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working on TED demo

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This commit is contained in:
Joseph Redmon 2017-04-12 14:22:53 -07:00
parent 77ee1118bc
commit addcc4ef96
24 changed files with 392 additions and 110 deletions
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205
cfg/darknet9000.cfg Normal file
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@ -0,0 +1,205 @@
[net]
# Training
# batch=128
# subdivisions=4
# Testing
batch = 1
subdivisions = 1
height=448
width=448
max_crop=512
channels=3
momentum=0.9
decay=0.0005
learning_rate=0.001
policy=poly
power=4
max_batches=100000
angle=7
hue=.1
saturation=.75
exposure=.75
aspect=.75
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
filters=9418
size=1
stride=1
pad=1
activation=linear
[avgpool]
[softmax]
groups=1
tree=data/9k.tree
[cost]
type=masked

9
cfg/imagenet9k.hierarchy.dataset Normal file
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@ -0,0 +1,9 @@
classes=9418
train = data/9k.train.list
valid = /data/imagenet/imagenet1k.valid.list
leaves = data/imagenet1k.labels
backup = /home/pjreddie/backup/
labels = data/9k.labels
names = data/9k.names
top=5

19
cfg/yolo9000.cfg
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@ -1,17 +1,24 @@
[net]
# Testing
# batch=1
# subdivisions=1
# Training
batch=64
subdivisions=8
batch=1
subdivisions=1
height=416
width=416
height=544
width=544
channels=3
momentum=0.9
decay=0.0005
learning_rate=0.00001
max_batches = 242200
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=500,200000,240000
scales=10,.1,.1
steps=400000,450000
scales=.1,.1
hue=.1
saturation=.75

24
src/classifier.c
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@ -1113,27 +1113,9 @@ void run_classifier(int argc, char **argv)
}
char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
int *gpus = 0;
int gpu = 0;
int ngpus = 0;
if(gpu_list){
printf("%s\n", gpu_list);
int len = strlen(gpu_list);
ngpus = 1;
int i;
for(i = 0; i < len; ++i){
if (gpu_list[i] == ',') ++ngpus;
}
gpus = calloc(ngpus, sizeof(int));
for(i = 0; i < ngpus; ++i){
gpus[i] = atoi(gpu_list);
gpu_list = strchr(gpu_list, ',')+1;
}
} else {
gpu = gpu_index;
gpus = &gpu;
ngpus = 1;
}
int ngpus;
int *gpus = read_intlist(gpu_list, &ngpus, gpu_index);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int top = find_int_arg(argc, argv, "-t", 0);

10
src/convolutional_kernels.cu
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@ -286,7 +286,7 @@ void push_convolutional_layer(convolutional_layer layer)
}
}
void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch)
void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t)
{
scal_ongpu(n, B1, m, 1);
scal_ongpu(n, B2, v, 1);
@ -296,7 +296,7 @@ void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2,
mul_ongpu(n, d, 1, d, 1);
axpy_ongpu(n, (1-B2), d, 1, v, 1);
adam_gpu(n, w, m, v, B1, B2, rate/batch, eps, 1000);
adam_gpu(n, w, m, v, B1, B2, rate/batch, eps, t);
fill_ongpu(n, 0, d, 1);
}
@ -305,10 +305,10 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate, flo
int size = l.size*l.size*l.c*l.n;
if(l.adam){
adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, size, batch);
adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch);
adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, size, batch, l.t);
adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t);
if(l.scales_gpu){
adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch);
adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t);
}
}else{
axpy_ongpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);

2
src/convolutional_layer.c
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@ -188,7 +188,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
// float scale = 1./sqrt(size*size*c);
float scale = sqrt(2./(size*size*c));
scale = .02;
//scale = .02;
//for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1);
for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal();
int out_w = convolutional_out_width(l);

2
src/convolutional_layer.h
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@ -19,7 +19,7 @@ void pull_convolutional_layer(convolutional_layer layer);
void add_bias_gpu(float *output, float *biases, int batch, int n, int size);
void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size);
void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch);
void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t);
#ifdef CUDNN
void cudnn_convolutional_setup(layer *l);
#endif

19
src/data.c
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@ -102,7 +102,7 @@ matrix load_image_paths(char **paths, int n, int w, int h)
return X;
}
matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center)
{
int i;
matrix X;
@ -112,7 +112,12 @@ matrix load_image_augment_paths(char **paths, int n, int min, int max, int size,
for(i = 0; i < n; ++i){
image im = load_image_color(paths[i], 0, 0);
image crop = random_augment_image(im, angle, aspect, min, max, size);
image crop;
if(center){
crop = center_crop_image(im, size, size);
} else {
crop = random_augment_image(im, angle, aspect, min, max, size);
}
int flip = rand()%2;
if (flip) flip_image(crop);
random_distort_image(crop, hue, saturation, exposure);
@ -742,7 +747,7 @@ void *load_thread(void *ptr)
} else if (a.type == REGRESSION_DATA){
*a.d = load_data_regression(a.paths, a.n, a.m, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
} else if (a.type == CLASSIFICATION_DATA){
*a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
*a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.center);
} else if (a.type == SUPER_DATA){
*a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale);
} else if (a.type == WRITING_DATA){
@ -890,18 +895,18 @@ data load_data_regression(char **paths, int n, int m, int min, int max, int size
if(m) paths = get_random_paths(paths, n, m);
data d = {0};
d.shallow = 0;
d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0);
d.y = load_regression_labels_paths(paths, n);
if(m) free(paths);
return d;
}
data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center)
{
if(m) paths = get_random_paths(paths, n, m);
data d = {0};
d.shallow = 0;
d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, center);
d.y = load_labels_paths(paths, n, labels, k, hierarchy);
if(m) free(paths);
return d;
@ -914,7 +919,7 @@ data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size
d.w = size;
d.h = size;
d.shallow = 0;
d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0);
d.y = load_tags_paths(paths, n, k);
if(m) free(paths);
return d;

5
src/data.h
View File

@ -49,6 +49,7 @@ typedef struct load_args{
int classes;
int background;
int scale;
int center;
float jitter;
float angle;
float aspect;
@ -80,9 +81,9 @@ data load_data_captcha_encode(char **paths, int n, int m, int w, int h);
data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h);
data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure);
data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center);
data load_data_super(char **paths, int n, int m, int w, int h, int scale);
data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center);
data load_data_regression(char **paths, int n, int m, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
data load_go(char *filename);

6
src/deconvolutional_kernels.cu
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@ -114,10 +114,10 @@ void update_deconvolutional_layer_gpu(layer l, int batch, float learning_rate, f
int size = l.size*l.size*l.c*l.n;
if(l.adam){
adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, size, batch);
adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch);
adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, size, batch, l.t);
adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t);
if(l.scales_gpu){
adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch);
adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t);
}
}else{
axpy_ongpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);

16
src/demo.c
View File

@ -10,6 +10,7 @@
#include <sys/time.h>
#define FRAMES 3
#define DEMO 1
#ifdef OPENCV
@ -37,7 +38,13 @@ static float *avg;
void *fetch_in_thread(void *ptr)
{
in = get_image_from_stream(cap);
image raw = get_image_from_stream(cap);
if(DEMO){
in = center_crop_image(raw, 1440, 1080);
free_image(raw);
}else{
in = raw;
}
if(!in.data){
error("Stream closed.");
}
@ -65,7 +72,7 @@ void *detect_in_thread(void *ptr)
} else {
error("Last layer must produce detections\n");
}
if (nms > 0) do_nms(boxes, probs, l.w*l.h*l.n, l.classes, nms);
if (nms > 0) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
printf("\033[2J");
printf("\033[1;1H");
printf("\nFPS:%.1f\n",fps);
@ -113,6 +120,11 @@ void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const ch
cap = cvCaptureFromFile(filename);
}else{
cap = cvCaptureFromCAM(cam_index);
if(DEMO){
cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, 1920);
cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, 1080);
cvSetCaptureProperty(cap, CV_CAP_PROP_FPS, 60);
}
}
if(!cap) error("Couldn't connect to webcam.\n");

4
src/demo.h
View File

@ -1,5 +1,5 @@
#ifndef DEMO
#define DEMO
#ifndef DEMO_H
#define DEMO_H
#include "image.h"
void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, float hier_thresh);

4
src/detector.c
View File

@ -625,8 +625,8 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0, hier_thresh, 0);
if (l.softmax_tree && nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
//else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
draw_detections(sized, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
if(outfile){
save_image(sized, outfile);

9
src/image.c
View File

@ -187,6 +187,7 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs,
alphabet = 0;
}
//printf("%d %s: %.0f%%\n", i, names[class], prob*100);
printf("%s: %.0f%%\n", names[class], prob*100);
int offset = class*123457 % classes;
float red = get_color(2,offset,classes);
@ -641,6 +642,14 @@ void place_image(image im, int w, int h, int dx, int dy, image canvas)
}
}
image center_crop_image(image im, int w, int h)
{
int m = (im.w < im.h) ? im.w : im.h;
image c = crop_image(im, (im.w - m) / 2, (im.h - m)/2, m, m);
image r = resize_image(c, w, h);
free_image(c);
return r;
}
image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect)
{

1
src/image.h
View File

@ -44,6 +44,7 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs,
image image_distance(image a, image b);
void scale_image(image m, float s);
image crop_image(image im, int dx, int dy, int w, int h);
image center_crop_image(image im, int w, int h);
image random_crop_image(image im, int w, int h);
image random_augment_image(image im, float angle, float aspect, int low, int high, int size);
void random_distort_image(image im, float hue, float saturation, float exposure);

70
src/lsd.c
View File

@ -599,14 +599,14 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
aloss_avg = aloss_avg*.9 + aloss*.1;
printf("%d: adv: %f | adv_avg: %f, %f rate, %lf seconds, %d images\n", i, aloss, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs);
if(i%1000==0){
if(i%10000==0){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(gnet, buff);
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
save_weights(anet, buff);
}
if(i%100==0){
if(i%1000==0){
char buff[256];
sprintf(buff, "%s/%s.backup", backup_directory, base);
save_weights(gnet, buff);
@ -620,8 +620,7 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
#endif
}
/*
void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int clear)
void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display)
{
#ifdef GPU
//char *train_images = "/home/pjreddie/data/coco/train1.txt";
@ -668,31 +667,19 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
pthread_t load_thread = load_data_in_thread(args);
clock_t time;
network_state gstate = {0};
gstate.index = 0;
gstate.net = net;
int x_size = get_network_input_size(net)*net.batch;
int x_size = net.inputs*net.batch;
int y_size = x_size;
gstate.input = cuda_make_array(0, x_size);
gstate.truth = cuda_make_array(0, y_size);
gstate.delta = 0;
gstate.train = 1;
net.delta = 0;
net.train = 1;
float *pixs = calloc(x_size, sizeof(float));
float *graypixs = calloc(x_size, sizeof(float));
float *y = calloc(y_size, sizeof(float));
network_state astate = {0};
astate.index = 0;
astate.net = anet;
int ay_size = get_network_output_size(anet)*anet.batch;
astate.input = 0;
astate.truth = 0;
astate.delta = 0;
astate.train = 1;
int ay_size = anet.outputs*anet.batch;
anet.delta = 0;
anet.train = 1;
float *imerror = cuda_make_array(0, imlayer.outputs*imlayer.batch);
float *ones_gpu = cuda_make_array(0, ay_size);
fill_ongpu(ay_size, 1, ones_gpu, 1);
float aloss_avg = -1;
float gloss_avg = -1;
@ -712,8 +699,8 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
for(j = 0; j < imgs; ++j){
image gim = float_to_image(net.w, net.h, net.c, gray.X.vals[j]);
grayscale_image_3c(gim);
train.y.vals[j][0] = 1;
gray.y.vals[j][0] = 0;
train.y.vals[j][0] = .95;
gray.y.vals[j][0] = .05;
}
time=clock();
float gloss = 0;
@ -721,9 +708,8 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
for(j = 0; j < net.subdivisions; ++j){
get_next_batch(train, net.batch, j*net.batch, pixs, 0);
get_next_batch(gray, net.batch, j*net.batch, graypixs, 0);
cuda_push_array(gstate.input, graypixs, x_size);
cuda_push_array(gstate.truth, pixs, y_size);
*/
cuda_push_array(net.input_gpu, graypixs, net.inputs*net.batch);
cuda_push_array(net.truth_gpu, pixs, net.truths*net.batch);
/*
image origi = float_to_image(net.w, net.h, 3, pixs);
image grayi = float_to_image(net.w, net.h, 3, graypixs);
@ -731,16 +717,15 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
show_image(origi, "orig");
cvWaitKey(0);
*/
/*
*net.seen += net.batch;
forward_network_gpu(net, gstate);
forward_network_gpu(net);
fill_ongpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
astate.input = imlayer.output_gpu;
astate.delta = imerror;
astate.truth = ones_gpu;
forward_network_gpu(anet, astate);
backward_network_gpu(anet, astate);
copy_ongpu(anet.inputs*anet.batch, imlayer.output_gpu, 1, anet.input_gpu, 1);
fill_ongpu(anet.inputs*anet.batch, .95, anet.truth_gpu, 1);
anet.delta_gpu = imerror;
forward_network_gpu(anet);
backward_network_gpu(anet);
scal_ongpu(imlayer.outputs*imlayer.batch, 1./100., net.layers[net.n-1].delta_gpu, 1);
@ -751,12 +736,11 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
axpy_ongpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, net.layers[net.n-1].delta_gpu, 1);
backward_network_gpu(net, gstate);
backward_network_gpu(net);
gloss += get_network_cost(net) /(net.subdivisions*net.batch);
gloss += *net.cost /(net.subdivisions*net.batch);
cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
for(k = 0; k < net.batch; ++k){
int index = j*net.batch + k;
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1);
@ -769,6 +753,16 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
float aloss = train_network(anet, merge);
update_network_gpu(net);
#ifdef OPENCV
if(display){
image im = float_to_image(anet.w, anet.h, anet.c, gray.X.vals[0]);
image im2 = float_to_image(anet.w, anet.h, anet.c, train.X.vals[0]);
show_image(im, "gen");
show_image(im2, "train");
cvWaitKey(50);
}
#endif
free_data(merge);
free_data(train);
free_data(gray);
@ -797,7 +791,6 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
save_weights(net, buff);
#endif
}
*/
/*
void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfile, int clear)
@ -1136,6 +1129,7 @@ void run_lsd(int argc, char **argv)
//else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear);
//else if(0==strcmp(argv[2], "train3")) train_lsd3(argv[3], argv[4], argv[5], argv[6], argv[7], argv[8], clear);
if(0==strcmp(argv[2], "traingan")) train_dcgan(cfg, weights, acfg, aweights, clear, display, file);
else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear, display);
else if(0==strcmp(argv[2], "gan")) test_dcgan(cfg, weights);
else if(0==strcmp(argv[2], "test")) test_lsd(cfg, weights, filename, 0);
else if(0==strcmp(argv[2], "color")) test_lsd(cfg, weights, filename, 1);

3
src/network.c
View File

@ -42,6 +42,7 @@ load_args get_base_args(network net)
args.angle = net.angle;
args.aspect = net.aspect;
args.exposure = net.exposure;
args.center = net.center;
args.saturation = net.saturation;
args.hue = net.hue;
return args;
@ -385,7 +386,7 @@ image get_network_image_layer(network net, int i)
{
layer l = net.layers[i];
#ifdef GPU
cuda_pull_array(l.output_gpu, l.output, l.outputs);
//cuda_pull_array(l.output_gpu, l.output, l.outputs);
#endif
if (l.out_w && l.out_h && l.out_c){
return float_to_image(l.out_w, l.out_h, l.out_c, l.output);

1
src/network.h
View File

@ -47,6 +47,7 @@ typedef struct network{
int h, w, c;
int max_crop;
int min_crop;
int center;
float angle;
float aspect;
float exposure;

36
src/nightmare.c
View File

@ -2,6 +2,7 @@
#include "parser.h"
#include "blas.h"
#include "utils.h"
#include "region_layer.h"
// ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2
@ -137,11 +138,11 @@ void reconstruct_picture(network net, float *features, image recon, image update
#ifdef GPU
cuda_push_array(net.input_gpu, recon.data, recon.w*recon.h*recon.c);
cuda_push_array(net.truth_gpu, features, net.truths);
//cuda_push_array(net.truth_gpu, features, net.truths);
net.delta_gpu = cuda_make_array(delta.data, delta.w*delta.h*delta.c);
forward_network_gpu(net);
copy_ongpu(l.outputs, net.truth_gpu, 1, l.delta_gpu, 1);
cuda_push_array(l.delta_gpu, features, l.outputs);
axpy_ongpu(l.outputs, -1, l.output_gpu, 1, l.delta_gpu, 1);
backward_network_gpu(net);
@ -157,13 +158,15 @@ void reconstruct_picture(network net, float *features, image recon, image update
backward_network(net);
#endif
//normalize_array(delta.data, delta.w*delta.h*delta.c);
axpy_cpu(recon.w*recon.h*recon.c, 1, delta.data, 1, update.data, 1);
smooth(recon, update, lambda, smooth_size);
//smooth(recon, update, lambda, smooth_size);
axpy_cpu(recon.w*recon.h*recon.c, rate, update.data, 1, recon.data, 1);
scal_cpu(recon.w*recon.h*recon.c, momentum, update.data, 1);
//float mag = mag_array(recon.data, recon.w*recon.h*recon.c);
float mag = mag_array(delta.data, recon.w*recon.h*recon.c);
printf("mag: %f\n", mag);
//scal_cpu(recon.w*recon.h*recon.c, 600/mag, recon.data, 1);
constrain_image(recon);
@ -330,28 +333,33 @@ void run_nightmare(int argc, char **argv)
image update;
if (reconstruct){
net.n = max_layer;
resize_network(&net, im.w, im.h);
im = letterbox_image(im, net.w, net.h);
//resize_network(&net, im.w, im.h);
int zz = 0;
network_predict(net, im.data);
image out_im = get_network_image(net);
image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz);
if(net.layers[net.n-1].type == REGION){
printf("region!\n");
zero_objectness(net.layers[net.n-1]);
}
image out_im = copy_image(get_network_image(net));
/*
image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz);
//flip_image(crop);
image f_im = resize_image(crop, out_im.w, out_im.h);
free_image(crop);
*/
printf("%d features\n", out_im.w*out_im.h*out_im.c);
features = out_im.data;
im = resize_image(im, im.w, im.h);
f_im = resize_image(f_im, f_im.w, f_im.h);
features = f_im.data;
/*
int i;
for(i = 0; i < 14*14*512; ++i){
//features[i] += rand_uniform(-.19, .19);
for(i = 0; i < 14*14*512; ++i){
//features[i] += rand_uniform(-.19, .19);
}
free_image(im);
im = make_random_image(im.w, im.h, im.c);
*/
update = make_image(im.w, im.h, im.c);
}

9
src/parser.c
View File

@ -558,6 +558,7 @@ void parse_net_options(list *options, network *net)
net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c);
net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2);
net->min_crop = option_find_int_quiet(options, "min_crop",net->w);
net->center = option_find_int_quiet(options, "center",0);
net->angle = option_find_float_quiet(options, "angle", 0);
net->aspect = option_find_float_quiet(options, "aspect", 1);
@ -835,8 +836,8 @@ void save_convolutional_weights(layer l, FILE *fp)
}
fwrite(l.weights, sizeof(float), num, fp);
if(l.adam){
fwrite(l.m, sizeof(float), num, fp);
fwrite(l.v, sizeof(float), num, fp);
//fwrite(l.m, sizeof(float), num, fp);
//fwrite(l.v, sizeof(float), num, fp);
}
}
@ -1039,8 +1040,8 @@ void load_convolutional_weights(layer l, FILE *fp)
}
fread(l.weights, sizeof(float), num, fp);
if(l.adam){
fread(l.m, sizeof(float), num, fp);
fread(l.v, sizeof(float), num, fp);
//fread(l.m, sizeof(float), num, fp);
//fread(l.v, sizeof(float), num, fp);
}
//if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1);
if (l.flipped) {

23
src/region_layer.c
View File

@ -384,14 +384,24 @@ void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *b
probs[index][l.classes] = scale;
}
} else {
float max = 0;
for(j = 0; j < l.classes; ++j){
int class_index = entry_index(l, 0, n*l.w*l.h + i, 5 + j);
float prob = scale*predictions[class_index];
probs[index][j] = (prob > thresh) ? prob : 0;
if(prob > max) max = prob;
// TODO REMOVE
// if (j != 15 && j != 16) probs[index][j] = 0;
// if (j != 0) probs[index][j] = 0;
/*
if (j != 0) probs[index][j] = 0;
int blacklist[] = {121, 497, 482, 504, 122, 518,481, 418, 542, 491, 914, 478, 120, 510,500};
int bb;
for (bb = 0; bb < sizeof(blacklist)/sizeof(int); ++bb){
if(index == blacklist[bb]) probs[index][j] = 0;
}
*/
}
probs[index][l.classes] = max;
}
if(only_objectness){
probs[index][0] = scale;
@ -461,3 +471,14 @@ void backward_region_layer_gpu(const layer l, network net)
}
#endif
void zero_objectness(layer l)
{
int i, n;
for (i = 0; i < l.w*l.h; ++i){
for(n = 0; n < l.n; ++n){
int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4);
l.output[obj_index] = 0;
}
}
}

1
src/region_layer.h
View File

@ -9,6 +9,7 @@ void forward_region_layer(const layer l, network net);
void backward_region_layer(const layer l, network net);
void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map, float tree_thresh, int nomult);
void resize_region_layer(layer *l, int w, int h);
void zero_objectness(layer l);
#ifdef GPU
void forward_region_layer_gpu(const layer l, network net);

23
src/utils.c
View File

@ -9,6 +9,29 @@
#include "utils.h"
int *read_intlist(char *gpu_list, int *ngpus, int d)
{
int *gpus = 0;
if(gpu_list){
int len = strlen(gpu_list);
*ngpus = 1;
int i;
for(i = 0; i < len; ++i){
if (gpu_list[i] == ',') ++*ngpus;
}
gpus = calloc(*ngpus, sizeof(int));
for(i = 0; i < *ngpus; ++i){
gpus[i] = atoi(gpu_list);
gpu_list = strchr(gpu_list, ',')+1;
}
} else {
gpus = calloc(1, sizeof(float));
*gpus = d;
*ngpus = 1;
}
return gpus;
}
int *read_map(char *filename)
{
int n = 0;

1
src/utils.h
View File

@ -7,6 +7,7 @@
#define SECRET_NUM -1234
#define TWO_PI 6.2831853071795864769252866
int *read_intlist(char *s, int *n, int d);
int *read_map(char *filename);
void shuffle(void *arr, size_t n, size_t size);
void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections);