Merge pull request #4 from uphantom/master

fastnn first version, include follow submodular

1 files changed, 258 insertions, 0 deletions

diff --git a/fastnn/example/fastnn_baseline.lua b/fastnn/example/fastnn_baseline.lua
new file mode 100644
index 0000000..6e774de
--- /dev/null
+++ b/fastnn/example/fastnn_baseline.lua
@@ -0,0 +1,258 @@
+require 'htk_io'
+
+gconf = {lrate = 0.2, wcost = 1e-6, momentum = 0.9,
+        cumat_type = nerv.CuMatrixFloat,
+        mmat_type = nerv.MMatrixFloat,
+        frm_ext = 5,
+        frm_trim = 5,
+	batch_size = 256,
+	buffer_size = 81920,
+	rearrange = true,
+        tr_scp = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/train.scp",
+        cv_scp = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/train_cv.scp",
+        htk_conf = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/fbank_d_a_z.conf",
+	initialized_param = {"/sgfs/users/wd007/src/nerv/tools/nerv.global.transf",
+                             "/sgfs/users/wd007/src/nerv/tools/nerv.svd0.55_3000h_iter1.init"},
+        debug = false}
+
+function make_layer_repo(param_repo) 
+    local layer_repo = nerv.LayerRepo( 
+    {
+        -- global transf
+        ["nerv.BiasLayer"] =
+        {   
+            blayer1 = {{bias = "bias1"}, {dim_in = {1320}, dim_out = {1320}}},
+        },  
+        ["nerv.WindowLayer"] =
+        {   
+            wlayer1 = {{window = "window1"}, {dim_in = {1320}, dim_out = {1320}}},
+	},
+        -- biased linearity
+        ["nerv.AffineLayer"] =
+        {
+            affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
+            {dim_in = {1320}, dim_out = {2048}}},
+            affine1 = {{ltp = "affine1_ltp", bp = "affine1_bp"},
+            {dim_in = {2048}, dim_out = {367}}},
+            affine2 = {{ltp = "affine2_ltp", bp = "affine2_bp"},
+            {dim_in = {367}, dim_out = {2048}}},
+            affine3 = {{ltp = "affine3_ltp", bp = "affine3_bp"},
+            {dim_in = {2048}, dim_out = {408}}},
+            affine4 = {{ltp = "affine4_ltp", bp = "affine4_bp"},
+            {dim_in = {408}, dim_out = {2048}}},
+            affine5 = {{ltp = "affine5_ltp", bp = "affine5_bp"},
+            {dim_in = {2048}, dim_out = {368}}},
+            affine6 = {{ltp = "affine6_ltp", bp = "affine6_bp"},
+            {dim_in = {368}, dim_out = {2048}}},
+            affine7 = {{ltp = "affine7_ltp", bp = "affine7_bp"},
+            {dim_in = {2048}, dim_out = {303}}},
+            affine8 = {{ltp = "affine8_ltp", bp = "affine8_bp"},
+            {dim_in = {303}, dim_out = {2048}}},
+            affine9 = {{ltp = "affine9_ltp", bp = "affine9_bp"},
+            {dim_in = {2048}, dim_out = {277}}},
+            affine10 = {{ltp = "affine10_ltp", bp = "affine10_bp"},
+            {dim_in = {277}, dim_out = {2048}}},
+            affine11 = {{ltp = "affine11_ltp", bp = "affine11_bp"},
+            {dim_in = {2048}, dim_out = {361}}},
+            affine12 = {{ltp = "affine12_ltp", bp = "affine12_bp"},
+            {dim_in = {361}, dim_out = {2048}}},
+            affine13 = {{ltp = "affine13_ltp", bp = "affine13_bp"},
+            {dim_in = {2048}, dim_out = {441}}},
+            affine14 = {{ltp = "affine14_ltp", bp = "affine14_bp"},
+            {dim_in = {441}, dim_out = {10092}}},
+	},
+        ["nerv.SigmoidLayer"] =
+        {
+            sigmoid0 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid1 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid2 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid3 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid4 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid5 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid6 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+        },
+        ["nerv.SoftmaxCELayer"] = -- softmax + ce criterion layer for finetune output
+        {
+            ce_crit = {{}, {dim_in = {10092, 1}, dim_out = {1}, compressed = true}}
+        },
+	["nerv.SoftmaxLayer"] = -- softmax for decode output
+        {
+            softmax = {{}, {dim_in = {10092}, dim_out = {10092}}}
+        }
+    }, param_repo, gconf)
+    
+    layer_repo:add_layers(
+    {
+        ["nerv.DAGLayer"] =
+        {
+            global_transf = {{}, {
+                dim_in = {1320}, dim_out = {1320},
+                sub_layers = layer_repo,
+                connections = 
+		{
+                    ["<input>[1]"] = "blayer1[1]",
+                    ["blayer1[1]"] = "wlayer1[1]",
+                    ["wlayer1[1]"] = "<output>[1]"
+                }
+            }},
+	    main = {{}, {
+                dim_in = {1320}, dim_out = {10092},
+                sub_layers = layer_repo,
+                connections = {
+                    ["<input>[1]"] = "affine0[1]",
+                    ["affine0[1]"] = "sigmoid0[1]",
+                    ["sigmoid0[1]"] = "affine1[1]",
+                    ["affine1[1]"] = "affine2[1]",
+                    ["affine2[1]"] = "sigmoid1[1]",
+                    ["sigmoid1[1]"] = "affine3[1]",
+                    ["affine3[1]"] = "affine4[1]",
+                    ["affine4[1]"] = "sigmoid2[1]",
+                    ["sigmoid2[1]"] = "affine5[1]",
+                    ["affine5[1]"] = "affine6[1]",
+                    ["affine6[1]"] = "sigmoid3[1]",
+                    ["sigmoid3[1]"] = "affine7[1]",
+                    ["affine7[1]"] = "affine8[1]",
+                    ["affine8[1]"] = "sigmoid4[1]",
+                    ["sigmoid4[1]"] = "affine9[1]",
+                    ["affine9[1]"] = "affine10[1]",
+                    ["affine10[1]"] = "sigmoid5[1]",
+                    ["sigmoid5[1]"] = "affine11[1]",
+                    ["affine11[1]"] = "affine12[1]",
+                    ["affine12[1]"] = "sigmoid6[1]",
+                    ["sigmoid6[1]"] = "affine13[1]",
+                    ["affine13[1]"] = "affine14[1]",
+                    ["affine14[1]"] = "<output>[1]",
+                }
+            }}
+        }
+    }, param_repo, gconf)
+
+    layer_repo:add_layers(
+    {
+        ["nerv.DAGLayer"] =
+        {
+            ce_output = {{}, {
+                dim_in = {1320, 1}, dim_out = {1},
+                sub_layers = layer_repo,
+                connections = {
+                    ["<input>[1]"] = "main[1]",
+                    ["main[1]"] = "ce_crit[1]",
+                    ["<input>[2]"] = "ce_crit[2]",
+                    ["ce_crit[1]"] = "<output>[1]"
+                }
+            }},
+            softmax_output = {{}, {
+                dim_in = {1320}, dim_out = {10092},
+                sub_layers = layer_repo,
+                connections = {
+                    ["<input>[1]"] = "main[1]",
+                    ["main[1]"] = "softmax[1]",
+                    ["softmax[1]"] = "<output>[1]"
+                }
+            }}
+        }
+    }, param_repo, gconf)
+
+    return layer_repo
+end
+
+
+function get_network(layer_repo)
+    return layer_repo:get_layer("ce_output")
+end
+
+function get_decode_network(layer_repo)
+    return layer_repo:get_layer("softmax_output")
+end
+
+function get_global_transf(layer_repo)
+    return layer_repo:get_layer("global_transf")
+end
+
+
+
+function make_readers(scp_file, layer_repo, feat_repo_shareid, data_mutex_shareid)
+    return {
+                {reader = nerv.TNetReader(gconf,
+                    {   
+                        id = "main_scp",
+                        scp_file = scp_file,
+                        conf_file = gconf.htk_conf,
+                        frm_ext = gconf.frm_ext,
+                        mlfs = { 
+                            phone_state = { 
+                                file = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/ref.mlf",
+                                format = "map",
+                                format_arg = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/dict",
+                                dir = "*/",
+                                ext = "lab"
+                            }   
+                        },  
+                        global_transf = layer_repo:get_layer("global_transf")
+                    }, feat_repo_shareid, data_mutex_shareid), 
+                data = {main_scp = 1320, phone_state = 1}} 
+            }   
+end
+
+function get_feat_id()
+        return {main_scp = true}
+end
+
+
+function make_buffer(readers)
+    return nerv.SGDBuffer(gconf,
+        {
+            buffer_size = gconf.buffer_size,
+            randomize = gconf.randomize,
+            readers = readers,
+            use_gpu = true
+        })
+end
+
+function get_input_order()
+    return {{id = "main_scp", global_transf = true},
+            {id = "phone_state"}}
+end
+
+function get_accuracy(layer_repo)
+    local ce_crit = layer_repo:get_layer("ce_crit")
+    return ce_crit.total_correct / ce_crit.total_frames * 100
+end
+
+function print_stat(layer_repo)
+    local ce_crit = layer_repo:get_layer("ce_crit")
+    nerv.info("*** training stat begin ***")
+    nerv.printf("cross entropy:\t\t%.8f\n", ce_crit.total_ce)
+    nerv.printf("correct:\t\t%d\n", ce_crit.total_correct)
+    nerv.printf("frames:\t\t\t%d\n", ce_crit.total_frames)
+    nerv.printf("err/frm:\t\t%.8f\n", ce_crit.total_ce / ce_crit.total_frames)
+    nerv.printf("accuracy:\t\t%.3f%%\n", get_accuracy(layer_repo))
+    nerv.info("*** training stat end ***")
+end
+
+function print_xent(xent)
+	local totalframes = xent:totalframes()
+	local loss = xent:loss()
+	local correct = xent:correct()
+	nerv.info_stderr("*** training statistics info begin ***")
+	nerv.info_stderr("total frames:\t\t%d", totalframes)
+	nerv.info_stderr("cross entropy:\t%.8f", loss/totalframes)
+	nerv.info_stderr("frame accuracy:\t%.3f%%", 100*correct/totalframes)
+	nerv.info_stderr("*** training statistics info end ***")
+end
+
+function frame_acc(xent)
+	local correct = xent:correct()
+	local totalframes = xent:totalframes()
+	return string.format("%.3f", 100*correct/totalframes)
+end
+
+function print_gconf()
+	nerv.info_stderr("%s \t:= %s", "network", gconf.initialized_param[1])
+	nerv.info_stderr("%s \t:= %s", "transf", gconf.initialized_param[2])
+	nerv.info_stderr("%s \t:= %s", "batch_size", gconf.batch_size)
+	nerv.info_stderr("%s \t:= %s", "buffer_size", gconf.buffer_size)
+	nerv.info_stderr("%s \t:= %s", "lrate", gconf.lrate)
+	nerv.info_stderr("%s \t:= %s", "tr_scp", gconf.tr_scp)
+	nerv.info_stderr("%s \t:= %s", "cv_scp", gconf.cv_scp)
+end