Tracking fixed

AlexeyAB

2018-01-04 e205c1e7aeb47e3dffd35d1b5ce7841d24b9aff4

 src/region_layer.c

@@ -44,7 +44,7 @@
    l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
#endif

    fprintf(stderr, "Region Layer\n");
    fprintf(stderr, "detection\n");
    srand(0);

    return l;
@@ -196,7 +196,8 @@
                if(truth.x > 100000 && truth.y > 100000){
                    for(n = 0; n < l.n*l.w*l.h; ++n){
                        int index = size*n + b*l.outputs + 5;
                        float p = get_hierarchy_probability(l.output + index, l.softmax_tree, class);
                        float scale =  l.output[index-1];
                        float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class);
                        if(p > maxp){
                            maxp = p;
                            maxi = n;
@@ -324,7 +325,7 @@
    axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
}

void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map)
{
    int i,j,n;
    float *predictions = l.output;
@@ -348,8 +349,13 @@

                hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);
                int found = 0;
                for(j = l.classes - 1; j >= 0; --j){
                    if(1){
                if(map){
                    for(j = 0; j < 200; ++j){
                        float prob = scale*predictions[class_index+map[j]];
                        probs[index][j] = (prob > thresh) ? prob : 0;
                    }
                } else {
                    for(j = l.classes - 1; j >= 0; --j){
                        if(!found && predictions[class_index + j] > .5){
                            found = 1;
                        } else {
@@ -357,12 +363,9 @@
                        }
                        float prob = predictions[class_index+j];
                        probs[index][j] = (scale > thresh) ? prob : 0;
                    }else{
                        float prob = scale*predictions[class_index+j];
                        probs[index][j] = (prob > thresh) ? prob : 0;
                    }
                }
            }else{
            } else {
                for(j = 0; j < l.classes; ++j){
                    float prob = scale*predictions[class_index+j];
                    probs[index][j] = (prob > thresh) ? prob : 0;
@@ -406,6 +409,7 @@
        cuda_pull_array(state.truth, truth_cpu, num_truth);
    }
    cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs);
   cudaStreamSynchronize(get_cuda_stream());
    network_state cpu_state = state;
    cpu_state.train = state.train;
    cpu_state.truth = truth_cpu;