@ -26,8 +26,9 @@
# pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") # pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
# endif # endif
IplImage * draw_train_chart ( float max_img_loss , int max_batches , int number_of_lines , int img_size ) ; IplImage * draw_train_chart ( float max_img_loss , int max_batches , int number_of_lines , int img_size , int dont_show ) ;
void draw_train_loss ( IplImage * img , int img_size , float avg_loss , float max_img_loss , int current_batch , int max_batches , float precision , int draw_precision ) ; void draw_train_loss ( IplImage * img , int img_size , float avg_loss , float max_img_loss , int current_batch , int max_batches ,
float precision , int draw_precision , int dont_show , int mjpeg_port ) ;
# define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) # define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 )
# endif // OPENCV
# endif // OPENCV
@ -38,7 +39,7 @@ int check_mistakes;
static int coco_ids [ ] = { 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 27 , 28 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 67 , 70 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 84 , 85 , 86 , 87 , 88 , 89 , 90 } ; static int coco_ids [ ] = { 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 27 , 28 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 67 , 70 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 84 , 85 , 86 , 87 , 88 , 89 , 90 } ;
void train_detector ( char * datacfg , char * cfgfile , char * weightfile , int * gpus , int ngpus , int clear , int dont_show , int calc_map ) 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 ) ; list * options = read_data_cfg ( datacfg ) ;
char * train_images = option_find_str ( options , " train " , " data/train.txt " ) ; char * train_images = option_find_str ( options , " train " , " data/train.txt " ) ;
@ -158,8 +159,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
float max_img_loss = 5 ; float max_img_loss = 5 ;
int number_of_lines = 100 ; int number_of_lines = 100 ;
int img_size = 1000 ; int img_size = 1000 ;
if ( ! dont_show ) img = draw_train_chart ( max_img_loss , net . max_batches , number_of_lines , img_size , dont_show ) ;
img = draw_train_chart ( max_img_loss , net . max_batches , number_of_lines , img_size ) ;
# endif //OPENCV
# endif //OPENCV
pthread_t load_thread = load_data ( args ) ; pthread_t load_thread = load_data ( args ) ;
@ -238,42 +238,42 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
avg_loss = avg_loss * .9 + loss * .1 ; avg_loss = avg_loss * .9 + loss * .1 ;
i = get_current_batch ( net ) ; 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 ( 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 ) ; 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. " ) ; else printf ( " \n Tensor Cores are used. " ) ;
} }
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 ) ; 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 ) ;
# ifdef OPENCV int draw_precision = 0 ;
if ( ! dont_show ) { int calc_map_for_each = 4 * train_images_num / ( net . batch * net . subdivisions ) ; // calculate mAP for each 4 Epochs
int draw_precision = 0 ; if ( calc_map & & ( i > = ( iter_map + calc_map_for_each ) | | i = = net . max_batches ) & & i > = net . burn_in & & i > = 1000 ) {
int calc_map_for_each = 4 * train_images_num / ( net . batch * net . subdivisions ) ; // calculate mAP for each 4 Epochs
if ( l . random ) {
if ( calc_map & & ( i > = ( iter_map + calc_map_for_each ) | | i = = net . max_batches ) & & i > = net . burn_in & & i > = 1000 ) { printf ( " Resizing to initial size: %d x %d \n " , init_w , init_h ) ;
if ( l . random ) { args . w = init_w ;
printf ( " Resizing to initial size: %d x %d \n " , init_w , init_h ) ; args . h = init_h ;
args . w = init_w ; pthread_join ( load_thread , 0 ) ;
args . h = init_h ; train = buffer ;
pthread_join ( load_thread , 0 ) ; load_thread = load_data ( args ) ;
train = buffer ; int k ;
load_thread = load_data ( args ) ; for ( k = 0 ; k < ngpus ; + + k ) {
int k ; resize_network ( nets + k , init_w , init_h ) ;
for ( k = 0 ; k < ngpus ; + + k ) {
resize_network ( nets + k , init_w , init_h ) ;
}
net = nets [ 0 ] ;
} }
net = nets [ 0 ] ;
// combine Training and Validation networks
network 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 ;
} }
draw_train_loss ( img , img_size , avg_loss , max_img_loss , i , net . max_batches , mean_average_precision , draw_precision ) ; // combine Training and Validation networks
network 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
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
# endif // OPENCV
//if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
//if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
@ -1378,7 +1378,7 @@ void run_detector(int argc, char **argv)
int show = find_arg ( argc , argv , " -show " ) ; int show = find_arg ( argc , argv , " -show " ) ;
int calc_map = find_arg ( argc , argv , " -map " ) ; int calc_map = find_arg ( argc , argv , " -map " ) ;
check_mistakes = find_arg ( argc , argv , " -check_mistakes " ) ; check_mistakes = find_arg ( argc , argv , " -check_mistakes " ) ;
int http_stream _port= find_int_arg ( argc , argv , " -http _port " , - 1 ) ; int mjpeg _port= find_int_arg ( argc , argv , " -mjpeg _port " , - 1 ) ;
int json_port = find_int_arg ( argc , argv , " -json_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 * out_filename = find_char_arg ( argc , argv , " -out_filename " , 0 ) ;
char * outfile = find_char_arg ( argc , argv , " -out " , 0 ) ; char * outfile = find_char_arg ( argc , argv , " -out " , 0 ) ;
@ -1433,7 +1433,7 @@ void run_detector(int argc, char **argv)
if ( weights [ strlen ( weights ) - 1 ] = = 0x0d ) weights [ strlen ( weights ) - 1 ] = 0 ; if ( weights [ strlen ( weights ) - 1 ] = = 0x0d ) weights [ strlen ( weights ) - 1 ] = 0 ;
char * filename = ( argc > 6 ) ? argv [ 6 ] : 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 ) ; 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 ) ; 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 ] , " 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 ] , " 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 ] , " map " ) ) validate_detector_map ( datacfg , cfg , weights , thresh , iou_thresh , NULL ) ;
@ -1447,7 +1447,7 @@ void run_detector(int argc, char **argv)
if ( strlen ( filename ) > 0 ) if ( strlen ( filename ) > 0 )
if ( filename [ strlen ( filename ) - 1 ] = = 0x0d ) filename [ strlen ( filename ) - 1 ] = 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 , demo ( cfg , weights , thresh , hier_thresh , cam_index , filename , names , classes , frame_skip , prefix , out_filename ,
http_stream _port, json_port , dont_show , ext_output ) ; mjpeg _port, json_port , dont_show , ext_output ) ;
free_list_contents_kvp ( options ) ; free_list_contents_kvp ( options ) ;
free_list ( options ) ; free_list ( options ) ;
AlexeyAB
6 years ago