mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

opencv is hell. this is why we can't have nice things.

Browse Source
This commit is contained in:
Joseph Redmon 2017-03-29 20:27:54 -07:00
parent 38802ef56a
commit 179ed8ec76
27 changed files with 297 additions and 138 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
cfg/yolo-voc.cfg
View File

@ -1,6 +1,10 @@
[net]
batch=64
subdivisions=8
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=8
height=416
width=416
channels=3

8
cfg/yolo.cfg
View File

@ -1,6 +1,10 @@
[net]
batch=64
subdivisions=8
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=8
height=416
width=416
channels=3

6
src/art.c
View File

@ -6,12 +6,6 @@
#include "classifier.h"
#include <sys/time.h>
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
image get_image_from_stream(CvCapture *cap);
#endif
void demo_art(char *cfgfile, char *weightfile, int cam_index)
{
#ifdef OPENCV

4
src/cifar.c
View File

@ -4,10 +4,6 @@
#include "option_list.h"
#include "blas.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
void train_cifar(char *cfgfile, char *weightfile)
{
srand(time(0));

5
src/classifier.c
View File

@ -8,11 +8,6 @@
#include "cuda.h"
#include <sys/time.h>
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
image get_image_from_stream(CvCapture *cap);
#endif
float *get_regression_values(char **labels, int n)
{
float *v = calloc(n, sizeof(float));

4
src/coco.c
View File

@ -8,10 +8,6 @@
#include "box.h"
#include "demo.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"};
int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};

16
src/connected_layer.c
View File

@ -97,6 +97,12 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT
l.x_gpu = cuda_make_array(l.output, l.batch*outputs);
l.x_norm_gpu = cuda_make_array(l.output, l.batch*outputs);
#ifdef CUDNN
cudnnCreateTensorDescriptor(&l.normTensorDesc);
cudnnCreateTensorDescriptor(&l.dstTensorDesc);
cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w);
cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1);
#endif
}
#endif
l.activation = activation;
@ -213,11 +219,11 @@ void statistics_connected_layer(layer l)
printf("Scales ");
print_statistics(l.scales, l.outputs);
/*
printf("Rolling Mean ");
print_statistics(l.rolling_mean, l.outputs);
printf("Rolling Variance ");
print_statistics(l.rolling_variance, l.outputs);
*/
printf("Rolling Mean ");
print_statistics(l.rolling_mean, l.outputs);
printf("Rolling Variance ");
print_statistics(l.rolling_variance, l.outputs);
*/
}
printf("Biases ");
print_statistics(l.biases, l.outputs);

4
src/darknet.c
View File

@ -8,10 +8,6 @@
#include "blas.h"
#include "connected_layer.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);
extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh);
extern void run_voxel(int argc, char **argv);

2
src/data.c
View File

@ -1017,7 +1017,7 @@ void get_next_batch(data d, int n, int offset, float *X, float *y)
for(j = 0; j < n; ++j){
int index = offset + j;
memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
if(y) memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
}
}

3
src/demo.c
View File

@ -12,9 +12,6 @@
#define FRAMES 3
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
image get_image_from_stream(CvCapture *cap);
static char **demo_names;
static image **demo_alphabet;

3
src/detector.c
View File

@ -8,9 +8,6 @@
#include "option_list.h"
#include "blas.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)

10
src/go.c
View File

@ -6,10 +6,6 @@
#include "data.h"
#include <unistd.h>
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
int inverted = 1;
int noi = 1;
static const int nind = 2;
@ -125,7 +121,7 @@ data random_go_moves(moves m, int n)
}
void train_go(char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
void train_go(char *cfgfile, char *weightfile, char *filename, int *gpus, int ngpus, int clear)
{
int i;
float avg_loss = -1;
@ -150,7 +146,7 @@ void train_go(char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
char *backup_directory = "/home/pjreddie/backup/";
char buff[256];
moves m = load_go_moves("/home/pjreddie/backup/go.train");
moves m = load_go_moves(filename);
//moves m = load_go_moves("games.txt");
int N = m.n;
@ -909,7 +905,7 @@ void run_go(int argc, char **argv)
char *c2 = (argc > 5) ? argv[5] : 0;
char *w2 = (argc > 6) ? argv[6] : 0;
int multi = find_arg(argc, argv, "-multi");
if(0==strcmp(argv[2], "train")) train_go(cfg, weights, gpus, ngpus, clear);
if(0==strcmp(argv[2], "train")) train_go(cfg, weights, c2, gpus, ngpus, clear);
else if(0==strcmp(argv[2], "valid")) valid_go(cfg, weights, multi, c2);
else if(0==strcmp(argv[2], "self")) self_go(cfg, weights, c2, w2, multi);
else if(0==strcmp(argv[2], "test")) test_go(cfg, weights, multi);

9
src/gru_layer.c
View File

@ -70,6 +70,15 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no
*(l.state_h_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.state_h_layer->batch = batch;
#ifdef CUDNN
cudnnSetTensor4dDescriptor(l.input_z_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.input_z_layer->out_c, l.input_z_layer->out_h, l.input_z_layer->out_w);
cudnnSetTensor4dDescriptor(l.input_h_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.input_h_layer->out_c, l.input_h_layer->out_h, l.input_h_layer->out_w);
cudnnSetTensor4dDescriptor(l.input_r_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.input_r_layer->out_c, l.input_r_layer->out_h, l.input_r_layer->out_w);
cudnnSetTensor4dDescriptor(l.state_z_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.state_z_layer->out_c, l.state_z_layer->out_h, l.state_z_layer->out_w);
cudnnSetTensor4dDescriptor(l.state_h_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.state_h_layer->out_c, l.state_h_layer->out_h, l.state_h_layer->out_w);
cudnnSetTensor4dDescriptor(l.state_r_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.state_r_layer->out_c, l.state_r_layer->out_h, l.state_r_layer->out_w);
#endif
l.batch_normalize = batch_normalize;

6
src/image.c
View File

@ -10,12 +10,6 @@
#define STB_IMAGE_WRITE_IMPLEMENTATION
#include "stb_image_write.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#endif
int windows = 0;
float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };

18
src/image.h
View File

@ -8,6 +8,17 @@
#include <math.h>
#include "box.h"
#ifndef __cplusplus
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/core/version.hpp"
#if CV_MAJOR_VERSION == 3
#include "opencv2/videoio/videoio_c.h"
#endif
#endif
#endif
typedef struct {
int h;
int w;
@ -15,6 +26,13 @@ typedef struct {
float *data;
} image;
#ifndef __cplusplus
#ifdef OPENCV
image get_image_from_stream(CvCapture *cap);
image ipl_to_image(IplImage* src);
#endif
#endif
float get_color(int c, int x, int max);
void flip_image(image a);
void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b);

287
src/lsd.c
View File

@ -4,10 +4,6 @@
#include "parser.h"
#include "blas.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg, char *aweight, int clear)
{
#ifdef GPU
@ -75,9 +71,7 @@ void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg
float *y = calloc(y_size, sizeof(float));
float *ones = cuda_make_array(0, anet.batch);
float *zeros = cuda_make_array(0, anet.batch);
fill_ongpu(anet.batch, .99, ones, 1);
fill_ongpu(anet.batch, .01, zeros, 1);
fill_ongpu(anet.batch, .9, ones, 1);
network_state astate = {0};
astate.index = 0;
@ -145,7 +139,7 @@ void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg
float *delta = imlayer.delta_gpu;
fill_ongpu(x_size, 0, delta, 1);
scal_ongpu(x_size, 100, astate.delta, 1);
scal_ongpu(x_size, .00001, fstate.delta, 1);
scal_ongpu(x_size, .001, fstate.delta, 1);
axpy_ongpu(x_size, 1, fstate.delta, 1, delta, 1);
axpy_ongpu(x_size, 1, astate.delta, 1, delta, 1);
@ -165,7 +159,8 @@ void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg
for(k = 0; k < gnet.batch; ++k){
int index = j*gnet.batch + k;
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
generated.y.vals[index][0] = .01;
generated.y.vals[index][0] = .1;
style.y.vals[index][0] = .9;
}
}
@ -346,7 +341,7 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
backward_network_gpu(net, gstate);
scal_ongpu(imlayer.outputs, 100, imerror, 1);
scal_ongpu(imlayer.outputs, 1000, imerror, 1);
printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
printf("features %f\n", cuda_mag_array(net.layers[net.n-1].delta_gpu, imlayer.outputs));
@ -399,6 +394,217 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
#endif
}
void test_dcgan(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int i, imlayer = 0;
for (i = 0; i < net.n; ++i) {
if (net.layers[i].out_c == 3) {
imlayer = i;
printf("%d\n", i);
break;
}
}
while(1){
image im = make_image(net.w, net.h, net.c);
int i;
for(i = 0; i < im.w*im.h*im.c; ++i){
im.data[i] = rand_normal();
}
float *X = im.data;
time=clock();
network_predict(net, X);
image out = get_network_image_layer(net, imlayer);
//yuv_to_rgb(out);
constrain_image(out);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
show_image(out, "out");
save_image(out, "out");
#ifdef OPENCV
cvWaitKey(0);
#endif
free_image(im);
}
}
void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear)
{
#ifdef GPU
//char *train_images = "/home/pjreddie/data/coco/train1.txt";
//char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list";
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
char *base = basecfg(cfg);
char *abase = basecfg(acfg);
printf("%s\n", base);
network net = load_network(cfg, weight, clear);
network anet = load_network(acfg, aweight, clear);
int i, j, k;
layer imlayer = {0};
for (i = 0; i < net.n; ++i) {
if (net.layers[i].out_c == 3) {
imlayer = net.layers[i];
break;
}
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch*net.subdivisions;
i = *net.seen/imgs;
data train, buffer;
list *plist = get_paths(train_images);
//int N = plist->size;
char **paths = (char **)list_to_array(plist);
load_args args= get_base_args(anet);
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.d = &buffer;
args.type = CLASSIFICATION_DATA;
args.classes = 2;
char *ls[2] = {"imagenet", "zzzzzzzz"};
args.labels = ls;
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 y_size = get_network_output_size(net)*net.batch;
gstate.input = cuda_make_array(0, x_size);
gstate.truth = cuda_make_array(0, y_size);
gstate.train = 1;
float *input = calloc(x_size, sizeof(float));
float *y = calloc(y_size, sizeof(float));
float *imerror = cuda_make_array(0, y_size);
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;
float *ones_gpu = cuda_make_array(0, ay_size);
fill_ongpu(ay_size, .1, ones_gpu, 1);
fill_ongpu(ay_size/2, .9, ones_gpu, 2);
float aloss_avg = -1;
//data generated = copy_data(train);
while (get_current_batch(net) < net.max_batches) {
i += 1;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
data gen = copy_data(train);
for(j = 0; j < imgs; ++j){
train.y.vals[j][0] = .9;
train.y.vals[j][1] = .1;
gen.y.vals[j][0] = .1;
gen.y.vals[j][1] = .9;
}
time=clock();
for(j = 0; j < net.subdivisions; ++j){
get_next_batch(train, net.batch, j*net.batch, y, 0);
int z;
for(z = 0; z < x_size; ++z){
input[z] = rand_normal();
}
cuda_push_array(gstate.input, input, x_size);
cuda_push_array(gstate.truth, y, y_size);
*net.seen += net.batch;
forward_network_gpu(net, gstate);
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);
scal_ongpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
scal_ongpu(imlayer.outputs*imlayer.batch, .001, net.layers[net.n-1].delta_gpu, 1);
printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch));
printf("features %f\n", cuda_mag_array(net.layers[net.n-1].delta_gpu, imlayer.outputs*imlayer.batch));
axpy_ongpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, net.layers[net.n-1].delta_gpu, 1);
backward_network_gpu(net, gstate);
cuda_pull_array(imlayer.output_gpu, imlayer.output, x_size);
for(k = 0; k < net.batch; ++k){
int index = j*net.batch + k;
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gen.X.vals[index], 1);
gen.y.vals[index][0] = .1;
}
}
harmless_update_network_gpu(anet);
data merge = concat_data(train, gen);
randomize_data(merge);
float aloss = train_network(anet, merge);
update_network_gpu(net);
free_data(merge);
free_data(train);
free_data(gen);
if (aloss_avg < 0) aloss_avg = aloss;
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(net), sec(clock()-time), i*imgs);
if(i%1000==0){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
save_weights(anet, buff);
}
if(i%100==0){
char buff[256];
sprintf(buff, "%s/%s.backup", backup_directory, base);
save_weights(net, buff);
sprintf(buff, "%s/%s.backup", backup_directory, abase);
save_weights(anet, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
#endif
}
void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int clear)
{
#ifdef GPU
@ -432,25 +638,15 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
//int N = plist->size;
char **paths = (char **)list_to_array(plist);
load_args args = {0};
args.w = net.w;
args.h = net.h;
load_args args= get_base_args(net);
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.d = &buffer;
args.min = net.min_crop;
args.max = net.max_crop;
args.angle = net.angle;
args.aspect = net.aspect;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
args.size = net.w;
args.type = CLASSIFICATION_DATA;
args.classes = 1;
char *ls[1] = {"imagenet"};
args.classes = 2;
char *ls[2] = {"imagenet", "zzzzzzz"};
args.labels = ls;
pthread_t load_thread = load_data_in_thread(args);
@ -478,9 +674,10 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
astate.delta = 0;
astate.train = 1;
float *imerror = cuda_make_array(0, imlayer.outputs);
float *imerror = cuda_make_array(0, imlayer.outputs*imlayer.batch);
float *ones_gpu = cuda_make_array(0, ay_size);
fill_ongpu(ay_size, .99, ones_gpu, 1);
fill_ongpu(ay_size, .1, ones_gpu, 1);
fill_ongpu(ay_size/2, .9, ones_gpu, 2);
float aloss_avg = -1;
float gloss_avg = -1;
@ -500,17 +697,17 @@ 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] = .99;
image yim = float_to_image(net.w, net.h, net.c, train.X.vals[j]);
//rgb_to_yuv(yim);
train.y.vals[j][0] = .9;
train.y.vals[j][1] = .1;
gray.y.vals[j][0] = .1;
gray.y.vals[j][1] = .9;
}
time=clock();
float gloss = 0;
for(j = 0; j < net.subdivisions; ++j){
get_next_batch(train, net.batch, j*net.batch, pixs, y);
get_next_batch(gray, net.batch, j*net.batch, graypixs, y);
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, x_size);
/*
@ -523,23 +720,24 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
*net.seen += net.batch;
forward_network_gpu(net, gstate);
fill_ongpu(imlayer.outputs, 0, imerror, 1);
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);
scal_ongpu(imlayer.outputs, .1, net.layers[net.n-1].delta_gpu, 1);
scal_ongpu(imlayer.outputs*imlayer.batch, 1./1000., net.layers[net.n-1].delta_gpu, 1);
scal_ongpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch));
printf("features %f\n", cuda_mag_array(net.layers[net.n-1].delta_gpu, imlayer.outputs*imlayer.batch));
axpy_ongpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, net.layers[net.n-1].delta_gpu, 1);
backward_network_gpu(net, gstate);
scal_ongpu(imlayer.outputs, 100, imerror, 1);
printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
printf("features %f\n", cuda_mag_array(net.layers[net.n-1].delta_gpu, imlayer.outputs));
axpy_ongpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1);
gloss += get_network_cost(net) /(net.subdivisions*net.batch);
@ -547,7 +745,6 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
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);
gray.y.vals[index][0] = .01;
}
}
harmless_update_network_gpu(anet);
@ -557,7 +754,6 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
float aloss = train_network(anet, merge);
update_network_gpu(net);
update_network_gpu(anet);
free_data(merge);
free_data(train);
free_data(gray);
@ -840,7 +1036,7 @@ void train_lsd(char *cfgfile, char *weightfile, int clear)
save_weights(net, buff);
}
void test_lsd(char *cfgfile, char *weightfile, char *filename)
void test_lsd(char *cfgfile, char *weightfile, char *filename, int gray)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
@ -875,7 +1071,7 @@ void test_lsd(char *cfgfile, char *weightfile, char *filename)
image im = load_image_color(input, 0, 0);
image resized = resize_min(im, net.w);
image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
//grayscale_image_3c(crop);
if(gray) grayscale_image_3c(crop);
float *X = crop.data;
time=clock();
@ -916,8 +1112,11 @@ void run_lsd(int argc, char **argv)
if(0==strcmp(argv[2], "train")) train_lsd(cfg, weights, clear);
else if(0==strcmp(argv[2], "train2")) train_lsd2(cfg, weights, acfg, aweights, clear);
else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear);
else if(0==strcmp(argv[2], "traingan")) train_dcgan(cfg, weights, acfg, aweights, clear);
else if(0==strcmp(argv[2], "gan")) test_dcgan(cfg, weights);
else if(0==strcmp(argv[2], "train3")) train_lsd3(argv[3], argv[4], argv[5], argv[6], argv[7], argv[8], clear);
else if(0==strcmp(argv[2], "test")) test_lsd(cfg, weights, filename);
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);
/*
else if(0==strcmp(argv[2], "valid")) validate_lsd(cfg, weights);
*/

1
src/network_kernels.cu
View File

@ -8,7 +8,6 @@ extern "C" {
#include <assert.h>
#include "network.h"
#include "image.h"
#include "data.h"
#include "utils.h"
#include "parser.h"

5
src/nightmare.c
View File

@ -1,13 +1,8 @@
#include "network.h"
#include "parser.h"
#include "blas.h"
#include "utils.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
// ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2
float abs_mean(float *x, int n)

6
src/regressor.c
View File

@ -7,11 +7,6 @@
#include "cuda.h"
#include <sys/time.h>
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
image get_image_from_stream(CvCapture *cap);
#endif
void train_regressor(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
int i;
@ -185,7 +180,6 @@ void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
cvNamedWindow("Regressor", CV_WINDOW_NORMAL);
cvResizeWindow("Regressor", 512, 512);
float fps = 0;
int i;
while(1){
struct timeval tval_before, tval_after, tval_result;

4
src/rnn.c
View File

@ -4,10 +4,6 @@
#include "blas.h"
#include "parser.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
typedef struct {
float *x;
float *y;

1
src/rnn_vid.c
View File

@ -5,7 +5,6 @@
#include "blas.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
image get_image_from_stream(CvCapture *cap);
image ipl_to_image(IplImage* src);

4
src/super.c
View File

@ -3,10 +3,6 @@
#include "utils.h"
#include "parser.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
void train_super(char *cfgfile, char *weightfile, int clear)
{
char *train_images = "/data/imagenet/imagenet1k.train.list";

4
src/swag.c
View File

@ -5,10 +5,6 @@
#include "parser.h"
#include "box.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
void train_swag(char *cfgfile, char *weightfile)
{
char *train_images = "data/voc.0712.trainval";

4
src/tag.c
View File

@ -2,10 +2,6 @@
#include "utils.h"
#include "parser.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
void train_tag(char *cfgfile, char *weightfile, int clear)
{
srand(time(0));

5
src/voxel.c
View File

@ -3,11 +3,6 @@
#include "utils.h"
#include "parser.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
image get_image_from_stream(CvCapture *cap);
#endif
void extract_voxel(char *lfile, char *rfile, char *prefix)
{
#ifdef OPENCV

4
src/writing.c
View File

@ -2,10 +2,6 @@
#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/";

4
src/yolo.c
View File

@ -6,10 +6,6 @@
#include "box.h"
#include "demo.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
void train_yolo(char *cfgfile, char *weightfile)