...

9 files changed, 220 insertions, 9 deletions

diff --git a/examples/tnet_preprocessing_example.lua b/examples/tnet_preprocessing_example.lua
index 8a65b44..5f20910 100644
--- a/examples/tnet_preprocessing_example.lua
+++ b/examples/tnet_preprocessing_example.lua
@@ -1,6 +1,6 @@
 require 'libspeech'
 frm_ext = 5
-gconf = {mat_type = nerv.CuMatrixFloat,
+gconf = {cumat_type = nerv.CuMatrixFloat,
         batch_size = 158}
 param_repo = nerv.ParamRepo({"global_transf.nerv"})
 sublayer_repo = nerv.LayerRepo(
diff --git a/examples/tnet_preprocessing_example2.lua b/examples/tnet_preprocessing_example2.lua
index ae8d86b..8e1bc85 100644
--- a/examples/tnet_preprocessing_example2.lua
+++ b/examples/tnet_preprocessing_example2.lua
@@ -1,5 +1,5 @@
 require 'speech.init'
-gconf = {mat_type = nerv.CuMatrixFloat,
+gconf = {cumat_type = nerv.CuMatrixFloat,
         batch_size = 158}
 param_repo = nerv.ParamRepo({"global_transf.nerv"})
 
diff --git a/examples/tnet_sgd_buffer.lua b/examples/tnet_sgd_buffer.lua
new file mode 100644
index 0000000..152d2f5
--- /dev/null
+++ b/examples/tnet_sgd_buffer.lua
@@ -0,0 +1,70 @@
+require 'speech.init'
+gconf = {cumat_type = nerv.CuMatrixFloat,
+        mmat_type = nerv.MMatrixFloat,
+        batch_size = 256}
+param_repo = nerv.ParamRepo({"global_transf.nerv"})
+
+sublayer_repo = nerv.LayerRepo(
+    {
+        ["nerv.BiasLayer"] =
+        {
+            blayer1 = {{bias = "bias1"}, {dim_in = {429}, dim_out = {429}}},
+            blayer2 = {{bias = "bias2"}, {dim_in = {429}, dim_out = {429}}}
+        },
+        ["nerv.WindowLayer"] =
+        {
+            wlayer1 = {{window = "window1"}, {dim_in = {429}, dim_out = {429}}},
+            wlayer2 = {{window = "window2"}, {dim_in = {429}, dim_out = {429}}}
+        }
+    }, param_repo, gconf)
+
+layer_repo = nerv.LayerRepo(
+    {
+        ["nerv.DAGLayer"] =
+        {
+            main = {{}, {
+                dim_in = {429}, dim_out = {429},
+                sub_layers = sublayer_repo,
+                connections = {
+                    ["<input>[1]"] = "blayer1[1]",
+                    ["blayer1[1]"] = "wlayer1[1]",
+                    ["wlayer1[1]"] = "blayer2[1]",
+                    ["blayer2[1]"] = "wlayer2[1]",
+                    ["wlayer2[1]"] = "<output>[1]"
+                }
+            }}
+        }
+    }, param_repo, gconf)
+
+tnet_reader = nerv.TNetReader({},
+    {
+        id = "main_scp",
+--        scp_file = "/slfs1/users/mfy43/swb_ivec/train_bp.scp",
+        scp_file = "t.scp",
+        conf_file = "/slfs1/users/mfy43/swb_ivec/plp_0_d_a.conf",
+        frm_ext = 5,
+        mlfs = {
+            ref = {
+                file = "/slfs1/users/mfy43/swb_ivec/ref.mlf",
+                format = "map",
+                format_arg = "/slfs1/users/mfy43/swb_ivec/dict",
+                dir = "*/",
+                ext = "lab"
+            }
+        },
+        global_transf = layer_repo:get_layer("main")
+    })
+
+buffer = nerv.SGDBuffer(gconf,
+    {
+        buffer_size = 1024,
+        readers = {
+            { reader = tnet_reader,
+              data = {main_scp = 429, ref = 1}}
+        }
+    })
+
+for data in buffer.get_data, buffer do
+    print(data.main_scp)
+--    print(data.ref)
+end
diff --git a/init.lua b/init.lua
index 1f20096..39a1e9e 100644
--- a/init.lua
+++ b/init.lua
@@ -4,6 +4,8 @@ local TNetReader = nerv.class("nerv.TNetReader", "nerv.DataReader")
 function TNetReader:__init(global_conf, reader_conf)
     self.feat_id = reader_conf.id
     self.frm_ext = reader_conf.frm_ext
+    self.gconf = global_conf
+    self.global_transf = reader_conf.global_transf
     self.feat_repo = nerv.TNetFeatureRepo(reader_conf.scp_file,
                                           reader_conf.conf_file,
                                           reader_conf.frm_ext)
@@ -15,24 +17,26 @@ function TNetReader:__init(global_conf, reader_conf)
                                             mlf_spec.dir,
                                             mlf_spec.ext)
     end
-    self.global_transf = reader_conf.global_transf
 end
 
 function TNetReader:get_data()
+    if self.feat_repo:is_end() then
+        return nil
+    end
     local res = {}
     local frm_ext = self.frm_ext
     local step = frm_ext * 2 + 1
     local feat_utter = self.feat_repo:cur_utter()
-    local expanded = nerv.CuMatrixFloat(feat_utter:nrow(), feat_utter:ncol() * step)
-    expanded:expand_frm(nerv.CuMatrixFloat.new_from_host(feat_utter), frm_ext)
+    local expanded = self.gconf.cumat_type(feat_utter:nrow(), feat_utter:ncol() * step)
+    expanded:expand_frm(self.gconf.cumat_type.new_from_host(feat_utter), frm_ext)
     local rearranged = expanded:create()
     rearranged:rearrange_frm(expanded, step)
     local input = {rearranged}
     local output = {rearranged:create()}
-    self.global_transf:init()
+    self.global_transf:init(input[1]:nrow())
     self.global_transf:propagate(input, output)
-    expanded = nerv.CuMatrixFloat(output[1]:nrow() - frm_ext * 2, output[1]:ncol())
-    expanded:copy_fromd(output[1], frm_ext, feat_utter:nrow() - frm_ext)
+    expanded = self.gconf.mmat_type(output[1]:nrow() - frm_ext * 2, output[1]:ncol())
+    output[1]:copy_toh(expanded, frm_ext, feat_utter:nrow() - frm_ext)
     res[self.feat_id] = expanded
     for id, repo in pairs(self.lab_repo) do
         local lab_utter = repo:get_utter(self.feat_repo, expanded:nrow())
diff --git a/tnet_io/cwrapper.cpp b/tnet_io/cwrapper.cpp
index e82f3f8..4149557 100644
--- a/tnet_io/cwrapper.cpp
+++ b/tnet_io/cwrapper.cpp
@@ -2,13 +2,13 @@
 #include "KaldiLib/Labels.h"
 #include "KaldiLib/Common.h"
 #include "KaldiLib/UserInterface.h"
-#include "../../common.h"
 #include <string>
 #define SNAME "TNET"
 
 extern "C" {
 #include "cwrapper.h"
 #include "string.h"
+#include "../../common.h"
 
     extern Matrix *nerv_matrix_host_float_new_(lua_State *L, long nrow, long ncol);
 
@@ -77,6 +77,10 @@ extern "C" {
         repo->feature_repo.MoveNext();
     }
 
+    int tnet_feature_repo_is_end(TNetFeatureRepo *repo) {
+        return repo->feature_repo.EndOfList();
+    }
+
     size_t tnet_feature_repo_current_samplerate(TNetFeatureRepo *repo) {
         return repo->feature_repo.CurrentHeader().mSamplePeriod;
     }
diff --git a/tnet_io/cwrapper.h b/tnet_io/cwrapper.h
index a34f090..54fb69b 100644
--- a/tnet_io/cwrapper.h
+++ b/tnet_io/cwrapper.h
@@ -14,6 +14,7 @@ extern "C" {
     size_t tnet_feature_repo_current_samplerate(TNetFeatureRepo *repo);
     const char *tnet_feature_repo_current_tag(TNetFeatureRepo *repo);
     void tnet_feature_repo_next(TNetFeatureRepo *repo);
+    int tnet_feature_repo_is_end(TNetFeatureRepo *repo);
     void tnet_feature_repo_destroy(TNetFeatureRepo *repo);
 
     typedef struct TNetLabelRepo TNetLabelRepo;
diff --git a/tnet_io/init.c b/tnet_io/init.c
index 3fa7cb8..16f6f37 100644
--- a/tnet_io/init.c
+++ b/tnet_io/init.c
@@ -40,10 +40,17 @@ static int feat_repo_next(lua_State *L) {
     return 0;
 }
 
+static int feat_repo_is_end(lua_State *L) {
+    TNetFeatureRepo *repo = luaT_checkudata(L, 1, nerv_tnet_feat_repo_tname);
+    lua_pushboolean(L, tnet_feature_repo_is_end(repo));
+    return 1;
+}
+
 static const luaL_Reg feat_repo_methods[] = {
     {"cur_utter", feat_repo_current_utterance},
     {"cur_tag", feat_repo_current_tag},
     {"next", feat_repo_next},
+    {"is_end", feat_repo_is_end},
     {NULL, NULL}
 };
 
diff --git a/tools/tnet_to_nerv.c b/tools/tnet_to_nerv.c
new file mode 100644
index 0000000..f781236
--- /dev/null
+++ b/tools/tnet_to_nerv.c
@@ -0,0 +1,57 @@
+#include <stdio.h>
+#include <string.h>
+#include <stdlib.h>
+char token[1024];
+double mat[4096][4096];
+int main() {
+    FILE *fout = fopen("converted.nerv", "w");
+    int cnt = 0;
+    while (scanf("%s", token) != EOF)
+    {
+        int nrow, ncol;
+        int i, j;
+        if (strcmp(token, "<biasedlinearity>") == 0)
+        {
+            scanf("%d %d", &ncol, &nrow);
+            scanf("%s %d %d", token, &ncol, &nrow);
+            printf("%d %d\n", nrow, ncol);
+            for (j = 0; j < ncol; j++)
+                for (i = 0; i < nrow; i++)
+                    scanf("%lf", mat[i] + j);
+            off_t base = ftello(fout);
+            fprintf(fout, "%16d", 0);
+            fprintf(fout, "{type=\"nerv.LinearTransParam\",id=\"affine%d_ltp\"}\n",
+                            cnt);
+            fprintf(fout, "%d %d\n", nrow, ncol);
+            for (i = 0; i < nrow; i++)
+            {
+                for (j = 0; j < ncol; j++)
+                    fprintf(fout, "%.8f ", mat[i][j]);
+                fprintf(fout, "\n");
+            }
+            size_t length = ftello(fout) - base;
+            fseeko(fout, base, SEEK_SET);
+            fprintf(fout, "[%13lu]\n", length);
+            fseeko(fout, 0, SEEK_END);
+            if (scanf("%s %d", token, &ncol) == 2 && *token == 'v')
+            {
+                base = ftello(fout);
+                for (j = 0; j < ncol; j++)
+                    scanf("%lf", mat[0] + j);
+                fprintf(fout, "%16d", 0);
+                fprintf(fout, "{type=\"nerv.BiasParam\",id=\"affine%d_bp\"}\n",
+                        cnt);
+                fprintf(fout, "1 %d\n", nrow, ncol);
+                for (j = 0; j < ncol; j++)
+                    fprintf(fout, "%.8f ", mat[0][j]);
+                fprintf(fout, "\n");
+                length = ftello(fout) - base;
+                fseeko(fout, base, SEEK_SET);
+                fprintf(fout, "[%13lu]\n", length);
+                cnt++;
+                fseeko(fout, 0, SEEK_END);
+            }
+        }
+    }
+    return 0;
+}
diff --git a/tools/tnet_to_nerv.cpp b/tools/tnet_to_nerv.cpp
new file mode 100644
index 0000000..cedf27a
--- /dev/null
+++ b/tools/tnet_to_nerv.cpp
@@ -0,0 +1,68 @@
+#include <cstdio>
+#include <fstream>
+#include <string>
+#include <cstring>
+char token[1024];
+char output[1024];
+double mat[4096][4096];
+int main() {
+    std::ofstream fout;
+    fout.open("converted.nerv");
+    int cnt = 0;
+    while (scanf("%s", token) != EOF)
+    {
+        int nrow, ncol;
+        int i, j;
+        if (strcmp(token, "<biasedlinearity>") == 0)
+        {
+            scanf("%d %d", &ncol, &nrow);
+            scanf("%s %d %d", token, &ncol, &nrow);
+            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);
+            if (scanf("%s %d", token, &ncol) == 2 && *token == 'v')
+            {
+                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++;
+            }
+        }
+    }
+    return 0;
+}