一. 整体代码逻辑

yolo中源码分为三个部分,\example,\include,以及\src文件夹下都有源代码存在.

结构如下所示

├── examples

│   ├── darknet.c(主程序)

│   │── xxx1.c

│   └── xxx2.c

│

├── include

│   ├── darknet.h

│

│

├── Makefile

│

│

└── src

    ├── yyy1.c

    ├── yyy2.h

    └──......

\include文件夹中没有.h头文件, 里边的内容算作一个整体, 都是darknet.c中的一部分, 每个文件的内容共darknet.c调用, 除了darknet.c外, \include文件夹中的文件不存在互相调用, 各自完成不同的功能,如检测视频, 检测图片, 检测艺术品等, 通过darknet.c中的if条件进行选择调用. 因为这部分算作一个整体, 所以共用darknet.h这个头文件. 如果\include需要用到\src中的函数, 则在darknet.h中进行声明

在\src文件夹中, 每个c文件都对应一个同名的.h头文件; main函数存在于\example文件夹下的darknet.c文件中.

\include文件夹下的darknet.h的作用是联系\example与\src两部分, 在这两部分中都需要用的函数则在darknet.h中进行声明, 例如\example中有xxx1.c, \src中有yyy1.c及yyy1.h, xxx1.c与yyy1.c中都需要用到func()这个函数, 那么func()的声明需要放在darknet.h中, 然后在xxx1.c与yyy1.h分别引入头文件#include "darknet.h"

而如果\example\darknet.c中需要调用\example\xxx1.c中的函数, 则需要在\example\darknet.c加extern字段

多文件的实现方式(头文件的使用)

在本项目中, \includes\darknet.h是\examples中文件的头文件, 而在\includes\darknet.h中, 又对部分函数(例如 void forward_network(network *net); )进行了声明, 但是 forward_network 函数的定义是在\src\network.c中, 因为定义是在\src中, 所以定义时\src中的文件需要引入darknet.h这个头文件; 由此, \examples中的文件便可通过darknet.h中的声明调用\src中的函数了

举例

对于 ./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg 这条命令,首先传送到darknet.c文件, 然后darknet.c文件检测到含有detect字符, 所以进入if语句. 使用\src\utils.c中的find_char_arg函数来获取输出文件名等信息, 然后调用detector.c文件中的test_detector函数, 该函数负责检测并进行输出.

二. main函数

唉唉唉

三. makefile文件

入门见<并行程序设计(第四版)>

以yolo源码中的makefile文件为例

GPU=

CUDNN=

OPENCV=

OPENMP=

DEBUG=

ARCH= -gencode arch=compute_30,code=sm_30 \

      -gencode arch=compute_35,code=sm_35 \

      -gencode arch=compute_50,code=[sm_50,compute_50] \

      -gencode arch=compute_52,code=[sm_52,compute_52]

#      -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?

# This is what I use, uncomment if you know your arch and want to specify

# ARCH= -gencode arch=compute_52,code=compute_52

VPATH=./src/:./examples

# VTATH用来告诉make,源文件的路径, 参考https://blog.csdn.net/mcgrady_tracy/article/details/27240139

SLIB=libdarknet.so

ALIB=libdarknet.a

EXEC=darknet

OBJDIR=./obj/

CC=gcc

NVCC=nvcc

AR=ar

ARFLAGS=rcs

OPTS=-Ofast

LDFLAGS= -lm -pthread       #gcc等编译器会用到的一些优化参数，也可以在里面指定库文件的位置

COMMON= -Iinclude/ -Isrc/

CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC   #指定头文件（.h文件）的路径，如：CFLAGS=-I/usr/include -I/path/include。

ifeq ($(OPENMP), )

CFLAGS+= -fopenmp

endif

ifeq ($(DEBUG), )

OPTS=-O0 -g

endif

CFLAGS+=$(OPTS)

ifeq ($(OPENCV), )

COMMON+= -DOPENCV

CFLAGS+= -DOPENCV

LDFLAGS+= `pkg-config --libs opencv`

COMMON+= `pkg-config --cflags opencv`

endif

ifeq ($(GPU), )

COMMON+= -DGPU -I/usr/local/cuda/include/

CFLAGS+= -DGPU

LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand

endif

ifeq ($(CUDNN), )

COMMON+= -DCUDNN

CFLAGS+= -DCUDNN

LDFLAGS+= -lcudnn

endif

OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o l2norm_layer.o yolo_layer.o

EXECOBJA=my_test.o  captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o darknet.o

ifeq ($(GPU), )

LDFLAGS+= -lstdc++

OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o avgpool_layer_kernels.o

endif

EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))   #加前缀函数: $(addprefix <prefix>,<names...>),OBJDIR是obj存放的地址

OBJS = $(addprefix $(OBJDIR), $(OBJ))

DEPS = $(wildcard src/*.h) Makefile include/darknet.h

#all: obj backup results $(SLIB) $(ALIB) $(EXEC)

all: obj  results $(SLIB) $(ALIB) $(EXEC)

$(EXEC): $(EXECOBJ) $(ALIB)

    $(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB)

$(ALIB): $(OBJS)

    $(AR) $(ARFLAGS) $@ $^

$(SLIB): $(OBJS)

    $(CC) $(CFLAGS) -shared $^ -o $@ $(LDFLAGS)

$(OBJDIR)%.o: %.c $(DEPS)

    $(CC) $(COMMON) $(CFLAGS) -c $< -o $@

$(OBJDIR)%.o: %.cu $(DEPS)

    $(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@

obj:

    mkdir -p obj

backup:

    mkdir -p backup

results:

    mkdir -p results

.PHONY: clean

clean:

    rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/*

关于vpath,参考https://blog.csdn.net/mcgrady_tracy/article/details/27240139

(1)修改代码的第一次尝试

在\examples文件夹下新建my_test.c文件, 内容如下

#include "darknet.h"

void output_to_file()

{

    FILE *fp;

    fp=fopen("output.txt","w");

    fprintf(fp,"adfsss");

    printf("test\n");

    fclose(fp);

}

在darknet.c中进行调用, 如下

#include "darknet.h"

#include <time.h>

#include <stdlib.h>

#include <stdio.h>

//

extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);     // 在\examples\classifier.c中

extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen);     // 在\examples\detector.c中

extern void run_yolo(int argc, char **argv);     // 在\examples\yolo.c中

extern void run_detector(int argc, char **argv);     // 在\examples\detector.c中

extern void run_coco(int argc, char **argv);         // 在\examples\coco.c中

extern void run_captcha(int argc, char **argv);      // 在\examples\captcha.c中

extern void run_nightmare(int argc, char **argv);        // 在\examples\nightmare.c中

extern void run_classifier(int argc, char **argv);       // 在\examples\classifier.c中

extern void run_regressor(int argc, char **argv);        // 在\examples\regressor.c中

extern void run_segmenter(int argc, char **argv);        // 在\examples\segmenter.c中

extern void run_char_rnn(int argc, char **argv);         // 在\examples\rnn.c中

extern void run_tag(int argc, char **argv);              // 在\examples\tag.c中

extern void run_cifar(int argc, char **argv);            // 在\examples\fun_cifar.c中

extern void run_go(int argc, char **argv);               // 在\examples\go.c中

extern void run_art(int argc, char **argv);              // 在\examples\art.c中

extern void run_super(int argc, char **argv);            // 在\examples\super.c中

extern void run_lsd(int argc, char **argv);              // 在\examples\nightmare.c中

extern void output_to_file();              // 在\examples\my_test.c中

void average(int argc, char *argv[])

{

    char *cfgfile = argv[];

    char *outfile = argv[];

    gpu_index = -;

    network *net = parse_network_cfg(cfgfile);

    network *sum = parse_network_cfg(cfgfile);

    char *weightfile = argv[];

    load_weights(sum, weightfile);

    int i, j;

    int n = argc - ;

    for(i = ; i < n; ++i){

        weightfile = argv[i+];

        load_weights(net, weightfile);

        for(j = ; j < net->n; ++j){

            layer l = net->layers[j];

            layer out = sum->layers[j];

            if(l.type == CONVOLUTIONAL){

                int num = l.n*l.c*l.size*l.size;

                axpy_cpu(l.n, , l.biases, , out.biases, );

                axpy_cpu(num, , l.weights, , out.weights, );

                if(l.batch_normalize){

                    axpy_cpu(l.n, , l.scales, , out.scales, );

                    axpy_cpu(l.n, , l.rolling_mean, , out.rolling_mean, );

                    axpy_cpu(l.n, , l.rolling_variance, , out.rolling_variance, );

                }

            }

            if(l.type == CONNECTED){

                axpy_cpu(l.outputs, , l.biases, , out.biases, );

                axpy_cpu(l.outputs*l.inputs, , l.weights, , out.weights, );

            }

        }

    }

    n = n+;

    for(j = ; j < net->n; ++j){

        layer l = sum->layers[j];

        if(l.type == CONVOLUTIONAL){

            int num = l.n*l.c*l.size*l.size;

            scal_cpu(l.n, ./n, l.biases, );

            scal_cpu(num, ./n, l.weights, );

                if(l.batch_normalize){

                    scal_cpu(l.n, ./n, l.scales, );

                    scal_cpu(l.n, ./n, l.rolling_mean, );

                    scal_cpu(l.n, ./n, l.rolling_variance, );

                }

        }

        if(l.type == CONNECTED){

            scal_cpu(l.outputs, ./n, l.biases, );

            scal_cpu(l.outputs*l.inputs, ./n, l.weights, );

        }

    }

    save_weights(sum, outfile);

}

long numops(network *net)

{

    int i;

    long ops = ;

    for(i = ; i < net->n; ++i){

        layer l = net->layers[i];

        if(l.type == CONVOLUTIONAL){

            ops += 2l * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w;

        } else if(l.type == CONNECTED){

            ops += 2l * l.inputs * l.outputs;

        } else if (l.type == RNN){

            ops += 2l * l.input_layer->inputs * l.input_layer->outputs;

            ops += 2l * l.self_layer->inputs * l.self_layer->outputs;

            ops += 2l * l.output_layer->inputs * l.output_layer->outputs;

        } else if (l.type == GRU){

            ops += 2l * l.uz->inputs * l.uz->outputs;

            ops += 2l * l.uh->inputs * l.uh->outputs;

            ops += 2l * l.ur->inputs * l.ur->outputs;

            ops += 2l * l.wz->inputs * l.wz->outputs;

            ops += 2l * l.wh->inputs * l.wh->outputs;

            ops += 2l * l.wr->inputs * l.wr->outputs;

        } else if (l.type == LSTM){

            ops += 2l * l.uf->inputs * l.uf->outputs;

            ops += 2l * l.ui->inputs * l.ui->outputs;

            ops += 2l * l.ug->inputs * l.ug->outputs;

            ops += 2l * l.uo->inputs * l.uo->outputs;

            ops += 2l * l.wf->inputs * l.wf->outputs;

            ops += 2l * l.wi->inputs * l.wi->outputs;

            ops += 2l * l.wg->inputs * l.wg->outputs;

            ops += 2l * l.wo->inputs * l.wo->outputs;

        }

    }

    return ops;

}

void speed(char *cfgfile, int tics)

{

    if (tics == ) tics = ;

    network *net = parse_network_cfg(cfgfile);

    set_batch_network(net, );

    int i;

    double time=what_time_is_it_now();

    image im = make_image(net->w, net->h, net->c*net->batch);

    for(i = ; 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/.);

    printf("FLOPS: %.2f Bn\n", (float)ops/.*tics/t);

    printf("Speed: %f sec/eval\n", t/tics);

    printf("Speed: %f Hz\n", tics/t);

}

void operations(char *cfgfile)

{

    gpu_index = -;

    network *net = parse_network_cfg(cfgfile);

    long ops = numops(net);

    printf("Floating Point Operations: %ld\n", ops);

    printf("Floating Point Operations: %.2f Bn\n", (float)ops/.);

}

void oneoff(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = parse_network_cfg(cfgfile);

    int oldn = net->layers[net->n - ].n;

    int c = net->layers[net->n - ].c;

    scal_cpu(oldn*c, ., net->layers[net->n - ].weights, );

    scal_cpu(oldn, , net->layers[net->n - ].biases, );

    net->layers[net->n - ].n = ;

    net->layers[net->n - ].biases += ;

    net->layers[net->n - ].weights += *c;

    if(weightfile){

        load_weights(net, weightfile);

    }

    net->layers[net->n - ].biases -= ;

    net->layers[net->n - ].weights -= *c;

    net->layers[net->n - ].n = oldn;

    printf("%d\n", oldn);

    layer l = net->layers[net->n - ];

    copy_cpu(l.n/, l.biases, , l.biases +   l.n/, );

    copy_cpu(l.n/, l.biases, , l.biases + *l.n/, );

    copy_cpu(l.n/*l.c, l.weights, , l.weights +   l.n/*l.c, );

    copy_cpu(l.n/*l.c, l.weights, , l.weights + *l.n/*l.c, );

    *net->seen = ;

    save_weights(net, outfile);

}

void oneoff2(char *cfgfile, char *weightfile, char *outfile, int l)

{

    gpu_index = -;

    network *net = parse_network_cfg(cfgfile);

    if(weightfile){

        load_weights_upto(net, weightfile, , net->n);

        load_weights_upto(net, weightfile, l, net->n);

    }

    *net->seen = ;

    save_weights_upto(net, outfile, net->n);

}

void partial(char *cfgfile, char *weightfile, char *outfile, int max)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    save_weights_upto(net, outfile, max);

}

void print_weights(char *cfgfile, char *weightfile, int n)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    layer l = net->layers[n];

    int i, j;

    //printf("[");

    for(i = ; i < l.n; ++i){

        //printf("[");

        for(j = ; j < l.size*l.size*l.c; ++j){

            //if(j > 0) printf(",");

            printf("%g ", l.weights[i*l.size*l.size*l.c + j]);

        }

        printf("\n");

        //printf("]%s\n", (i == l.n-1)?"":",");

    }

    //printf("]");

}

void rescale_net(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for(i = ; i < net->n; ++i){

        layer l = net->layers[i];

        if(l.type == CONVOLUTIONAL){

            rescale_weights(l, , -.);

            break;

        }

    }

    save_weights(net, outfile);

}

void rgbgr_net(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for(i = ; i < net->n; ++i){

        layer l = net->layers[i];

        if(l.type == CONVOLUTIONAL){

            rgbgr_weights(l);

            break;

        }

    }

    save_weights(net, outfile);

}

void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for (i = ; i < net->n; ++i) {

        layer l = net->layers[i];

        if (l.type == CONVOLUTIONAL && l.batch_normalize) {

            denormalize_convolutional_layer(l);

        }

        if (l.type == CONNECTED && l.batch_normalize) {

            denormalize_connected_layer(l);

        }

        if (l.type == GRU && l.batch_normalize) {

            denormalize_connected_layer(*l.input_z_layer);

            denormalize_connected_layer(*l.input_r_layer);

            denormalize_connected_layer(*l.input_h_layer);

            denormalize_connected_layer(*l.state_z_layer);

            denormalize_connected_layer(*l.state_r_layer);

            denormalize_connected_layer(*l.state_h_layer);

        }

    }

    save_weights(net, outfile);

}

layer normalize_layer(layer l, int n)

{

    int j;

    l.batch_normalize=;

    l.scales = calloc(n, sizeof(float));

    for(j = ; j < n; ++j){

        l.scales[j] = ;

    }

    l.rolling_mean = calloc(n, sizeof(float));

    l.rolling_variance = calloc(n, sizeof(float));

    return l;

}

void normalize_net(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for(i = ; i < net->n; ++i){

        layer l = net->layers[i];

        if(l.type == CONVOLUTIONAL && !l.batch_normalize){

            net->layers[i] = normalize_layer(l, l.n);

        }

        if (l.type == CONNECTED && !l.batch_normalize) {

            net->layers[i] = normalize_layer(l, l.outputs);

        }

        if (l.type == GRU && l.batch_normalize) {

            *l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs);

            *l.input_r_layer = normalize_layer(*l.input_r_layer, l.input_r_layer->outputs);

            *l.input_h_layer = normalize_layer(*l.input_h_layer, l.input_h_layer->outputs);

            *l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs);

            *l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs);

            *l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs);

            net->layers[i].batch_normalize=;

        }

    }

    save_weights(net, outfile);

}

void statistics_net(char *cfgfile, char *weightfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for (i = ; i < net->n; ++i) {

        layer l = net->layers[i];

        if (l.type == CONNECTED && l.batch_normalize) {

            printf("Connected Layer %d\n", i);

            statistics_connected_layer(l);

        }

        if (l.type == GRU && l.batch_normalize) {

            printf("GRU Layer %d\n", i);

            printf("Input Z\n");

            statistics_connected_layer(*l.input_z_layer);

            printf("Input R\n");

            statistics_connected_layer(*l.input_r_layer);

            printf("Input H\n");

            statistics_connected_layer(*l.input_h_layer);

            printf("State Z\n");

            statistics_connected_layer(*l.state_z_layer);

            printf("State R\n");

            statistics_connected_layer(*l.state_r_layer);

            printf("State H\n");

            statistics_connected_layer(*l.state_h_layer);

        }

        printf("\n");

    }

}

void denormalize_net(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for (i = ; i < net->n; ++i) {

        layer l = net->layers[i];

        if ((l.type == DECONVOLUTIONAL || l.type == CONVOLUTIONAL) && l.batch_normalize) {

            denormalize_convolutional_layer(l);

            net->layers[i].batch_normalize=;

        }

        if (l.type == CONNECTED && l.batch_normalize) {

            denormalize_connected_layer(l);

            net->layers[i].batch_normalize=;

        }

        if (l.type == GRU && l.batch_normalize) {

            denormalize_connected_layer(*l.input_z_layer);

            denormalize_connected_layer(*l.input_r_layer);

            denormalize_connected_layer(*l.input_h_layer);

            denormalize_connected_layer(*l.state_z_layer);

            denormalize_connected_layer(*l.state_r_layer);

            denormalize_connected_layer(*l.state_h_layer);

            l.input_z_layer->batch_normalize = ;

            l.input_r_layer->batch_normalize = ;

            l.input_h_layer->batch_normalize = ;

            l.state_z_layer->batch_normalize = ;

            l.state_r_layer->batch_normalize = ;

            l.state_h_layer->batch_normalize = ;

            net->layers[i].batch_normalize=;

        }

    }

    save_weights(net, outfile);

}

void mkimg(char *cfgfile, char *weightfile, int h, int w, int num, char *prefix)

{

    network *net = load_network(cfgfile, weightfile, );

    image *ims = get_weights(net->layers[]);

    int n = net->layers[].n;

    int z;

    for(z = ; z < num; ++z){

        image im = make_image(h, w, );

        fill_image(im, .);

        int i;

        for(i = ; i < ; ++i){

            image r = copy_image(ims[rand()%n]);

            rotate_image_cw(r, rand()%);

            random_distort_image(r, , 1.5, 1.5);

            int dx = rand()%(w-r.w);

            int dy = rand()%(h-r.h);

            ghost_image(r, im, dx, dy);

            free_image(r);

        }

        char buff[];

        sprintf(buff, "%s/gen_%d", prefix, z);

        save_image(im, buff);

        free_image(im);

    }

}

void visualize(char *cfgfile, char *weightfile)

{

    network *net = load_network(cfgfile, weightfile, );

    visualize_network(net);

#ifdef OPENCV

    cvWaitKey();

#endif

}

int main(int argc, char **argv)

{

    // argv[0] 指向程序运行的全路径名;argv[1] 指向在DOS命令行中执行程序名后的第一个字符串;argv[2]第二个

    //test_resize("data/bad.jpg");

    //test_box();

    //test_convolutional_layer();

    if(argc < ){

        fprintf(stderr, "usage: %s <function>\n", argv[]);

        return ;

    }

    gpu_index = find_int_arg(argc, argv, "-i", );

    if(find_arg(argc, argv, "-nogpu")) {

        gpu_index = -;

    }

#ifndef GPU

    gpu_index = -;

#else

    if(gpu_index >= ){

        cuda_set_device(gpu_index);

    }

#endif

    if ( == strcmp(argv[], "average")){

        average(argc, argv);

    } else if ( == strcmp(argv[], "yolo")){

        run_yolo(argc, argv);

    } else if ( == strcmp(argv[], "super")){

        run_super(argc, argv);

    } else if ( == strcmp(argv[], "lsd")){

        run_lsd(argc, argv);

    } else if ( == strcmp(argv[], "detector")){

        run_detector(argc, argv);

    } else if ( == strcmp(argv[], "detect")){

        float thresh = find_float_arg(argc, argv, "-thresh", .);       //thresh用来控制检测的阈值

        char *filename = (argc > ) ? argv[]: ;

        char *outfile = find_char_arg(argc, argv, "-out", );           // 定义在\src\utils.c中

        int fullscreen = find_arg(argc, argv, "-fullscreen");

        test_detector("cfg/coco.data", argv[], argv[], filename, thresh, ., outfile, fullscreen);      // 函数定义位于detector.c中

        // 命令举例./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

        //*修改//

        output_to_file();

        //*//

    } else if ( == strcmp(argv[], "cifar")){

        run_cifar(argc, argv);

    } else if ( == strcmp(argv[], "go")){

        run_go(argc, argv);

    } else if ( == strcmp(argv[], "rnn")){

        run_char_rnn(argc, argv);

    } else if ( == strcmp(argv[], "coco")){

        run_coco(argc, argv);

    } else if ( == strcmp(argv[], "classify")){

        predict_classifier("cfg/imagenet1k.data", argv[], argv[], argv[], );

    } else if ( == strcmp(argv[], "classifier")){

        run_classifier(argc, argv);

    } else if ( == strcmp(argv[], "regressor")){

        run_regressor(argc, argv);

    } else if ( == strcmp(argv[], "segmenter")){

        run_segmenter(argc, argv);

    } else if ( == strcmp(argv[], "art")){

        run_art(argc, argv);

    } else if ( == strcmp(argv[], "tag")){

        run_tag(argc, argv);

    } else if ( == strcmp(argv[], "3d")){

        composite_3d(argv[], argv[], argv[], (argc > ) ? atof(argv[]) : );

    } else if ( == strcmp(argv[], "test")){

        test_resize(argv[]);

    } else if ( == strcmp(argv[], "captcha")){

        run_captcha(argc, argv);

    } else if ( == strcmp(argv[], "nightmare")){

        run_nightmare(argc, argv);

    } else if ( == strcmp(argv[], "rgbgr")){

        rgbgr_net(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "reset")){

        reset_normalize_net(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "denormalize")){

        denormalize_net(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "statistics")){

        statistics_net(argv[], argv[]);

    } else if ( == strcmp(argv[], "normalize")){

        normalize_net(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "rescale")){

        rescale_net(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "ops")){

        operations(argv[]);

    } else if ( == strcmp(argv[], "speed")){

        speed(argv[], (argc >  && argv[]) ? atoi(argv[]) : );

    } else if ( == strcmp(argv[], "oneoff")){

        oneoff(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "oneoff2")){

        oneoff2(argv[], argv[], argv[], atoi(argv[]));

    } else if ( == strcmp(argv[], "print")){

        print_weights(argv[], argv[], atoi(argv[]));

    } else if ( == strcmp(argv[], "partial")){

        partial(argv[], argv[], argv[], atoi(argv[]));

    } else if ( == strcmp(argv[], "average")){

        average(argc, argv);

    } else if ( == strcmp(argv[], "visualize")){

        visualize(argv[], (argc > ) ? argv[] : );

    } else if ( == strcmp(argv[], "mkimg")){

        mkimg(argv[], argv[], atoi(argv[]), atoi(argv[]), atoi(argv[]), argv[]);

    } else if ( == strcmp(argv[], "imtest")){

        test_resize(argv[]);

    } else {

        fprintf(stderr, "Not an option: %s\n", argv[]);

    }

    return ;

}

然后修改Makefile文件, 在EXECOBJA=后追加my_test.o字段. 注意不可将该字段放在EXECOBJA=的最后, 否则编译不通过. 内容如下

GPU=

CUDNN=

OPENCV=

OPENMP=

DEBUG=

ARCH= -gencode arch=compute_30,code=sm_30 \

      -gencode arch=compute_35,code=sm_35 \

      -gencode arch=compute_50,code=[sm_50,compute_50] \

      -gencode arch=compute_52,code=[sm_52,compute_52]

#      -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?

# This is what I use, uncomment if you know your arch and want to specify

# ARCH= -gencode arch=compute_52,code=compute_52

VPATH=./src/:./examples

# VTATH用来告诉make,源文件的路径, 参考https://blog.csdn.net/mcgrady_tracy/article/details/27240139

SLIB=libdarknet.so

ALIB=libdarknet.a

EXEC=darknet

OBJDIR=./obj/

CC=gcc

NVCC=nvcc

AR=ar

ARFLAGS=rcs

OPTS=-Ofast

LDFLAGS= -lm -pthread       #gcc等编译器会用到的一些优化参数，也可以在里面指定库文件的位置

COMMON= -Iinclude/ -Isrc/

CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC   #指定头文件（.h文件）的路径，如：CFLAGS=-I/usr/include -I/path/include。

ifeq ($(OPENMP), )

CFLAGS+= -fopenmp

endif

ifeq ($(DEBUG), )

OPTS=-O0 -g

endif

CFLAGS+=$(OPTS)

ifeq ($(OPENCV), )

COMMON+= -DOPENCV

CFLAGS+= -DOPENCV

LDFLAGS+= `pkg-config --libs opencv`

COMMON+= `pkg-config --cflags opencv`

endif

ifeq ($(GPU), )

COMMON+= -DGPU -I/usr/local/cuda/include/

CFLAGS+= -DGPU

LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand

endif

ifeq ($(CUDNN), )

COMMON+= -DCUDNN

CFLAGS+= -DCUDNN

LDFLAGS+= -lcudnn

endif

OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o l2norm_layer.o yolo_layer.o

EXECOBJA=my_test.o  captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o darknet.o

ifeq ($(GPU), )

LDFLAGS+= -lstdc++

OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o avgpool_layer_kernels.o

endif

EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))   #加前缀函数: $(addprefix <prefix>,<names...>),OBJDIR是obj存放的地址

OBJS = $(addprefix $(OBJDIR), $(OBJ))

DEPS = $(wildcard src/*.h) Makefile include/darknet.h

#all: obj backup results $(SLIB) $(ALIB) $(EXEC)

all: obj  results $(SLIB) $(ALIB) $(EXEC)

$(EXEC): $(EXECOBJ) $(ALIB)

    $(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB)

$(ALIB): $(OBJS)

    $(AR) $(ARFLAGS) $@ $^

$(SLIB): $(OBJS)

    $(CC) $(CFLAGS) -shared $^ -o $@ $(LDFLAGS)

$(OBJDIR)%.o: %.c $(DEPS)

    $(CC) $(COMMON) $(CFLAGS) -c $< -o $@

$(OBJDIR)%.o: %.cu $(DEPS)

    $(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@

obj:

    mkdir -p obj

backup:

    mkdir -p backup

results:

    mkdir -p results

.PHONY: clean

clean:

    rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/*

编译并可成功运行.

 (2)修改代码的第二次尝试

在\src目录下新建my_testinsrc.c以及my_testinsrc.h, 内容如下

// my_testinsrc.h

#include "darknet.h"

// my_testinsrc.c

#include <stdio.h>

void my_testinsrc(){

    printf("test in src\n");

}

修改Makefile, 在最后声明新加的函数

修改后内容如下

GPU=

CUDNN=

OPENCV=

OPENMP=

DEBUG=

ARCH= -gencode arch=compute_30,code=sm_30 \

      -gencode arch=compute_35,code=sm_35 \

      -gencode arch=compute_50,code=[sm_50,compute_50] \

      -gencode arch=compute_52,code=[sm_52,compute_52]

#      -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?

# This is what I use, uncomment if you know your arch and want to specify

# ARCH= -gencode arch=compute_52,code=compute_52

VPATH=./src/:./examples

# VTATH用来告诉make,源文件的路径, 参考https://blog.csdn.net/mcgrady_tracy/article/details/27240139

SLIB=libdarknet.so

ALIB=libdarknet.a

EXEC=darknet

OBJDIR=./obj/

CC=gcc

NVCC=nvcc

AR=ar

ARFLAGS=rcs

OPTS=-Ofast

LDFLAGS= -lm -pthread       #gcc等编译器会用到的一些优化参数，也可以在里面指定库文件的位置

COMMON= -Iinclude/ -Isrc/

CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC   #指定头文件（.h文件）的路径，如：CFLAGS=-I/usr/include -I/path/include。

ifeq ($(OPENMP), )

CFLAGS+= -fopenmp

endif

ifeq ($(DEBUG), )

OPTS=-O0 -g

endif

CFLAGS+=$(OPTS)

ifeq ($(OPENCV), )

COMMON+= -DOPENCV

CFLAGS+= -DOPENCV

LDFLAGS+= `pkg-config --libs opencv`

COMMON+= `pkg-config --cflags opencv`

endif

ifeq ($(GPU), )

COMMON+= -DGPU -I/usr/local/cuda/include/

CFLAGS+= -DGPU

LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand

endif

ifeq ($(CUDNN), )

COMMON+= -DCUDNN

CFLAGS+= -DCUDNN

LDFLAGS+= -lcudnn

endif

OBJ=my_testinsrc.o  gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o l2norm_layer.o yolo_layer.o

EXECOBJA=my_test.o  captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o darknet.o

ifeq ($(GPU), )

LDFLAGS+= -lstdc++

OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o avgpool_layer_kernels.o

endif

EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))   #加前缀函数: $(addprefix <prefix>,<names...>),OBJDIR是obj存放的地址

OBJS = $(addprefix $(OBJDIR), $(OBJ))

DEPS = $(wildcard src/*.h) Makefile include/darknet.h

#all: obj backup results $(SLIB) $(ALIB) $(EXEC)

all: obj  results $(SLIB) $(ALIB) $(EXEC)

$(EXEC): $(EXECOBJ) $(ALIB)

    $(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB)

$(ALIB): $(OBJS)

    $(AR) $(ARFLAGS) $@ $^

$(SLIB): $(OBJS)

    $(CC) $(CFLAGS) -shared $^ -o $@ $(LDFLAGS)

$(OBJDIR)%.o: %.c $(DEPS)

    $(CC) $(COMMON) $(CFLAGS) -c $< -o $@

$(OBJDIR)%.o: %.cu $(DEPS)

    $(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@

obj:

    mkdir -p obj

backup:

    mkdir -p backup

results:

    mkdir -p results

.PHONY: clean

clean:

    rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/*

在darknet.c中进行调用, 内容如下

#include "darknet.h"

#include <time.h>

#include <stdlib.h>

#include <stdio.h>

//

extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);     // 在\examples\classifier.c中

extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen);     // 在\examples\detector.c中

extern void run_yolo(int argc, char **argv);     // 在\examples\yolo.c中

extern void run_detector(int argc, char **argv);     // 在\examples\detector.c中

extern void run_coco(int argc, char **argv);         // 在\examples\coco.c中

extern void run_captcha(int argc, char **argv);      // 在\examples\captcha.c中

extern void run_nightmare(int argc, char **argv);        // 在\examples\nightmare.c中

extern void run_classifier(int argc, char **argv);       // 在\examples\classifier.c中

extern void run_regressor(int argc, char **argv);        // 在\examples\regressor.c中

extern void run_segmenter(int argc, char **argv);        // 在\examples\segmenter.c中

extern void run_char_rnn(int argc, char **argv);         // 在\examples\rnn.c中

extern void run_tag(int argc, char **argv);              // 在\examples\tag.c中

extern void run_cifar(int argc, char **argv);            // 在\examples\fun_cifar.c中

extern void run_go(int argc, char **argv);               // 在\examples\go.c中

extern void run_art(int argc, char **argv);              // 在\examples\art.c中

extern void run_super(int argc, char **argv);            // 在\examples\super.c中

extern void run_lsd(int argc, char **argv);              // 在\examples\nightmare.c中

extern void output_to_file();              // 在\examples\my_test.c中

void average(int argc, char *argv[])

{

    char *cfgfile = argv[];

    char *outfile = argv[];

    gpu_index = -;

    network *net = parse_network_cfg(cfgfile);

    network *sum = parse_network_cfg(cfgfile);

    char *weightfile = argv[];

    load_weights(sum, weightfile);

    int i, j;

    int n = argc - ;

    for(i = ; i < n; ++i){

        weightfile = argv[i+];

        load_weights(net, weightfile);

        for(j = ; j < net->n; ++j){

            layer l = net->layers[j];

            layer out = sum->layers[j];

            if(l.type == CONVOLUTIONAL){

                int num = l.n*l.c*l.size*l.size;

                axpy_cpu(l.n, , l.biases, , out.biases, );

                axpy_cpu(num, , l.weights, , out.weights, );

                if(l.batch_normalize){

                    axpy_cpu(l.n, , l.scales, , out.scales, );

                    axpy_cpu(l.n, , l.rolling_mean, , out.rolling_mean, );

                    axpy_cpu(l.n, , l.rolling_variance, , out.rolling_variance, );

                }

            }

            if(l.type == CONNECTED){

                axpy_cpu(l.outputs, , l.biases, , out.biases, );

                axpy_cpu(l.outputs*l.inputs, , l.weights, , out.weights, );

            }

        }

    }

    n = n+;

    for(j = ; j < net->n; ++j){

        layer l = sum->layers[j];

        if(l.type == CONVOLUTIONAL){

            int num = l.n*l.c*l.size*l.size;

            scal_cpu(l.n, ./n, l.biases, );

            scal_cpu(num, ./n, l.weights, );

                if(l.batch_normalize){

                    scal_cpu(l.n, ./n, l.scales, );

                    scal_cpu(l.n, ./n, l.rolling_mean, );

                    scal_cpu(l.n, ./n, l.rolling_variance, );

                }

        }

        if(l.type == CONNECTED){

            scal_cpu(l.outputs, ./n, l.biases, );

            scal_cpu(l.outputs*l.inputs, ./n, l.weights, );

        }

    }

    save_weights(sum, outfile);

}

long numops(network *net)

{

    int i;

    long ops = ;

    for(i = ; i < net->n; ++i){

        layer l = net->layers[i];

        if(l.type == CONVOLUTIONAL){

            ops += 2l * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w;

        } else if(l.type == CONNECTED){

            ops += 2l * l.inputs * l.outputs;

        } else if (l.type == RNN){

            ops += 2l * l.input_layer->inputs * l.input_layer->outputs;

            ops += 2l * l.self_layer->inputs * l.self_layer->outputs;

            ops += 2l * l.output_layer->inputs * l.output_layer->outputs;

        } else if (l.type == GRU){

            ops += 2l * l.uz->inputs * l.uz->outputs;

            ops += 2l * l.uh->inputs * l.uh->outputs;

            ops += 2l * l.ur->inputs * l.ur->outputs;

            ops += 2l * l.wz->inputs * l.wz->outputs;

            ops += 2l * l.wh->inputs * l.wh->outputs;

            ops += 2l * l.wr->inputs * l.wr->outputs;

        } else if (l.type == LSTM){

            ops += 2l * l.uf->inputs * l.uf->outputs;

            ops += 2l * l.ui->inputs * l.ui->outputs;

            ops += 2l * l.ug->inputs * l.ug->outputs;

            ops += 2l * l.uo->inputs * l.uo->outputs;

            ops += 2l * l.wf->inputs * l.wf->outputs;

            ops += 2l * l.wi->inputs * l.wi->outputs;

            ops += 2l * l.wg->inputs * l.wg->outputs;

            ops += 2l * l.wo->inputs * l.wo->outputs;

        }

    }

    return ops;

}

void speed(char *cfgfile, int tics)

{

    if (tics == ) tics = ;

    network *net = parse_network_cfg(cfgfile);

    set_batch_network(net, );

    int i;

    double time=what_time_is_it_now();

    image im = make_image(net->w, net->h, net->c*net->batch);

    for(i = ; 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/.);

    printf("FLOPS: %.2f Bn\n", (float)ops/.*tics/t);

    printf("Speed: %f sec/eval\n", t/tics);

    printf("Speed: %f Hz\n", tics/t);

}

void operations(char *cfgfile)

{

    gpu_index = -;

    network *net = parse_network_cfg(cfgfile);

    long ops = numops(net);

    printf("Floating Point Operations: %ld\n", ops);

    printf("Floating Point Operations: %.2f Bn\n", (float)ops/.);

}

void oneoff(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = parse_network_cfg(cfgfile);

    int oldn = net->layers[net->n - ].n;

    int c = net->layers[net->n - ].c;

    scal_cpu(oldn*c, ., net->layers[net->n - ].weights, );

    scal_cpu(oldn, , net->layers[net->n - ].biases, );

    net->layers[net->n - ].n = ;

    net->layers[net->n - ].biases += ;

    net->layers[net->n - ].weights += *c;

    if(weightfile){

        load_weights(net, weightfile);

    }

    net->layers[net->n - ].biases -= ;

    net->layers[net->n - ].weights -= *c;

    net->layers[net->n - ].n = oldn;

    printf("%d\n", oldn);

    layer l = net->layers[net->n - ];

    copy_cpu(l.n/, l.biases, , l.biases +   l.n/, );

    copy_cpu(l.n/, l.biases, , l.biases + *l.n/, );

    copy_cpu(l.n/*l.c, l.weights, , l.weights +   l.n/*l.c, );

    copy_cpu(l.n/*l.c, l.weights, , l.weights + *l.n/*l.c, );

    *net->seen = ;

    save_weights(net, outfile);

}

void oneoff2(char *cfgfile, char *weightfile, char *outfile, int l)

{

    gpu_index = -;

    network *net = parse_network_cfg(cfgfile);

    if(weightfile){

        load_weights_upto(net, weightfile, , net->n);

        load_weights_upto(net, weightfile, l, net->n);

    }

    *net->seen = ;

    save_weights_upto(net, outfile, net->n);

}

void partial(char *cfgfile, char *weightfile, char *outfile, int max)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    save_weights_upto(net, outfile, max);

}

void print_weights(char *cfgfile, char *weightfile, int n)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    layer l = net->layers[n];

    int i, j;

    //printf("[");

    for(i = ; i < l.n; ++i){

        //printf("[");

        for(j = ; j < l.size*l.size*l.c; ++j){

            //if(j > 0) printf(",");

            printf("%g ", l.weights[i*l.size*l.size*l.c + j]);

        }

        printf("\n");

        //printf("]%s\n", (i == l.n-1)?"":",");

    }

    //printf("]");

}

void rescale_net(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for(i = ; i < net->n; ++i){

        layer l = net->layers[i];

        if(l.type == CONVOLUTIONAL){

            rescale_weights(l, , -.);

            break;

        }

    }

    save_weights(net, outfile);

}

void rgbgr_net(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for(i = ; i < net->n; ++i){

        layer l = net->layers[i];

        if(l.type == CONVOLUTIONAL){

            rgbgr_weights(l);

            break;

        }

    }

    save_weights(net, outfile);

}

void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for (i = ; i < net->n; ++i) {

        layer l = net->layers[i];

        if (l.type == CONVOLUTIONAL && l.batch_normalize) {

            denormalize_convolutional_layer(l);

        }

        if (l.type == CONNECTED && l.batch_normalize) {

            denormalize_connected_layer(l);

        }

        if (l.type == GRU && l.batch_normalize) {

            denormalize_connected_layer(*l.input_z_layer);

            denormalize_connected_layer(*l.input_r_layer);

            denormalize_connected_layer(*l.input_h_layer);

            denormalize_connected_layer(*l.state_z_layer);

            denormalize_connected_layer(*l.state_r_layer);

            denormalize_connected_layer(*l.state_h_layer);

        }

    }

    save_weights(net, outfile);

}

layer normalize_layer(layer l, int n)

{

    int j;

    l.batch_normalize=;

    l.scales = calloc(n, sizeof(float));

    for(j = ; j < n; ++j){

        l.scales[j] = ;

    }

    l.rolling_mean = calloc(n, sizeof(float));

    l.rolling_variance = calloc(n, sizeof(float));

    return l;

}

void normalize_net(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for(i = ; i < net->n; ++i){

        layer l = net->layers[i];

        if(l.type == CONVOLUTIONAL && !l.batch_normalize){

            net->layers[i] = normalize_layer(l, l.n);

        }

        if (l.type == CONNECTED && !l.batch_normalize) {

            net->layers[i] = normalize_layer(l, l.outputs);

        }

        if (l.type == GRU && l.batch_normalize) {

            *l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs);

            *l.input_r_layer = normalize_layer(*l.input_r_layer, l.input_r_layer->outputs);

            *l.input_h_layer = normalize_layer(*l.input_h_layer, l.input_h_layer->outputs);

            *l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs);

            *l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs);

            *l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs);

            net->layers[i].batch_normalize=;

        }

    }

    save_weights(net, outfile);

}

void statistics_net(char *cfgfile, char *weightfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for (i = ; i < net->n; ++i) {

        layer l = net->layers[i];

        if (l.type == CONNECTED && l.batch_normalize) {

            printf("Connected Layer %d\n", i);

            statistics_connected_layer(l);

        }

        if (l.type == GRU && l.batch_normalize) {

            printf("GRU Layer %d\n", i);

            printf("Input Z\n");

            statistics_connected_layer(*l.input_z_layer);

            printf("Input R\n");

            statistics_connected_layer(*l.input_r_layer);

            printf("Input H\n");

            statistics_connected_layer(*l.input_h_layer);

            printf("State Z\n");

            statistics_connected_layer(*l.state_z_layer);

            printf("State R\n");

            statistics_connected_layer(*l.state_r_layer);

            printf("State H\n");

            statistics_connected_layer(*l.state_h_layer);

        }

        printf("\n");

    }

}

void denormalize_net(char *cfgfile, char *weightfile, char *outfile)

{

    gpu_index = -;

    network *net = load_network(cfgfile, weightfile, );

    int i;

    for (i = ; i < net->n; ++i) {

        layer l = net->layers[i];

        if ((l.type == DECONVOLUTIONAL || l.type == CONVOLUTIONAL) && l.batch_normalize) {

            denormalize_convolutional_layer(l);

            net->layers[i].batch_normalize=;

        }

        if (l.type == CONNECTED && l.batch_normalize) {

            denormalize_connected_layer(l);

            net->layers[i].batch_normalize=;

        }

        if (l.type == GRU && l.batch_normalize) {

            denormalize_connected_layer(*l.input_z_layer);

            denormalize_connected_layer(*l.input_r_layer);

            denormalize_connected_layer(*l.input_h_layer);

            denormalize_connected_layer(*l.state_z_layer);

            denormalize_connected_layer(*l.state_r_layer);

            denormalize_connected_layer(*l.state_h_layer);

            l.input_z_layer->batch_normalize = ;

            l.input_r_layer->batch_normalize = ;

            l.input_h_layer->batch_normalize = ;

            l.state_z_layer->batch_normalize = ;

            l.state_r_layer->batch_normalize = ;

            l.state_h_layer->batch_normalize = ;

            net->layers[i].batch_normalize=;

        }

    }

    save_weights(net, outfile);

}

void mkimg(char *cfgfile, char *weightfile, int h, int w, int num, char *prefix)

{

    network *net = load_network(cfgfile, weightfile, );

    image *ims = get_weights(net->layers[]);

    int n = net->layers[].n;

    int z;

    for(z = ; z < num; ++z){

        image im = make_image(h, w, );

        fill_image(im, .);

        int i;

        for(i = ; i < ; ++i){

            image r = copy_image(ims[rand()%n]);

            rotate_image_cw(r, rand()%);

            random_distort_image(r, , 1.5, 1.5);

            int dx = rand()%(w-r.w);

            int dy = rand()%(h-r.h);

            ghost_image(r, im, dx, dy);

            free_image(r);

        }

        char buff[];

        sprintf(buff, "%s/gen_%d", prefix, z);

        save_image(im, buff);

        free_image(im);

    }

}

void visualize(char *cfgfile, char *weightfile)

{

    network *net = load_network(cfgfile, weightfile, );

    visualize_network(net);

#ifdef OPENCV

    cvWaitKey();

#endif

}

int main(int argc, char **argv)

{

    // argv[0] 指向程序运行的全路径名;argv[1] 指向在DOS命令行中执行程序名后的第一个字符串;argv[2]第二个

    //test_resize("data/bad.jpg");

    //test_box();

    //test_convolutional_layer();

    if(argc < ){

        fprintf(stderr, "usage: %s <function>\n", argv[]);

        return ;

    }

    gpu_index = find_int_arg(argc, argv, "-i", );

    if(find_arg(argc, argv, "-nogpu")) {

        gpu_index = -;

    }

#ifndef GPU

    gpu_index = -;

#else

    if(gpu_index >= ){

        cuda_set_device(gpu_index);

    }

#endif

    if ( == strcmp(argv[], "average")){

        average(argc, argv);

    } else if ( == strcmp(argv[], "yolo")){

        run_yolo(argc, argv);

    } else if ( == strcmp(argv[], "super")){

        run_super(argc, argv);

    } else if ( == strcmp(argv[], "lsd")){

        run_lsd(argc, argv);

    } else if ( == strcmp(argv[], "detector")){

        run_detector(argc, argv);

    } else if ( == strcmp(argv[], "detect")){

        float thresh = find_float_arg(argc, argv, "-thresh", .);       //thresh用来控制检测的阈值

        char *filename = (argc > ) ? argv[]: ;

        char *outfile = find_char_arg(argc, argv, "-out", );           // 定义在\src\utils.c中

        int fullscreen = find_arg(argc, argv, "-fullscreen");

        test_detector("cfg/coco.data", argv[], argv[], filename, thresh, ., outfile, fullscreen);      // 函数定义位于detector.c中

        // 命令举例./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

        //*修改//

        //output_to_file();

        my_testinsrc();

        //*//

    } else if ( == strcmp(argv[], "cifar")){

        run_cifar(argc, argv);

    } else if ( == strcmp(argv[], "go")){

        run_go(argc, argv);

    } else if ( == strcmp(argv[], "rnn")){

        run_char_rnn(argc, argv);

    } else if ( == strcmp(argv[], "coco")){

        run_coco(argc, argv);

    } else if ( == strcmp(argv[], "classify")){

        predict_classifier("cfg/imagenet1k.data", argv[], argv[], argv[], );

    } else if ( == strcmp(argv[], "classifier")){

        run_classifier(argc, argv);

    } else if ( == strcmp(argv[], "regressor")){

        run_regressor(argc, argv);

    } else if ( == strcmp(argv[], "segmenter")){

        run_segmenter(argc, argv);

    } else if ( == strcmp(argv[], "art")){

        run_art(argc, argv);

    } else if ( == strcmp(argv[], "tag")){

        run_tag(argc, argv);

    } else if ( == strcmp(argv[], "3d")){

        composite_3d(argv[], argv[], argv[], (argc > ) ? atof(argv[]) : );

    } else if ( == strcmp(argv[], "test")){

        test_resize(argv[]);

    } else if ( == strcmp(argv[], "captcha")){

        run_captcha(argc, argv);

    } else if ( == strcmp(argv[], "nightmare")){

        run_nightmare(argc, argv);

    } else if ( == strcmp(argv[], "rgbgr")){

        rgbgr_net(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "reset")){

        reset_normalize_net(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "denormalize")){

        denormalize_net(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "statistics")){

        statistics_net(argv[], argv[]);

    } else if ( == strcmp(argv[], "normalize")){

        normalize_net(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "rescale")){

        rescale_net(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "ops")){

        operations(argv[]);

    } else if ( == strcmp(argv[], "speed")){

        speed(argv[], (argc >  && argv[]) ? atoi(argv[]) : );

    } else if ( == strcmp(argv[], "oneoff")){

        oneoff(argv[], argv[], argv[]);

    } else if ( == strcmp(argv[], "oneoff2")){

        oneoff2(argv[], argv[], argv[], atoi(argv[]));

    } else if ( == strcmp(argv[], "print")){

        print_weights(argv[], argv[], atoi(argv[]));

    } else if ( == strcmp(argv[], "partial")){

        partial(argv[], argv[], argv[], atoi(argv[]));

    } else if ( == strcmp(argv[], "average")){

        average(argc, argv);

    } else if ( == strcmp(argv[], "visualize")){

        visualize(argv[], (argc > ) ? argv[] : );

    } else if ( == strcmp(argv[], "mkimg")){

        mkimg(argv[], argv[], atoi(argv[]), atoi(argv[]), atoi(argv[]), argv[]);

    } else if ( == strcmp(argv[], "imtest")){

        test_resize(argv[]);

    } else {

        fprintf(stderr, "Not an option: %s\n", argv[]);

    }

    return ;

}

可成功编译并运行

(3)修改代码的第三次尝试

在darknet下新建目录\my, 用于存放自己新写的代码. 新建两个文件my_tofile.c与my_file.h, 其内容如下

//my_tofile.h

#ifndef TOFILE

#define TOFLIE

#include "darknet.h"

void my_output_to_file();

#endif

// my_tofile.c

#include "my_tofile.h"

void my_output_to_file()

{

    FILE *fp;

    fp=fopen("output.txt","w");

    fprintf(fp,"adfsss");

    fclose(fp);

    printf("test in \\my\n");

}

修改Makefile文件, 在最后对函数进行声明, 在VPATH处添加路径 VPATH=./src/:./examples:./my , 修改完后内容如下

GPU=

CUDNN=

OPENCV=

OPENMP=

DEBUG=

ARCH= -gencode arch=compute_30,code=sm_30 \

      -gencode arch=compute_35,code=sm_35 \

      -gencode arch=compute_50,code=[sm_50,compute_50] \

      -gencode arch=compute_52,code=[sm_52,compute_52]

#      -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?

# This is what I use, uncomment if you know your arch and want to specify

# ARCH= -gencode arch=compute_52,code=compute_52

VPATH=./src/:./examples:./my

# VTATH用来告诉make,源文件的路径, 参考https://blog.csdn.net/mcgrady_tracy/article/details/27240139

SLIB=libdarknet.so

ALIB=libdarknet.a

EXEC=darknet

OBJDIR=./obj/

CC=gcc

NVCC=nvcc

AR=ar

ARFLAGS=rcs

OPTS=-Ofast

LDFLAGS= -lm -pthread       #gcc等编译器会用到的一些优化参数，也可以在里面指定库文件的位置

COMMON= -Iinclude/ -Isrc/

CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC   #指定头文件（.h文件）的路径，如：CFLAGS=-I/usr/include -I/path/include。

ifeq ($(OPENMP), )

CFLAGS+= -fopenmp

endif

ifeq ($(DEBUG), )

OPTS=-O0 -g

endif

CFLAGS+=$(OPTS)

ifeq ($(OPENCV), )

COMMON+= -DOPENCV

CFLAGS+= -DOPENCV

LDFLAGS+= `pkg-config --libs opencv`

COMMON+= `pkg-config --cflags opencv`

endif

ifeq ($(GPU), )

COMMON+= -DGPU -I/usr/local/cuda/include/

CFLAGS+= -DGPU

LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand

endif

ifeq ($(CUDNN), )

COMMON+= -DCUDNN

CFLAGS+= -DCUDNN

LDFLAGS+= -lcudnn

endif

OBJ=my_tofile.o    my_testinsrc.o  gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o l2norm_layer.o yolo_layer.o

EXECOBJA=my_test.o  captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o darknet.o

ifeq ($(GPU), )

LDFLAGS+= -lstdc++

OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o avgpool_layer_kernels.o

endif

EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))   #加前缀函数: $(addprefix <prefix>,<names...>),OBJDIR是obj存放的地址

OBJS = $(addprefix $(OBJDIR), $(OBJ))

DEPS = $(wildcard src/*.h) Makefile include/darknet.h

#all: obj backup results $(SLIB) $(ALIB) $(EXEC)

all: obj  results $(SLIB) $(ALIB) $(EXEC)

$(EXEC): $(EXECOBJ) $(ALIB)

    $(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB)

$(ALIB): $(OBJS)

    $(AR) $(ARFLAGS) $@ $^

$(SLIB): $(OBJS)

    $(CC) $(CFLAGS) -shared $^ -o $@ $(LDFLAGS)

$(OBJDIR)%.o: %.c $(DEPS)

    $(CC) $(COMMON) $(CFLAGS) -c $< -o $@

$(OBJDIR)%.o: %.cu $(DEPS)

    $(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@

obj:

    mkdir -p obj

backup:

    mkdir -p backup

results:

    mkdir -p results

.PHONY: clean

clean:

    rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/*

最后在\exampes中的文件中进行调用, 可顺利编译并运行

├── examples

│   ├── darknet.c(主程序)

│   │── xxx1.c

│   └── xxx2.c

│

├── include

│   ├── darknet.h

│

│

├── Makefile

│

├── my

│   ├── my_zzz1.c

│   │── my_zzz1.h

│   └── ......

│

└── src

    ├── yyy1.c

    ├── yyy2.h

    └──......

最终代码结构会如下所示

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