YOLO v3代码学习

本人使用的版本是https://github.com/AlexeyAB/darknet

源码在darknet-master\src目录下

还记得我们用YOLO v3训练自己的数据集的过程，控制台下使用以下代码进行训练

darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74

上面代码的意思是启动darknet.exe这个文件并以后面作为参数开始运行。参数位置对应，如下：

argv[1] = dectector ; argv[2] = train ; argv[3] = data/obj.data ; argv[4] = yolo-obj.cfg ; argv[5] = darknet53.conv.74 ;

1、darknet.exe就是对应darknet.c的main函数

int main(int argc, char **argv)
{
#ifdef _DEBUG_CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF);
#endifint i;for (i = 0; i < argc; ++i) {if (!argv[i]) continue;strip_args(argv[i]);}//test_resize("data/bad.jpg");//test_box();//test_convolutional_layer();if(argc < 2){fprintf(stderr, "usage: %s <function>\n", argv[0]);return 0;}gpu_index = find_int_arg(argc, argv, "-i", 0);if(find_arg(argc, argv, "-nogpu")) {gpu_index = -1;printf("\n Currently Darknet doesn't support -nogpu flag. If you want to use CPU - please compile Darknet with GPU=0 in the Makefile, or compile darknet_no_gpu.sln on Windows.\n");exit(-1);}#ifndef GPUgpu_index = -1;
#elseif(gpu_index >= 0){cuda_set_device(gpu_index);check_error(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync));}
#endifif (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], "detector")){run_detector(argc, argv);} else if (0 == strcmp(argv[1], "detect")){float thresh = find_float_arg(argc, argv, "-thresh", .24);int ext_output = find_arg(argc, argv, "-ext_output");char *filename = (argc > 4) ? argv[4]: 0;test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, 1, 0, NULL);} 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], "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], "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], "imtest")){test_resize(argv[2]);} else {fprintf(stderr, "Not an option: %s\n", argv[1]);}return 0;
}

2、根据参数argv[1]可以看出，将执行run_detector(argc, argv)，它在detector.c文件里

void run_detector(int argc, char **argv)
{    //查看argv里有没有'-dont_show'这个参数，有返回1，无0，下同int dont_show = find_arg(argc, argv, "-dont_show");int show = find_arg(argc, argv, "-show");int calc_map = find_arg(argc, argv, "-map");check_mistakes = find_arg(argc, argv, "-check_mistakes");int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);int json_port = find_int_arg(argc, argv, "-json_port", -1);char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);char *outfile = find_char_arg(argc, argv, "-out", 0);char *prefix = find_char_arg(argc, argv, "-prefix", 0);float thresh = find_float_arg(argc, argv, "-thresh", .25);    // 0.24float iou_thresh = find_float_arg(argc, argv, "-iou_thresh", .5);    // 0.5 for mAPfloat hier_thresh = find_float_arg(argc, argv, "-hier", .5);int cam_index = find_int_arg(argc, argv, "-c", 0);int frame_skip = find_int_arg(argc, argv, "-s", 0);int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);int width = find_int_arg(argc, argv, "-width", -1);int height = find_int_arg(argc, argv, "-height", -1);// extended output in test mode (output of rect bound coords)// and for recall mode (extended output table-like format with results for best_class fit)int ext_output = find_arg(argc, argv, "-ext_output");int save_labels = find_arg(argc, argv, "-save_labels");if (argc < 4) {fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]\n", argv[0], argv[1]);return;}char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);int *gpus = 0;int gpu = 0;int ngpus = 0;if (gpu_list) {printf("%s\n", gpu_list);int len = strlen(gpu_list);ngpus = 1;int i;for (i = 0; i < len; ++i) {if (gpu_list[i] == ',') ++ngpus;}gpus = calloc(ngpus, sizeof(int));for (i = 0; i < ngpus; ++i) {gpus[i] = atoi(gpu_list);gpu_list = strchr(gpu_list, ',') + 1;}}else {gpu = gpu_index;gpus = &gpu;ngpus = 1;//不使用gpu,故这里ngpus = 1}int clear = find_arg(argc, argv, "-clear");char *datacfg = argv[3];char *cfg = argv[4];char *weights = (argc > 5) ? argv[5] : 0;if (weights)if (strlen(weights) > 0)if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;char *filename = (argc > 6) ? argv[6] : 0;if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile);else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port);else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, NULL);else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);else if (0 == strcmp(argv[2], "demo")) {list *options = read_data_cfg(datacfg);int classes = option_find_int(options, "classes", 20);char *name_list = option_find_str(options, "names", "data/names.list");char **names = get_labels(name_list);if (filename)if (strlen(filename) > 0)if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename,mjpeg_port, json_port, dont_show, ext_output);free_list_contents_kvp(options);free_list(options);}else printf(" There isn't such command: %s", argv[2]);
}

3、参数argv[2]是train，所以接下来执行train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port);

也是在detector.c文件中

void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port)
{list *options = read_data_cfg(datacfg);//读入obj.data配置文件//查找配置文件里train的值，如果没有则使用第三个参数"data/train.txt"作为默认值char *train_images = option_find_str(options, "train", "data/train.txt");char *valid_images = option_find_str(options, "valid", train_images);char *backup_directory = option_find_str(options, "backup", "/backup/");int train_images_num = 0;network net_map;if (calc_map) {FILE* valid_file = fopen(valid_images, "r");if (!valid_file) {printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg);getchar();exit(-1);}else fclose(valid_file);//读入train.txt文件中每行的图片地址list *plist = get_paths(train_images);train_images_num = plist->size;free_list(plist);cuda_set_device(gpus[0]);printf(" Prepare additional network for mAP calculation...\n");//解析yolo-obj.cfg配置文件（下面讲）net_map = parse_network_cfg_custom(cfgfile, 1);int k;  // free memory unnecessary arraysfor (k = 0; k < net_map.n; ++k) {free_layer(net_map.layers[k]);}
#ifdef GPUcuda_free(net_map.workspace);cuda_free(net_map.input_state_gpu);if (*net_map.input16_gpu) cuda_free(*net_map.input16_gpu);if (*net_map.output16_gpu) cuda_free(*net_map.output16_gpu);
#elsefree(net_map.workspace);
#endif}srand(time(0));//随机数种子，配合下面的rand()实现随机数的产生char *base = basecfg(cfgfile);printf("%s\n", base);float avg_loss = -1;network *nets = calloc(ngpus, sizeof(network));srand(time(0));int seed = rand();int i;for (i = 0; i < ngpus; ++i) {srand(seed);
#ifdef GPUcuda_set_device(gpus[i]);
#endifnets[i] = parse_network_cfg(cfgfile);if (weightfile) {load_weights(&nets[i], weightfile);}if (clear) *nets[i].seen = 0;nets[i].learning_rate *= ngpus;}srand(time(0));network net = nets[0];const int actual_batch_size = net.batch * net.subdivisions;if (actual_batch_size == 1) {printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n");getchar();}else if (actual_batch_size < 64) {printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size);}int imgs = net.batch * net.subdivisions * ngpus;printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);data train, buffer;layer l = net.layers[net.n - 1];int classes = l.classes;float jitter = l.jitter;//抖动产生额外的数据list *plist = get_paths(train_images);//得到训练数据路径//int N = plist->size;char **paths = (char **)list_to_array(plist);int init_w = net.w;int init_h = net.h;int iter_save, iter_save_last, iter_map;iter_save = get_current_batch(net);iter_save_last = get_current_batch(net);iter_map = get_current_batch(net);float mean_average_precision = -1;load_args args = { 0 };args.w = net.w;args.h = net.h;args.c = net.c;args.paths = paths;args.n = imgs;args.m = plist->size;args.classes = classes;args.flip = net.flip;args.jitter = jitter;args.num_boxes = l.max_boxes;args.small_object = net.small_object;args.d = &buffer;args.type = DETECTION_DATA;args.threads = 64;    // 16 or 64//数据扩增，角度，曝光，饱和，灰度args.angle = net.angle;args.exposure = net.exposure;args.saturation = net.saturation;args.hue = net.hue;#ifdef OPENCVargs.threads = 3 * ngpus;   // Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge//args.threads = 12 * ngpus;    // Ryzen 7 2700X (16 logical cores)IplImage* img = NULL;float max_img_loss = 5;int number_of_lines = 100;int img_size = 1000;img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show);
#endif    //OPENCVpthread_t load_thread = load_data(args);double time;int count = 0;//while(i*imgs < N*120){//这里的net.max_batches就是你yolo-obj.cfg里定义的max_batches的值while (get_current_batch(net) < net.max_batches) {//是否多尺度训练if (l.random && count++ % 10 == 0) {printf("Resizing\n");//int dim = (rand() % 12 + (init_w/32 - 5)) * 32;    // +-160//int dim = (rand() % 4 + 16) * 32;//if (get_current_batch(net)+100 > net.max_batches) dim = 544;//int random_val = rand() % 12;//int dim_w = (random_val + (init_w / 32 - 5)) * 32;    // +-160//int dim_h = (random_val + (init_h / 32 - 5)) * 32;    // +-160float random_val = rand_scale(1.4);    // *x or /xint dim_w = roundl(random_val*init_w / 32 + 1) * 32;int dim_h = roundl(random_val*init_h / 32 + 1) * 32;// at the beginningif (avg_loss < 0) {dim_w = roundl(1.4*init_w / 32 + 1) * 32;dim_h = roundl(1.4*init_h / 32 + 1) * 32;}if (dim_w < 32) dim_w = 32;if (dim_h < 32) dim_h = 32;printf("%d x %d \n", dim_w, dim_h);args.w = dim_w;args.h = dim_h;pthread_join(load_thread, 0);train = buffer;free_data(train);load_thread = load_data(args);for (i = 0; i < ngpus; ++i) {resize_network(nets + i, dim_w, dim_h);}net = nets[0];}time = what_time_is_it_now();//线程相关pthread_join(load_thread, 0);train = buffer;load_thread = load_data(args);/*int k;for(k = 0; k < l.max_boxes; ++k){box b = float_to_box(train.y.vals[10] + 1 + k*5);if(!b.x) break;printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);}image im = float_to_image(448, 448, 3, train.X.vals[10]);int k;for(k = 0; k < l.max_boxes; ++k){box b = float_to_box(train.y.vals[10] + 1 + k*5);printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);draw_bbox(im, b, 8, 1,0,0);}save_image(im, "truth11");*/printf("Loaded: %lf seconds\n", (what_time_is_it_now() - time));time = what_time_is_it_now();float loss = 0;
#ifdef GPUif (ngpus == 1) {loss = train_network(net, train);}else {loss = train_networks(nets, ngpus, train, 4);}
#else//训练的函数，返回损失值（下面讲）loss = train_network(net, train);
#endifif (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss;    // if(-inf or nan)avg_loss = avg_loss*.9 + loss*.1;i = get_current_batch(net);if (mean_average_precision > 0) {printf("\n Last accuracy mAP@0.5 = %2.2f %% ", mean_average_precision*100);}if (net.cudnn_half) {if (i < net.burn_in * 3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in);else printf("\n Tensor Cores are used.");}//输出损失等数值printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs);int draw_precision = 0;int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions);  // calculate mAP for each 4 Epochsif (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) {if (l.random) {printf("Resizing to initial size: %d x %d \n", init_w, init_h);args.w = init_w;args.h = init_h;pthread_join(load_thread, 0);train = buffer;load_thread = load_data(args);int k;for (k = 0; k < ngpus; ++k) {resize_network(nets + k, init_w, init_h);}net = nets[0];}// combine Training and Validation networksnetwork net_combined = combine_train_valid_networks(net, net_map);iter_map = i;mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_combined);printf("\n mean_average_precision = %f \n", mean_average_precision);draw_precision = 1;}
#ifdef OPENCV//画出损失函数图draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, dont_show, mjpeg_port);
#endif    // OPENCV//if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {//if (i % 100 == 0) {//每1000轮保存一次权重文件，名字为yolo-obj(i).weightsif (i >= (iter_save + 1000)) {iter_save = i;
#ifdef GPUif (ngpus != 1) sync_nets(nets, ngpus, 0);
#endifchar buff[256];sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);save_weights(net, buff);}//每100轮保存一次权重文件，名字为yolo-obj_last.weightsif (i >= (iter_save_last + 100)) {iter_save_last = i;
#ifdef GPUif (ngpus != 1) sync_nets(nets, ngpus, 0);
#endifchar buff[256];sprintf(buff, "%s/%s_last.weights", backup_directory, base, i);save_weights(net, buff);}free_data(train);}
#ifdef GPUif (ngpus != 1) sync_nets(nets, ngpus, 0);
#endifchar buff[256];sprintf(buff, "%s/%s_final.weights", backup_directory, base);save_weights(net, buff);#ifdef OPENCVcvReleaseImage(&img);cvDestroyAllWindows();
#endif// free memorypthread_join(load_thread, 0);free_data(buffer);free(base);free(paths);free_list_contents(plist);free_list(plist);free_list_contents_kvp(options);free_list(options);for (i = 0; i < ngpus; ++i) free_network(nets[i]);free(nets);//free_network(net);
}

上面调用了很多函数，如parse_network_cfg_custom、train_network等，下面我们分别看看，

4、首先是parse_network_cfg_custom(char *filename, int batch)，它在parser.c文件中

network parse_network_cfg_custom(char *filename, int batch)
{list *sections = read_cfg(filename);node *n = sections->front;if(!n) error("Config file has no sections");network net = make_network(sections->size - 1);net.gpu_index = gpu_index;size_params params;section *s = (section *)n->val;list *options = s->options;if(!is_network(s)) error("First section must be [net] or [network]");parse_net_options(options, &net);params.h = net.h;params.w = net.w;params.c = net.c;params.inputs = net.inputs;if (batch > 0) net.batch = batch;params.batch = net.batch;params.time_steps = net.time_steps;params.net = net;float bflops = 0;size_t workspace_size = 0;size_t max_inputs = 0;size_t max_outputs = 0;n = n->next;int count = 0;free_section(s);fprintf(stderr, "layer     filters    size              input                output\n");//循环打印所有层的信息while(n){params.index = count;fprintf(stderr, "%4d ", count);s = (section *)n->val;options = s->options;layer l = {0};LAYER_TYPE lt = string_to_layer_type(s->type);if(lt == CONVOLUTIONAL){//这里使用的很多是convolutionl = parse_convolutional(options, params);}else if(lt == LOCAL){l = parse_local(options, params);}else if(lt == ACTIVE){l = parse_activation(options, params);}else if(lt == RNN){l = parse_rnn(options, params);}else if(lt == GRU){l = parse_gru(options, params);}else if(lt == CRNN){l = parse_crnn(options, params);}else if(lt == CONNECTED){l = parse_connected(options, params);}else if(lt == CROP){l = parse_crop(options, params);}else if(lt == COST){l = parse_cost(options, params);}else if(lt == REGION){l = parse_region(options, params);}else if (lt == YOLO) {l = parse_yolo(options, params);}else if(lt == DETECTION){l = parse_detection(options, params);}else if(lt == SOFTMAX){l = parse_softmax(options, params);net.hierarchy = l.softmax_tree;}else if(lt == NORMALIZATION){l = parse_normalization(options, params);}else if(lt == BATCHNORM){l = parse_batchnorm(options, params);}else if(lt == MAXPOOL){l = parse_maxpool(options, params);}else if(lt == REORG){l = parse_reorg(options, params);        }else if (lt == REORG_OLD) {l = parse_reorg_old(options, params);}else if(lt == AVGPOOL){l = parse_avgpool(options, params);}else if(lt == ROUTE){l = parse_route(options, params, net);int k;for (k = 0; k < l.n; ++k) net.layers[l.input_layers[k]].use_bin_output = 0;}else if (lt == UPSAMPLE) {l = parse_upsample(options, params, net);}else if(lt == SHORTCUT){l = parse_shortcut(options, params, net);net.layers[count - 1].use_bin_output = 0;net.layers[l.index].use_bin_output = 0;}else if(lt == DROPOUT){l = parse_dropout(options, params);l.output = net.layers[count-1].output;l.delta = net.layers[count-1].delta;
#ifdef GPUl.output_gpu = net.layers[count-1].output_gpu;l.delta_gpu = net.layers[count-1].delta_gpu;
#endif}else{fprintf(stderr, "Type not recognized: %s\n", s->type);}l.onlyforward = option_find_int_quiet(options, "onlyforward", 0);l.stopbackward = option_find_int_quiet(options, "stopbackward", 0);l.dontload = option_find_int_quiet(options, "dontload", 0);l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1);option_unused(options);net.layers[count] = l;if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;if (l.inputs > max_inputs) max_inputs = l.inputs;if (l.outputs > max_outputs) max_outputs = l.outputs;free_section(s);n = n->next;++count;if(n){params.h = l.out_h;params.w = l.out_w;params.c = l.out_c;params.inputs = l.outputs;}if (l.bflops > 0) bflops += l.bflops;}free_list(sections);net.outputs = get_network_output_size(net);net.output = get_network_output(net);printf("Total BFLOPS %5.3f \n", bflops);if(workspace_size){//printf("%ld\n", workspace_size);
#ifdef GPUget_cuda_stream();get_cuda_memcpy_stream();if(gpu_index >= 0){net.workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1);int size = get_network_input_size(net) * net.batch;net.input_state_gpu = cuda_make_array(0, size);if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size*sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;else net.input_pinned_cpu = calloc(size, sizeof(float));// pre-allocate memory for inference on Tensor Cores (fp16)if (net.cudnn_half) {*net.max_input16_size = max_inputs;check_error(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half)*net.max_output16_size = max_outputs;check_error(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half)}}else {net.workspace = calloc(1, workspace_size);}
#elsenet.workspace = calloc(1, workspace_size);
#endif}LAYER_TYPE lt = net.layers[net.n - 1].type;if ((net.w % 32 != 0 || net.h % 32 != 0) && (lt == YOLO || lt == REGION || lt == DETECTION)) {printf("\n Warning: width=%d and height=%d in cfg-file must be divisible by 32 for default networks Yolo v1/v2/v3!!! \n\n",net.w, net.h);}return net;
}

parse_convolutional(options, params)主要是cfg配置文件中主要是[convolutional]

size表示卷积层大小，filters是过滤器数量，batch_normalize 表示是否批归一化处理，stride是步长，pad是外围填充层数，activation是激活函数的类型。

其它shortcut、route、yolo等类似

5、下面再来看train_network(network net, data d)，它在network.c文件中

float train_network(network net, data d)
{assert(d.X.rows % net.batch == 0);int batch = net.batch;int n = d.X.rows / batch;float *X = calloc(batch*d.X.cols, sizeof(float));float *y = calloc(batch*d.y.cols, sizeof(float));int i;float sum = 0;for(i = 0; i < n; ++i){get_next_batch(d, batch, i*batch, X, y);float err = train_network_datum(net, X, y);//具体训练函数sum += err;}free(X);free(y);return (float)sum/(n*batch);
}

6、其中，train_network_datum()细节如下：

float train_network_datum(network net, float *x, float *y)
{
#ifdef GPUif(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
#endifnetwork_state state;*net.seen += net.batch;state.index = 0;state.net = net;state.input = x;state.delta = 0;state.truth = y;state.train = 1;forward_network(net, state);//前向传播backward_network(net, state);//反向函数float error = get_network_cost(net);if(((*net.seen)/net.batch)%net.subdivisions == 0) update_network(net);return error;
}

到这来就能基本修改实现自己的功能了。

还有很多细节，有空再更新了！

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