From c40cdeb4021fc1a638969563972f13c9f5e90d74 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 9 Oct 2015 12:50:43 -0700
Subject: [PATCH] lots of comparator stuff
---
Makefile | 2 +-
cfg/darknet.cfg | 1 +
src/coco.c | 1 +
src/compare.c | 110 +++++++++++++++++++++++++++-----------
src/convolutional_layer.c | 2 +-
src/darknet.c | 3 ++
src/data.c | 8 +--
src/data.h | 1 +
src/dice.c | 2 +-
src/imagenet.c | 2 +-
src/layer.h | 4 ++
src/network.c | 6 +--
src/network.h | 2 +-
src/option_list.c | 18 +++++++
src/option_list.h | 1 +
src/parser.c | 23 ++------
src/region_layer.c | 38 ++++++++++++-
src/swag.c | 99 +++++++++++++++++++++++++++++++++-
18 files changed, 258 insertions(+), 65 deletions(-)
diff --git a/Makefile b/Makefile
index 22e89a1..26c4076 100644
--- a/Makefile
+++ b/Makefile
@@ -34,7 +34,7 @@ CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif
-OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o swag.o
+OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o swag.o classifier.o
ifeq ($(GPU), 1)
OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
endif
diff --git a/cfg/darknet.cfg b/cfg/darknet.cfg
index 0b3c46c..00e9c36 100644
--- a/cfg/darknet.cfg
+++ b/cfg/darknet.cfg
@@ -104,6 +104,7 @@ output=1000
activation=leaky
[softmax]
+groups=1
[cost]
type=sse
diff --git a/src/coco.c b/src/coco.c
index c016548..f6b135f 100644
--- a/src/coco.c
+++ b/src/coco.c
@@ -135,6 +135,7 @@ void get_probs(float *predictions, int total, int classes, int inc, float **prob
}
}
}
+
void get_boxes(float *predictions, int n, int num_boxes, int per_box, box *boxes)
{
int i,j;
diff --git a/src/compare.c b/src/compare.c
index 74c1cf5..76e0b60 100644
--- a/src/compare.c
+++ b/src/compare.c
@@ -150,17 +150,20 @@ typedef struct {
network net;
char *filename;
int class;
+ int classes;
float elo;
+ float *elos;
} sortable_bbox;
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.elo == box2.elo) return 0;
- if(box1.elo > box2.elo) return -1;
+ if(box1.elos[current_class] == box2.elos[current_class]) return 0;
+ if(box1.elos[current_class] > box2.elos[current_class]) return -1;
return 1;
}
@@ -188,16 +191,38 @@ int bbox_comparator(const void *a, const void *b)
return -1;
}
-void bbox_fight(sortable_bbox *a, sortable_bbox *b)
+void bbox_update(sortable_bbox *a, sortable_bbox *b, int class, int result)
{
int k = 32;
- int result = bbox_comparator(a,b);
- float EA = 1./(1+pow(10, (b->elo - a->elo)/400.));
- float EB = 1./(1+pow(10, (a->elo - b->elo)/400.));
- float SA = 1.*(result > 0);
- float SB = 1.*(result < 0);
- a->elo = a->elo + k*(SA - EA);
- b->elo = b->elo + k*(SB - EB);
+ 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);
+}
+
+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);
+ ++total_compares;
+
+ int i;
+ 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)
@@ -233,7 +258,8 @@ void SortMaster3000(char *filename, char *weightfile)
void BattleRoyaleWithCheese(char *filename, char *weightfile)
{
- int i = 0;
+ int classes = 20;
+ int i,j;
network net = parse_network_cfg(filename);
if(weightfile){
load_weights(&net, weightfile);
@@ -241,47 +267,67 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile)
srand(time(0));
set_batch_network(&net, 1);
- //list *plist = get_paths("data/compare.sort.list");
- list *plist = get_paths("data/compare.cat.list");
+ 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);
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){
boxes[i].filename = paths[i];
boxes[i].net = net;
- boxes[i].class = 7;
- boxes[i].elo = 1500;
+ boxes[i].classes = classes;
+ boxes[i].elos = calloc(classes, sizeof(float));;
+ for(j = 0; j < classes; ++j){
+ boxes[i].elos[j] = 1500;
+ }
}
int round;
clock_t time=clock();
- for(round = 1; round <= 500; ++round){
+ for(round = 1; round <= 4; ++round){
clock_t round_time=clock();
printf("Round: %d\n", round);
- qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
- sorta_shuffle(boxes, N, sizeof(sortable_bbox), 10);
shuffle(boxes, N, sizeof(sortable_bbox));
for(i = 0; i < N/2; ++i){
- bbox_fight(boxes+i*2, boxes+i*2+1);
- }
- if(round >= 4 && 0){
- qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
- if(round == 4){
- N = N/2;
- }else{
- N = (N*9/10)/2*2;
- }
+ bbox_fight(net, boxes+i*2, boxes+i*2+1, classes, -1);
}
printf("Round: %f secs, %d remaining\n", sec(clock()-round_time), N);
}
- qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
- FILE *outfp = fopen("results/battle.log", "w");
- for(i = 0; i < N; ++i){
- fprintf(outfp, "%s %f\n", boxes[i].filename, boxes[i].elo);
+
+ int 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 <= 20; ++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);
+ }
+ qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
+ N = (N*9/10)/2*2;
+
+ 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){
+ fprintf(outfp, "%s %f\n", boxes[i].filename, boxes[i].elos[class]);
+ }
+ fclose(outfp);
}
- fclose(outfp);
printf("Tournament in %d compares, %f secs\n", total_compares, sec(clock()-time));
}
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 6e3f38b..f3609ea 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -61,7 +61,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.biases = calloc(n, sizeof(float));
l.bias_updates = calloc(n, sizeof(float));
- //float scale = 1./sqrt(size*size*c);
+ // float scale = 1./sqrt(size*size*c);
float scale = sqrt(2./(size*size*c));
for(i = 0; i < c*n*size*size; ++i) l.filters[i] = 2*scale*rand_uniform() - scale;
for(i = 0; i < n; ++i){
diff --git a/src/darknet.c b/src/darknet.c
index 9632f91..073156b 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -20,6 +20,7 @@ extern void run_captcha(int argc, char **argv);
extern void run_nightmare(int argc, char **argv);
extern void run_dice(int argc, char **argv);
extern void run_compare(int argc, char **argv);
+extern void run_classifier(int argc, char **argv);
void change_rate(char *filename, float scale, float add)
{
@@ -183,6 +184,8 @@ int main(int argc, char **argv)
run_swag(argc, argv);
} else if (0 == strcmp(argv[1], "coco")){
run_coco(argc, argv);
+ } else if (0 == strcmp(argv[1], "classifier")){
+ run_classifier(argc, argv);
} else if (0 == strcmp(argv[1], "compare")){
run_compare(argc, argv);
} else if (0 == strcmp(argv[1], "dice")){
diff --git a/src/data.c b/src/data.c
index 2853d72..92c3d95 100644
--- a/src/data.c
+++ b/src/data.c
@@ -366,7 +366,7 @@ void free_data(data d)
}
}
-data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes)
+data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes, float jitter)
{
char **random_paths = get_random_paths(paths, n, m);
int i;
@@ -385,8 +385,8 @@ data load_data_region(int n, char **paths, int m, int w, int h, int size, int cl
int oh = orig.h;
int ow = orig.w;
- int dw = ow/10;
- int dh = oh/10;
+ int dw = (ow*jitter);
+ int dh = (oh*jitter);
int pleft = (rand_uniform() * 2*dw - dw);
int pright = (rand_uniform() * 2*dw - dw);
@@ -556,7 +556,7 @@ void *load_thread(void *ptr)
} else if (a.type == WRITING_DATA){
*a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h);
} else if (a.type == REGION_DATA){
- *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes);
+ *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter);
} else if (a.type == COMPARE_DATA){
*a.d = load_data_compare(a.n, a.paths, a.m, a.classes, a.w, a.h);
} else if (a.type == IMAGE_DATA){
diff --git a/src/data.h b/src/data.h
index b91819f..0dacea2 100644
--- a/src/data.h
+++ b/src/data.h
@@ -44,6 +44,7 @@ typedef struct load_args{
int num_boxes;
int classes;
int background;
+ float jitter;
data *d;
image *im;
image *resized;
diff --git a/src/dice.c b/src/dice.c
index fdc535e..6f148b0 100644
--- a/src/dice.c
+++ b/src/dice.c
@@ -61,7 +61,7 @@ void validate_dice(char *filename, char *weightfile)
free_list(plist);
data val = load_data(paths, m, 0, labels, 6, net.w, net.h);
- float *acc = network_accuracies(net, val);
+ float *acc = network_accuracies(net, val, 2);
printf("Validation Accuracy: %f, %d images\n", acc[0], m);
free_data(val);
}
diff --git a/src/imagenet.c b/src/imagenet.c
index 567a8c4..1701a2a 100644
--- a/src/imagenet.c
+++ b/src/imagenet.c
@@ -133,7 +133,7 @@ void validate_imagenet(char *filename, char *weightfile)
printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
time=clock();
- float *acc = network_accuracies(net, val);
+ float *acc = network_accuracies(net, val, 5);
avg_acc += acc[0];
avg_top5 += acc[1];
printf("%d: top1: %f, top5: %f, %lf seconds, %d images\n", i, avg_acc/i, avg_top5/i, sec(clock()-time), val.X.rows);
diff --git a/src/layer.h b/src/layer.h
index 808aba4..49f144d 100644
--- a/src/layer.h
+++ b/src/layer.h
@@ -29,6 +29,9 @@ typedef struct {
COST_TYPE cost_type;
int batch;
int forced;
+ int object_logistic;
+ int class_logistic;
+ int coord_logistic;
int inputs;
int outputs;
int truths;
@@ -45,6 +48,7 @@ typedef struct {
int sqrt;
int flip;
float angle;
+ float jitter;
float saturation;
float exposure;
int softmax;
diff --git a/src/network.c b/src/network.c
index 7f19318..063a1bb 100644
--- a/src/network.c
+++ b/src/network.c
@@ -540,12 +540,12 @@ float network_accuracy(network net, data d)
return acc;
}
-float *network_accuracies(network net, data d)
+float *network_accuracies(network net, data d, int n)
{
static float acc[2];
matrix guess = network_predict_data(net, d);
- acc[0] = matrix_topk_accuracy(d.y, guess,1);
- acc[1] = matrix_topk_accuracy(d.y, guess,5);
+ acc[0] = matrix_topk_accuracy(d.y, guess, 1);
+ acc[1] = matrix_topk_accuracy(d.y, guess, n);
free_matrix(guess);
return acc;
}
diff --git a/src/network.h b/src/network.h
index 5a39f08..78ad0fe 100644
--- a/src/network.h
+++ b/src/network.h
@@ -70,7 +70,7 @@ float train_network_sgd(network net, data d, int n);
matrix network_predict_data(network net, data test);
float *network_predict(network net, float *input);
float network_accuracy(network net, data d);
-float *network_accuracies(network net, data d);
+float *network_accuracies(network net, data d, int n);
float network_accuracy_multi(network net, data d, int n);
void top_predictions(network net, int n, int *index);
float *get_network_output(network net);
diff --git a/src/option_list.c b/src/option_list.c
index f5536e1..7d68ead 100644
--- a/src/option_list.c
+++ b/src/option_list.c
@@ -3,6 +3,24 @@
#include <string.h>
#include "option_list.h"
+int read_option(char *s, list *options)
+{
+ size_t i;
+ size_t len = strlen(s);
+ char *val = 0;
+ for(i = 0; i < len; ++i){
+ if(s[i] == '='){
+ s[i] = '\0';
+ val = s+i+1;
+ break;
+ }
+ }
+ if(i == len-1) return 0;
+ char *key = s;
+ option_insert(options, key, val);
+ return 1;
+}
+
void option_insert(list *l, char *key, char *val)
{
kvp *p = malloc(sizeof(kvp));
diff --git a/src/option_list.h b/src/option_list.h
index 4441462..d0417aa 100644
--- a/src/option_list.h
+++ b/src/option_list.h
@@ -9,6 +9,7 @@ typedef struct{
} kvp;
+int read_option(char *s, list *options);
void option_insert(list *l, char *key, char *val);
char *option_find(list *l, char *key);
char *option_find_str(list *l, char *key, char *def);
diff --git a/src/parser.c b/src/parser.c
index 6daeb13..a3400d0 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -186,11 +186,16 @@ region_layer parse_region(list *options, size_params params)
layer.softmax = option_find_int(options, "softmax", 0);
layer.sqrt = option_find_int(options, "sqrt", 0);
+ layer.object_logistic = option_find_int(options, "object_logistic", 0);
+ layer.class_logistic = option_find_int(options, "class_logistic", 0);
+ layer.coord_logistic = option_find_int(options, "coord_logistic", 0);
+
layer.coord_scale = option_find_float(options, "coord_scale", 1);
layer.forced = option_find_int(options, "forced", 0);
layer.object_scale = option_find_float(options, "object_scale", 1);
layer.noobject_scale = option_find_float(options, "noobject_scale", 1);
layer.class_scale = option_find_float(options, "class_scale", 1);
+ layer.jitter = option_find_float(options, "jitter", .1);
return layer;
}
@@ -532,24 +537,6 @@ int is_route(section *s)
return (strcmp(s->type, "[route]")==0);
}
-int read_option(char *s, list *options)
-{
- size_t i;
- size_t len = strlen(s);
- char *val = 0;
- for(i = 0; i < len; ++i){
- if(s[i] == '='){
- s[i] = '\0';
- val = s+i+1;
- break;
- }
- }
- if(i == len-1) return 0;
- char *key = s;
- option_insert(options, key, val);
- return 1;
-}
-
list *read_cfg(char *filename)
{
FILE *file = fopen(filename, "r");
diff --git a/src/region_layer.c b/src/region_layer.c
index 4d8c2a4..3239f87 100644
--- a/src/region_layer.c
+++ b/src/region_layer.c
@@ -57,6 +57,28 @@ void forward_region_layer(const region_layer l, network_state state)
activate_array(l.output + index + offset, locations*l.n*(1+l.coords), LOGISTIC);
}
}
+ if (l.object_logistic) {
+ for(b = 0; b < l.batch; ++b){
+ int index = b*l.inputs;
+ int p_index = index + locations*l.classes;
+ activate_array(l.output + p_index, locations*l.n, LOGISTIC);
+ }
+ }
+
+ if (l.coord_logistic) {
+ for(b = 0; b < l.batch; ++b){
+ int index = b*l.inputs;
+ int coord_index = index + locations*(l.classes + l.n);
+ activate_array(l.output + coord_index, locations*l.n*l.coords, LOGISTIC);
+ }
+ }
+
+ if (l.class_logistic) {
+ for(b = 0; b < l.batch; ++b){
+ int class_index = b*l.inputs;
+ activate_array(l.output + class_index, locations*l.classes, LOGISTIC);
+ }
+ }
if(state.train){
float avg_iou = 0;
@@ -85,7 +107,6 @@ void forward_region_layer(const region_layer l, network_state state)
float best_rmse = 20;
if (!is_obj){
- //printf(".");
continue;
}
@@ -113,6 +134,7 @@ void forward_region_layer(const region_layer l, network_state state)
}
float iou = box_iou(out, truth);
+ //iou = 0;
float rmse = box_rmse(out, truth);
if(best_iou > 0 || iou > 0){
if(iou > best_iou){
@@ -175,6 +197,20 @@ void forward_region_layer(const region_layer l, network_state state)
gradient_array(l.output + index + locations*l.classes, locations*l.n*(1+l.coords),
LOGISTIC, l.delta + index + locations*l.classes);
}
+ if (l.object_logistic) {
+ int p_index = index + locations*l.classes;
+ gradient_array(l.output + p_index, locations*l.n, LOGISTIC, l.delta + p_index);
+ }
+
+ if (l.class_logistic) {
+ int class_index = index;
+ gradient_array(l.output + class_index, locations*l.classes, LOGISTIC, l.delta + class_index);
+ }
+
+ if (l.coord_logistic) {
+ int coord_index = index + locations*(l.classes + l.n);
+ gradient_array(l.output + coord_index, locations*l.n*l.coords, LOGISTIC, l.delta + coord_index);
+ }
//printf("\n");
}
printf("Region Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count);
diff --git a/src/swag.c b/src/swag.c
index ec58f0d..8c9ce3c 100644
--- a/src/swag.c
+++ b/src/swag.c
@@ -73,6 +73,7 @@ void train_swag(char *cfgfile, char *weightfile)
int side = l.side;
int classes = l.classes;
+ float jitter = l.jitter;
list *plist = get_paths(train_images);
//int N = plist->size;
@@ -85,6 +86,7 @@ void train_swag(char *cfgfile, char *weightfile)
args.n = imgs;
args.m = plist->size;
args.classes = classes;
+ args.jitter = jitter;
args.num_boxes = side;
args.d = &buffer;
args.type = REGION_DATA;
@@ -127,7 +129,7 @@ void train_swag(char *cfgfile, char *weightfile)
save_weights(net, buff);
}
-void convert_swag_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes)
+void convert_swag_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
{
int i,j,n;
//int per_cell = 5*num+classes;
@@ -148,6 +150,9 @@ void convert_swag_detections(float *predictions, int classes, int num, int squar
float prob = scale*predictions[class_index+j];
probs[index][j] = (prob > thresh) ? prob : 0;
}
+ if(only_objectness){
+ probs[index][0] = scale;
+ }
}
}
}
@@ -250,7 +255,7 @@ void validate_swag(char *cfgfile, char *weightfile)
float *predictions = network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
- convert_swag_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes);
+ convert_swag_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0);
if (nms) do_nms(boxes, probs, side*side*l.n, classes, iou_thresh);
print_swag_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h);
free(id);
@@ -261,6 +266,95 @@ void validate_swag(char *cfgfile, char *weightfile)
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}
+void validate_swag_recall(char *cfgfile, char *weightfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ srand(time(0));
+
+ char *base = "results/comp4_det_test_";
+ list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
+ char **paths = (char **)list_to_array(plist);
+
+ layer l = net.layers[net.n-1];
+ int classes = l.classes;
+ int square = l.sqrt;
+ int side = l.side;
+
+ int j, k;
+ FILE **fps = calloc(classes, sizeof(FILE *));
+ for(j = 0; j < classes; ++j){
+ char buff[1024];
+ snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
+ fps[j] = fopen(buff, "w");
+ }
+ box *boxes = calloc(side*side*l.n, sizeof(box));
+ float **probs = calloc(side*side*l.n, sizeof(float *));
+ for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
+
+ int m = plist->size;
+ int i=0;
+
+ float thresh = .001;
+ int nms = 0;
+ float iou_thresh = .5;
+ float nms_thresh = .5;
+
+ int total = 0;
+ int correct = 0;
+ int proposals = 0;
+ float avg_iou = 0;
+
+ for(i = 0; i < m; ++i){
+ char *path = paths[i];
+ image orig = load_image_color(path, 0, 0);
+ image sized = resize_image(orig, net.w, net.h);
+ char *id = basecfg(path);
+ float *predictions = network_predict(net, sized.data);
+ int w = orig.w;
+ int h = orig.h;
+ convert_swag_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1);
+ if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh);
+
+ char *labelpath = find_replace(path, "images", "labels");
+ labelpath = find_replace(labelpath, "JPEGImages", "labels");
+ labelpath = find_replace(labelpath, ".jpg", ".txt");
+ labelpath = find_replace(labelpath, ".JPEG", ".txt");
+
+ int num_labels = 0;
+ box_label *truth = read_boxes(labelpath, &num_labels);
+ for(k = 0; k < side*side*l.n; ++k){
+ if(probs[k][0] > thresh){
+ ++proposals;
+ }
+ }
+ for (j = 0; j < num_labels; ++j) {
+ ++total;
+ box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
+ float best_iou = 0;
+ for(k = 0; k < side*side*l.n; ++k){
+ float iou = box_iou(boxes[k], t);
+ if(probs[k][0] > thresh && iou > best_iou){
+ best_iou = iou;
+ }
+ }
+ avg_iou += best_iou;
+ if(best_iou > iou_thresh){
+ ++correct;
+ }
+ }
+
+ fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
+ free(id);
+ free_image(orig);
+ free_image(sized);
+ }
+}
+
void test_swag(char *cfgfile, char *weightfile, char *filename, float thresh)
{
@@ -316,4 +410,5 @@ void run_swag(int argc, char **argv)
if(0==strcmp(argv[2], "test")) test_swag(cfg, weights, filename, thresh);
else if(0==strcmp(argv[2], "train")) train_swag(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_swag(cfg, weights);
+ else if(0==strcmp(argv[2], "recall")) validate_swag_recall(cfg, weights);
}
--
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