mirror of
https://github.com/pjreddie/darknet.git
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add CMakeLists.txt
This commit is contained in:
caesar
parent
61c9d02ec4
commit
ae2b0a7679
92
CMakeLists.txt
Normal file
92
CMakeLists.txt
Normal file
@ -0,0 +1,92 @@
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CMAKE_MINIMUM_REQUIRED(VERSION 3.10)
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PROJECT(darknet)
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# C++和CUDA的编译参数，可选。
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SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC -std=c++11")
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SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC")
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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;)
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# 头文件路径，按需
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include_directories(
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./src
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./include
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)
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# 库文件路径，按需
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link_directories(/usr/lib
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/usr/local/lib)
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# 主要就是这个，教cmake去找nvcc来编译这些东西
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set(darknet_lib libDarkNet)
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option(BUILD_SHARED_LIBS "BUILD_SHARED_LIBS" ON)
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if (BUILD_SHARED_LIBS)
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set(darknet_LIB_TYPE SHARED)
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else ()
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set(darknet_LIB_TYPE STATIC)
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endif ()
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FILE(GLOB C_SrcSource "src/*.c")
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list(FILTER C_SrcSource EXCLUDE REGEX ".*compare.c")
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FILE(GLOB CU_SrcSource "src/*.cu")
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option(ENABLE_OPENCV "option for OpenCV" OFF)
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if (ENABLE_OPENCV)
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find_package(OpenCV)
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if (OpenCV_FOUND)
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set(ENABLE_OPENCV ON)
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add_definitions(-DENABLE_OPENCV -DOPENCV)
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message(STATUS "OpenCV library status:")
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message(STATUS " version: ${OpenCV_VERSION}")
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message(STATUS " libraries: ${OpenCV_LIBS}")
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message(STATUS " libraries: ${OpenCV_LIBRARIES}")
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message(STATUS " lib_dir: ${OpenCV_LIB_DIR}")
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message(STATUS " include path: ${OpenCV_INCLUDE_DIRS}")
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link_directories(${OpenCV_DIR})
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include_directories(
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${OpenCV_INCLUDE_DIRS}
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)
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endif ()
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endif ()
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find_package(CUDA)
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option(ENABLE_CUDA "option for CUDA" OFF)
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if (ENABLE_CUDA AND CUDA_FOUND)
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add_definitions(-DENABLE_CUDA -DCUDNN -DGPU)
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include_directories(${CUDA_INCLUDE_DIRS})
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if ("${Tools_Other_Project}" STREQUAL "ON")
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message(STATUS "CUDA library status:")
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message(STATUS " ${CUDA_VERSION}")
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message(STATUS " libraries: ${CUDA_LIBS}")
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message(STATUS " libraries: ${CUDA_LIBRARIES}")
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message(STATUS " lib_dir: ${CUDA_LIBRARY_DIRS}")
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message(STATUS " include path: ${CUDA_INCLUDE_DIRS}")
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endif ()
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include_directories(${CUDA_INCLUDE_DIRS})
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link_directories(${CUDA_LIBRARY_DIRS})
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cuda_add_library(${darknet_lib} ${darknet_LIB_TYPE}
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${C_SrcSource}
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${CU_SrcSource}
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)
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# 链接外部库，按需
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target_link_libraries(${darknet_lib} ${CUDA_LIBRARIES} ${OpenCV_LIBRARIES})
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else ()
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add_library(${darknet_lib} ${darknet_LIB_TYPE}
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${C_SrcSource}
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)
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# 链接外部库，按需
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target_link_libraries(${darknet_lib} ${OpenCV_LIBRARIES})
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endif ()
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FILE(GLOB example_SrcSource "examples/*.c")
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list(FILTER example_SrcSource EXCLUDE REGEX ".*attention.c")
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list(FILTER example_SrcSource EXCLUDE REGEX ".*dice.c")
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list(FILTER example_SrcSource EXCLUDE REGEX ".*swag.c")
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list(FILTER example_SrcSource EXCLUDE REGEX ".*writing.c")
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list(FILTER example_SrcSource EXCLUDE REGEX ".*voxel.c")
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add_executable(darknet ${example_SrcSource})
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target_link_libraries(darknet ${darknet_lib})
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239
src/compare.c
239
src/compare.c
@ -7,21 +7,20 @@
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#include "parser.h"
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#include "box.h"
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void train_compare(char *cfgfile, char *weightfile)
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{
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void train_compare(char *cfgfile, char *weightfile) {
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srand(time(0));
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float avg_loss = -1;
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char *base = basecfg(cfgfile);
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char *backup_directory = "/home/pjreddie/backup/";
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printf("%s\n", base);
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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network *net = parse_network_cfg(cfgfile);
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if (weightfile) {
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load_weights(net, weightfile);
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}
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
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int imgs = 1024;
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list *plist = get_paths("data/compare.train.list");
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char **paths = (char **)list_to_array(plist);
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char **paths = (char **) list_to_array(plist);
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int N = plist->size;
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printf("%d\n", N);
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clock_t time;
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@ -30,8 +29,8 @@ void train_compare(char *cfgfile, char *weightfile)
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data buffer;
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.w = net->w;
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args.h = net->h;
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args.paths = paths;
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args.classes = 20;
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args.n = imgs;
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@ -40,70 +39,70 @@ void train_compare(char *cfgfile, char *weightfile)
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args.type = COMPARE_DATA;
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load_thread = load_data_in_thread(args);
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int epoch = *net.seen/N;
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int epoch = *(net->seen) / N;
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int i = 0;
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while(1){
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while (1) {
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++i;
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time=clock();
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time = clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data_in_thread(args);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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printf("Loaded: %lf seconds\n", sec(clock() - time));
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time = clock();
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float loss = train_network(net, train);
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if(avg_loss == -1) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen);
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if (avg_loss == -1) avg_loss = loss;
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avg_loss = avg_loss * .9 + loss * .1;
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printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float) *net->seen / N, loss, avg_loss,
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sec(clock() - time), *net->seen);
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free_data(train);
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if(i%100 == 0){
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if (i % 100 == 0) {
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char buff[256];
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sprintf(buff, "%s/%s_%d_minor_%d.weights",backup_directory,base, epoch, i);
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sprintf(buff, "%s/%s_%d_minor_%d.weights", backup_directory, base, epoch, i);
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save_weights(net, buff);
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}
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if(*net.seen/N > epoch){
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epoch = *net.seen/N;
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if (*net->seen / N > epoch) {
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epoch = *net->seen / N;
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i = 0;
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
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sprintf(buff, "%s/%s_%d.weights", backup_directory, base, epoch);
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save_weights(net, buff);
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if(epoch%22 == 0) net.learning_rate *= .1;
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if (epoch % 22 == 0) net->learning_rate *= .1;
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}
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}
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pthread_join(load_thread, 0);
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free_data(buffer);
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free_network(net);
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free_ptrs((void**)paths, plist->size);
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free_ptrs((void **) paths, plist->size);
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free_list(plist);
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free(base);
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}
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void validate_compare(char *filename, char *weightfile)
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{
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void validate_compare(char *filename, char *weightfile) {
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int i = 0;
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network net = parse_network_cfg(filename);
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if(weightfile){
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load_weights(&net, weightfile);
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network *net = parse_network_cfg(filename);
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if (weightfile) {
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load_weights(net, weightfile);
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}
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srand(time(0));
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list *plist = get_paths("data/compare.val.list");
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//list *plist = get_paths("data/compare.val.old");
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char **paths = (char **)list_to_array(plist);
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int N = plist->size/2;
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char **paths = (char **) list_to_array(plist);
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int N = plist->size / 2;
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free_list(plist);
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clock_t time;
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int correct = 0;
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int total = 0;
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int splits = 10;
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int num = (i+1)*N/splits - i*N/splits;
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int num = (i + 1) * N / splits - i * N / splits;
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data val, buffer;
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.w = net->w;
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args.h = net->h;
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args.paths = paths;
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args.classes = 20;
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args.n = num;
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@ -112,35 +111,36 @@ void validate_compare(char *filename, char *weightfile)
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args.type = COMPARE_DATA;
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pthread_t load_thread = load_data_in_thread(args);
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for(i = 1; i <= splits; ++i){
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time=clock();
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for (i = 1; i <= splits; ++i) {
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time = clock();
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pthread_join(load_thread, 0);
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val = buffer;
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num = (i+1)*N/splits - i*N/splits;
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char **part = paths+(i*N/splits);
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if(i != splits){
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num = (i + 1) * N / splits - i * N / splits;
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char **part = paths + (i * N / splits);
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if (i != splits) {
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args.paths = part;
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load_thread = load_data_in_thread(args);
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}
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printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
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printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock() - time));
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time=clock();
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time = clock();
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matrix pred = network_predict_data(net, val);
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int j,k;
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for(j = 0; j < val.y.rows; ++j){
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for(k = 0; k < 20; ++k){
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if(val.y.vals[j][k*2] != val.y.vals[j][k*2+1]){
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int j, k;
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for (j = 0; j < val.y.rows; ++j) {
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for (k = 0; k < 20; ++k) {
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if (val.y.vals[j][k * 2] != val.y.vals[j][k * 2 + 1]) {
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++total;
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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])){
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if ((val.y.vals[j][k * 2] < val.y.vals[j][k * 2 + 1]) ==
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(pred.vals[j][k * 2] < pred.vals[j][k * 2 + 1])) {
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++correct;
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}
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}
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}
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}
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free_matrix(pred);
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printf("%d: Acc: %f, %lf seconds, %d images\n", i, (float)correct/total, sec(clock()-time), val.X.rows);
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printf("%d: Acc: %f, %lf seconds, %d images\n", i, (float) correct / total, sec(clock() - time), val.X.rows);
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free_data(val);
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}
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}
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@ -157,64 +157,60 @@ typedef struct {
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int total_compares = 0;
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int current_class = 0;
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int elo_comparator(const void*a, const void *b)
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{
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sortable_bbox box1 = *(sortable_bbox*)a;
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sortable_bbox box2 = *(sortable_bbox*)b;
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if(box1.elos[current_class] == box2.elos[current_class]) return 0;
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if(box1.elos[current_class] > box2.elos[current_class]) return -1;
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int elo_comparator(const void *a, const void *b) {
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sortable_bbox box1 = *(sortable_bbox *) a;
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sortable_bbox box2 = *(sortable_bbox *) b;
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if (box1.elos[current_class] == box2.elos[current_class]) return 0;
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if (box1.elos[current_class] > box2.elos[current_class]) return -1;
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return 1;
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}
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int bbox_comparator(const void *a, const void *b)
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{
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int bbox_comparator(const void *a, const void *b) {
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++total_compares;
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sortable_bbox box1 = *(sortable_bbox*)a;
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sortable_bbox box2 = *(sortable_bbox*)b;
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sortable_bbox box1 = *(sortable_bbox *) a;
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sortable_bbox box2 = *(sortable_bbox *) b;
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network net = box1.net;
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int class = box1.class;
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int class = box1.class;
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image im1 = load_image_color(box1.filename, net.w, net.h);
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image im2 = load_image_color(box2.filename, net.w, net.h);
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float *X = calloc(net.w*net.h*net.c, sizeof(float));
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memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float));
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memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float));
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float *predictions = network_predict(net, X);
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float *X = calloc(net.w * net.h * net.c, sizeof(float));
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memcpy(X, im1.data, im1.w * im1.h * im1.c * sizeof(float));
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memcpy(X + im1.w * im1.h * im1.c, im2.data, im2.w * im2.h * im2.c * sizeof(float));
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float *predictions = network_predict(&net, X);
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free_image(im1);
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free_image(im2);
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free(X);
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if (predictions[class*2] > predictions[class*2+1]){
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if (predictions[class * 2] > predictions[class * 2 + 1]) {
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return 1;
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}
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return -1;
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}
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void bbox_update(sortable_bbox *a, sortable_bbox *b, int class, int result)
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{
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void bbox_update(sortable_bbox *a, sortable_bbox *b, int class, int result) {
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int k = 32;
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float EA = 1./(1+pow(10, (b->elos[class] - a->elos[class])/400.));
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float EB = 1./(1+pow(10, (a->elos[class] - b->elos[class])/400.));
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float EA = 1. / (1 + pow(10, (b->elos[class] - a->elos[class]) / 400.));
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float EB = 1. / (1 + pow(10, (a->elos[class] - b->elos[class]) / 400.));
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float SA = result ? 1 : 0;
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float SB = result ? 0 : 1;
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a->elos[class] += k*(SA - EA);
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b->elos[class] += k*(SB - EB);
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a->elos[class] += k * (SA - EA);
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b->elos[class] += k * (SB - EB);
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}
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void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, int class)
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{
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void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, int class) {
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image im1 = load_image_color(a->filename, net.w, net.h);
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image im2 = load_image_color(b->filename, net.w, net.h);
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float *X = calloc(net.w*net.h*net.c, sizeof(float));
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memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float));
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memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float));
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float *predictions = network_predict(net, X);
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float *X = calloc(net.w * net.h * net.c, sizeof(float));
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memcpy(X, im1.data, im1.w * im1.h * im1.c * sizeof(float));
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memcpy(X + im1.w * im1.h * im1.c, im2.data, im2.w * im2.h * im2.c * sizeof(float));
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float *predictions = network_predict(&net, X);
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++total_compares;
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int i;
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for(i = 0; i < classes; ++i){
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if(class < 0 || class == i){
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int result = predictions[i*2] > predictions[i*2+1];
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for (i = 0; i < classes; ++i) {
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if (class < 0 || class == i) {
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int result = predictions[i * 2] > predictions[i * 2 + 1];
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bbox_update(a, b, i, result);
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}
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}
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@ -224,115 +220,112 @@ void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, in
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free(X);
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}
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void SortMaster3000(char *filename, char *weightfile)
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{
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void SortMaster3000(char *filename, char *weightfile) {
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int i = 0;
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network net = parse_network_cfg(filename);
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if(weightfile){
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load_weights(&net, weightfile);
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network *net = parse_network_cfg(filename);
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if (weightfile) {
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load_weights(net, weightfile);
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}
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srand(time(0));
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set_batch_network(&net, 1);
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set_batch_network(net, 1);
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|
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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);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user