C++ set_batch_network函数代码示例

void label_classifier(char *datacfg, char *filename, char *weightfile)
{
    int i;
    network *net = load_network(filename, weightfile, 0);
    set_batch_network(net, 1);
    srand(time(0));

    list *options = read_data_cfg(datacfg);

    char *label_list = option_find_str(options, "names", "data/labels.list");
    char *test_list = option_find_str(options, "test", "data/train.list");
    int classes = option_find_int(options, "classes", 2);

    char **labels = get_labels(label_list);
    list *plist = get_paths(test_list);

    char **paths = (char **)list_to_array(plist);
    int m = plist->size;
    free_list(plist);

    for(i = 0; i < m; ++i){
        image im = load_image_color(paths[i], 0, 0);
        image resized = resize_min(im, net->w);
        image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
        float *pred = network_predict(net, crop.data);

        if(resized.data != im.data) free_image(resized);
        free_image(im);
        free_image(crop);
        int ind = max_index(pred, classes);

        printf("%s\n", labels[ind]);
    }
}

void decode_captcha(char *cfgfile, char *weightfile)
{
    setbuf(stdout, NULL);
    srand(time(0));
    network net = parse_network_cfg(cfgfile);
    set_batch_network(&net, 1);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    char filename[256];
    while(1){
        printf("Enter filename: ");
        fgets(filename, 256, stdin);
        strtok(filename, "\n");
        image im = load_image_color(filename, 300, 57);
        scale_image(im, 1./255.);
        float *X = im.data;
        float *predictions = network_predict(net, X);
        image out  = float_to_image(300, 57, 1, predictions);
        show_image(out, "decoded");
        #ifdef OPENCV
        cvWaitKey(0);
        #endif
        free_image(im);
    }
}

void test_cifar_multi(char *filename, char *weightfile)
{
    network net = parse_network_cfg(filename);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);
    srand(time(0));

    float avg_acc = 0;
    data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");

    int i;
    for(i = 0; i < test.X.rows; ++i){
        image im = float_to_image(32, 32, 3, test.X.vals[i]);

        float pred[10] = {0};

        float *p = network_predict(net, im.data);
        axpy_cpu(10, 1, p, 1, pred, 1);
        flip_image(im);
        p = network_predict(net, im.data);
        axpy_cpu(10, 1, p, 1, pred, 1);

        int index = max_index(pred, 10);
        int class = max_index(test.y.vals[i], 10);
        if(index == class) avg_acc += 1;
        free_image(im);
        printf("%4d: %.2f%%\n", i, 100.*avg_acc/(i+1));
    }
}

void predict_regressor(char *cfgfile, char *weightfile, char *filename)
{
    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 1);
    srand(2222222);

    clock_t time;
    char buff[256];
    char *input = buff;
    while(1){
        if(filename){
            strncpy(input, filename, 256);
        }else{
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "\n");
        }
        image im = load_image_color(input, 0, 0);
        image sized = letterbox_image(im, net->w, net->h);

        float *X = sized.data;
        time=clock();
        float *predictions = network_predict(net, X);
        printf("Predicted: %f\n", predictions[0]);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        free_image(im);
        free_image(sized);
        if (filename) break;
    }
    free_network(net);
}

void valid_go(char *cfgfile, char *weightfile, int multi)
{
    srand(time(0));
    char *base = basecfg(cfgfile);
    printf("%s\n", base);
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);
    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);

    float *board = calloc(19*19, sizeof(float));
    float *move = calloc(19*19, sizeof(float));
    moves m = load_go_moves("/home/pjreddie/backup/go.test");

    int N = m.n;
    int i;
    int correct = 0;
    for(i = 0; i <N; ++i){
        char *b = m.data[i];
        int row = b[0];
        int col = b[1];
        int truth = col + 19*row;
        string_to_board(b+2, board);
        predict_move(net, board, move, multi);
        int index = max_index(move, 19*19);
        if(index == truth) ++correct;
        printf("%d Accuracy %f\n", i, (float) correct/(i+1));
    }
}

void test_dice(char *cfgfile, char *weightfile, char *filename)
{
    network * net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(net, weightfile);
    }
    set_batch_network(net, 1);
    srand(2222222);
    int i = 0;
    char **names = dice_labels;
    char buff[256];
    char *input = buff;
    int indexes[6];
    while(1){
        if(filename){
            strncpy(input, filename, 256);
        }else{
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "\n");
        }
        image im = load_image_color(input, net->w, net->h);
        float *X = im.data;
        float *predictions = network_predict(net, X);
        top_predictions(net, 6, indexes);
        for(i = 0; i < 6; ++i){
            int index = indexes[i];
            printf("%s: %f\n", names[index], predictions[index]);
        }
        free_image(im);
        if (filename) break;
    }
}

void test_captcha(char *cfgfile, char *weightfile)
{
    setbuf(stdout, NULL);
    srand(time(0));
    //char *base = basecfg(cfgfile);
    //printf("%s\n", base);
    network net = parse_network_cfg(cfgfile);
    set_batch_network(&net, 1);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    char filename[256];
    while(1){
        //printf("Enter filename: ");
        fgets(filename, 256, stdin);
        strtok(filename, "\n");
        image im = load_image_color(filename, 200, 60);
        translate_image(im, -128);
        scale_image(im, 1/128.);
        float *X = im.data;
        float *predictions = network_predict(net, X);
        print_letters(predictions, 10);
        free_image(im);
    }
}

/*
 * @param[in]: ctx
*/
void createYoloNetwork(context_param_yolo_t *yoloctx, char* cfgfile, char* weightfile)
{
	printf("Create YOLO network\n");
	network net = parse_network_cfg(cfgfile);
	if (weightfile)
	{
		load_weights(&net, weightfile);
	}

	set_batch_network(&net, 1);
	detection_layer l = net.layers[net.n - 1];

	yoloGrid grid;
	grid.grids = l.side;
	grid.bbs = l.n;
	grid.classes = l.classes;

	box		*boxes = malloc(l.side * l.side * l.n * sizeof(box));
	float	**probs = malloc(l.side * l.side * l.n * sizeof(float *));
	for (int j = 0; j < l.side * l.side * l.n; j++)
	{
		probs[j] = malloc(l.classes*sizeof(float *));
	}
	
	yoloctx->_net	= net;
	yoloctx->_grid	= grid;
	yoloctx->_grid.boxes	= boxes;
	yoloctx->_grid.probs	= probs;

	yoloctx->_nwidth	= net.w;
	yoloctx->_nheight	= net.h;

	yoloctx->_sqrt		= l.sqrt;
	yoloctx->_nms		= .5f;		// non maximal suppression
}

void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
{
    int i, j;
    network net = parse_network_cfg(filename);
    set_batch_network(&net, 1);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    srand(time(0));

    list *options = read_data_cfg(datacfg);

    char *label_list = option_find_str(options, "labels", "data/labels.list");
    char *valid_list = option_find_str(options, "valid", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    int topk = option_find_int(options, "top", 1);

    char **labels = get_labels(label_list);
    list *plist = get_paths(valid_list);

    char **paths = (char **)list_to_array(plist);
    int m = plist->size;
    free_list(plist);

    float avg_acc = 0;
    float avg_topk = 0;
    int *indexes = calloc(topk, sizeof(int));

    int size = net.w;
    for(i = 0; i < m; ++i){
        int class = -1;
        char *path = paths[i];
        for(j = 0; j < classes; ++j){
            if(strstr(path, labels[j])){
                class = j;
                break;
            }
        }
        image im = load_image_color(paths[i], 0, 0);
        image resized = resize_min(im, size);
        resize_network(&net, resized.w, resized.h);
        //show_image(im, "orig");
        //show_image(crop, "cropped");
        //cvWaitKey(0);
        float *pred = network_predict(net, resized.data);
        if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1, 1);

        free_image(im);
        free_image(resized);
        top_k(pred, classes, topk, indexes);

        if(indexes[0] == class) avg_acc += 1;
        for(j = 0; j < topk; ++j){
            if(indexes[j] == class) avg_topk += 1;
        }

        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
    }
}

void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh)
{
	int show_flag = 1;
    list *options = read_data_cfg(datacfg);
    char *name_list = option_find_str(options, "names", "data/names.list");
    char **names = get_labels(name_list);

    image **alphabet = load_alphabet();
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);
    srand(2222222);
    clock_t time;
    char buff[256];
    char *input = buff;
    int j;
    float nms=.4;
    while(1){
        if(filename){
            strncpy(input, filename, 256);
        } else {
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "\n");
        }
        image im = load_image_color(input,0,0);
        image sized = resize_image(im, net.w, net.h);
        layer l = net.layers[net.n-1];

        box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
        float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
        for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes + 1, sizeof(float *));

        float *X = sized.data;
        time=clock();
        network_predict(net, X);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0, hier_thresh);
        if (l.softmax_tree && nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
        else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
        draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes, show_flag);
        save_image(im, "predictions");
        show_image(im, "predictions");

        free_image(im);
        free_image(sized);
        free(boxes);
        free_ptrs((void **)probs, l.w*l.h*l.n);
#ifdef OPENCV
        cvWaitKey(0);
        cvDestroyAllWindows();
#endif
        if (filename) break;
    }
}

float *network_predict_image(network *net, image im)
{
    image imr = letterbox_image(im, net->w, net->h);
    set_batch_network(net, 1);
    float *p = network_predict(*net, imr.data);
    free_image(imr);
    return p;
}

void inter_dcgan(char *cfgfile, char *weightfile)
{
    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 1);
    srand(2222222);

    clock_t time;
    char buff[256];
    char *input = buff;
    int i, imlayer = 0;

    for (i = 0; i < net->n; ++i) {
        if (net->layers[i].out_c == 3) {
            imlayer = i;
            printf("%d\n", i);
            break;
        }
    }
    image start = random_unit_vector_image(net->w, net->h, net->c);
    image end = random_unit_vector_image(net->w, net->h, net->c);
        image im = make_image(net->w, net->h, net->c);
        image orig = copy_image(start);

    int c = 0;
    int count = 0;
    int max_count = 15;
    while(1){
        ++c;

        if(count == max_count){
            count = 0;
            free_image(start);
            start = end;
            end = random_unit_vector_image(net->w, net->h, net->c);
            if(c > 300){
                end = orig;
            }
            if(c>300 + max_count) return;
        }
        ++count;

        slerp(start.data, end.data, (float)count / max_count, im.w*im.h*im.c, im.data);

        float *X = im.data;
        time=clock();
        network_predict(net, X);
        image out = get_network_image_layer(net, imlayer);
        //yuv_to_rgb(out);
        normalize_image(out);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        //char buff[256];
        sprintf(buff, "out%05d", c);
        save_image(out, "out");
        save_image(out, buff);
        show_image(out, "out", 0);
    }
}

void test_lsd(char *cfgfile, char *weightfile, char *filename)
{
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);
    srand(2222222);

    clock_t time;
    char buff[256];
    char *input = buff;
    int i, imlayer = 0;

    for (i = 0; i < net.n; ++i) {
        if (net.layers[i].out_c == 3) {
            imlayer = i;
            printf("%d\n", i);
            break;
        }
    }

    while(1){
        if(filename){
            strncpy(input, filename, 256);
        }else{
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "\n");
        }
        image im = load_image_color(input, 0, 0);
        image resized = resize_min(im, net.w);
        image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
        //grayscale_image_3c(crop);

        float *X = crop.data;
        time=clock();
        network_predict(net, X);
        image out = get_network_image_layer(net, imlayer);
        //yuv_to_rgb(out);
        constrain_image(out);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        show_image(out, "out");
        show_image(crop, "crop");
        save_image(out, "out");
#ifdef OPENCV
        cvWaitKey(0);
#endif

        free_image(im);
        free_image(resized);
        free_image(crop);
        if (filename) break;
    }
}

void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
{
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);
    srand(2222222);

    list *options = read_data_cfg(datacfg);

    char *name_list = option_find_str(options, "names", 0);
    if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
    if(top == 0) top = option_find_int(options, "top", 1);

    int i = 0;
    char **names = get_labels(name_list);
    clock_t time;
    int *indexes = calloc(top, sizeof(int));
    char buff[256];
    char *input = buff;
    while(1){
        if(filename){
            strncpy(input, filename, 256);
        }else{
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "\n");
        }
        image im = load_image_color(input, 0, 0);
        image r = letterbox_image(im, net.w, net.h);
        //resize_network(&net, r.w, r.h);
        //printf("%d %d\n", r.w, r.h);

        float *X = r.data;
        time=clock();
        float *predictions = network_predict(net, X);
        if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1, 1);
        top_k(predictions, net.outputs, top, indexes);
        fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        for(i = 0; i < top; ++i){
            int index = indexes[i];
            //if(net.hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root");
            //else printf("%s: %f\n",names[index], predictions[index]);
            printf("%5.2f%%: %s\n", predictions[index]*100, names[index]);
        }
        if(r.data != im.data) free_image(r);
        free_image(im);
        if (filename) break;
    }
}

void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
{
#ifdef OPENCV
    printf("Regressor Demo\n");
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);

    srand(2222222);
    CvCapture * cap;

    if(filename){
        cap = cvCaptureFromFile(filename);
    }else{
        cap = cvCaptureFromCAM(cam_index);
    }

    if(!cap) error("Couldn't connect to webcam.\n");
    cvNamedWindow("Regressor", CV_WINDOW_NORMAL); 
    cvResizeWindow("Regressor", 512, 512);
    float fps = 0;

    while(1){
        struct timeval tval_before, tval_after, tval_result;
        gettimeofday(&tval_before, NULL);

        image in = get_image_from_stream(cap);
        image in_s = letterbox_image(in, net.w, net.h);
        show_image(in, "Regressor");

        float *predictions = network_predict(net, in_s.data);

        printf("\033[2J");
        printf("\033[1;1H");
        printf("\nFPS:%.0f\n",fps);

        printf("People: %f\n", predictions[0]);

        free_image(in_s);
        free_image(in);

        cvWaitKey(10);

        gettimeofday(&tval_after, NULL);
        timersub(&tval_after, &tval_before, &tval_result);
        float curr = 1000000.f/((long int)tval_result.tv_usec);
        fps = .9*fps + .1*curr;
    }
#endif
}