matrix-in-rxfilename1> <matrix-in-rxfilename2> ..."
" <matrix-out-wxfilename>
"
" e.g.: matrix-sum --binary=false 1.mat 2.mat 3.mat sum.mat
"
"See also: matrix-sum-rows, copy-matrix
";
BaseFloat scale1 = 1.0, scale2 = 1.0;
bool average = false;
bool binary = true;
ParseOptions po(usage);
po.Register("scale1", &scale1, "Scale applied to first matrix "
"(only for type one usage)");
po.Register("scale2", &scale2, "Scale applied to second matrix "
"(only for type one usage)");
po.Register("binary", &binary, "If true, write output as binary (only "
"relevant for usage types two or three");
po.Register("average", &average, "If true, compute average instead of "
"sum; currently compatible with type 3 or type 1 usage.");
po.Read(argc, argv);
int32 N = po.NumArgs(), exit_status;
if (po.NumArgs() >= 2 &&
ClassifyWspecifier(po.GetArg(N), NULL, NULL, NULL) != kNoWspecifier) {
if (average)
// average option with type one usage.";
exit_status = TypeOneUsageAverage(po);
else
// output to table.
exit_status = TypeOneUsage(po, scale1, scale2);
} else if (po.NumArgs() == 2 &&
ClassifyRspecifier(po.GetArg(1), NULL, NULL) != kNoRspecifier &&
ClassifyWspecifier(po.GetArg(N), NULL, NULL, NULL) ==
kNoWspecifier) {
KALDI_ASSERT(scale1 == 1.0 && scale2 == 1.0);
if (average)
KALDI_ERR << "--average option not compatible with type two usage.";
// input from a single table, output not to table.
exit_status = TypeTwoUsage(po, binary);
} else if (po.NumArgs() >= 2 &&
ClassifyRspecifier(po.GetArg(1), NULL, NULL) == kNoRspecifier &&
ClassifyWspecifier(po.GetArg(N), NULL, NULL, NULL) == kNoWspecifier) {
KALDI_ASSERT(scale1 == 1.0 && scale2 == 1.0);
// summing flat files.
exit_status = TypeThreeUsage(po, binary, average);
} else {
po.PrintUsage();
exit(1);
}
return exit_status;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}
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