Fixed xnor for random=1

AlexeyAB

2018-05-19 c1bb8c129d7d4d8fc85348d5e7ac5441fa36ead5

Fixed xnor for random=1

 src/convolutional_layer.c

@@ -465,6 +465,10 @@
        l->x_norm  = realloc(l->x_norm, l->batch*l->outputs*sizeof(float));
    }

    if (l->xnor) {
        //l->binary_input = realloc(l->inputs*l->batch, sizeof(float));
    }

#ifdef GPU
    if (old_w < w || old_h < h) {
        cuda_free(l->delta_gpu);
@@ -480,6 +484,11 @@
            l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs);
            l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs);
        }

        if (l->xnor) {
            cuda_free(l->binary_input_gpu);
            l->binary_input_gpu = cuda_make_array(0, l->inputs*l->batch);
        }
    }
#ifdef CUDNN
    cudnn_convolutional_setup(l, cudnn_fastest);

 src/detector.c

@@ -1109,12 +1109,42 @@
        detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
        draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);
        free_detections(dets, nboxes);
        save_image(im, "predictions");
        if (!dont_show) {
            show_image(im, "predictions");
        }
/*
        // pseudo labeling concept - fast.ai
        {
            char labelpath[4096];
            find_replace(input, ".jpg", ".txt", labelpath);
            find_replace(labelpath, ".png", ".txt", labelpath);
            find_replace(labelpath, ".bmp", ".txt", labelpath);
            find_replace(labelpath, ".JPG", ".txt", labelpath);
            find_replace(labelpath, ".JPEG", ".txt", labelpath);
            find_replace(labelpath, ".ppm", ".txt", labelpath);

            FILE* fw = fopen(labelpath, "wb");
            int i;
            for (i = 0; i < nboxes; ++i) {
                char buff[1024];
                int class_id = -1;
                float prob = 0;
                for (j = 0; j < l.classes; ++j) {
                    if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) {
                        prob = dets[i].prob[j];
                        class_id = j;
                    }
                }
                if (class_id >= 0) {
                    sprintf(buff, "%d %2.4f %2.4f %2.4f %2.4f\n", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h);
                    fwrite(buff, sizeof(char), strlen(buff), fw);
                }
            }
            fclose(fw);
        }
*/
        free_detections(dets, nboxes);
        free_image(im);
        free_image(sized);
        //free(boxes);