solve dependencies

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()