yolo3源码分析
要想更好地调参或者修改相应的功能就必须对源码有所了解，为此先看一下安装目录再往下讲解是很必要的。
顺便粘贴一下makefile
GPU=0
CUDNN=0
OPENCV=0
OPENMP=0
DEBUG=0
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
SLIB=libdarknet.so
ALIB=libdarknet.a
EXEC=darknet
OBJDIR=./obj/
CC=gcc
NVCC=nvcc
AR=ar
ARFLAGS=rcs
OPTS=-Ofast
LDFLAGS= -lm -pthread
COMMON= -Iinclude/ -Isrc/
CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC
ifeq ($(OPENMP), 1)
CFLAGS+= -fopenmp
endif
ifeq ($(DEBUG), 1)
OPTS=-O0 -g
endif
CFLAGS+=$(OPTS)
ifeq ($(OPENCV), 1)
COMMON+= -DOPENCV
CFLAGS+= -DOPENCV
LDFLAGS+= `pkg-config --libs opencv`
COMMON+= `pkg-config --cflags opencv`
endif
ifeq ($(GPU), 1)
COMMON+= -DGPU -I/usr/local/cuda/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif
ifeq ($(CUDNN), 1)
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=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), 1)
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))
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)/*
分析过程
首先我们从yolo的训练命令开始分析（yolo的源码是用c++写的，c占大部分）：
- ./darknet detector train cfg/voc.data cfg/yolo-voc.cfg darknet19_448.conv.23
总结：根据参数选择执行函数，加载数据，配置，权重
int main(int argc, char **argv)
{
//test_resize("data/bad.jpg");
//test_box();
//test_convolutional_layer();
if(argc < 2){
fprintf(stderr, "usage: %s \n", argv[0]);
return 0;
}
gpu_index = find_int_arg(argc, argv, "-i", 0);
if(find_arg(argc, argv, "-nogpu")) {
gpu_index = -1;
}
#ifndef GPU
gpu_index = -1;
#else
if(gpu_index >= 0){
cuda_set_device(gpu_index);
}
#endif
if (0 == strcmp(argv[1], "average")){
average(argc, argv);
} else if (0 == strcmp(argv[1], "yolo")){
run_yolo(argc, argv);
} else if (0 == strcmp(argv[1], "voxel")){
run_voxel(argc, argv);
} else if (0 == strcmp(argv[1], "super")){
run_super(argc, argv);
} else if (0 == strcmp(argv[1], "lsd")){
run_lsd(argc, argv);
} else if (0 == strcmp(argv[1], "detector")){
run_detector(argc, argv);
} else if (0 == strcmp(argv[1], "detect")){
float thresh = find_float_arg(argc, argv, "-thresh", .24);
char *filename = (argc > 4) ? argv[4]: 0;
char *outfile = find_char_arg(argc, argv, "-out", 0);
int fullscreen = find_arg(argc, argv, "-fullscreen");
test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5, outfile, fullscreen);
} else if (0 == strcmp(argv[1], "cifar")){
run_cifar(argc, argv);
} else if (0 == strcmp(argv[1], "go")){
run_go(argc, argv);
} else if (0 == strcmp(argv[1], "rnn")){
run_char_rnn(argc, argv);
} else if (0 == strcmp(argv[1], "vid")){
run_vid_rnn(argc, argv);
} else if (0 == strcmp(argv[1], "coco")){
run_coco(argc, argv);
} else if (0 == strcmp(argv[1], "classify")){
predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
} else if (0 == strcmp(argv[1], "classifier")){
run_classifier(argc, argv);
} else if (0 == strcmp(argv[1], "regressor")){
run_regressor(argc, argv);
} else if (0 == strcmp(argv[1], "segmenter")){
run_segmenter(argc, argv);
} else if (0 == strcmp(argv[1], "art")){
run_art(argc, argv);
} else if (0 == strcmp(argv[1], "tag")){
run_tag(argc, argv);
} else if (0 == strcmp(argv[1], "compare")){
run_compare(argc, argv);
} else if (0 == strcmp(argv[1], "dice")){
run_dice(argc, argv);
} else if (0 == strcmp(argv[1], "writing")){
run_writing(argc, argv);
} else if (0 == strcmp(argv[1], "3d")){
composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
} else if (0 == strcmp(argv[1], "test")){
test_resize(argv[2]);
} else if (0 == strcmp(argv[1], "captcha")){
run_captcha(argc, argv);
} else if (0 == strcmp(argv[1], "nightmare")){
run_nightmare(argc, argv);
} else if (0 == strcmp(argv[1], "rgbgr")){
rgbgr_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "reset")){
reset_normalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "denormalize")){
denormalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "statistics")){
statistics_net(argv[2], argv[3]);
} else if (0 == strcmp(argv[1], "normalize")){
normalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "rescale")){
rescale_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "ops")){
operations(argv[2]);
} else if (0 == strcmp(argv[1], "speed")){
speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
} else if (0 == strcmp(argv[1], "oneoff")){
oneoff(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "oneoff2")){
oneoff2(argv[2], argv[3], argv[4], atoi(argv[5]));
} else if (0 == strcmp(argv[1], "partial")){
partial(argv[2], argv[3], argv[4], atoi(argv[5]));
} else if (0 == strcmp(argv[1], "average")){
average(argc, argv);
} else if (0 == strcmp(argv[1], "visualize")){
visualize(argv[2], (argc > 3) ? argv[3] : 0);
} else if (0 == strcmp(argv[1], "mkimg")){
mkimg(argv[2], argv[3], atoi(argv[4]), atoi(argv[5]), atoi(argv[6]), argv[7]);
} else if (0 == strcmp(argv[1], "imtest")){
test_resize(argv[2]);
} else {
fprintf(stderr, "Not an option: %s\n", argv[1]);
}
return 0;
} 这里先不展开，可以参考下面的文章，讲的很详细了。
https://blog.csdn.net/u014540717/article/details/53114067
https://blog.csdn.net/syoung9029/article/details/70338061
理解到这里基本下就可以对源码（darknet.c）修改实现自己的功能了。
YOLOv3批量测试图片并保存在自定义文件夹下
转子：https://blog.csdn.net/mieleizhi0522/article/details/79989754
先说测试并返回评价指标的3个命令
1) ./darknet detector test cfg/voc.data cfg/yolo-voc.cfg ./svt/backup/yolo-voc_final.weights
/*不现实评价指标，输入图片路径，只显示框好后的图片和类别、置信率*/
2) ./darknet detector valid cfg/voc.data cfg/yolo-voc.cfg backup/yolo-voc_final.weights
/*在终端只返回用时，在./results/comp4_det_test_[类名].txt里保存测试结果*/
3) ./darknet detector recall cfg/voc.data cfg/yolo-voc.cfg backup/yolo-voc_final.weights（这个命令需修改dectector.c文件）
/*依次ID:图片序号从0开始，correct:累计检测正确的总框数，total:累计的总ground truth数，RPs/Img: 累计的总proposals/已检测图片数，IOU，Recall: correct / total，proposals:累计的总框数，Precision: correct / proposals*/
*************************************************************************************************
命令参数总结
- 训练模型
- 单GPU训练：
./darknet -idetector train 举例： ./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74 - 多GPU训练，格式为
0,1,2,3：./darknet detector train-gpus 举例 ./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74 -gpus 0,1,2,3 - CPU训练：
./darknet -nogpu detector train
- 单GPU训练：
- 测试图片
- 测试单张图片，需要编译时有OpenCV支持：
./darknet detector test batch和subdivisions两项必须为1。- 测试时还可以用
-thresh和-hier选项指定对应参数。
- 测试单张图片，需要编译时有OpenCV支持：
- 生成预测结果
./darknet detector validbatch和subdivisions两项必须为1。- 结果生成在
results指定的目录下以results，那么默认为/results
- 计算recall（执行这个命令需要修改detector.c文件，修改信息请参考“detector.c修改”）
./darknet detector recallbatch和subdivisions两项必须为1。- 输出在stderr里，重定向时请注意。
RPs/Img、IOU、Recall都是到当前测试图片的均值。detector.c中对目录处理有错误，可以参照validate_detector对validate_detector_recall最开始几行的处理进行修改。
- 执行这些命令的时候在
**************************************************************************************************************
detector.c修改（example文件夹下）
validate_detector_recall函数定义和调用改为：
- void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
- validate_detector_recall(datacfg, cfg, weights);
void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
validate_detector_recall(datacfg, cfg, weights);validate_detector_recall内的plist和paths的如下初始化代码：
- list *plist = get_paths("data/voc.2007.test");
- char **paths = (char **)list_to_array(plist);
list *plist = get_paths("data/voc.2007.test");
char **paths = (char **)list_to_array(plist);修改为：
- list *options = read_data_cfg(datacfg);
- char *valid_images = option_find_str(options, "valid", "data/train.list");
- list *plist = get_paths(valid_images);
- char **paths = (char **)list_to_array(plist);
list *options = read_data_cfg(datacfg);
char *valid_images = option_find_str(options, "valid", "data/train.list");
list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);上述修改完之后务必记住要在darknet下重新make一下就可以进行recall命令了，
- ./darknet detector recall cfg/voc.data cfg/yolo-voc.cfg backup/yolo-voc_final.weights
批量测试图片并保存在自定义文件夹下
1.用下面代码替换detector.c文件（example文件夹下）的void test_detector函数（注意有3处要改成自己的路径）
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)
{
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 = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
double time;
char buff[256];
char *input = buff;
float nms=.45;
int i=0;
while(1){
if(filename){
strncpy(input, filename, 256);
image im = load_image_color(input,0,0);
image sized = letterbox_image(im, net->w, net->h);
//image sized = resize_image(im, net->w, net->h);
//image sized2 = resize_max(im, net->w);
//image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
//resize_network(net, sized.w, sized.h);
layer l = net->layers[net->n-1];
float *X = sized.data;
time=what_time_is_it_now();
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);
int nboxes = 0;
detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
//printf("%d\n", nboxes);
//if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
free_detections(dets, nboxes);
if(outfile)
{
save_image(im, outfile);
}
else{
save_image(im, "predictions");
#ifdef OPENCV
cvNamedWindow("predictions", CV_WINDOW_NORMAL);
if(fullscreen){
cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
}
show_image(im, "predictions");
cvWaitKey(0);
cvDestroyAllWindows();
#endif
}
free_image(im);
free_image(sized);
if (filename) break;
}
else {
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, "\n");
list *plist = get_paths(input);
char **paths = (char **)list_to_array(plist);
printf("Start Testing!\n");
int m = plist->size;
if(access("/home/FENGsl/darknet/data/out",0)==-1)//"/home/FENGsl/darknet/data"修改成自己的路径
{
if (mkdir("/home/FENGsl/darknet/data/out",0777))//"/home/FENGsl/darknet/data"修改成自己的路径
{
printf("creat file bag failed!!!");
}
}
for(i = 0; i < m; ++i){
char *path = paths[i];
image im = load_image_color(path,0,0);
image sized = letterbox_image(im, net->w, net->h);
//image sized = resize_image(im, net->w, net->h);
//image sized2 = resize_max(im, net->w);
//image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
//resize_network(net, sized.w, sized.h);
layer l = net->layers[net->n-1];
float *X = sized.data;
time=what_time_is_it_now();
network_predict(net, X);
printf("Try Very Hard:");
printf("%s: Predicted in %f seconds.\n", path, what_time_is_it_now()-time);
int nboxes = 0;
detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
//printf("%d\n", nboxes);
//if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
free_detections(dets, nboxes);
if(outfile){
save_image(im, outfile);
}
else{
char b[2048];
sprintf(b,"/home/FENGsl/darknet/data/out/%s",GetFilename(path));//"/home/FENGsl/darknet/data"修改成自己的路径
save_image(im, b);
printf("save %s successfully!\n",GetFilename(path));
#ifdef OPENCV
cvNamedWindow("predictions", CV_WINDOW_NORMAL);
if(fullscreen){
cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
}
show_image(im, "predictions");
cvWaitKey(0);
cvDestroyAllWindows();
#endif
}
free_image(im);
free_image(sized);
if (filename) break;
}
}
}
}2，在函数前面添加*GetFilename(char *p)函数
#include "darknet.h"
#include
#include
#include
#include
static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
char *GetFilename(char *p)
{
char name[20]={""};
char *q = strrchr(p,'/') + 1;
strncpy(name,q,6);
return name;
} 3.在darknet下重新make
4.执行批量测试命令如下
./darknet detector test cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc_final.weights
layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BFLOPs
1 conv 64 3 x 3 / 2 416 x 416 x 32 -> 208 x 208 x 64 1.595 BFLOPs
.......
104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
105 conv 255 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 255 0.353 BFLOPs
106 detection
Loading weights from yolov3.weights...Done!
Enter Image Path:Enter Image Path：后面输入你的txt文件路径（你准备好的所有测试图片的路径全部存放在一个txt文件里），你可以复制voc.data文件里的valid后面的路径，就可以了，如下
- classes= 3
- train =/home/FENGsl/darknet/data/train.txt
- valid = /home/FENGsl/darknet/data/2007_test.txt
- names = data/voc.names
- backup = backup
classes= 3
train =/home/FENGsl/darknet/data/train.txt
valid = /home/FENGsl/darknet/data/2007_test.txt
names = data/voc.names
backup = backup你就可以看到如下结果：
- 101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
- 102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
- 103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
- 104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
- 105 conv 24 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 24 0.033 BFLOPs
- 106 detection
- Loading weights from backup/yolov3-voc_final.weights...Done!
- Enter Image Path: /home/FENGsl/darknet/data/2007_test.txt
- Start Testing!
- Try Very Hard:/home/FENGsl/darknet/data/VOCdevkit/VOC2007/JPEGImages/000013.jpg: Predicted in 0.085814 seconds.
- PED: 100%
- save 000013 successfully!
- Try Very Hard:/home/FENGsl/darknet/data/VOCdevkit/VOC2007/JPEGImages/000016.jpg: Predicted in 0.084692 seconds.
- save 000016 successfully!
101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
105 conv 24 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 24 0.033 BFLOPs
106 detection
Loading weights from backup/yolov3-voc_final.weights...Done!
Enter Image Path: /home/FENGsl/darknet/data/2007_test.txt
Start Testing!
Try Very Hard:/home/FENGsl/darknet/data/VOCdevkit/VOC2007/JPEGImages/000013.jpg: Predicted in 0.085814 seconds.
PED: 100%
save 000013 successfully!
Try Very Hard:/home/FENGsl/darknet/data/VOCdevkit/VOC2007/JPEGImages/000016.jpg: Predicted in 0.084692 seconds.
save 000016 successfully! 然后你所有的图片都保存在了data/out文件夹下，你可以打开看看，展示一下我的结果
