From b5936b499abc94c0efffbcc99b5698574b59d860 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sat, 05 Sep 2015 00:52:44 +0000
Subject: [PATCH] lots of stuff
---
src/network.c | 37 ++
src/yolo.c | 3
src/cost_layer.c | 7
src/utils.h | 2
src/network.h | 16 +
Makefile | 6
src/network_kernels.cu | 9
src/data.c | 57 ++++-
src/dice.c | 5
src/data.h | 2
src/image.c | 5
src/coco.c | 37 ++
src/writing.c | 5
src/imagenet.c | 17
src/parser.c | 39 ++
src/captcha.c | 6
cfg/darknet.cfg | 21 +
src/darknet.c | 5
src/compare.c | 303 +++++++++++++++++++++++++++
src/cost_layer.h | 2
src/utils.c | 23 ++
21 files changed, 529 insertions(+), 78 deletions(-)
diff --git a/Makefile b/Makefile
index 116d3bc..65264de 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,5 @@
-GPU=0
-OPENCV=0
+GPU=1
+OPENCV=1
DEBUG=0
ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20
@@ -34,7 +34,7 @@
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
+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
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 2e0a624..f52ff3f 100644
--- a/cfg/darknet.cfg
+++ b/cfg/darknet.cfg
@@ -1,12 +1,15 @@
[net]
batch=128
-subdivisions=32
+subdivisions=1
height=256
width=256
channels=3
-learning_rate=0.01
momentum=0.9
decay=0.0005
+learning_rate=0.01
+policy=poly
+power=.5
+max_batches=600000
[crop]
crop_height=224
@@ -24,8 +27,8 @@
activation=leaky
[maxpool]
+size=3
stride=2
-size=2
[convolutional]
filters=32
@@ -35,8 +38,8 @@
activation=leaky
[maxpool]
+size=3
stride=2
-size=2
[convolutional]
filters=64
@@ -46,8 +49,8 @@
activation=leaky
[maxpool]
+size=3
stride=2
-size=2
[convolutional]
filters=128
@@ -57,8 +60,8 @@
activation=leaky
[maxpool]
+size=3
stride=2
-size=2
[convolutional]
filters=256
@@ -68,8 +71,8 @@
activation=leaky
[maxpool]
+size=3
stride=2
-size=2
[convolutional]
filters=512
@@ -79,8 +82,8 @@
activation=leaky
[maxpool]
+size=3
stride=2
-size=2
[convolutional]
filters=1024
@@ -96,7 +99,7 @@
[connected]
output=1000
-activation=leaky
+activation=linear
[softmax]
diff --git a/src/captcha.c b/src/captcha.c
index 68d8915..4e77ce2 100644
--- a/src/captcha.c
+++ b/src/captcha.c
@@ -38,9 +38,8 @@
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- //net.seen=0;
int imgs = 1024;
- int i = net.seen/imgs;
+ int i = *net.seen/imgs;
int solved = 1;
list *plist;
char **labels = get_labels("/data/captcha/reimgs.labels.list");
@@ -85,10 +84,9 @@
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
- net.seen += imgs;
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
- printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
+ printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen);
free_data(train);
if(i%100==0){
char buff[256];
diff --git a/src/coco.c b/src/coco.c
index 62ae429..87f3dca 100644
--- a/src/coco.c
+++ b/src/coco.c
@@ -62,7 +62,7 @@
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 128;
- int i = net.seen/imgs;
+ int i = *net.seen/imgs;
data train, buffer;
@@ -107,7 +107,6 @@
time=clock();
float loss = train_network(net, train);
- net.seen += imgs;
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
@@ -253,8 +252,9 @@
int total = 0;
int correct = 0;
float avg_iou = 0;
- int nms = 0;
+ int nms = 1;
int proposals = 0;
+ int save = 1;
for (i = 0; i < N; ++i) {
char *path = paths[i];
@@ -277,6 +277,27 @@
for(k = 0; k < num_boxes*num_boxes*num; ++k){
if(probs[k][0] > thresh){
++proposals;
+ if(save){
+ char buff[256];
+ sprintf(buff, "/data/extracted/nms_preds/%d", proposals);
+ int dx = (boxes[k].x - boxes[k].w/2) * orig.w;
+ int dy = (boxes[k].y - boxes[k].h/2) * orig.h;
+ int w = boxes[k].w * orig.w;
+ int h = boxes[k].h * orig.h;
+ image cropped = crop_image(orig, dx, dy, w, h);
+ image sized = resize_image(cropped, 224, 224);
+#ifdef OPENCV
+ save_image_jpg(sized, buff);
+#endif
+ free_image(sized);
+ free_image(cropped);
+ sprintf(buff, "/data/extracted/nms_pred_boxes/%d.txt", proposals);
+ char *im_id = basecfg(path);
+ FILE *fp = fopen(buff, "w");
+ fprintf(fp, "%s %d %d %d %d\n", im_id, dx, dy, dx+w, dy+h);
+ fclose(fp);
+ free(im_id);
+ }
}
}
for (j = 0; j < num_labels; ++j) {
@@ -332,7 +353,7 @@
int k;
int count = 0;
- float iou_thresh = .1;
+ float iou_thresh = .3;
for (i = 0; i < N; ++i) {
fprintf(stderr, "%5d %5d\n", i, count);
@@ -361,7 +382,7 @@
if (iou > iou_thresh){
if (!overlaps) {
char buff[256];
- sprintf(buff, "/home/pjreddie/extracted/labels/%d.txt", count);
+ sprintf(buff, "/data/extracted/labels/%d.txt", count);
label = fopen(buff, "w");
overlaps = 1;
}
@@ -370,16 +391,16 @@
}
if (overlaps) {
char buff[256];
- sprintf(buff, "/home/pjreddie/extracted/imgs/%d", count++);
+ sprintf(buff, "/data/extracted/imgs/%d", count++);
int dx = (boxes[k].x - boxes[k].w/2) * orig.w;
int dy = (boxes[k].y - boxes[k].h/2) * orig.h;
int w = boxes[k].w * orig.w;
int h = boxes[k].h * orig.h;
image cropped = crop_image(orig, dx, dy, w, h);
image sized = resize_image(cropped, 224, 224);
- #ifdef OPENCV
+#ifdef OPENCV
save_image_jpg(sized, buff);
- #endif
+#endif
free_image(sized);
free_image(cropped);
fclose(label);
diff --git a/src/compare.c b/src/compare.c
new file mode 100644
index 0000000..9b6d6bf
--- /dev/null
+++ b/src/compare.c
@@ -0,0 +1,303 @@
+#include <stdio.h>
+
+#include "network.h"
+#include "detection_layer.h"
+#include "cost_layer.h"
+#include "utils.h"
+#include "parser.h"
+#include "box.h"
+
+void train_compare(char *cfgfile, char *weightfile)
+{
+ data_seed = time(0);
+ 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);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ 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);
+ int N = plist->size;
+ printf("%d\n", N);
+ clock_t time;
+ pthread_t load_thread;
+ data train;
+ data buffer;
+
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.paths = paths;
+ args.classes = 20;
+ args.n = imgs;
+ args.m = N;
+ args.d = &buffer;
+ args.type = COMPARE_DATA;
+
+ load_thread = load_data_in_thread(args);
+ int epoch = *net.seen/N;
+ int i = 0;
+ while(1){
+ ++i;
+ time=clock();
+ pthread_join(load_thread, 0);
+ train = buffer;
+
+ load_thread = load_data_in_thread(args);
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+ 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, %d 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;
+ 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;
+ }
+ }
+ pthread_join(load_thread, 0);
+ free_data(buffer);
+ free_network(net);
+ free_ptrs((void**)paths, plist->size);
+ free_list(plist);
+ free(base);
+}
+
+void validate_compare(char *filename, char *weightfile)
+{
+ int i = 0;
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *plist = get_paths("data/compare.val.list");
+ //list *plist = get_paths("data/compare.val.old");
+ char **paths = (char **)list_to_array(plist);
+ int N = plist->size/2;
+ free_list(plist);
+
+ clock_t time;
+ int correct = 0;
+ int total = 0;
+ int splits = 10;
+ int num = (i+1)*N/splits - i*N/splits;
+
+ data val, buffer;
+
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.paths = paths;
+ args.classes = 20;
+ args.n = num;
+ args.m = 0;
+ args.d = &buffer;
+ args.type = COMPARE_DATA;
+
+ pthread_t load_thread = load_data_in_thread(args);
+ for(i = 1; i <= splits; ++i){
+ time=clock();
+
+ pthread_join(load_thread, 0);
+ val = buffer;
+
+ num = (i+1)*N/splits - i*N/splits;
+ char **part = paths+(i*N/splits);
+ if(i != splits){
+ args.paths = part;
+ load_thread = load_data_in_thread(args);
+ }
+ printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
+
+ time=clock();
+ matrix pred = network_predict_data(net, val);
+ int j,k;
+ for(j = 0; j < val.y.rows; ++j){
+ 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])){
+ ++correct;
+ }
+ }
+ }
+ }
+ free_matrix(pred);
+ printf("%d: Acc: %f, %lf seconds, %d images\n", i, (float)correct/total, sec(clock()-time), val.X.rows);
+ free_data(val);
+ }
+}
+
+typedef struct {
+ network net;
+ char *filename;
+ int class;
+ float elo;
+} sortable_bbox;
+
+int total_compares = 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;
+ return 1;
+}
+
+int bbox_comparator(const void *a, const void *b)
+{
+ ++total_compares;
+ sortable_bbox box1 = *(sortable_bbox*)a;
+ sortable_bbox box2 = *(sortable_bbox*)b;
+ network net = box1.net;
+ int class = box1.class;
+
+ image im1 = load_image_color(box1.filename, net.w, net.h);
+ image im2 = load_image_color(box2.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);
+ memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c);
+ float *predictions = network_predict(net, X);
+
+ free_image(im1);
+ free_image(im2);
+ free(X);
+ if (predictions[class*2] > predictions[class*2+1]){
+ return 1;
+ }
+ return -1;
+}
+
+void bbox_fight(sortable_bbox *a, sortable_bbox *b)
+{
+ 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);
+}
+
+void SortMaster3000(char *filename, char *weightfile)
+{
+ int i = 0;
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+ set_batch_network(&net, 1);
+
+ list *plist = get_paths("data/compare.sort.list");
+ //list *plist = get_paths("data/compare.val.old");
+ 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){
+ boxes[i].filename = paths[i];
+ boxes[i].net = net;
+ boxes[i].class = 7;
+ boxes[i].elo = 1500;
+ }
+ clock_t time=clock();
+ qsort(boxes, N, sizeof(sortable_bbox), bbox_comparator);
+ 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));
+}
+
+void BattleRoyaleWithCheese(char *filename, char *weightfile)
+{
+ int i = 0;
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+ set_batch_network(&net, 1);
+
+ list *plist = get_paths("data/compare.sort.list");
+ //list *plist = get_paths("data/compare.val.old");
+ char **paths = (char **)list_to_array(plist);
+ int N = plist->size;
+ 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;
+ }
+ int round;
+ clock_t time=clock();
+ for(round = 1; round <= 40; ++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);
+ for(i = 0; i < N/2; ++i){
+ bbox_fight(boxes+i*2, boxes+i*2+1);
+ }
+ if(round >= 4){
+ qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
+ if(round == 4){
+ N = N/2;
+ }else{
+ N = (N*9/10)/2*2;
+ }
+ }
+ printf("Round: %f secs, %d remaining\n", sec(clock()-round_time), N);
+ }
+ qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
+ for(i = 0; i < N; ++i){
+ printf("%s %f\n", boxes[i].filename, boxes[i].elo);
+ }
+ printf("Tournament in %d compares, %f secs\n", total_compares, sec(clock()-time));
+}
+
+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;
+ }
+
+ 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);
+ /*
+ else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
+ else if(0==strcmp(argv[2], "extract")) extract_boxes(cfg, weights);
+ else if(0==strcmp(argv[2], "valid")) validate_recall(cfg, weights);
+ */
+}
diff --git a/src/cost_layer.c b/src/cost_layer.c
index d1ae6e5..4ec0ac4 100644
--- a/src/cost_layer.c
+++ b/src/cost_layer.c
@@ -26,12 +26,13 @@
return "sse";
}
-cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type)
+cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale)
{
fprintf(stderr, "Cost Layer: %d inputs\n", inputs);
cost_layer l = {0};
l.type = COST;
+ l.scale = scale;
l.batch = batch;
l.inputs = inputs;
l.outputs = inputs;
@@ -61,7 +62,7 @@
void backward_cost_layer(const cost_layer l, network_state state)
{
- axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
+ axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, state.delta, 1);
}
#ifdef GPU
@@ -92,7 +93,7 @@
void backward_cost_layer_gpu(const cost_layer l, network_state state)
{
- axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1);
+ axpy_ongpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, state.delta, 1);
}
#endif
diff --git a/src/cost_layer.h b/src/cost_layer.h
index 0732323..9ad3124 100644
--- a/src/cost_layer.h
+++ b/src/cost_layer.h
@@ -7,7 +7,7 @@
COST_TYPE get_cost_type(char *s);
char *get_cost_string(COST_TYPE a);
-cost_layer make_cost_layer(int batch, int inputs, COST_TYPE type);
+cost_layer make_cost_layer(int batch, int inputs, COST_TYPE type, float scale);
void forward_cost_layer(const cost_layer l, network_state state);
void backward_cost_layer(const cost_layer l, network_state state);
diff --git a/src/darknet.c b/src/darknet.c
index f87afc6..3709ed1 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -18,6 +18,7 @@
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);
void change_rate(char *filename, float scale, float add)
{
@@ -86,7 +87,7 @@
if(weightfile){
load_weights_upto(&net, weightfile, max);
}
- net.seen = 0;
+ *net.seen = 0;
save_weights_upto(net, outfile, max);
}
@@ -179,6 +180,8 @@
run_yolo(argc, argv);
} else if (0 == strcmp(argv[1], "coco")){
run_coco(argc, argv);
+ } else if (0 == strcmp(argv[1], "compare")){
+ run_compare(argc, argv);
} else if (0 == strcmp(argv[1], "dice")){
run_dice(argc, argv);
} else if (0 == strcmp(argv[1], "writing")){
diff --git a/src/data.c b/src/data.c
index ec2b304..003338e 100644
--- a/src/data.c
+++ b/src/data.c
@@ -413,8 +413,8 @@
data load_data_compare(int n, char **paths, int m, int classes, int w, int h)
{
- char **random_paths = get_random_paths(paths, 2*n, m);
- int i;
+ if(m) paths = get_random_paths(paths, 2*n, m);
+ int i,j;
data d;
d.shallow = 0;
@@ -425,20 +425,51 @@
int k = 2*(classes);
d.y = make_matrix(n, k);
for(i = 0; i < n; ++i){
- image im1 = load_image_color(random_paths[i*2], w, h);
- image im2 = load_image_color(random_paths[i*2+1], w, h);
+ image im1 = load_image_color(paths[i*2], w, h);
+ image im2 = load_image_color(paths[i*2+1], w, h);
d.X.vals[i] = calloc(d.X.cols, sizeof(float));
memcpy(d.X.vals[i], im1.data, h*w*3*sizeof(float));
memcpy(d.X.vals[i] + h*w*3, im2.data, h*w*3*sizeof(float));
- //char *imlabel1 = find_replace(random_paths[i*2], "imgs", "labels");
- //char *imlabel2 = find_replace(random_paths[i*2+1], "imgs", "labels");
+ int id;
+ float iou;
+
+ char *imlabel1 = find_replace(paths[i*2], "imgs", "labels");
+ imlabel1 = find_replace(imlabel1, "jpg", "txt");
+ FILE *fp1 = fopen(imlabel1, "r");
+
+ while(fscanf(fp1, "%d %f", &id, &iou) == 2){
+ if (d.y.vals[i][2*id] < iou) d.y.vals[i][2*id] = iou;
+ }
+
+ char *imlabel2 = find_replace(paths[i*2+1], "imgs", "labels");
+ imlabel2 = find_replace(imlabel2, "jpg", "txt");
+ FILE *fp2 = fopen(imlabel2, "r");
+
+ while(fscanf(fp2, "%d %f", &id, &iou) == 2){
+ if (d.y.vals[i][2*id + 1] < iou) d.y.vals[i][2*id + 1] = iou;
+ }
+
+ for (j = 0; j < classes; ++j){
+ if (d.y.vals[i][2*j] > .5 && d.y.vals[i][2*j+1] < .5){
+ d.y.vals[i][2*j] = 1;
+ d.y.vals[i][2*j+1] = 0;
+ } else if (d.y.vals[i][2*j] < .5 && d.y.vals[i][2*j+1] > .5){
+ d.y.vals[i][2*j] = 0;
+ d.y.vals[i][2*j+1] = 1;
+ } else {
+ d.y.vals[i][2*j] = SECRET_NUM;
+ d.y.vals[i][2*j+1] = SECRET_NUM;
+ }
+ }
+ fclose(fp1);
+ fclose(fp2);
free_image(im1);
free_image(im2);
}
- free(random_paths);
+ if(m) free(paths);
return d;
}
@@ -503,11 +534,11 @@
void *load_thread(void *ptr)
{
-
- #ifdef GPU
- cudaError_t status = cudaSetDevice(gpu_index);
- check_error(status);
- #endif
+
+#ifdef GPU
+ cudaError_t status = cudaSetDevice(gpu_index);
+ check_error(status);
+#endif
printf("Loading data: %d\n", rand_r(&data_seed));
load_args a = *(struct load_args*)ptr;
@@ -517,6 +548,8 @@
*a.d = load_data_detection(a.n, a.paths, a.m, a.classes, a.w, a.h, a.num_boxes, a.background);
} 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);
+ } 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){
*(a.im) = load_image_color(a.path, 0, 0);
*(a.resized) = resize_image(*(a.im), a.w, a.h);
diff --git a/src/data.h b/src/data.h
index 7c425ba..216ab0c 100644
--- a/src/data.h
+++ b/src/data.h
@@ -26,7 +26,7 @@
} data;
typedef enum {
- CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA
+ CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA
} data_type;
typedef struct load_args{
diff --git a/src/dice.c b/src/dice.c
index 3283fe9..7948741 100644
--- a/src/dice.c
+++ b/src/dice.c
@@ -18,7 +18,7 @@
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1024;
- int i = net.seen/imgs;
+ int i = *net.seen/imgs;
char **labels = dice_labels;
list *plist = get_paths("data/dice/dice.train.list");
char **paths = (char **)list_to_array(plist);
@@ -32,10 +32,9 @@
time=clock();
float loss = train_network(net, train);
- net.seen += imgs;
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
- printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
+ printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen);
free_data(train);
if((i % 100) == 0) net.learning_rate *= .1;
if(i%100==0){
diff --git a/src/image.c b/src/image.c
index fa0bceb..b6d7577 100644
--- a/src/image.c
+++ b/src/image.c
@@ -274,6 +274,8 @@
#ifdef OPENCV
void save_image_jpg(image p, char *name)
{
+ image copy = copy_image(p);
+ rgbgr_image(copy);
int x,y,k;
char buff[256];
@@ -284,12 +286,13 @@
for(y = 0; y < p.h; ++y){
for(x = 0; x < p.w; ++x){
for(k= 0; k < p.c; ++k){
- disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(p,x,y,k)*255);
+ disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255);
}
}
}
cvSaveImage(buff, disp,0);
cvReleaseImage(&disp);
+ free_image(copy);
}
#endif
diff --git a/src/imagenet.c b/src/imagenet.c
index 5d79483..c826a0f 100644
--- a/src/imagenet.c
+++ b/src/imagenet.c
@@ -19,7 +19,6 @@
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- //net.seen=0;
int imgs = 1024;
char **labels = get_labels("data/inet.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
@@ -43,8 +42,8 @@
args.type = CLASSIFICATION_DATA;
load_thread = load_data_in_thread(args);
- int epoch = net.seen/N;
- while(1){
+ int epoch = (*net.seen)/N;
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
time=clock();
pthread_join(load_thread, 0);
train = buffer;
@@ -59,19 +58,21 @@
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
- net.seen += imgs;
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
- printf("%.3f: %f, %f avg, %lf seconds, %d images\n", (float)net.seen/N, loss, avg_loss, sec(clock()-time), net.seen);
+ printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
free_data(train);
- if(net.seen/N > epoch){
- epoch = net.seen/N;
+ if(*net.seen/N > epoch){
+ epoch = *net.seen/N;
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;
}
}
+ char buff[256];
+ sprintf(buff, "%s/%s.weights", backup_directory, base);
+ save_weights(net, buff);
+
pthread_join(load_thread, 0);
free_data(buffer);
free_network(net);
diff --git a/src/network.c b/src/network.c
index 70bcb58..d823c15 100644
--- a/src/network.c
+++ b/src/network.c
@@ -20,6 +20,30 @@
#include "dropout_layer.h"
#include "route_layer.h"
+int get_current_batch(network net)
+{
+ int batch_num = (*net.seen)/(net.batch*net.subdivisions);
+ return batch_num;
+}
+
+float get_current_rate(network net)
+{
+ int batch_num = get_current_batch(net);
+ switch (net.policy) {
+ case CONSTANT:
+ return net.learning_rate;
+ case STEP:
+ return net.learning_rate * pow(net.gamma, batch_num/net.step);
+ case EXP:
+ return net.learning_rate * pow(net.gamma, batch_num);
+ case POLY:
+ return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+ default:
+ fprintf(stderr, "Policy is weird!\n");
+ return net.learning_rate;
+ }
+}
+
char *get_layer_string(LAYER_TYPE a)
{
switch(a){
@@ -60,6 +84,7 @@
network net = {0};
net.n = n;
net.layers = calloc(net.n, sizeof(layer));
+ net.seen = calloc(1, sizeof(int));
#ifdef GPU
net.input_gpu = calloc(1, sizeof(float *));
net.truth_gpu = calloc(1, sizeof(float *));
@@ -110,14 +135,15 @@
{
int i;
int update_batch = net.batch*net.subdivisions;
+ float rate = get_current_rate(net);
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
- update_convolutional_layer(l, update_batch, net.learning_rate, net.momentum, net.decay);
+ update_convolutional_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == DECONVOLUTIONAL){
- update_deconvolutional_layer(l, net.learning_rate, net.momentum, net.decay);
+ update_deconvolutional_layer(l, rate, net.momentum, net.decay);
} else if(l.type == CONNECTED){
- update_connected_layer(l, update_batch, net.learning_rate, net.momentum, net.decay);
+ update_connected_layer(l, update_batch, rate, net.momentum, net.decay);
}
}
}
@@ -203,6 +229,7 @@
float train_network_datum(network net, float *x, float *y)
{
+ *net.seen += net.batch;
#ifdef GPU
if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
#endif
@@ -214,7 +241,7 @@
forward_network(net, state);
backward_network(net, state);
float error = get_network_cost(net);
- if((net.seen/net.batch)%net.subdivisions == 0) update_network(net);
+ if(((*net.seen)/net.batch)%net.subdivisions == 0) update_network(net);
return error;
}
@@ -227,7 +254,6 @@
int i;
float sum = 0;
for(i = 0; i < n; ++i){
- net.seen += batch;
get_random_batch(d, batch, X, y);
float err = train_network_datum(net, X, y);
sum += err;
@@ -248,7 +274,6 @@
float sum = 0;
for(i = 0; i < n; ++i){
get_next_batch(d, batch, i*batch, X, y);
- net.seen += batch;
float err = train_network_datum(net, X, y);
sum += err;
}
diff --git a/src/network.h b/src/network.h
index 1d960c0..85e5dbc 100644
--- a/src/network.h
+++ b/src/network.h
@@ -7,17 +7,27 @@
#include "layer.h"
#include "data.h"
+typedef enum {
+ CONSTANT, STEP, EXP, POLY
+} learning_rate_policy;
+
typedef struct {
int n;
int batch;
- int seen;
+ int *seen;
int subdivisions;
- float learning_rate;
float momentum;
float decay;
layer *layers;
int outputs;
float *output;
+ learning_rate_policy policy;
+
+ float learning_rate;
+ float gamma;
+ float power;
+ int step;
+ int max_batches;
int inputs;
int h, w, c;
@@ -38,6 +48,8 @@
void backward_network_gpu(network net, network_state state);
#endif
+float get_current_rate(network net);
+int get_current_batch(network net);
void free_network(network net);
void compare_networks(network n1, network n2, data d);
char *get_layer_string(LAYER_TYPE a);
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index a73ddd9..1f0a654 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -116,14 +116,15 @@
{
int i;
int update_batch = net.batch*net.subdivisions;
+ float rate = get_current_rate(net);
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
- update_convolutional_layer_gpu(l, update_batch, net.learning_rate, net.momentum, net.decay);
+ update_convolutional_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == DECONVOLUTIONAL){
- update_deconvolutional_layer_gpu(l, net.learning_rate, net.momentum, net.decay);
+ update_deconvolutional_layer_gpu(l, rate, net.momentum, net.decay);
} else if(l.type == CONNECTED){
- update_connected_layer_gpu(l, update_batch, net.learning_rate, net.momentum, net.decay);
+ update_connected_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
}
}
}
@@ -147,7 +148,7 @@
forward_network_gpu(net, state);
backward_network_gpu(net, state);
float error = get_network_cost(net);
- if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
+ if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net);
return error;
}
diff --git a/src/parser.c b/src/parser.c
index ad324e9..b9f6cb6 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -189,7 +189,8 @@
{
char *type_s = option_find_str(options, "type", "sse");
COST_TYPE type = get_cost_type(type_s);
- cost_layer layer = make_cost_layer(params.batch, params.inputs, type);
+ float scale = option_find_float_quiet(options, "scale",1);
+ cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale);
return layer;
}
@@ -305,6 +306,16 @@
return layer;
}
+learning_rate_policy get_policy(char *s)
+{
+ if (strcmp(s, "poly")==0) return POLY;
+ if (strcmp(s, "constant")==0) return CONSTANT;
+ if (strcmp(s, "step")==0) return STEP;
+ if (strcmp(s, "exp")==0) return EXP;
+ fprintf(stderr, "Couldn't find policy %s, going with constant\n", s);
+ return CONSTANT;
+}
+
void parse_net_options(list *options, network *net)
{
net->batch = option_find_int(options, "batch",1);
@@ -319,7 +330,20 @@
net->w = option_find_int_quiet(options, "width",0);
net->c = option_find_int_quiet(options, "channels",0);
net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c);
+
if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
+
+ char *policy_s = option_find_str(options, "policy", "constant");
+ net->policy = get_policy(policy_s);
+ if(net->policy == STEP){
+ net->step = option_find_int(options, "step", 1);
+ net->gamma = option_find_float(options, "gamma", 1);
+ } else if (net->policy == EXP){
+ net->gamma = option_find_float(options, "gamma", 1);
+ } else if (net->policy == POLY){
+ net->power = option_find_float(options, "power", 1);
+ }
+ net->max_batches = option_find_int(options, "max_batches", 0);
}
network parse_network_cfg(char *filename)
@@ -532,7 +556,7 @@
fwrite(&net.learning_rate, sizeof(float), 1, fp);
fwrite(&net.momentum, sizeof(float), 1, fp);
fwrite(&net.decay, sizeof(float), 1, fp);
- fwrite(&net.seen, sizeof(int), 1, fp);
+ fwrite(net.seen, sizeof(int), 1, fp);
int i,j,k;
for(i = 0; i < net.n; ++i){
@@ -571,7 +595,7 @@
fwrite(&net.learning_rate, sizeof(float), 1, fp);
fwrite(&net.momentum, sizeof(float), 1, fp);
fwrite(&net.decay, sizeof(float), 1, fp);
- fwrite(&net.seen, sizeof(int), 1, fp);
+ fwrite(net.seen, sizeof(int), 1, fp);
int i;
for(i = 0; i < net.n && i < cutoff; ++i){
@@ -620,10 +644,11 @@
FILE *fp = fopen(filename, "r");
if(!fp) file_error(filename);
- fread(&net->learning_rate, sizeof(float), 1, fp);
- fread(&net->momentum, sizeof(float), 1, fp);
- fread(&net->decay, sizeof(float), 1, fp);
- fread(&net->seen, sizeof(int), 1, fp);
+ float garbage;
+ fread(&garbage, sizeof(float), 1, fp);
+ fread(&garbage, sizeof(float), 1, fp);
+ fread(&garbage, sizeof(float), 1, fp);
+ fread(net->seen, sizeof(int), 1, fp);
int i;
for(i = 0; i < net->n && i < cutoff; ++i){
diff --git a/src/utils.c b/src/utils.c
index d54e966..3121ef6 100644
--- a/src/utils.c
+++ b/src/utils.c
@@ -8,6 +8,29 @@
#include "utils.h"
+void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections)
+{
+ size_t i;
+ for(i = 0; i < sections; ++i){
+ size_t start = n*i/sections;
+ size_t end = n*(i+1)/sections;
+ size_t num = end-start;
+ shuffle(arr+(start*size), num, size);
+ }
+}
+
+void shuffle(void *arr, size_t n, size_t size)
+{
+ size_t i;
+ void *swp = calloc(1, size);
+ for(i = 0; i < n-1; ++i){
+ size_t j = i + rand()/(RAND_MAX / (n-i)+1);
+ memcpy(swp, arr+(j*size), size);
+ memcpy(arr+(j*size), arr+(i*size), size);
+ memcpy(arr+(i*size), swp, size);
+ }
+}
+
void del_arg(int argc, char **argv, int index)
{
int i;
diff --git a/src/utils.h b/src/utils.h
index 9332702..1b9ba08 100644
--- a/src/utils.h
+++ b/src/utils.h
@@ -6,6 +6,8 @@
#define SECRET_NUM -1234
+void shuffle(void *arr, size_t n, size_t size);
+void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections);
void free_ptrs(void **ptrs, int n);
char *basecfg(char *cfgfile);
int alphanum_to_int(char c);
diff --git a/src/writing.c b/src/writing.c
index 1c1684b..cfbc5fd 100644
--- a/src/writing.c
+++ b/src/writing.c
@@ -15,7 +15,7 @@
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1024;
- int i = net.seen/imgs;
+ int i = *net.seen/imgs;
list *plist = get_paths("figures.list");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
@@ -44,10 +44,9 @@
cvWaitKey(0);
*/
- net.seen += imgs;
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
- printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
+ printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen);
free_data(train);
if((i % 20000) == 0) net.learning_rate *= .1;
//if(i%100 == 0 && net.learning_rate > .00001) net.learning_rate *= .97;
diff --git a/src/yolo.c b/src/yolo.c
index 9bf96de..61a5344 100644
--- a/src/yolo.c
+++ b/src/yolo.c
@@ -68,7 +68,7 @@
detection_layer layer = get_network_detection_layer(net);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 128;
- int i = net.seen/imgs;
+ int i = *net.seen/imgs;
char **paths;
list *plist = get_paths(train_images);
@@ -112,7 +112,6 @@
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
- net.seen += imgs;
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
--
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