path: root/htk_io/tools/tnet_to_nerv.cpp

#include <cstdio>
#include <fstream>
#include <string>
#include <cstring>
#include <cassert>
#include <cstdlib>

char token[1024];
char output[1024];

double **new_matrix(int nrow, int ncol) {
    double **mat = new double *[nrow];
    int i;
    for (i = 0; i < nrow; i++)
        mat[i] = new double[ncol];
    return mat;
}

void free_matrix(double **mat, int nrow, int ncol) {
    int i;
    for (i = 0; i < nrow; i++)
        delete [] mat[i];
    delete [] mat;
}

int main(int argc, char **argv) {
    FILE *fin;
    std::ofstream fout;
    assert(argc >= 3);
    fin = fopen(argv[1], "r");
    fout.open(argv[2]);
    assert(fin != NULL);
    int cnt = argc > 3 ? atoi(argv[3]) : 0;
    while (fscanf(fin, "%s", token) != EOF)
    {
        int nrow, ncol;
        int i, j;
        double **mat;
        if (strcmp(token, "<biasedlinearity>") == 0)
        {
            fscanf(fin, "%d %d", &ncol, &nrow);
            fscanf(fin, "%s %d %d", token, &ncol, &nrow);
            printf("%d %d\n", nrow, ncol);
            mat = new_matrix(nrow, ncol);
            for (j = 0; j < ncol; j++)
                for (i = 0; i < nrow; i++)
                    fscanf(fin, "%lf", mat[i] + j);
            long base = fout.tellp();
            sprintf(output, "%16d", 0);
            fout << output;
            sprintf(output, "{type=\"nerv.LinearTransParam\",id=\"affine%d_ltp\"}\n",
                    cnt);
            fout << output;
            sprintf(output, "%d %d\n", nrow, ncol);
            fout << output;
            for (i = 0; i < nrow; i++)
            {
                for (j = 0; j < ncol; j++)
                    fout << mat[i][j] << " ";
                fout << std::endl;
            }
            long length = fout.tellp() - base;
            fout.seekp(base);
            sprintf(output, "[%13lu]\n", length);
            fout << output;
            fout.seekp(0, std::ios_base::end);
            if (fscanf(fin, "%s %d", token, &ncol) == 2 && *token == 'v')
            {
                base = fout.tellp();
                for (j = 0; j < ncol; j++)
                    fscanf(fin, "%lf", mat[0] + j);
                sprintf(output, "%16d", 0);
                fout << output;
                sprintf(output, "{type=\"nerv.BiasParam\",id=\"affine%d_bp\"}\n",
                        cnt);
                fout << output;
                sprintf(output, "1 %d\n", ncol);
                fout << output;
                for (j = 0; j < ncol; j++)
                    fout << mat[0][j] << " ";
                fout << std::endl;
                length = fout.tellp() - base;
                fout.seekp(base);
                sprintf(output, "[%13lu]\n", length);
                fout << output;
                fout.seekp(0, std::ios_base::end);
                cnt++;
            }
            free_matrix(mat, nrow, ncol);
        }
    }
    return 0;
}