From ae2b0a76794ab64d6c97feb8c7bfacacdfe3e521 Mon Sep 17 00:00:00 2001 From: caesar Date: Thu, 9 Apr 2020 11:21:54 +0800 Subject: [PATCH] add CMakeLists.txt --- CMakeLists.txt | 92 +++++++++++++++++++ src/compare.c | 243 ++++++++++++++++++++++++------------------------- 2 files changed, 210 insertions(+), 125 deletions(-) create mode 100644 CMakeLists.txt diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 00000000..38a9497e --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,92 @@ +CMAKE_MINIMUM_REQUIRED(VERSION 3.10) +PROJECT(darknet) +# C++和CUDA的编译参数，可选。 +SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC -std=c++11") +SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC") +SET(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-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];-std=c++11;) +# 头文件路径，按需 +include_directories( + ./src + ./include +) +# 库文件路径，按需 +link_directories(/usr/lib + /usr/local/lib) +# 主要就是这个，教cmake去找nvcc来编译这些东西 + +set(darknet_lib libDarkNet) +option(BUILD_SHARED_LIBS "BUILD_SHARED_LIBS" ON) +if (BUILD_SHARED_LIBS) + set(darknet_LIB_TYPE SHARED) +else () + set(darknet_LIB_TYPE STATIC) +endif () + +FILE(GLOB C_SrcSource "src/*.c") +list(FILTER C_SrcSource EXCLUDE REGEX ".*compare.c") + +FILE(GLOB CU_SrcSource "src/*.cu") + +option(ENABLE_OPENCV "option for OpenCV" OFF) +if (ENABLE_OPENCV) + find_package(OpenCV) + if (OpenCV_FOUND) + set(ENABLE_OPENCV ON) + add_definitions(-DENABLE_OPENCV -DOPENCV) + message(STATUS "OpenCV library status:") + message(STATUS " version: ${OpenCV_VERSION}") + message(STATUS " libraries: ${OpenCV_LIBS}") + message(STATUS " libraries: ${OpenCV_LIBRARIES}") + message(STATUS " lib_dir: ${OpenCV_LIB_DIR}") + message(STATUS " include path: ${OpenCV_INCLUDE_DIRS}") + + link_directories(${OpenCV_DIR}) + include_directories( + ${OpenCV_INCLUDE_DIRS} + ) + endif () +endif () + + +find_package(CUDA) +option(ENABLE_CUDA "option for CUDA" OFF) +if (ENABLE_CUDA AND CUDA_FOUND) + add_definitions(-DENABLE_CUDA -DCUDNN -DGPU) + + include_directories(${CUDA_INCLUDE_DIRS}) + if ("${Tools_Other_Project}" STREQUAL "ON") + message(STATUS "CUDA library status:") + message(STATUS " ${CUDA_VERSION}") + message(STATUS " libraries: ${CUDA_LIBS}") + message(STATUS " libraries: ${CUDA_LIBRARIES}") + message(STATUS " lib_dir: ${CUDA_LIBRARY_DIRS}") + message(STATUS " include path: ${CUDA_INCLUDE_DIRS}") + endif () + include_directories(${CUDA_INCLUDE_DIRS}) + link_directories(${CUDA_LIBRARY_DIRS}) + + cuda_add_library(${darknet_lib} ${darknet_LIB_TYPE} + ${C_SrcSource} + ${CU_SrcSource} + ) + # 链接外部库，按需 + target_link_libraries(${darknet_lib} ${CUDA_LIBRARIES} ${OpenCV_LIBRARIES}) +else () + add_library(${darknet_lib} ${darknet_LIB_TYPE} + ${C_SrcSource} + ) + # 链接外部库，按需 + target_link_libraries(${darknet_lib} ${OpenCV_LIBRARIES}) +endif () + + +FILE(GLOB example_SrcSource "examples/*.c") +list(FILTER example_SrcSource EXCLUDE REGEX ".*attention.c") +list(FILTER example_SrcSource EXCLUDE REGEX ".*dice.c") +list(FILTER example_SrcSource EXCLUDE REGEX ".*swag.c") +list(FILTER example_SrcSource EXCLUDE REGEX ".*writing.c") +list(FILTER example_SrcSource EXCLUDE REGEX ".*voxel.c") +add_executable(darknet ${example_SrcSource}) +target_link_libraries(darknet ${darknet_lib}) + + diff --git a/src/compare.c b/src/compare.c index d2d2b3bd..6e220adf 100644 --- a/src/compare.c +++ b/src/compare.c @@ -7,21 +7,20 @@ #include "parser.h" #include "box.h" -void train_compare(char *cfgfile, char *weightfile) -{ +void train_compare(char *cfgfile, char *weightfile) { srand(time(0)); float avg_loss = -1; char *base = basecfg(cfgfile); char *backup_directory = "/home/pjreddie/backup/"; printf("%s\n", base); - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); + network *net = parse_network_cfg(cfgfile); + if (weightfile) { + load_weights(net, weightfile); } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); int imgs = 1024; list *plist = get_paths("data/compare.train.list"); - char **paths = (char **)list_to_array(plist); + char **paths = (char **) list_to_array(plist); int N = plist->size; printf("%d\n", N); clock_t time; @@ -30,8 +29,8 @@ void train_compare(char *cfgfile, char *weightfile) data buffer; load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.paths = paths; args.classes = 20; args.n = imgs; @@ -40,70 +39,70 @@ void train_compare(char *cfgfile, char *weightfile) args.type = COMPARE_DATA; load_thread = load_data_in_thread(args); - int epoch = *net.seen/N; + int epoch = *(net->seen) / N; int i = 0; - while(1){ + while (1) { ++i; - time=clock(); + time = clock(); pthread_join(load_thread, 0); train = buffer; load_thread = load_data_in_thread(args); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); + printf("Loaded: %lf seconds\n", sec(clock() - time)); + time = clock(); float loss = train_network(net, train); - if(avg_loss == -1) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); + if (avg_loss == -1) avg_loss = loss; + avg_loss = avg_loss * .9 + loss * .1; + printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float) *net->seen / N, loss, avg_loss, + sec(clock() - time), *net->seen); free_data(train); - if(i%100 == 0){ + if (i % 100 == 0) { char buff[256]; - sprintf(buff, "%s/%s_%d_minor_%d.weights",backup_directory,base, epoch, i); + sprintf(buff, "%s/%s_%d_minor_%d.weights", backup_directory, base, epoch, i); save_weights(net, buff); } - if(*net.seen/N > epoch){ - epoch = *net.seen/N; + if (*net->seen / N > epoch) { + epoch = *net->seen / N; i = 0; char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, epoch); save_weights(net, buff); - if(epoch%22 == 0) net.learning_rate *= .1; + if (epoch % 22 == 0) net->learning_rate *= .1; } } pthread_join(load_thread, 0); free_data(buffer); free_network(net); - free_ptrs((void**)paths, plist->size); + free_ptrs((void **) paths, plist->size); free_list(plist); free(base); } -void validate_compare(char *filename, char *weightfile) -{ +void validate_compare(char *filename, char *weightfile) { int i = 0; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); + network *net = parse_network_cfg(filename); + if (weightfile) { + load_weights(net, weightfile); } srand(time(0)); list *plist = get_paths("data/compare.val.list"); //list *plist = get_paths("data/compare.val.old"); - char **paths = (char **)list_to_array(plist); - int N = plist->size/2; + char **paths = (char **) list_to_array(plist); + int N = plist->size / 2; free_list(plist); clock_t time; int correct = 0; int total = 0; int splits = 10; - int num = (i+1)*N/splits - i*N/splits; + int num = (i + 1) * N / splits - i * N / splits; data val, buffer; load_args args = {0}; - args.w = net.w; - args.h = net.h; + args.w = net->w; + args.h = net->h; args.paths = paths; args.classes = 20; args.n = num; @@ -112,35 +111,36 @@ void validate_compare(char *filename, char *weightfile) args.type = COMPARE_DATA; pthread_t load_thread = load_data_in_thread(args); - for(i = 1; i <= splits; ++i){ - time=clock(); + for (i = 1; i <= splits; ++i) { + time = clock(); pthread_join(load_thread, 0); val = buffer; - num = (i+1)*N/splits - i*N/splits; - char **part = paths+(i*N/splits); - if(i != splits){ + num = (i + 1) * N / splits - i * N / splits; + char **part = paths + (i * N / splits); + if (i != splits) { args.paths = part; load_thread = load_data_in_thread(args); } - printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time)); + printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock() - time)); - time=clock(); + time = clock(); matrix pred = network_predict_data(net, val); - int j,k; - for(j = 0; j < val.y.rows; ++j){ - for(k = 0; k < 20; ++k){ - if(val.y.vals[j][k*2] != val.y.vals[j][k*2+1]){ + int j, k; + for (j = 0; j < val.y.rows; ++j) { + for (k = 0; k < 20; ++k) { + if (val.y.vals[j][k * 2] != val.y.vals[j][k * 2 + 1]) { ++total; - if((val.y.vals[j][k*2] < val.y.vals[j][k*2+1]) == (pred.vals[j][k*2] < pred.vals[j][k*2+1])){ + if ((val.y.vals[j][k * 2] < val.y.vals[j][k * 2 + 1]) == + (pred.vals[j][k * 2] < pred.vals[j][k * 2 + 1])) { ++correct; } } } } free_matrix(pred); - printf("%d: Acc: %f, %lf seconds, %d images\n", i, (float)correct/total, sec(clock()-time), val.X.rows); + printf("%d: Acc: %f, %lf seconds, %d images\n", i, (float) correct / total, sec(clock() - time), val.X.rows); free_data(val); } } @@ -157,182 +157,175 @@ typedef struct { int total_compares = 0; int current_class = 0; -int elo_comparator(const void*a, const void *b) -{ - sortable_bbox box1 = *(sortable_bbox*)a; - sortable_bbox box2 = *(sortable_bbox*)b; - if(box1.elos[current_class] == box2.elos[current_class]) return 0; - if(box1.elos[current_class] > box2.elos[current_class]) return -1; +int elo_comparator(const void *a, const void *b) { + sortable_bbox box1 = *(sortable_bbox *) a; + sortable_bbox box2 = *(sortable_bbox *) b; + if (box1.elos[current_class] == box2.elos[current_class]) return 0; + if (box1.elos[current_class] > box2.elos[current_class]) return -1; return 1; } -int bbox_comparator(const void *a, const void *b) -{ +int bbox_comparator(const void *a, const void *b) { ++total_compares; - sortable_bbox box1 = *(sortable_bbox*)a; - sortable_bbox box2 = *(sortable_bbox*)b; + sortable_bbox box1 = *(sortable_bbox *) a; + sortable_bbox box2 = *(sortable_bbox *) b; network net = box1.net; - int class = box1.class; + int class = box1.class; image im1 = load_image_color(box1.filename, net.w, net.h); image im2 = load_image_color(box2.filename, net.w, net.h); - float *X = calloc(net.w*net.h*net.c, sizeof(float)); - memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float)); - memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float)); - float *predictions = network_predict(net, X); - + float *X = calloc(net.w * net.h * net.c, sizeof(float)); + memcpy(X, im1.data, im1.w * im1.h * im1.c * sizeof(float)); + memcpy(X + im1.w * im1.h * im1.c, im2.data, im2.w * im2.h * im2.c * sizeof(float)); + float *predictions = network_predict(&net, X); + free_image(im1); free_image(im2); free(X); - if (predictions[class*2] > predictions[class*2+1]){ + if (predictions[class * 2] > predictions[class * 2 + 1]) { return 1; } return -1; } -void bbox_update(sortable_bbox *a, sortable_bbox *b, int class, int result) -{ +void bbox_update(sortable_bbox *a, sortable_bbox *b, int class, int result) { int k = 32; - float EA = 1./(1+pow(10, (b->elos[class] - a->elos[class])/400.)); - float EB = 1./(1+pow(10, (a->elos[class] - b->elos[class])/400.)); + float EA = 1. / (1 + pow(10, (b->elos[class] - a->elos[class]) / 400.)); + float EB = 1. / (1 + pow(10, (a->elos[class] - b->elos[class]) / 400.)); float SA = result ? 1 : 0; float SB = result ? 0 : 1; - a->elos[class] += k*(SA - EA); - b->elos[class] += k*(SB - EB); + a->elos[class] += k * (SA - EA); + b->elos[class] += k * (SB - EB); } -void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, int class) -{ +void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, int class) { image im1 = load_image_color(a->filename, net.w, net.h); image im2 = load_image_color(b->filename, net.w, net.h); - float *X = calloc(net.w*net.h*net.c, sizeof(float)); - memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float)); - memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float)); - float *predictions = network_predict(net, X); + float *X = calloc(net.w * net.h * net.c, sizeof(float)); + memcpy(X, im1.data, im1.w * im1.h * im1.c * sizeof(float)); + memcpy(X + im1.w * im1.h * im1.c, im2.data, im2.w * im2.h * im2.c * sizeof(float)); + float *predictions = network_predict(&net, X); ++total_compares; int i; - for(i = 0; i < classes; ++i){ - if(class < 0 || class == i){ - int result = predictions[i*2] > predictions[i*2+1]; + for (i = 0; i < classes; ++i) { + if (class < 0 || class == i) { + int result = predictions[i * 2] > predictions[i * 2 + 1]; bbox_update(a, b, i, result); } } - + free_image(im1); free_image(im2); free(X); } -void SortMaster3000(char *filename, char *weightfile) -{ +void SortMaster3000(char *filename, char *weightfile) { int i = 0; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); + network *net = parse_network_cfg(filename); + if (weightfile) { + load_weights(net, weightfile); } srand(time(0)); - set_batch_network(&net, 1); + set_batch_network(net, 1); list *plist = get_paths("data/compare.sort.list"); //list *plist = get_paths("data/compare.val.old"); - char **paths = (char **)list_to_array(plist); + char **paths = (char **) list_to_array(plist); int N = plist->size; free_list(plist); sortable_bbox *boxes = calloc(N, sizeof(sortable_bbox)); printf("Sorting %d boxes...\n", N); - for(i = 0; i < N; ++i){ + for (i = 0; i < N; ++i) { boxes[i].filename = paths[i]; - boxes[i].net = net; + boxes[i].net = *net; boxes[i].class = 7; boxes[i].elo = 1500; } - clock_t time=clock(); + clock_t time = clock(); qsort(boxes, N, sizeof(sortable_bbox), bbox_comparator); - for(i = 0; i < N; ++i){ + for (i = 0; i < N; ++i) { printf("%s\n", boxes[i].filename); } - printf("Sorted in %d compares, %f secs\n", total_compares, sec(clock()-time)); + printf("Sorted in %d compares, %f secs\n", total_compares, sec(clock() - time)); } -void BattleRoyaleWithCheese(char *filename, char *weightfile) -{ +void BattleRoyaleWithCheese(char *filename, char *weightfile) { int classes = 20; - int i,j; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); + int i, j; + network *net = parse_network_cfg(filename); + if (weightfile) { + load_weights(net, weightfile); } srand(time(0)); - set_batch_network(&net, 1); + set_batch_network(net, 1); list *plist = get_paths("data/compare.sort.list"); //list *plist = get_paths("data/compare.small.list"); //list *plist = get_paths("data/compare.cat.list"); //list *plist = get_paths("data/compare.val.old"); - char **paths = (char **)list_to_array(plist); + char **paths = (char **) list_to_array(plist); int N = plist->size; int total = N; free_list(plist); sortable_bbox *boxes = calloc(N, sizeof(sortable_bbox)); printf("Battling %d boxes...\n", N); - for(i = 0; i < N; ++i){ + for (i = 0; i < N; ++i) { boxes[i].filename = paths[i]; - boxes[i].net = net; + boxes[i].net = *net; boxes[i].classes = classes; boxes[i].elos = calloc(classes, sizeof(float));; - for(j = 0; j < classes; ++j){ + for (j = 0; j < classes; ++j) { boxes[i].elos[j] = 1500; } } int round; - clock_t time=clock(); - for(round = 1; round <= 4; ++round){ - clock_t round_time=clock(); + clock_t time = clock(); + for (round = 1; round <= 4; ++round) { + clock_t round_time = clock(); printf("Round: %d\n", round); shuffle(boxes, N, sizeof(sortable_bbox)); - for(i = 0; i < N/2; ++i){ - bbox_fight(net, boxes+i*2, boxes+i*2+1, classes, -1); + for (i = 0; i < N / 2; ++i) { + bbox_fight(*net, boxes + i * 2, boxes + i * 2 + 1, classes, -1); } - printf("Round: %f secs, %d remaining\n", sec(clock()-round_time), N); + printf("Round: %f secs, %d remaining\n", sec(clock() - round_time), N); } int class; - for (class = 0; class < classes; ++class){ + for (class = 0; class < classes; ++class) { N = total; current_class = class; qsort(boxes, N, sizeof(sortable_bbox), elo_comparator); N /= 2; - for(round = 1; round <= 100; ++round){ - clock_t round_time=clock(); + for (round = 1; round <= 100; ++round) { + clock_t round_time = clock(); printf("Round: %d\n", round); sorta_shuffle(boxes, N, sizeof(sortable_bbox), 10); - for(i = 0; i < N/2; ++i){ - bbox_fight(net, boxes+i*2, boxes+i*2+1, classes, class); + for (i = 0; i < N / 2; ++i) { + bbox_fight(*net, boxes + i * 2, boxes + i * 2 + 1, classes, class); } qsort(boxes, N, sizeof(sortable_bbox), elo_comparator); - if(round <= 20) N = (N*9/10)/2*2; + if (round <= 20) N = (N * 9 / 10) / 2 * 2; - printf("Round: %f secs, %d remaining\n", sec(clock()-round_time), N); + printf("Round: %f secs, %d remaining\n", sec(clock() - round_time), N); } char buff[256]; sprintf(buff, "results/battle_%d.log", class); FILE *outfp = fopen(buff, "w"); - for(i = 0; i < N; ++i){ + for (i = 0; i < N; ++i) { fprintf(outfp, "%s %f\n", boxes[i].filename, boxes[i].elos[class]); } fclose(outfp); } - printf("Tournament in %d compares, %f secs\n", total_compares, sec(clock()-time)); + printf("Tournament in %d compares, %f secs\n", total_compares, sec(clock() - time)); } -void run_compare(int argc, char **argv) -{ - if(argc < 4){ +void run_compare(int argc, char **argv) { + if (argc < 4) { fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; } @@ -340,10 +333,10 @@ void run_compare(int argc, char **argv) char *cfg = argv[3]; char *weights = (argc > 4) ? argv[4] : 0; //char *filename = (argc > 5) ? argv[5]: 0; - if(0==strcmp(argv[2], "train")) train_compare(cfg, weights); - else if(0==strcmp(argv[2], "valid")) validate_compare(cfg, weights); - else if(0==strcmp(argv[2], "sort")) SortMaster3000(cfg, weights); - else if(0==strcmp(argv[2], "battle")) BattleRoyaleWithCheese(cfg, weights); + if (0 == strcmp(argv[2], "train")) train_compare(cfg, weights); + else if (0 == strcmp(argv[2], "valid")) validate_compare(cfg, weights); + else if (0 == strcmp(argv[2], "sort")) SortMaster3000(cfg, weights); + else if (0 == strcmp(argv[2], "battle")) BattleRoyaleWithCheese(cfg, weights); /* else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights); else if(0==strcmp(argv[2], "extract")) extract_boxes(cfg, weights);