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add CMakeLists.txt

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This commit is contained in:
caesar
2020-04-09 11:21:54 +08:00
parent 61c9d02ec4
commit ae2b0a7679
2 changed files with 210 additions and 125 deletions
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92
CMakeLists.txt Normal file
View File

@@ -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})

83
src/compare.c
View File

@@ -7,18 +7,17 @@
#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);
network *net = parse_network_cfg(cfgfile);
if (weightfile) {
load_weights(&net, 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);
@@ -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;
@@ -40,7 +39,7 @@ 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) {
++i;
@@ -54,20 +53,21 @@ void train_compare(char *cfgfile, char *weightfile)
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);
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) {
char buff[256];
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);
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);
@@ -78,12 +78,11 @@ void train_compare(char *cfgfile, char *weightfile)
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);
network *net = parse_network_cfg(filename);
if (weightfile) {
load_weights(&net, weightfile);
load_weights(net, weightfile);
}
srand(time(0));
@@ -102,8 +101,8 @@ void validate_compare(char *filename, char *weightfile)
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;
@@ -133,7 +132,8 @@ void validate_compare(char *filename, char *weightfile)
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;
}
}
@@ -157,8 +157,7 @@ typedef struct {
int total_compares = 0;
int current_class = 0;
int elo_comparator(const void*a, const void *b)
{
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;
@@ -166,8 +165,7 @@ int elo_comparator(const void*a, const void *b)
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;
@@ -179,7 +177,7 @@ int bbox_comparator(const void *a, const void *b)
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 *predictions = network_predict(&net, X);
free_image(im1);
free_image(im2);
@@ -190,8 +188,7 @@ int bbox_comparator(const void *a, const void *b)
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.));
@@ -201,14 +198,13 @@ void bbox_update(sortable_bbox *a, sortable_bbox *b, int class, int result)
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 *predictions = network_predict(&net, X);
++total_compares;
int i;
@@ -224,15 +220,14 @@ void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, in
free(X);
}
void SortMaster3000(char *filename, char *weightfile)
{
void SortMaster3000(char *filename, char *weightfile) {
int i = 0;
network net = parse_network_cfg(filename);
network *net = parse_network_cfg(filename);
if (weightfile) {
load_weights(&net, 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");
@@ -243,7 +238,7 @@ void SortMaster3000(char *filename, char *weightfile)
printf("Sorting %d boxes...\n", N);
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;
}
@@ -255,16 +250,15 @@ void SortMaster3000(char *filename, char *weightfile)
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);
network *net = parse_network_cfg(filename);
if (weightfile) {
load_weights(&net, 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");
@@ -278,7 +272,7 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile)
printf("Battling %d boxes...\n", N);
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) {
@@ -292,7 +286,7 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile)
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);
bbox_fight(*net, boxes + i * 2, boxes + i * 2 + 1, classes, -1);
}
printf("Round: %f secs, %d remaining\n", sec(clock() - round_time), N);
}
@@ -312,7 +306,7 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile)
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);
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;
@@ -330,8 +324,7 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile)
printf("Tournament in %d compares, %f secs\n", total_compares, sec(clock() - time));
}
void run_compare(int argc, char **argv)
{
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;