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idk man

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This commit is contained in:
Joseph Redmon 2015-09-21 17:30:32 -07:00
parent c53e03348c
commit fed6d6e31d
11 changed files with 131 additions and 104 deletions
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8
Makefile
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@ -1,5 +1,5 @@
GPU=1 GPU=0
OPENCV=1 OPENCV=0
DEBUG=0 DEBUG=0
ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20 ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20
@ -12,7 +12,7 @@ CC=gcc
NVCC=nvcc NVCC=nvcc
OPTS=-Ofast OPTS=-Ofast
LDFLAGS= -lm -pthread -lstdc++ LDFLAGS= -lm -pthread -lstdc++
COMMON= -I/usr/local/cuda/include/ COMMON=
CFLAGS=-Wall -Wfatal-errors CFLAGS=-Wall -Wfatal-errors
ifeq ($(DEBUG), 1) ifeq ($(DEBUG), 1)
@ -29,7 +29,7 @@ COMMON+= `pkg-config --cflags opencv`
endif endif
ifeq ($(GPU), 1) ifeq ($(GPU), 1)
COMMON+= -DGPU COMMON+= -DGPU -I/usr/local/cuda/include/
CFLAGS+= -DGPU CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif endif

10
cfg/darknet.cfg
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@ -1,5 +1,5 @@
[net] [net]
batch=256 batch=128
subdivisions=1 subdivisions=1
height=256 height=256
width=256 width=256
@ -8,10 +8,10 @@ momentum=0.9
decay=0.0005 decay=0.0005
learning_rate=0.01 learning_rate=0.01
policy=step policy=sigmoid
scale=.1 gamma=.00002
step=100000 step=400000
max_batches=400000 max_batches=800000
[crop] [crop]
crop_height=224 crop_height=224

1
cfg/yolo.cfg
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@ -206,4 +206,5 @@ coords=4
rescore=0 rescore=0
joint=0 joint=0
objectness=1 objectness=1
background=0

10
src/box.c
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@ -2,6 +2,16 @@
#include <stdio.h> #include <stdio.h>
#include <math.h> #include <math.h>
box float_to_box(float *f)
{
box b;
b.x = f[0];
b.y = f[1];
b.w = f[2];
b.h = f[3];
return b;
}
dbox derivative(box a, box b) dbox derivative(box a, box b)
{ {
dbox d; dbox d;

1
src/box.h
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@ -9,6 +9,7 @@ typedef struct{
float dx, dy, dw, dh; float dx, dy, dw, dh;
} dbox; } dbox;
box float_to_box(float *f);
float box_iou(box a, box b); float box_iou(box a, box b);
float box_rmse(box a, box b); float box_rmse(box a, box b);
dbox diou(box a, box b); dbox diou(box a, box b);

12
src/compare.c
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@ -241,7 +241,8 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile)
srand(time(0)); 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.sort.list");
list *plist = get_paths("data/compare.cat.list");
//list *plist = get_paths("data/compare.val.old"); //list *plist = get_paths("data/compare.val.old");
char **paths = (char **)list_to_array(plist); char **paths = (char **)list_to_array(plist);
int N = plist->size; int N = plist->size;
@ -256,15 +257,16 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile)
} }
int round; int round;
clock_t time=clock(); clock_t time=clock();
for(round = 1; round <= 40; ++round){ for(round = 1; round <= 500; ++round){
clock_t round_time=clock(); clock_t round_time=clock();
printf("Round: %d\n", round); printf("Round: %d\n", round);
qsort(boxes, N, sizeof(sortable_bbox), elo_comparator); qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
sorta_shuffle(boxes, N, sizeof(sortable_bbox), 10); sorta_shuffle(boxes, N, sizeof(sortable_bbox), 10);
shuffle(boxes, N, sizeof(sortable_bbox));
for(i = 0; i < N/2; ++i){ for(i = 0; i < N/2; ++i){
bbox_fight(boxes+i*2, boxes+i*2+1); bbox_fight(boxes+i*2, boxes+i*2+1);
} }
if(round >= 4){ if(round >= 4 && 0){
qsort(boxes, N, sizeof(sortable_bbox), elo_comparator); qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
if(round == 4){ if(round == 4){
N = N/2; N = N/2;
@ -275,9 +277,11 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile)
printf("Round: %f secs, %d remaining\n", sec(clock()-round_time), N); printf("Round: %f secs, %d remaining\n", sec(clock()-round_time), N);
} }
qsort(boxes, N, sizeof(sortable_bbox), elo_comparator); qsort(boxes, N, sizeof(sortable_bbox), elo_comparator);
FILE *outfp = fopen("results/battle.log", "w");
for(i = 0; i < N; ++i){ for(i = 0; i < N; ++i){
printf("%s %f\n", boxes[i].filename, boxes[i].elo); fprintf(outfp, "%s %f\n", boxes[i].filename, boxes[i].elo);
} }
fclose(outfp);
printf("Tournament in %d compares, %f secs\n", total_compares, sec(clock()-time)); printf("Tournament in %d compares, %f secs\n", total_compares, sec(clock()-time));
} }

5
src/layer.h
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@ -60,6 +60,11 @@ typedef struct {
float beta; float beta;
float kappa; float kappa;
float coord_scale;
float object_scale;
float noobject_scale;
float class_scale;
int dontload; int dontload;
float probability; float probability;

12
src/parser.c
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@ -182,10 +182,14 @@ region_layer parse_region(list *options, size_params params)
int num = option_find_int(options, "num", 1); int num = option_find_int(options, "num", 1);
int side = option_find_int(options, "side", 7); int side = option_find_int(options, "side", 7);
region_layer layer = make_region_layer(params.batch, params.inputs, num, side, classes, coords, rescore); region_layer layer = make_region_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
int softmax = option_find_int(options, "softmax", 0);
int sqrt = option_find_int(options, "sqrt", 0); layer.softmax = option_find_int(options, "softmax", 0);
layer.softmax = softmax; layer.sqrt = option_find_int(options, "sqrt", 0);
layer.sqrt = sqrt;
layer.coord_scale = option_find_float(options, "coord_scale", 1);
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);
return layer; return layer;
} }

172
src/region_layer.c
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@ -44,15 +44,20 @@ void forward_region_layer(const region_layer l, network_state state)
int locations = l.side*l.side; int locations = l.side*l.side;
int i,j; int i,j;
memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
for(i = 0; i < l.batch*locations; ++i){ int b;
int index = i*((1+l.coords)*l.n + l.classes); if (l.softmax){
if(l.softmax){ for(b = 0; b < l.batch; ++b){
activate_array(l.output + index, l.n*(1+l.coords), LOGISTIC); int index = b*l.inputs;
int offset = l.n*(1+l.coords); for (i = 0; i < locations; ++i) {
softmax_array(l.output + index + offset, l.classes, int offset = i*l.classes;
l.output + index + offset); softmax_array(l.output + index + offset, l.classes,
l.output + index + offset);
}
int offset = locations*l.classes;
activate_array(l.output + index + offset, locations*l.n*(1+l.coords), LOGISTIC);
} }
} }
if(state.train){ if(state.train){
float avg_iou = 0; float avg_iou = 0;
float avg_cat = 0; float avg_cat = 0;
@ -62,94 +67,91 @@ void forward_region_layer(const region_layer l, network_state state)
*(l.cost) = 0; *(l.cost) = 0;
int size = l.inputs * l.batch; int size = l.inputs * l.batch;
memset(l.delta, 0, size * sizeof(float)); memset(l.delta, 0, size * sizeof(float));
for (i = 0; i < l.batch*locations; ++i) { for (b = 0; b < l.batch; ++b){
int index = i*((1+l.coords)*l.n + l.classes); int index = b*l.inputs;
for(j = 0; j < l.n; ++j){ for (i = 0; i < locations; ++i) {
int prob_index = index + j*(1 + l.coords); int truth_index = (b*locations + i)*(1+l.coords+l.classes);
l.delta[prob_index] = (1./l.n)*(0-l.output[prob_index]); int is_obj = state.truth[truth_index];
if(l.softmax){ for (j = 0; j < l.n; ++j) {
l.delta[prob_index] = 1./(l.n*l.side)*(0-l.output[prob_index]); int p_index = index + locations*l.classes + i*l.n + j;
} l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]);
*(l.cost) += (1./l.n)*pow(l.output[prob_index], 2); *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2);
//printf("%f\n", l.output[prob_index]); avg_anyobj += l.output[p_index];
avg_anyobj += l.output[prob_index];
}
int truth_index = i*(1 + l.coords + l.classes);
int best_index = -1;
float best_iou = 0;
float best_rmse = 4;
int bg = !state.truth[truth_index];
if(bg) {
continue;
}
int class_index = index + l.n*(1+l.coords);
for(j = 0; j < l.classes; ++j) {
l.delta[class_index+j] = state.truth[truth_index+1+j] - l.output[class_index+j];
*(l.cost) += pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2);
if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j];
}
truth_index += l.classes + 1;
box truth = {state.truth[truth_index+0], state.truth[truth_index+1], state.truth[truth_index+2], state.truth[truth_index+3]};
truth.x /= l.side;
truth.y /= l.side;
for(j = 0; j < l.n; ++j){
int out_index = index + j*(1+l.coords);
box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
out.x /= l.side;
out.y /= l.side;
if (l.sqrt){
out.w = out.w*out.w;
out.h = out.h*out.h;
} }
float iou = box_iou(out, truth); int best_index = -1;
float rmse = box_rmse(out, truth); float best_iou = 0;
if(best_iou > 0 || iou > 0){ float best_rmse = 4;
if(iou > best_iou){
best_iou = iou; if (!is_obj) continue;
best_index = j;
int class_index = index + i*l.classes;
for(j = 0; j < l.classes; ++j) {
l.delta[class_index+j] = l.class_scale * (state.truth[truth_index+1+j] - l.output[class_index+j]);
*(l.cost) += l.class_scale * pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2);
if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j];
}
box truth = float_to_box(state.truth + truth_index + 1 + l.classes);
truth.x /= l.side;
truth.y /= l.side;
for(j = 0; j < l.n; ++j){
int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords;
box out = float_to_box(l.output + box_index);
out.x /= l.side;
out.y /= l.side;
if (l.sqrt){
out.w = out.w*out.w;
out.h = out.h*out.h;
} }
}else{
if(rmse < best_rmse){ float iou = box_iou(out, truth);
best_rmse = rmse; float rmse = box_rmse(out, truth);
best_index = j; if(best_iou > 0 || iou > 0){
if(iou > best_iou){
best_iou = iou;
best_index = j;
}
}else{
if(rmse < best_rmse){
best_rmse = rmse;
best_index = j;
}
} }
} }
} int p_index = index + locations*l.classes + i*l.n + best_index;
//printf("%d", best_index); *(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2);
int in_index = index + best_index*(1+l.coords); *(l.cost) += l.object_scale * pow(1-l.output[p_index], 2);
*(l.cost) -= pow(l.output[in_index], 2); avg_obj += l.output[p_index];
*(l.cost) += pow(1-l.output[in_index], 2); l.delta[p_index+0] = l.object_scale * (1.-l.output[p_index]);
avg_obj += l.output[in_index];
l.delta[in_index+0] = (1.-l.output[in_index]);
if(l.softmax){
l.delta[in_index+0] = 5*(1.-l.output[in_index]);
}
//printf("%f\n", l.output[in_index]);
l.delta[in_index+1] = 5*(state.truth[truth_index+0] - l.output[in_index+1]); if(l.rescore){
l.delta[in_index+2] = 5*(state.truth[truth_index+1] - l.output[in_index+2]); l.delta[p_index+0] = l.object_scale * (best_iou - l.output[p_index]);
if(l.sqrt){ }
l.delta[in_index+3] = 5*(sqrt(state.truth[truth_index+2]) - l.output[in_index+3]);
l.delta[in_index+4] = 5*(sqrt(state.truth[truth_index+3]) - l.output[in_index+4]);
}else{
l.delta[in_index+3] = 5*(state.truth[truth_index+2] - l.output[in_index+3]);
l.delta[in_index+4] = 5*(state.truth[truth_index+3] - l.output[in_index+4]);
}
*(l.cost) += pow(1-best_iou, 2); int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords;
avg_iou += best_iou; int tbox_index = truth_index + 1 + l.classes;
++count; l.delta[box_index+0] = l.coord_scale*(state.truth[tbox_index + 0] - l.output[box_index + 0]);
l.delta[box_index+1] = l.coord_scale*(state.truth[tbox_index + 1] - l.output[box_index + 1]);
l.delta[box_index+2] = l.coord_scale*(state.truth[tbox_index + 2] - l.output[box_index + 2]);
l.delta[box_index+3] = l.coord_scale*(state.truth[tbox_index + 3] - l.output[box_index + 3]);
if(l.sqrt){
l.delta[box_index+2] = l.coord_scale*(sqrt(state.truth[tbox_index + 2]) - l.output[box_index + 2]);
l.delta[box_index+3] = l.coord_scale*(sqrt(state.truth[tbox_index + 3]) - l.output[box_index + 3]);
}
*(l.cost) += pow(1-best_iou, 2);
avg_iou += best_iou;
++count;
}
if(l.softmax){ if(l.softmax){
gradient_array(l.output + index, l.n*(1+l.coords), LOGISTIC, l.delta + index); gradient_array(l.output + index + locations*l.classes, locations*l.n*(1+l.coords),
LOGISTIC, l.delta + index + locations*l.classes);
} }
} }
printf("Avg IOU: %f, Avg Cat Pred: %f, Avg Obj: %f, Avg Any: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count); printf("Region Avg IOU: %f, Avg Cat Pred: %f, Avg Obj: %f, Avg Any: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count);
} }
} }

2
src/stb_image_write.h
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@ -279,7 +279,7 @@ void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scra
{ {
unsigned char scanlineheader[4] = { 2, 2, 0, 0 }; unsigned char scanlineheader[4] = { 2, 2, 0, 0 };
unsigned char rgbe[4]; unsigned char rgbe[4];
float linear[3]; float linear[3] = {0};
int x; int x;
scanlineheader[2] = (width&0xff00)>>8; scanlineheader[2] = (width&0xff00)>>8;

2
src/swag.c
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@ -77,7 +77,7 @@ void train_swag(char *cfgfile, char *weightfile)
int classes = l.classes; int classes = l.classes;
list *plist = get_paths(train_images); list *plist = get_paths(train_images);
int N = plist->size; //int N = plist->size;
char **paths = (char **)list_to_array(plist); char **paths = (char **)list_to_array(plist);
load_args args = {0}; load_args args = {0};