MultinomialLogisticLossLayer.cs

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using MyCaffe.basecode;
using MyCaffe.common;
using MyCaffe.param;
 
namespace MyCaffe.layers
{
    public class MultinomialLogisticLossLayer<T> : LossLayer<T>
    {
        public MultinomialLogisticLossLayer(CudaDnn<T> cuda, Log log, LayerParameter p)
            : base(cuda, log, p)
        {
            m_type = LayerParameter.LayerType.MULTINOMIALLOGISTIC_LOSS;
        }
 
        public override void Reshape(BlobCollection<T> colBottom, BlobCollection<T> colTop)
        {
            base.Reshape(colBottom, colTop);
 
            m_log.CHECK_EQ(1, colBottom[1].channels, "The bottom[1] should have 1 channel.");
            m_log.CHECK_EQ(1, colBottom[1].height, "The bottom[1] should have height = 1.");
            m_log.CHECK_EQ(1, colBottom[1].width, "The bottom[1] should have width = 1.");
        }
 
        protected override void forward(BlobCollection<T> colBottom, BlobCollection<T> colTop)
        {
            int nNum = colBottom[0].num;
            int nDim = colBottom[0].count() / nNum;
            double dfLoss = 0;
 
            if (typeof(T) == typeof(double))
            {
                double[] rgBottomData = (double[])Convert.ChangeType(colBottom[0].update_cpu_data(), typeof(double[]));
                double[] rgBottomLabel = (double[])Convert.ChangeType(colBottom[1].update_cpu_data(), typeof(double[]));
 
                for (int i = 0; i < nNum; i++)
                {
                    int nLabel = (int)rgBottomLabel[i];
 
                    double dfProb = Math.Max(rgBottomData[i * nDim + nLabel], kLOG_THRESHOLD);
                    dfLoss -= Math.Log(dfProb);
                }
            }
            else
            {
                float[] rgBottomData = (float[])Convert.ChangeType(colBottom[0].update_cpu_data(), typeof(float[]));
                float[] rgBottomLabel = (float[])Convert.ChangeType(colBottom[1].update_cpu_data(), typeof(float[]));
 
                for (int i = 0; i < nNum; i++)
                {
                    int nLabel = (int)rgBottomLabel[i];
 
                    double dfProb = Math.Max(rgBottomData[i * nDim + nLabel], (float)kLOG_THRESHOLD);
                    dfLoss -= Math.Log(dfProb);
                }
            }
 
            colTop[0].SetData(dfLoss / nNum, 0);
        }
 
        protected override void backward(BlobCollection<T> colTop, List<bool> rgbPropagateDown, BlobCollection<T> colBottom)
        {
            if (rgbPropagateDown[1])
                m_log.FAIL(type.ToString() + " Layer cannot backpropagate to label inputs.");
 
            if (rgbPropagateDown[0])
            {
                int nNum = colBottom[0].num;
                int nDim = colBottom[0].count() / nNum;
                double dfScale = -1 * convertD(colTop[0].GetDiff(0)) / nNum;
 
                colBottom[0].SetDiff(0);
 
                if (typeof(T) == typeof(double))
                {
                    double[] rgBottomData = (double[])Convert.ChangeType(colBottom[0].update_cpu_data(), typeof(double[]));
                    double[] rgBottomLabel = (double[])Convert.ChangeType(colBottom[1].update_cpu_data(), typeof(double[]));
                    double[] rgBottomDiff = (double[])Convert.ChangeType(colBottom[0].mutable_cpu_diff, typeof(double[]));
 
                    for (int i = 0; i < nNum; i++)
                    {
                        int nLabel = (int)rgBottomLabel[i];
 
                        double dfProb = Math.Max(rgBottomData[i * nDim + nLabel], kLOG_THRESHOLD);
                        rgBottomDiff[i * nDim + nLabel] = dfScale / dfProb;
                    }
 
                    colBottom[0].mutable_cpu_data = (T[])Convert.ChangeType(rgBottomDiff, typeof(T[]));
                }
                else
                {
                    float[] rgBottomData = (float[])Convert.ChangeType(colBottom[0].update_cpu_data(), typeof(float[]));
                    float[] rgBottomLabel = (float[])Convert.ChangeType(colBottom[1].update_cpu_data(), typeof(float[]));
                    float[] rgBottomDiff = (float[])Convert.ChangeType(colBottom[0].mutable_cpu_diff, typeof(float[]));
 
                    for (int i = 0; i < nNum; i++)
                    {
                        int nLabel = (int)rgBottomLabel[i];
 
                        double dfProb = Math.Max(rgBottomData[i * nDim + nLabel], kLOG_THRESHOLD);
                        rgBottomDiff[i * nDim + nLabel] = (float)(dfScale / dfProb);
                    }
 
                    colBottom[0].mutable_cpu_data = (T[])Convert.ChangeType(rgBottomDiff, typeof(T[]));
                }
            }
        }
    }
}