diff options
Diffstat (limited to 'kaldi_io/tools')
| -rw-r--r-- | kaldi_io/tools/kaldi_to_nerv.cpp | 109 | ||||
| -rw-r--r-- | kaldi_io/tools/nerv_to_kaldi.lua | 66 |
2 files changed, 175 insertions, 0 deletions
diff --git a/kaldi_io/tools/kaldi_to_nerv.cpp b/kaldi_io/tools/kaldi_to_nerv.cpp new file mode 100644 index 0000000..1edb0f2 --- /dev/null +++ b/kaldi_io/tools/kaldi_to_nerv.cpp @@ -0,0 +1,109 @@ +#include <cstdio> +#include <fstream> +#include <string> +#include <cstring> +#include <cassert> + +char token[1024]; +char output[1024]; +double mat[4096][4096]; +int main(int argc, char **argv) { + std::ofstream fout; + fout.open(argv[1]); + int cnt = 0; + bool shift; + while (scanf("%s", token) != EOF) + { + int nrow, ncol; + int i, j; + if (strcmp(token, "<AffineTransform>") == 0) + { + double lrate, blrate, mnorm; + scanf("%d %d", &ncol, &nrow); + scanf("%s %lf %s %lf %s %lf", + token, &lrate, token, &blrate, token, &mnorm); + scanf("%s", token); + assert(*token == '['); + printf("%d %d\n", nrow, ncol); + for (j = 0; j < ncol; j++) + for (i = 0; i < nrow; i++) + scanf("%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); + scanf("%s", token); + assert(*token == ']'); + if (scanf("%s", token) == 1 && *token == '[') + { + base = fout.tellp(); + for (j = 0; j < ncol; j++) + scanf("%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++; + } + } + else if ((shift = (strcmp(token, "<AddShift>") == 0)) || + strcmp(token, "<Rescale>") == 0) + { + double lrate, blrate, mnorm; + scanf("%d %d", &ncol, &ncol); + scanf("%s %lf", + token, &lrate); + scanf("%s", token); + assert(*token == '['); + printf("%d\n", ncol); + for (j = 0; j < ncol; j++) + scanf("%lf", mat[0] + j); + long base = fout.tellp(); + sprintf(output, "%16d", 0); + fout << output; + sprintf(output, "{type=\"nerv.BiasParam\",id=\"%s%d\"}\n", + shift ? "bias" : "window", + cnt); + fout << output; + sprintf(output, "%d %d\n", 1, ncol); + fout << output; + for (j = 0; j < ncol; j++) + fout << mat[0][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); + scanf("%s", token); + assert(*token == ']'); + } + } + return 0; +} diff --git a/kaldi_io/tools/nerv_to_kaldi.lua b/kaldi_io/tools/nerv_to_kaldi.lua new file mode 100644 index 0000000..804f09b --- /dev/null +++ b/kaldi_io/tools/nerv_to_kaldi.lua @@ -0,0 +1,66 @@ +-- usage: nerv config_file nerv_param_input tnet_output + +dofile(arg[1]) +param_repo = nerv.ParamRepo() +param_repo:import({arg[2], gconf.initialized_param[2]}, nil, gconf) +layer_repo = make_layer_repo(param_repo) +f = assert(io.open(arg[3], "w")) + +function print_tnet_matrix(cumat) + local strs = {} + collectgarbage() + if cumat:nrow() == 1 then + local mat = nerv.MMatrixFloat(1, cumat:ncol()) + cumat:copy_toh(mat) + table.insert(strs, "[ ") + for j = 0, mat:ncol() - 1 do + table.insert(strs, string.format("%.8f ", mat[0][j])) + end + table.insert(strs, " ]\n") + f:write(table.concat(strs)) + else + cumat = cumat:trans() + local mat = nerv.MMatrixFloat(cumat:nrow(), cumat:ncol()) + cumat:copy_toh(mat) + table.insert(strs, string.format(" [\n", mat:nrow(), mat:ncol())) + for i = 0, mat:nrow() - 1 do + local row = mat[i] + for j = 0, mat:ncol() - 1 do + table.insert(strs, string.format("%.8f ", row[j])) + end + if i == mat:nrow() - 1 then + table.insert(strs, " ]\n") + else + table.insert(strs, "\n") + end + f:write(table.concat(strs)) + strs = {} + end + end +end +local lnames = {"affine0", "sigmoid0", + "affine1", "sigmoid1", + "affine2", "sigmoid2", + "affine3", "sigmoid3", + "affine4", "sigmoid4", + "affine5", "sigmoid5", + "affine6", "ce_crit"} +f:write("<Nnet>\n") +for i, name in ipairs(lnames) do + local layer = layer_repo:get_layer(name) + local layer_type = layer.__typename + if layer_type == "nerv.AffineLayer" then + f:write(string.format("<AffineTransform> %d %d\n<LearnRateCoef> 1 <BiasLearnRateCoef> 1 <MaxNorm> 0", + layer.dim_out[1], layer.dim_in[1])) + print_tnet_matrix(layer.ltp.trans) + print_tnet_matrix(layer.bp.trans) + elseif layer_type == "nerv.SigmoidLayer" then + f:write(string.format("<Sigmoid> %d %d\n", layer.dim_out[1], layer.dim_in[1])) + elseif layer_type == "nerv.SoftmaxCELayer" then + f:write(string.format("<Softmax> %d %d\n", layer.dim_in[1], layer.dim_in[1])) + else + nerv.error("unknown layer type %s", layer_type) + end +end +f:write("</Nnet>\n") +f:close() |