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writing stuff

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This commit is contained in:
Joseph Redmon 2015-09-22 15:22:50 -07:00
parent fed6d6e31d
commit 59e356730f
7 changed files with 701 additions and 602 deletions
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49
cfg/writing.cfg Normal file
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@ -0,0 +1,49 @@
[net]
batch=64
subdivisions=1
height=256
width=256
channels=3
learning_rate=0.00001
momentum=0.9
decay=0.0005
seen=0
[crop]
crop_height=256
crop_width=256
flip=0
angle=0
saturation=1
exposure=1
[convolutional]
filters=32
size=3
stride=1
pad=1
activation=ramp
[convolutional]
filters=32
size=3
stride=1
pad=1
activation=ramp
[convolutional]
filters=32
size=3
stride=1
pad=1
activation=ramp
[convolutional]
filters=1
size=5
stride=1
pad=1
activation=logistic
[cost]

11
src/data.c
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@ -54,7 +54,12 @@ matrix load_image_paths_gray(char **paths, int n, int w, int h)
X.cols = 0;
for(i = 0; i < n; ++i){
image im = load_image(paths[i], w, h, 1);
image im = load_image(paths[i], w, h, 3);
image gray = grayscale_image(im);
free_image(im);
im = gray;
X.vals[i] = im.data;
X.cols = im.h*im.w*im.c;
}
@ -571,14 +576,14 @@ pthread_t load_data_in_thread(load_args args)
return thread;
}
data load_data_writing(char **paths, int n, int m, int w, int h)
data load_data_writing(char **paths, int n, int m, int w, int h, int downsample)
{
if(m) paths = get_random_paths(paths, n, m);
char **replace_paths = find_replace_paths(paths, n, ".png", "-label.png");
data d;
d.shallow = 0;
d.X = load_image_paths(paths, n, w, h);
d.y = load_image_paths_gray(replace_paths, n, w/8, h/8);
d.y = load_image_paths_gray(replace_paths, n, w/downsample, h/downsample);
if(m) free(paths);
int i;
for(i = 0; i < n; ++i) free(replace_paths[i]);

2
src/data.h
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@ -68,7 +68,7 @@ box_label *read_boxes(char *filename, int *n);
data load_cifar10_data(char *filename);
data load_all_cifar10();
data load_data_writing(char **paths, int n, int m, int w, int h);
data load_data_writing(char **paths, int n, int m, int w, int h, int downsample);
list *get_paths(char *filename);
char **get_labels(char *filename);

1138
src/image.c
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File diff suppressed because it is too large Load Diff

4
src/region_layer.c
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@ -61,6 +61,7 @@ void forward_region_layer(const region_layer l, network_state state)
if(state.train){
float avg_iou = 0;
float avg_cat = 0;
float avg_allcat = 0;
float avg_obj = 0;
float avg_anyobj = 0;
int count = 0;
@ -90,6 +91,7 @@ void forward_region_layer(const region_layer l, network_state state)
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];
avg_allcat += l.output[class_index+j];
}
box truth = float_to_box(state.truth + truth_index + 1 + l.classes);
@ -151,7 +153,7 @@ void forward_region_layer(const region_layer l, network_state state)
LOGISTIC, l.delta + index + locations*l.classes);
}
}
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);
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);
}
}

19
src/swag.c
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@ -132,21 +132,22 @@ void train_swag(char *cfgfile, char *weightfile)
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 i,j,n;
int per_cell = 5*num+classes;
//int per_cell = 5*num+classes;
for (i = 0; i < side*side; ++i){
int row = i / side;
int col = i % side;
for(n = 0; n < num; ++n){
int offset = i*per_cell + 5*n;
float scale = predictions[offset];
int index = i*num + n;
boxes[index].x = (predictions[offset + 1] + col) / side * w;
boxes[index].y = (predictions[offset + 2] + row) / side * h;
boxes[index].w = pow(predictions[offset + 3], (square?2:1)) * w;
boxes[index].h = pow(predictions[offset + 4], (square?2:1)) * h;
int p_index = side*side*classes + i*num + n;
float scale = predictions[p_index];
int box_index = side*side*(classes + num) + (i*num + n)*4;
boxes[index].x = (predictions[box_index + 0] + col) / side * w;
boxes[index].y = (predictions[box_index + 1] + row) / side * h;
boxes[index].w = pow(predictions[box_index + 2], (square?2:1)) * w;
boxes[index].h = pow(predictions[box_index + 3], (square?2:1)) * h;
for(j = 0; j < classes; ++j){
offset = i*per_cell + 5*num;
float prob = scale*predictions[offset+j];
int class_index = i*classes;
float prob = scale*predictions[class_index+j];
probs[index][j] = (prob > thresh) ? prob : 0;
}
}

80
src/writing.c
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@ -2,8 +2,13 @@
#include "utils.h"
#include "parser.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
void train_writing(char *cfgfile, char *weightfile)
{
char *backup_directory = "/home/pjreddie/backup/";
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
@ -23,41 +28,78 @@ void train_writing(char *cfgfile, char *weightfile)
while(1){
++i;
time=clock();
data train = load_data_writing(paths, imgs, plist->size, 512, 512);
data train = load_data_writing(paths, imgs, plist->size, 256, 256, 1);
printf("Loaded %lf seconds\n",sec(clock()-time));
time=clock();
float loss = train_network(net, train);
#ifdef GPU
float *out = get_network_output_gpu(net);
#else
float *out = get_network_output(net);
#endif
/*
image pred = float_to_image(64, 64, 1, out);
print_image(pred);
/*
image im = float_to_image(256, 256, 3, train.X.vals[0]);
image lab = float_to_image(64, 64, 1, train.y.vals[0]);
image pred = float_to_image(64, 64, 1, out);
show_image(im, "image");
show_image(lab, "label");
print_image(lab);
show_image(pred, "pred");
cvWaitKey(0);
*/
/*
image im = float_to_image(256, 256, 3, train.X.vals[0]);
image lab = float_to_image(64, 64, 1, train.y.vals[0]);
image pred = float_to_image(64, 64, 1, out);
show_image(im, "image");
show_image(lab, "label");
print_image(lab);
show_image(pred, "pred");
cvWaitKey(0);
*/
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);
free_data(train);
if((i % 20000) == 0) net.learning_rate *= .1;
//if(i%100 == 0 && net.learning_rate > .00001) net.learning_rate *= .97;
if(i%1000==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
}
}
void test_writing(char *cfgfile, char *weightfile, char *outfile)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
char filename[256];
fgets(filename, 256, stdin);
strtok(filename, "\n");
image im = load_image_color(filename, 0, 0);
//image im = load_image_color("/home/pjreddie/darknet/data/figs/C02-1001-Figure-1.png", 0, 0);
image sized = resize_image(im, net.w, net.h);
printf("%d %d %d\n", im.h, im.w, im.c);
float *X = sized.data;
time=clock();
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
image pred = get_network_image(net);
if (outfile) {
printf("Save image as %s.png (shape: %d %d)\n", outfile, pred.w, pred.h);
save_image(pred, outfile);
} else {
show_image(pred, "prediction");
#ifdef OPENCV
cvWaitKey(0);
cvDestroyAllWindows();
#endif
}
free_image(im);
free_image(sized);
}
void run_writing(int argc, char **argv)
{
if(argc < 4){
@ -67,6 +109,8 @@ void run_writing(int argc, char **argv)
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
char *outfile = (argc > 5) ? argv[5] : 0;
if(0==strcmp(argv[2], "train")) train_writing(cfg, weights);
else if(0==strcmp(argv[2], "test")) test_writing(cfg, weights, outfile);
}