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This commit is contained in:
Joseph Redmon 2015-09-28 14:32:28 -07:00
parent f996bd59a6
commit 40cc104639
7 changed files with 84 additions and 55 deletions
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4
Makefile
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@ -1,5 +1,5 @@
GPU=0
OPENCV=0
GPU=1
OPENCV=1
DEBUG=0
ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20

10
cfg/yolo.cfg
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@ -1,17 +1,17 @@
[net]
batch=64
subdivisions=64
subdivisions=4
height=448
width=448
channels=3
learning_rate=0.001
learning_rate=0.01
momentum=0.9
decay=0.0005
policy=steps
steps=50, 5000
scales=10, .1
max_batches = 8000
steps=20000
scales=.1
max_batches = 35000
[crop]
crop_width=448

1
src/layer.h
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@ -28,6 +28,7 @@ typedef struct {
ACTIVATION activation;
COST_TYPE cost_type;
int batch;
int forced;
int inputs;
int outputs;
int truths;

1
src/parser.c
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@ -187,6 +187,7 @@ region_layer parse_region(list *options, size_params params)
layer.sqrt = option_find_int(options, "sqrt", 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);

46
src/region_layer.c
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@ -82,9 +82,12 @@ void forward_region_layer(const region_layer l, network_state state)
int best_index = -1;
float best_iou = 0;
float best_rmse = 4;
float best_rmse = 20;
if (!is_obj) continue;
if (!is_obj){
//printf(".");
continue;
}
int class_index = index + i*l.classes;
for(j = 0; j < l.classes; ++j) {
@ -123,18 +126,38 @@ void forward_region_layer(const region_layer l, network_state state)
}
}
}
int p_index = index + locations*l.classes + i*l.n + best_index;
*(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2);
*(l.cost) += l.object_scale * pow(1-l.output[p_index], 2);
avg_obj += l.output[p_index];
l.delta[p_index+0] = l.object_scale * (1.-l.output[p_index]);
if(l.rescore){
l.delta[p_index+0] = l.object_scale * (best_iou - l.output[p_index]);
if(l.forced){
if(truth.w*truth.h < .1){
best_index = 1;
}else{
best_index = 0;
}
}
int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords;
int tbox_index = truth_index + 1 + l.classes;
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;
}
float iou = box_iou(out, truth);
//printf("%d", best_index);
int p_index = index + locations*l.classes + i*l.n + best_index;
*(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2);
*(l.cost) += l.object_scale * pow(1-l.output[p_index], 2);
avg_obj += l.output[p_index];
l.delta[p_index] = l.object_scale * (1.-l.output[p_index]);
if(l.rescore){
l.delta[p_index] = l.object_scale * (iou - l.output[p_index]);
}
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]);
@ -144,14 +167,15 @@ void forward_region_layer(const region_layer l, network_state state)
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;
*(l.cost) += pow(1-iou, 2);
avg_iou += iou;
++count;
}
if(l.softmax){
gradient_array(l.output + index + locations*l.classes, locations*l.n*(1+l.coords),
LOGISTIC, l.delta + index + locations*l.classes);
}
//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);
}

71
src/swag.c
View File

@ -1,4 +1,5 @@
#include "network.h"
#include "region_layer.h"
#include "detection_layer.h"
#include "cost_layer.h"
#include "utils.h"
@ -11,40 +12,37 @@
char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
void draw_swag(image im, float *box, int side, int objectness, char *label, float thresh)
void draw_swag(image im, float *predictions, int side, int num, char *label, float thresh)
{
int classes = 20;
int elems = 4+classes+objectness;
int j;
int r, c;
int i,n;
for(r = 0; r < side; ++r){
for(c = 0; c < side; ++c){
j = (r*side + c) * elems;
float scale = 1;
if(objectness) scale = 1 - box[j++];
int class = max_index(box+j, classes);
if(scale * box[j+class] > thresh){
int width = sqrt(scale*box[j+class])*5 + 1;
printf("%f %s\n", scale * box[j+class], voc_names[class]);
for(i = 0; i < side*side; ++i){
int row = i / side;
int col = i % side;
for(n = 0; n < num; ++n){
int p_index = side*side*classes + i*num + n;
int box_index = side*side*(classes + num) + (i*num + n)*4;
int class_index = i*classes;
float scale = predictions[p_index];
int class = max_index(predictions+class_index, classes);
float prob = scale * predictions[class_index + class];
if(prob > thresh){
int width = sqrt(prob)*5 + 1;
printf("%f %s\n", prob, voc_names[class]);
float red = get_color(0,class,classes);
float green = get_color(1,class,classes);
float blue = get_color(2,class,classes);
box b = float_to_box(predictions+box_index);
b.x = (b.x + col)/side;
b.y = (b.y + row)/side;
b.w = b.w*b.w;
b.h = b.h*b.h;
j += classes;
float x = box[j+0];
float y = box[j+1];
x = (x+c)/side;
y = (y+r)/side;
float w = box[j+2]; //*maxwidth;
float h = box[j+3]; //*maxheight;
h = h*h;
w = w*w;
int left = (x-w/2)*im.w;
int right = (x+w/2)*im.w;
int top = (y-h/2)*im.h;
int bot = (y+h/2)*im.h;
int left = (b.x-b.w/2)*im.w;
int right = (b.x+b.w/2)*im.w;
int top = (b.y-b.h/2)*im.h;
int bot = (b.y+b.h/2)*im.h;
draw_box_width(im, left, top, right, bot, width, red, green, blue);
}
}
@ -103,13 +101,13 @@ void train_swag(char *cfgfile, char *weightfile)
printf("Loaded: %lf seconds\n", sec(clock()-time));
/*
image im = float_to_image(net.w, net.h, 3, train.X.vals[113]);
image copy = copy_image(im);
draw_swag(copy, train.y.vals[113], 7, "truth");
cvWaitKey(0);
free_image(copy);
*/
/*
image im = float_to_image(net.w, net.h, 3, train.X.vals[113]);
image copy = copy_image(im);
draw_swag(copy, train.y.vals[113], 7, "truth");
cvWaitKey(0);
free_image(copy);
*/
time=clock();
float loss = train_network(net, train);
@ -270,7 +268,7 @@ void test_swag(char *cfgfile, char *weightfile, char *filename, float thresh)
if(weightfile){
load_weights(&net, weightfile);
}
detection_layer layer = get_network_detection_layer(net);
region_layer layer = net.layers[net.n-1];
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
@ -292,7 +290,8 @@ void test_swag(char *cfgfile, char *weightfile, char *filename, float thresh)
time=clock();
float *predictions = network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
draw_swag(im, predictions, 7, layer.objectness, "predictions", thresh);
draw_swag(im, predictions, layer.side, layer.n, "predictions", thresh);
show_image(sized, "resized");
free_image(im);
free_image(sized);
#ifdef OPENCV

6
src/yolo.c
View File

@ -65,7 +65,6 @@ void train_yolo(char *cfgfile, char *weightfile)
if(weightfile){
load_weights(&net, weightfile);
}
detection_layer layer = get_network_detection_layer(net);
int imgs = 128;
int i = *net.seen/imgs;
@ -74,11 +73,16 @@ void train_yolo(char *cfgfile, char *weightfile)
int N = plist->size;
paths = (char **)list_to_array(plist);
if(i*imgs > N*80){
net.layers[net.n-1].objectness = 0;
net.layers[net.n-1].joint = 1;
}
if(i*imgs > N*120){
net.layers[net.n-1].rescore = 1;
}
data train, buffer;
detection_layer layer = get_network_detection_layer(net);
int classes = layer.classes;
int background = layer.objectness;
int side = sqrt(get_detection_layer_locations(layer));