前面介绍了论文的思想与模型,以及使用darknet
实践,接下来让我们深入到源码。
首先追踪主函数。主函数开始解析命令行参数,然后根据不同的命令行参数进入不同的调用方法。接下来我们以YOLO为主线,进行追踪与分析。
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){
cudaError_t status = cudaSetDevice(gpu_index);
check_error(status);
}
#endif
if(0==strcmp(argv[1], "imagenet")){
run_imagenet(argc, argv);
} else if (0 == strcmp(argv[1], "average")){
average(argc, argv);
} else if (0 == strcmp(argv[1], "yolo")){
// 检测到命令行第一个参数输入的是yolo,则进入run_yolo函数
run_yolo(argc, argv);
} 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], "classifier")){
run_classifier(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]);
} 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], "change")){
change_rate(argv[2], atof(argv[3]), (argc > 4) ? atof(argv[4]) : 0);
} else if (0 == strcmp(argv[1], "rgbgr")){
rgbgr_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], "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], "partial")){
partial(argv[2], argv[3], argv[4], atoi(argv[5]));
} else if (0 == strcmp(argv[1], "stacked")){
stacked(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "visualize")){
visualize(argv[2], (argc > 3) ? argv[3] : 0);
} else if (0 == strcmp(argv[1], "imtest")){
test_resize(argv[2]);
} else {
fprintf(stderr, "Not an option: %s\n", argv[1]);
}
return 0;
}
接下来分析yolo.c文件,该文件主要是使用darknet
实现YOLO论文的思想。
该函数是yolo.c文件的主函数。
char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
void run_yolo(int argc, char **argv)
{
int i;
// 1. 提取类别标签
for(i = 0; i < 20; ++i){
char buff[256];
sprintf(buff, "data/labels/%s.png", voc_names[i]);
voc_labels[i] = load_image_color(buff, 0, 0);
}
// 2.读取得分阈值,默认是0.2
float thresh = find_float_arg(argc, argv, "-thresh", .2);
// 3. 读取是否指定摄像头,默认是0
int cam_index = find_int_arg(argc, argv, "-c", 0);
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
}
char *cfg = argv[3]; // 读取配置文件
// 读取权重文件,默认是0
char *weights = (argc > 4) ? argv[4] : 0;
// 读取测试文件,默认是0
char *filename = (argc > 5) ? argv[5]: 0;
// 根据不同的需求(test,train,validate,demo),进入不同的函数
if(0==strcmp(argv[2], "test")) test_yolo(cfg, weights, filename, thresh);
else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights);
else if(0==strcmp(argv[2], "demo")) demo_yolo(cfg, weights, thresh, cam_index, filename);
}
该函数主要
void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
{
// 解析配置文件的网络结构
network net = parse_network_cfg(cfgfile);
// 加载权重文件到网络
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;
bounding_box *boxes = calloc(l.side*l.side*l.n, sizeof(bounding_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);
draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);
show_image(im, "predictions");
save_image(im, "predictions");
show_image(sized, "resized");
free_image(im);
free_image(sized);
#ifdef OPENCV
cvWaitKey(0);
cvDestroyAllWindows();
#endif
if (filename) break;
}
}
yolo_demo.c文件主要实现yolo处理视频文件。
demo_yolo函数是演示通过摄像头去做目标检测。
void demo_yolo(char *cfgfile, char *weightfile, float thresh, int cam_index, char *filename)
{
demo_thresh = thresh;
printf("YOLO demo\n");
net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
srand(2222222);
// 若有视频文件,打开视频文件;若没有视频文件,打开摄像头。
if(filename){
cap = cvCaptureFromFile(filename);
}else{
//驱动摄像头
cap = cvCaptureFromCAM(cam_index);
}
// 打开摄像头
if(!cap) error("Couldn't connect to webcam.\n");
cvNamedWindow("YOLO", CV_WINDOW_NORMAL);
cvResizeWindow("YOLO", 512, 512);
detection_layer l = net.layers[net.n-1];
int j;
// 开辟存储空间
boxes = (box *)calloc(l.side*l.side*l.n, sizeof(box));
probs = (float **)calloc(l.side*l.side*l.n, sizeof(float *));
for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));
// 多线程编程
pthread_t fetch_thread;
pthread_t detect_thread;
fetch_in_thread(0);
det = in;
det_s = in_s;
fetch_in_thread(0);
detect_in_thread(0);
disp = det;
det = in;
det_s = in_s;
while(1){
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed");
if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed");
show_image(disp, "YOLO");
free_image(disp);
cvWaitKey(1);
pthread_join(fetch_thread, 0);
pthread_join(detect_thread, 0);
disp = det;
det = in;
det_s = in_s;
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;
}
}
void *fetch_in_thread(void *ptr)
{
in = get_image_from_stream(cap);
in_s = resize_image(in, net.w, net.h);
return 0;
}
void *detect_in_thread(void *ptr)
{
float nms = .4;
detection_layer l = net.layers[net.n-1];
float *X = det_s.data;
float *predictions = network_predict(net, X);
free_image(det_s);
convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, demo_thresh, probs, boxes, 0);
if (nms > 0) do_nms(boxes, probs, l.side*l.side*l.n, l.classes, nms);
printf("\033[2J");
printf("\033[1;1H");
printf("\nFPS:%.0f\n",fps);
printf("Objects:\n\n");
draw_detections(det, l.side*l.side*l.n, demo_thresh, boxes, probs, voc_names, voc_labels, 20);
return 0;
}
image.c文件主要处理图像相关的工作。
get_image_from_stream函数主要完成从视频内抽帧的工作。
image get_image_from_stream(CvCapture *cap)
{
IplImage* src = cvQueryFrame(cap);
image im = ipl_to_image(src);
rgbgr_image(im);
return im;
}
image resize_image(image im, int w, int h)
{
image resized = make_image(w, h, im.c);
image part = make_image(w, im.h, im.c);
int r, c, k;
float w_scale = (float)(im.w - 1) / (w - 1);
float h_scale = (float)(im.h - 1) / (h - 1);
for(k = 0; k < im.c; ++k){
for(r = 0; r < im.h; ++r){
for(c = 0; c < w; ++c){
float val = 0;
if(c == w-1 || im.w == 1){
val = get_pixel(im, im.w-1, r, k);
} else {
float sx = c*w_scale;
int ix = (int) sx;
float dx = sx - ix;
val = (1 - dx) * get_pixel(im, ix, r, k) + dx * get_pixel(im, ix+1, r, k);
}
set_pixel(part, c, r, k, val);
}
}
}
for(k = 0; k < im.c; ++k){
for(r = 0; r < h; ++r){
float sy = r*h_scale;
int iy = (int) sy;
float dy = sy - iy;
for(c = 0; c < w; ++c){
float val = (1-dy) * get_pixel(part, c, iy, k);
set_pixel(resized, c, r, k, val);
}
if(r == h-1 || im.h == 1) continue;
for(c = 0; c < w; ++c){
float val = dy * get_pixel(part, c, iy+1, k);
add_pixel(resized, c, r, k, val);
}
}
}
free_image(part);
return resized;
}
typedef struct network{
float *workspace;
int n;
int batch;
int *seen;
float epoch;
int subdivisions;
float momentum;
float decay;
layer *layers;
int outputs;
float *output;
learning_rate_policy policy;
float learning_rate;
float gamma;
float scale;
float power;
int time_steps;
int step;
int max_batches;
float *scales;
int *steps;
int num_steps;
int inputs;
int h, w, c;
int max_crop;
int min_crop;
#ifdef GPU
float **input_gpu;
float **truth_gpu;
#endif
} network;
struct layer{
LAYER_TYPE type;
ACTIVATION activation;
COST_TYPE cost_type;
int batch_normalize;
int shortcut;
int batch;
int forced;
int flipped;
int inputs;
int outputs;
int truths;
int h,w,c;
int out_h, out_w, out_c;
int n;
int max_boxes;
int groups;
int size;
int side;
int stride;
int pad;
int sqrt;
int flip;
int index;
int binary;
int xnor;
int steps;
int hidden;
float dot;
float angle;
float jitter;
float saturation;
float exposure;
float shift;
int softmax;
int classes;
int coords;
int background;
int rescore;
int objectness;
int does_cost;
int joint;
int noadjust;
float alpha;
float beta;
float kappa;
float coord_scale;
float object_scale;
float noobject_scale;
float class_scale;
int dontload;
int dontloadscales;
float temperature;
float probability;
float scale;
int *indexes;
float *rand;
float *cost;
float *filters;
char *cfilters;
float *filter_updates;
float *state;
float *state_delta;
float *concat;
float *concat_delta;
float *binary_filters;
float *biases;
float *bias_updates;
float *scales;
float *scale_updates;
float *weights;
float *weight_updates;
float *col_image;
int * input_layers;
int * input_sizes;
float * delta;
float * output;
float * squared;
float * norms;
float * spatial_mean;
float * mean;
float * variance;
float * mean_delta;
float * variance_delta;
float * rolling_mean;
float * rolling_variance;
float * x;
float * x_norm;
struct layer *input_layer;
struct layer *self_layer;
struct layer *output_layer;
struct layer *input_gate_layer;
struct layer *state_gate_layer;
struct layer *input_save_layer;
struct layer *state_save_layer;
struct layer *input_state_layer;
struct layer *state_state_layer;
struct layer *input_z_layer;
struct layer *state_z_layer;
struct layer *input_r_layer;
struct layer *state_r_layer;
struct layer *input_h_layer;
struct layer *state_h_layer;
size_t workspace_size;
#ifdef GPU
float *z_gpu;
float *r_gpu;
float *h_gpu;
int *indexes_gpu;
float * prev_state_gpu;
float * forgot_state_gpu;
float * forgot_delta_gpu;
float * state_gpu;
float * state_delta_gpu;
float * gate_gpu;
float * gate_delta_gpu;
float * save_gpu;
float * save_delta_gpu;
float * concat_gpu;
float * concat_delta_gpu;
float * filters_gpu;
float * filter_updates_gpu;
float *binary_input_gpu;
float *binary_filters_gpu;
float * mean_gpu;
float * variance_gpu;
float * rolling_mean_gpu;
float * rolling_variance_gpu;
float * variance_delta_gpu;
float * mean_delta_gpu;
float * col_image_gpu;
float * x_gpu;
float * x_norm_gpu;
float * weights_gpu;
float * weight_updates_gpu;
float * biases_gpu;
float * bias_updates_gpu;
float * scales_gpu;
float * scale_updates_gpu;
float * output_gpu;
float * delta_gpu;
float * rand_gpu;
float * squared_gpu;
float * norms_gpu;
#ifdef CUDNN
cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc;
cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc;
cudnnFilterDescriptor_t filterDesc;
cudnnFilterDescriptor_t dfilterDesc;
cudnnConvolutionDescriptor_t convDesc;
cudnnConvolutionFwdAlgo_t fw_algo;
cudnnConvolutionBwdDataAlgo_t bd_algo;
cudnnConvolutionBwdFilterAlgo_t bf_algo;
#endif
#endif
};