require 'htk_io'

gconf = {lrate = 0.2, wcost = 1e-6, momentum = 0.9,
        cumat_type = nerv.CuMatrixFloat,
        mmat_type = nerv.MMatrixFloat,
        frm_ext = 5,
	batch_size = 256,
	buffer_size = 81920,
        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",
	transf = {"/sgfs/users/wd007/src/nerv/tools/nerv.global.transf"},
        network = {"/sgfs/users/wd007/src/nerv/tools/nerv.svd0.55_3000h_iter1.init"},
        debug = false}

function make_transf_node_repo(param_transf_repo)
    return 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}}},
        },  

    }, param_transf_repo, gconf)
end

function make_network_node_repo(param_network_repo)
    return nerv.LayerRepo(
    {
        -- 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"] =
        {
            ce_crit = {{}, {dim_in = {10092, 1}, dim_out = {1}, compressed = true}}
        }

    }, param_network_repo, gconf)
end


function make_transf_link_repo(layer_transf_node_repo, param_transf_repo)
    return nerv.LayerRepo(
    {
        ["nerv.DAGLayer"] =
        {
            global_transf = {{}, {
                dim_in = {1320}, dim_out = {1320},
                sub_layers = layer_transf_node_repo,
                connections = 
		{
                    ["<input>[1]"] = "blayer1[1]",
                    ["blayer1[1]"] = "wlayer1[1]",
                    ["wlayer1[1]"] = "<output>[1]"
                }
            }},

        }
    }, param_transf_repo, gconf)
end


function make_network_link_repo(layer_network_node_repo, param_network_repo)
    return nerv.LayerRepo(
    {   
        ["nerv.DAGLayer"] =
        {   
		 main = {{}, {
                dim_in = {1320, 1}, dim_out = {1},
                sub_layers = layer_network_node_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]"] = "ce_crit[1]",
                    ["<input>[2]"] = "ce_crit[2]",
                    ["ce_crit[1]"] = "<output>[1]"
                }
            }}
        }
    }, param_network_repo, gconf)
end

function get_network(layer_repo)
    return layer_repo:get_layer("main")
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 make_buffer(readers)
    return nerv.SGDBuffer(gconf,
        {
            buffer_size = gconf.buffer_size,
            randomize = gconf.randomize,
            readers = readers
        })
end

function get_feat_id()
        return {main_scp = true}
end

function get_input_order()
    return {"main_scp", "phone_state"}
end

function get_accuracy(sublayer_repo)
    local ce_crit = sublayer_repo:get_layer("ce_crit")
    return ce_crit.total_correct / ce_crit.total_frames * 100
end

function print_stat(sublayer_repo)
    local ce_crit = sublayer_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(sublayer_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.network[1])
	nerv.info_stderr("%s \t:= %s", "transf", gconf.transf[1])
	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