darknet/src/softmax_layer.c

#include "softmax_layer.h"
#include "blas.h"
#include "cuda.h"
#include <float.h>
#include <math.h>
#include <stdlib.h>
#include <stdio.h>
#include <assert.h>

softmax_layer make_softmax_layer(int batch, int inputs, int groups)
{
    assert(inputs%groups == 0);
    fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
    softmax_layer l = {0};
    l.type = SOFTMAX;
    l.batch = batch;
    l.groups = groups;
    l.inputs = inputs;
    l.outputs = inputs;
    l.output = calloc(inputs*batch, sizeof(float));
    l.delta = calloc(inputs*batch, sizeof(float));

    l.forward = forward_softmax_layer;
    l.backward = backward_softmax_layer;
    #ifdef GPU
    l.forward_gpu = forward_softmax_layer_gpu;
    l.backward_gpu = backward_softmax_layer_gpu;

    l.output_gpu = cuda_make_array(l.output, inputs*batch); 
    l.delta_gpu = cuda_make_array(l.delta, inputs*batch); 
    #endif
    return l;
}

void forward_softmax_layer(const softmax_layer l, network_state state)
{
    int b;
    int inputs = l.inputs / l.groups;
    int batch = l.batch * l.groups;
    if(l.softmax_tree){
        for(b = 0; b < batch; ++b){
            int i;
            int count = 0;
            for(i = 0; i < l.softmax_tree->groups; ++i){
                int group_size = l.softmax_tree->group_size[i];
                softmax(state.input+b*inputs + count, group_size, l.temperature, l.output+b*inputs + count);
                count += group_size;
            }
        }
    } else {
        for(b = 0; b < batch; ++b){
            softmax(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs);
        }
    }
}

void backward_softmax_layer(const softmax_layer l, network_state state)
{
    int i;
    for(i = 0; i < l.inputs*l.batch; ++i){
        state.delta[i] += l.delta[i];
    }
}

#ifdef GPU

void pull_softmax_layer_output(const softmax_layer layer)
{
    cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch);
}

void forward_softmax_layer_gpu(const softmax_layer l, network_state state)
{
    int inputs = l.inputs / l.groups;
    int batch = l.batch * l.groups;
    int b;
    if(l.softmax_tree){
        if(0){
            float *buff = calloc(inputs * batch, sizeof(float));
            cuda_pull_array(state.input, buff, batch * inputs);
            state.input = buff;
            forward_softmax_layer(l, state);
            cuda_push_array(l.output_gpu, l.output, batch*inputs);
            free(buff);
        } else {
            int i;
            const int nstreams = 32;
            cudaStream_t streams[nstreams];
            for (i = 0; i < nstreams; ++i) {
                cudaStreamCreate(&streams[i]);
            }
            for (b = 0; b < batch; ++b) {
                int i;
                int count = 0;
                for (i = 0; i < l.softmax_tree->groups; ++i) {
                    int group_size = l.softmax_tree->group_size[i];
                    softmax_gpu(state.input+b*inputs + count, group_size, 1, l.temperature, l.output_gpu+b*inputs + count, streams[(b*l.softmax_tree->groups + i) % nstreams]);
                    count += group_size;
                }
            }
            for(i = 0; i < nstreams; ++i){
                cudaStreamDestroy(streams[i]);
            }
        }
    } else {
        softmax_gpu(state.input, inputs, batch, l.temperature, l.output_gpu, 0);
    }
}

void backward_softmax_layer_gpu(const softmax_layer layer, network_state state)
{
    axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1);
}

#endif