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Fixed mAP chart during training for CUDNN=1

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pull/2111/head
AlexeyAB 6 years ago
parent 78d1ade380
commit f38d060137
3 changed files with 120 additions and 108 deletions
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  1. 81
      src/detector.c
  2. 2
      src/maxpool_layer.c
  3. 1
      src/network.h

81
src/detector.c
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@ -37,8 +37,6 @@ 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 };
float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net);
void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map)
{
list *options = read_data_cfg(datacfg);
@ -46,20 +44,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
char *valid_images = option_find_str(options, "valid", train_images);
char *backup_directory = option_find_str(options, "backup", "/backup/");
int train_images_num = 0;
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);
list *plist = get_paths(train_images);
train_images_num = plist->size;
free_list(plist);
}
srand(time(0));
char *base = basecfg(cfgfile);
printf("%s\n", base);
@ -84,6 +68,43 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
srand(time(0));
network net = nets[0];
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);
list *plist = get_paths(train_images);
train_images_num = plist->size;
free_list(plist);
printf(" Prepare additional network for mAP calculation...\n");
net_map = parse_network_cfg_custom(cfgfile, 1);
int k;
for (k = 0; k < net.n; ++k) {
layer l = net.layers[k];
if (l.type == CONVOLUTIONAL) {
net_map.layers[k].biases = l.biases;
net_map.layers[k].scales = l.scales;
net_map.layers[k].rolling_mean = l.rolling_mean;
net_map.layers[k].rolling_variance = l.rolling_variance;
net_map.layers[k].weights = l.weights;
net_map.layers[k].biases_gpu = l.biases_gpu;
net_map.layers[k].scales_gpu = l.scales_gpu;
net_map.layers[k].rolling_mean_gpu = l.rolling_mean_gpu;
net_map.layers[k].rolling_variance_gpu = l.rolling_variance_gpu;
net_map.layers[k].weights_gpu = l.weights_gpu;
net_map.layers[k].weights_gpu16 = l.weights_gpu16;
}
}
}
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");
@ -211,7 +232,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
#ifdef GPU
if (ngpus == 1) {
loss = train_network(net, train);
} else {
}
else {
loss = train_networks(nets, ngpus, train, 4);
}
#else
@ -232,21 +254,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
int draw_precision = 0;
int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions);
if (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];
}
iter_map = i;
mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net);
mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_map);
printf("\n mean_average_precision = %f \n", mean_average_precision);
draw_precision = 1;
}
@ -632,8 +641,6 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa
char *train_images = option_find_str(options, "train", "data/train.txt");
char *valid_images = option_find_str(options, "valid", train_images);
net = *existing_net;
initial_batch = net.batch;
set_batch_network(&net, 1);
}
else {
net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
@ -970,7 +977,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa
free_list(options);
if (existing_net) {
set_batch_network(&net, initial_batch);
//set_batch_network(&net, initial_batch);
}
else {
free_network(net);
@ -1232,7 +1239,8 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
strncpy(input, filename, 256);
if (strlen(input) > 0)
if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
} else {
}
else {
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
@ -1368,7 +1376,8 @@ void run_detector(int argc, char **argv)
gpus[i] = atoi(gpu_list);
gpu_list = strchr(gpu_list, ',') + 1;
}
} else {
}
else {
gpu = gpu_index;
gpus = &gpu;
ngpus = 1;

2
src/maxpool_layer.c
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@ -104,6 +104,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h)
l->indexes_gpu = cuda_make_int_array(output_size);
l->output_gpu = cuda_make_array(l->output, output_size);
l->delta_gpu = cuda_make_array(l->delta, output_size);
cudnn_maxpool_setup(l);
#endif
}

1
src/network.h
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@ -145,6 +145,7 @@ YOLODLL_API void reset_rnn(network *net);
YOLODLL_API network *load_network_custom(char *cfg, char *weights, int clear, int batch);
YOLODLL_API network *load_network(char *cfg, char *weights, int clear);
YOLODLL_API float *network_predict_image(network *net, image im);
YOLODLL_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net);
YOLODLL_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map);
YOLODLL_API int network_width(network *net);
YOLODLL_API int network_height(network *net);

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