GluParameter.cs

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.ComponentModel;
using MyCaffe.basecode;
 
namespace MyCaffe.param.tft
{
    [Serializable]
    [TypeConverter(typeof(ExpandableObjectConverter))]
    public class GluParameter : LayerParameterBase
    {
        FillerParameter m_fillerParam_weights = new FillerParameter("xavier");
        FillerParameter m_fillerParam_bias = new FillerParameter("constant", 0.1);
        int m_nAxis = 1;
        int m_nInputDim;
        bool m_bBiasTerm = true;
        bool m_bEnableNoise = false;
        double m_dfSigmaInit = 0.017;
        MODULATION m_modulation = MODULATION.SIGMOID;
 
        public enum MODULATION
        {
            SIGMOID
        }
 
        public GluParameter()
        {
        }
 
        [Description("Specifies the input dimension.")]
        public int input_dim
        {
            get { return m_nInputDim; }
            set { m_nInputDim = value; }
        }
 
        [Description("Specifies the gate modulation type.")]
        public MODULATION modulation
        {
            get { return m_modulation; }
            set { m_modulation = value; }
        }
 
        [Description("Enable/disable noise in the inner-product layer (default = false).")]
        public bool enable_noise
        {
            get { return m_bEnableNoise; }
            set { m_bEnableNoise = value; }
        }
 
        [Description("Specifies the initialization value for the sigma weight and sigma bias used when 'enable_noise' = true.")]
        public double sigma_init
        {
            get { return m_dfSigmaInit; }
            set { m_dfSigmaInit = value; }
        }
 
        [Description("Whether to have bias terms or not.")]
        public bool bias_term
        {
            get { return m_bBiasTerm; }
            set { m_bBiasTerm = value; }
        }
 
        [Category("Fillers")]
        [Description("The filler for the weights.")]
        public FillerParameter weight_filler
        {
            get { return m_fillerParam_weights; }
            set { m_fillerParam_weights = value; }
        }
 
        [Category("Fillers")]
        [Description("The filler for the bias.")]
        public FillerParameter bias_filler
        {
            get { return m_fillerParam_bias; }
            set { m_fillerParam_bias = value; }
        }
 
        [Description("Specifies the first axis to be lumped into a single inner product computation; all preceding axes are retained in the output.")]
        public int axis
        {
            get { return m_nAxis; }
            set { m_nAxis = value; }
        }
 
 
        public override object Load(System.IO.BinaryReader br, bool bNewInstance = true)
        {
            RawProto proto = RawProto.Parse(br.ReadString());
            GluParameter p = FromProto(proto);
 
            if (!bNewInstance)
                Copy(p);
 
            return p;
        }
 
        public override void Copy(LayerParameterBase src)
        {
            GluParameter p = (GluParameter)src;
 
            m_modulation = p.modulation;
            m_nInputDim = p.input_dim;
            m_bBiasTerm = p.m_bBiasTerm;
 
            if (p.m_fillerParam_bias != null)
                m_fillerParam_bias = p.m_fillerParam_bias.Clone();
 
            if (p.m_fillerParam_weights != null)
                m_fillerParam_weights = p.m_fillerParam_weights.Clone();
 
            m_nAxis = p.m_nAxis;
            m_bEnableNoise = p.m_bEnableNoise;
            m_dfSigmaInit = p.m_dfSigmaInit;
        }
 
        public override LayerParameterBase Clone()
        {
            GluParameter p = new GluParameter();
            p.Copy(this);
            return p;
        }
 
        public override RawProto ToProto(string strName)
        {
            RawProtoCollection rgChildren = new RawProtoCollection();
 
            rgChildren.Add("modulation", modulation.ToString());
            rgChildren.Add("input_dim", input_dim.ToString());
            rgChildren.Add("bias_term", bias_term.ToString());
 
            if (weight_filler != null)
                rgChildren.Add(weight_filler.ToProto("weight_filler"));
 
            if (bias_filler != null)
                rgChildren.Add(bias_filler.ToProto("bias_filler"));
 
            rgChildren.Add("axis", axis.ToString());
 
            if (m_bEnableNoise)
            {
                rgChildren.Add("enable_noise", m_bEnableNoise.ToString());
                rgChildren.Add("sigma_init", m_dfSigmaInit.ToString());
            }
 
            return new RawProto(strName, "", rgChildren);
        }
 
        public static GluParameter FromProto(RawProto rp)
        {
            string strVal;
            GluParameter p = new GluParameter();
 
            if ((strVal = rp.FindValue("input_dim")) != null)
                p.input_dim = int.Parse(strVal);
 
            if ((strVal = rp.FindValue("modulation")) != null)
            {
                if (strVal == MODULATION.SIGMOID.ToString())
                    p.modulation = MODULATION.SIGMOID;
            }
 
            if ((strVal = rp.FindValue("bias_term")) != null)
                p.bias_term = bool.Parse(strVal);
 
            RawProto rpWeightFiller = rp.FindChild("weight_filler");
            if (rpWeightFiller != null)
                p.weight_filler = FillerParameter.FromProto(rpWeightFiller);
 
            RawProto rpBiasFiller = rp.FindChild("bias_filler");
            if (rpBiasFiller != null)
                p.bias_filler = FillerParameter.FromProto(rpBiasFiller);
 
            if ((strVal = rp.FindValue("axis")) != null)
                p.axis = int.Parse(strVal);
 
            if ((strVal = rp.FindValue("enable_noise")) != null)
                p.enable_noise = bool.Parse(strVal);
 
            if ((strVal = rp.FindValue("sigma_init")) != null)
                p.sigma_init = ParseDouble(strVal);
 
            return p;
        }
    }
}