big change to images

Joseph Redmon

2015-04-09 4f50e29365c8b8fd3aa9b67167701c1ada1e373f

 src/detection_layer.c

@@ -1,72 +1,197 @@
int detection_out_height(detection_layer layer)
#include "detection_layer.h"
#include "activations.h"
#include "softmax_layer.h"
#include "blas.h"
#include "cuda.h"
#include <stdio.h>
#include <stdlib.h>

int get_detection_layer_locations(detection_layer layer)
{
    return layer.size + layer.h*layer.stride;
    return layer.inputs / (layer.classes+layer.coords+layer.rescore+layer.background);
}

int detection_out_width(detection_layer layer)
int get_detection_layer_output_size(detection_layer layer)
{
    return layer.size + layer.w*layer.stride;
    return get_detection_layer_locations(layer)*(layer.background + layer.classes + layer.coords);
}

detection_layer *make_detection_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance)
{
    int i;
    size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
    detection_layer *layer = calloc(1, sizeof(detection_layer));
    layer->h = h;
    layer->w = w;
    layer->c = c;
    layer->n = n;
    
    layer->batch = batch;
    layer->stride = stride;
    layer->size = size;
    assert(c%n == 0);
    layer->inputs = inputs;
    layer->classes = classes;
    layer->coords = coords;
    layer->rescore = rescore;
    layer->nuisance = nuisance;
    layer->background = background;
    int outputs = get_detection_layer_output_size(*layer);
    layer->output = calloc(batch*outputs, sizeof(float));
    layer->delta = calloc(batch*outputs, sizeof(float));
    #ifdef GPU
    layer->output_gpu = cuda_make_array(0, batch*outputs);
    layer->delta_gpu = cuda_make_array(0, batch*outputs);
    #endif

    layer->filters = calloc(c*size*size, sizeof(float));
    layer->filter_updates = calloc(c*size*size, sizeof(float));
    layer->filter_momentum = calloc(c*size*size, sizeof(float));

    float scale = 1./(size*size*c);
    for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform());

    int out_h = detection_out_height(*layer);
    int out_w = detection_out_width(*layer);

    layer->output = calloc(layer->batch * out_h * out_w * n, sizeof(float));
    layer->delta  = calloc(layer->batch * out_h * out_w * n, sizeof(float));

    layer->activation = activation;

    fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
    fprintf(stderr, "Detection Layer\n");
    srand(0);

    return layer;
}

void forward_detection_layer(const detection_layer layer, float *in)
void dark_zone(detection_layer layer, int class, int start, network_state state)
{
    int out_h = detection_out_height(layer);
    int out_w = detection_out_width(layer);
    int i,j,fh, fw,c;
    memset(layer.output, 0, layer->batch*layer->n*out_h*out_w*sizeof(float));
    for(c = 0; c < layer.c; ++c){
        for(i = 0; i < layer.h; ++i){
            for(j = 0; j < layer.w; ++j){
                float val = layer->input[j+(i + c*layer.h)*layer.w];
                for(fh = 0; fh < layer.size; ++fh){
                    for(fw = 0; fw < layer.size; ++fw){
                        int h = i*layer.stride + fh;
                        int w = j*layer.stride + fw;
                        layer.output[w+(h+c/n*out_h)*out_w] += val*layer->filters[fw+(fh+c*layer.size)*layer.size];
                    }
                }
            }
    int index = start+layer.background+class;
    int size = layer.classes+layer.coords+layer.background;
    int location = (index%(7*7*size)) / size ;
    int r = location / 7;
    int c = location % 7;
    int dr, dc;
    for(dr = -1; dr <= 1; ++dr){
        for(dc = -1; dc <= 1; ++dc){
            if(!(dr || dc)) continue;
            if((r + dr) > 6 || (r + dr) < 0) continue;
            if((c + dc) > 6 || (c + dc) < 0) continue;
            int di = (dr*7 + dc) * size;
            if(state.truth[index+di]) continue;
            layer.output[index + di] = 0;
            //if(!state.truth[start+di]) continue;
            //layer.output[start + di] = 1;
        }
    }
}

void backward_detection_layer(const detection_layer layer, float *delta)
void forward_detection_layer(const detection_layer layer, network_state state)
{
    int in_i = 0;
    int out_i = 0;
    int locations = get_detection_layer_locations(layer);
    int i,j;
    for(i = 0; i < layer.batch*locations; ++i){
        int mask = (!state.truth || state.truth[out_i + layer.background + layer.classes + 2]);
        float scale = 1;
        if(layer.rescore) scale = state.input[in_i++];
        else if(layer.nuisance){
            layer.output[out_i++] = 1-state.input[in_i++];
            scale = mask;
        }
        else if(layer.background) layer.output[out_i++] = scale*state.input[in_i++];

        for(j = 0; j < layer.classes; ++j){
            layer.output[out_i++] = scale*state.input[in_i++];
        }
        if(layer.nuisance){
            
        }else if(layer.background){
            softmax_array(layer.output + out_i - layer.classes-layer.background, layer.classes+layer.background, layer.output + out_i - layer.classes-layer.background);
            activate_array(state.input+in_i, layer.coords, LOGISTIC);
        }
        for(j = 0; j < layer.coords; ++j){
            layer.output[out_i++] = mask*state.input[in_i++];
        }
    }
    /*
    int count = 0;
    for(i = 0; i < layer.batch*locations; ++i){
        for(j = 0; j < layer.classes+layer.background; ++j){
            printf("%f, ", layer.output[count++]);
        }
        printf("\n");
        for(j = 0; j < layer.coords; ++j){
            printf("%f, ", layer.output[count++]);
        }
        printf("\n");
    }
    */
    /*
    if(layer.background || 1){
        for(i = 0; i < layer.batch*locations; ++i){
            int index = i*(layer.classes+layer.coords+layer.background);
            for(j= 0; j < layer.classes; ++j){
                if(state.truth[index+j+layer.background]){
                    //dark_zone(layer, j, index, state);
                }
            }
        }
    }
    */
}

void backward_detection_layer(const detection_layer layer, network_state state)
{
    int locations = get_detection_layer_locations(layer);
    int i,j;
    int in_i = 0;
    int out_i = 0;
    for(i = 0; i < layer.batch*locations; ++i){
        float scale = 1;
        float latent_delta = 0;
        if(layer.rescore) scale = state.input[in_i++];
        else if (layer.nuisance)   state.delta[in_i++] = -layer.delta[out_i++];
        else if (layer.background) state.delta[in_i++] = scale*layer.delta[out_i++];
        for(j = 0; j < layer.classes; ++j){
            latent_delta += state.input[in_i]*layer.delta[out_i];
            state.delta[in_i++] = scale*layer.delta[out_i++];
        }

        if (layer.nuisance) {

        }else if (layer.background) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
        for(j = 0; j < layer.coords; ++j){
            state.delta[in_i++] = layer.delta[out_i++];
        }
        if(layer.rescore) state.delta[in_i-layer.coords-layer.classes-layer.rescore-layer.background] = latent_delta;
    }
}

#ifdef GPU

void forward_detection_layer_gpu(const detection_layer layer, network_state state)
{
    int outputs = get_detection_layer_output_size(layer);
    float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
    float *truth_cpu = 0;
    if(state.truth){
        truth_cpu = calloc(layer.batch*outputs, sizeof(float));
        cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
    }
    cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
    network_state cpu_state;
    cpu_state.train = state.train;
    cpu_state.truth = truth_cpu;
    cpu_state.input = in_cpu;
    forward_detection_layer(layer, cpu_state);
    cuda_push_array(layer.output_gpu, layer.output, layer.batch*outputs);
    free(cpu_state.input);
    if(cpu_state.truth) free(cpu_state.truth);
}

void backward_detection_layer_gpu(detection_layer layer, network_state state)
{
    int outputs = get_detection_layer_output_size(layer);

    float *in_cpu =    calloc(layer.batch*layer.inputs, sizeof(float));
    float *delta_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
    float *truth_cpu = 0;
    if(state.truth){
        truth_cpu = calloc(layer.batch*outputs, sizeof(float));
        cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
    }
    network_state cpu_state;
    cpu_state.train = state.train;
    cpu_state.input = in_cpu;
    cpu_state.truth = truth_cpu;
    cpu_state.delta = delta_cpu;

    cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
    cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
    backward_detection_layer(layer, cpu_state);
    cuda_push_array(state.delta, delta_cpu, layer.batch*layer.inputs);

    free(in_cpu);
    free(delta_cpu);
}
#endif