C++ parse_network_cfg函数代码示例

void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
{

    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    detection_layer l = net.layers[net.n-1];
    set_batch_network(&net, 1);
    srand(2222222);
    clock_t time;
    char buff[256];
    char *input = buff;
    int j;
    float nms=.5;
    box *boxes = calloc(l.side*l.side*l.n, sizeof(box));
    float **probs = calloc(l.side*l.side*l.n, sizeof(float *));
    for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
    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);
        float *X = sized.data;
        time=clock();
        float *predictions = network_predict(net, X);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
        if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
        draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);
        show_image(im, "predictions");

        show_image(sized, "resized");
        free_image(im);
        free_image(sized);
#ifdef OPENCV
        cvWaitKey(0);
        cvDestroyAllWindows();
#endif
        if (filename) break;
    }
}

int darknet_load_network(struct darknet_helper *dnet, const char *cfg, const char *weights)
{
	dnet->priv->net = parse_network_cfg((char *)cfg);
	load_weights(&dnet->priv->net, (char *)weights);
	set_batch_network(&dnet->priv->net, 1);

	detection_layer l = dnet->priv->net.layers[dnet->priv->net.n-1];
	dnet->priv->boxes = (box *)calloc(l.side*l.side*l.n, sizeof(box));
	dnet->priv->probs = (float **)calloc(l.side*l.side*l.n, sizeof(float *));
	int j;
	for(j = 0; j < l.side * l.side * l.n; ++j)
		dnet->priv->probs[j] = (float *)calloc(l.classes, sizeof(float *));

	return 0;
}

void vec_char_rnn(char *cfgfile, char *weightfile, char *seed)
{
    char *base = basecfg(cfgfile);
    fprintf(stderr, "%s\n", base);

    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    int inputs = get_network_input_size(net);

    int c;
    int seed_len = strlen(seed);
    float *input = calloc(inputs, sizeof(float));
    int i;
    char *line;
    while((line=fgetl(stdin)) != 0){
        reset_rnn_state(net, 0);
        for(i = 0; i < seed_len; ++i){
            c = seed[i];
            input[(int)c] = 1;
            network_predict(net, input);
            input[(int)c] = 0;
        }
        strip(line);
        int str_len = strlen(line);
        for(i = 0; i < str_len; ++i){
            c = line[i];
            input[(int)c] = 1;
            network_predict(net, input);
            input[(int)c] = 0;
        }
        c = ' ';
        input[(int)c] = 1;
        network_predict(net, input);
        input[(int)c] = 0;

        layer l = net.layers[0];
        #ifdef GPU
        cuda_pull_array(l.output_gpu, l.output, l.outputs);
        #endif
        printf("%s", line);
        for(i = 0; i < l.outputs; ++i){
            printf(",%g", l.output[i]);
        }
        printf("\n");
    }
}

void test_writing(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;
    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);
        resize_network(net, im.w, im.h);
        printf("%d %d %d\n", im.h, im.w, im.c);
        float *X = im.data;
        time=clock();
        network_predict(net, X);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        image pred = get_network_image(net);

        image upsampled = resize_image(pred, im.w, im.h);
        image thresh = threshold_image(upsampled, .5);
        pred = thresh;

        show_image(pred, "prediction");
        show_image(im, "orig");
#ifdef OPENCV
        cvWaitKey(0);
        cvDestroyAllWindows();
#endif

        free_image(upsampled);
        free_image(thresh);
        free_image(im);
        if (filename) break;
    }
}

void rgbgr_net(char *cfgfile, char *weightfile, char *outfile)
{
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    int i;
    for(i = 0; i < net.n; ++i){
        layer l = net.layers[i];
        if(l.type == CONVOLUTIONAL){
            rgbgr_filters(l);
            break;
        }
    }
    save_weights(net, outfile);
}

void test_mnist_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;
    test = load_mnist_data("data/mnist/t10k-images.idx3-ubyte", "data/mnist/t10k-labels.idx1-ubyte", 10000);

    int i;
    for(i = 0; i < test.X.rows; ++i){
        image im = float_to_image(28, 28, 1, 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);
        image im1 = rotate_image(im, -2.0*3.1415926/180.0);
        image im2 = rotate_image(im, 2.0*3.1415926/180.0);
        image im3 = rotate_image(im, -3.0*3.1415926/180.0);
        image im4 = rotate_image(im, 3.0*3.1415926/180.0);
        p = network_predict(net, im1.data);
        axpy_cpu(10, 1, p, 1, pred, 1);
        p = network_predict(net, im2.data);
        axpy_cpu(10, 1, p, 1, pred, 1);
        p = network_predict(net, im3.data);
        axpy_cpu(10, 1, p, 1, pred, 1);
        p = network_predict(net, im4.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);
        free_image(im1);
        free_image(im2);
        free_image(im3);
        free_image(im4);
        printf("%4d: %.2f%%\n", i, 100.*avg_acc/(i+1));
    }
    printf("%4d: %.2f%%\n", i, 100.*avg_acc/(i+1));
}

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

    char *backup_directory = "/home/pjreddie/backup/";
    int classes = 10;
    int N = 50000;

    char **labels = get_labels("data/cifar/labels.txt");
    int epoch = (*net.seen)/N;
    data train = load_all_cifar10();
    while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
        clock_t time=clock();

        float loss = train_network_sgd(net, train, 1);
        if(avg_loss == -1) avg_loss = loss;
        avg_loss = avg_loss*.95 + loss*.05;
        printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
        if(*net.seen/N > epoch){
            epoch = *net.seen/N;
            char buff[256];
            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
            save_weights(net, buff);
        }
        if(get_current_batch(net)%100 == 0){
            char buff[256];
            sprintf(buff, "%s/%s.backup",backup_directory,base);
            save_weights(net, buff);
        }
    }
    char buff[256];
    sprintf(buff, "%s/%s.weights", backup_directory, base);
    save_weights(net, buff);

    free_network(net);
    free_ptrs((void**)labels, classes);
    free(base);
    free_data(train);
}

static void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    int i;
    for (i = 0; i < net.n; ++i) {
        layer_t l = net.layers[i];
        if (l.type == CONVOLUTIONAL && l.batch_normalize) {
            denormalize_convolutional_layer(l);
            net.layers[i].batch_normalize=0;
        }
    }
    save_weights(net, outfile);
}

static void rescale_net(char *cfgfile, char *weightfile, char *outfile)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    int i;
    for(i = 0; i < net.n; ++i){
        layer_t l = net.layers[i];
        if(l.type == CONVOLUTIONAL){
            rescale_filters(l, 2, -.5);
            break;
        }
    }
    save_weights(net, outfile);
}

void train_writing(char *cfgfile, char *weightfile)
{
    data_seed = time(0);
    srand(time(0));
    float avg_loss = -1;
    char *base = basecfg(cfgfile);
    printf("%s\n", base);
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
    int imgs = 256;
    int i = net.seen/imgs;
    list *plist = get_paths("data/train.list");
    char **paths = (char **)list_to_array(plist);
    printf("%d\n", plist->size);
    clock_t time;
    while(1){
        ++i;
        time=clock();
        data train = load_data_writing(paths, imgs, plist->size, 256, 256, 4);
        float loss = train_network(net, train);
        #ifdef GPU
        float *out = get_network_output_gpu(net);
        #else
        float *out = get_network_output(net);
        #endif
        // image pred = float_to_image(32, 32, 1, out);
        // print_image(pred);

        net.seen += imgs;
        if(avg_loss == -1) avg_loss = loss;
        avg_loss = avg_loss*.9 + loss*.1;
        printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
        free_data(train);
        if((i % 20000) == 0) net.learning_rate *= .1;
        //if(i%100 == 0 && net.learning_rate > .00001) net.learning_rate *= .97;
        if(i%250==0){
            char buff[256];
            sprintf(buff, "/home/pjreddie/writing_backup/%s_%d.weights", base, i);
            save_weights(net, buff);
        }
    }
}

void predict_classifier(char *datacfg, 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);

    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");
    int 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, net.w, net.h);
        float *X = im.data;
        time=clock();
        float *predictions = network_predict(net, X);
        top_predictions(net, top, indexes);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        for(i = 0; i < top; ++i){
            int index = indexes[i];
            printf("%s: %f\n", names[index], predictions[index]);
        }
        free_image(im);
        if (filename) break;
    }
}

void visualize_learned_weights(char *cfgfile, char *weightfile, char *out_file){
	network net = parse_network_cfg(cfgfile);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	layer l = net.layers[0];
	int num = l.n*l.c*l.size*l.size;
	int filter_size = l.size*l.size*l.c;
	printf("Layer params: %dX%dX%d - %d\n",l.size,l.size,l.c,l.n);
	printf("Filter size: %d\n",filter_size);
	float * filters = net.layers[0].filters;

	FILE *f = fopen(out_file,"w");
	if(f==NULL){
		printf("Error opening file!\n");
		exit(1);
	}

	for (int f_num = 0; f_num<l.n; f_num++){ 
		fprintf(f,"###############FILTER %d###################\n",f_num);
		printf("###############FILTER %d###################\n",f_num);
		for(int channel=0; channel<l.c; channel++){
			printf("~~~~~~~~~~~~~CHANNEL %d~~~~~~~~~~~~\n",channel);
			printf("=========================================\n");
			for(int r=0; r<l.size; r++){
				printf("|");
				for(int c=0; c<l.size; c++){
					int target_index = c + l.size*r+l.size*l.size*channel+f_num*filter_size;
					if(target_index>=num)
						printf('Sono incapace di contare\n');
					printf("%f\t",filters[target_index]);
					fprintf(f,"%f;",filters[target_index]);
				}
				fprintf(f,"\n");
				printf("----------------------------------------\n");
			}
			//visualize filter
			fprintf(f,"\n");
			printf("=========================================\n");
		}
		fprintf(f,"\n\n");
		printf("\n\n");
	}
	fclose(f);
}

void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file)
{
    char **tokens = 0;
    if(token_file){
        size_t n;
        tokens = read_tokens(token_file, &n);
    }

    srand(rseed);
    char *base = basecfg(cfgfile);
    fprintf(stderr, "%s\n", base);

    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    int inputs = get_network_input_size(net);

    int i, j;
    for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
    int c = 0;
    float *input = calloc(inputs, sizeof(float));
    float *out = 0;

    while((c = getc(stdin)) != EOF){
        input[c] = 1;
        out = network_predict(net, input);
        input[c] = 0;
    }
    for(i = 0; i < num; ++i){
        for(j = 0; j < inputs; ++j){
            if (out[j] < .0001) out[j] = 0;
        }
        int next = sample_array(out, inputs);
        if(c == '.' && next == '\n') break;
        c = next;
        print_symbol(c, tokens);

        input[c] = 1;
        out = network_predict(net, input);
        input[c] = 0;
    }
    printf("\n");
}

void test_tag(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 = get_labels("data/tags.txt");
    clock_t time;
    int indexes[10];
    char buff[256];
    char *input = buff;
    int size = net.w;
    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 = resize_min(im, size);
        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);
        top_predictions(net, 10, indexes);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        for(i = 0; i < 10; ++i){
            int index = indexes[i];
            printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
        }
        if(r.data != im.data) free_image(r);
        free_image(im);
        if (filename) break;
    }
}

void speed(char *cfgfile, int tics)
{
    if (tics == 0) tics = 1000;
    network *net = parse_network_cfg(cfgfile);
    set_batch_network(net, 1);
    int i;
    double time=what_time_is_it_now();
    image im = make_image(net->w, net->h, net->c*net->batch);
    for(i = 0; i < tics; ++i){
        network_predict(net, im.data);
    }
    double t = what_time_is_it_now() - time;
    long ops = numops(net);
    printf("\n%d evals, %f Seconds\n", tics, t);
    printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
    printf("FLOPS: %.2f Bn\n", (float)ops/1000000000.*tics/t);
    printf("Speed: %f sec/eval\n", t/tics);
    printf("Speed: %f Hz\n", tics/t);
}