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This commit is contained in:
Joseph Redmon 2016-02-29 13:54:12 -08:00
parent 23955b9fa0
commit 16d06ec0db
30 changed files with 1453 additions and 148 deletions
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8
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
@ -34,9 +34,9 @@ CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif
OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o rnn.o
OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o rnn.o rnn_vid.o crnn_layer.o coco_demo.o tag.o cifar.o
ifeq ($(GPU), 1)
OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o yolo_kernels.o coco_kernels.o
OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o yolo_kernels.o
endif
OBJS = $(addprefix $(OBJDIR), $(OBJ))

23
src/blas.c
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@ -115,13 +115,30 @@ void copy_cpu(int N, float *X, int INCX, float *Y, int INCY)
for(i = 0; i < N; ++i) Y[i*INCY] = X[i*INCX];
}
void smooth_l1_cpu(int n, float *pred, float *truth, float *delta)
void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error)
{
int i;
for(i = 0; i < n; ++i){
float diff = truth[i] - pred[i];
if(fabs(diff) > 1) delta[i] = diff;
else delta[i] = (diff > 0) ? 1 : -1;
float abs_val = fabs(diff);
if(abs_val < 1) {
error[i] = diff * diff;
delta[i] = diff;
}
else {
error[i] = 2*abs_val - 1;
delta[i] = (diff < 0) ? -1 : 1;
}
}
}
void l2_cpu(int n, float *pred, float *truth, float *delta, float *error)
{
int i;
for(i = 0; i < n; ++i){
float diff = truth[i] - pred[i];
error[i] = diff * diff;
delta[i] = diff;
}
}

8
src/blas.h
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@ -17,7 +17,6 @@ void fill_cpu(int N, float ALPHA, float * X, int INCX);
float dot_cpu(int N, float *X, int INCX, float *Y, int INCY);
void test_gpu_blas();
void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out);
void smooth_l1_cpu(int n, float *pred, float *truth, float *delta);
void mean_cpu(float *x, int batch, int filters, int spatial, float *mean);
void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance);
@ -29,6 +28,9 @@ void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int s
void variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta);
void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta);
void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error);
void l2_cpu(int n, float *pred, float *truth, float *delta, float *error);
#ifdef GPU
void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY);
void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY);
@ -53,9 +55,11 @@ void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *varianc
void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance);
void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean);
void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out);
void smooth_l1_gpu(int n, float *pred, float *truth, float *delta);
void scale_bias_gpu(float *output, float *biases, int batch, int n, int size);
void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates);
void scale_bias_gpu(float *output, float *biases, int batch, int n, int size);
void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error);
void l2_gpu(int n, float *pred, float *truth, float *delta, float *error);
#endif
#endif

33
src/blas_kernels.cu
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@ -410,18 +410,41 @@ extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int
check_error(cudaPeekAtLastError());
}
__global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta)
__global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, float *error)
{
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if(i < n){
float diff = truth[i] - pred[i];
if(abs(diff) > 1) delta[i] = diff;
else delta[i] = (diff > 0) ? 1 : -1;
float abs_val = abs(diff);
if(abs_val < 1) {
error[i] = diff * diff;
delta[i] = diff;
}
else {
error[i] = 2*abs_val - 1;
delta[i] = (diff < 0) ? -1 : 1;
}
}
}
extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta)
extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error)
{
smooth_l1_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta);
smooth_l1_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
check_error(cudaPeekAtLastError());
}
__global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float *error)
{
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if(i < n){
float diff = truth[i] - pred[i];
error[i] = diff * diff; //I know this is technically wrong, deal with it.
delta[i] = diff;
}
}
extern "C" void l2_gpu(int n, float *pred, float *truth, float *delta, float *error)
{
l2_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
check_error(cudaPeekAtLastError());
}

95
src/cifar.c Normal file
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@ -0,0 +1,95 @@
#include "network.h"
#include "utils.h"
#include "parser.h"
#include "option_list.h"
#include "blas.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
void train_cifar(char *cfgfile, char *weightfile)
{
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
char *backup_directory = "/home/pjreddie/backup/";
int classes = 10;
int N = 50000;
char **labels = get_labels("data/cifar/labels.txt");
int epoch = (*net.seen)/N;
data train = load_all_cifar10();
while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
clock_t time=clock();
float loss = train_network_sgd(net, train, 1);
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
if(*net.seen/N > epoch){
epoch = *net.seen/N;
char buff[256];
sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
}
if(get_current_batch(net)%100 == 0){
char buff[256];
sprintf(buff, "%s/%s.backup",backup_directory,base);
save_weights(net, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
free_network(net);
free_ptrs((void**)labels, classes);
free(base);
free_data(train);
}
void test_cifar(char *filename, char *weightfile)
{
network net = parse_network_cfg(filename);
if(weightfile){
load_weights(&net, weightfile);
}
srand(time(0));
clock_t time;
float avg_acc = 0;
float avg_top5 = 0;
data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
time=clock();
float *acc = network_accuracies(net, test, 2);
avg_acc += acc[0];
avg_top5 += acc[1];
printf("top1: %f, %lf seconds, %d images\n", avg_acc, sec(clock()-time), test.X.rows);
free_data(test);
}
void run_cifar(int argc, char **argv)
{
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
}
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
if(0==strcmp(argv[2], "train")) train_cifar(cfg, weights);
else if(0==strcmp(argv[2], "test")) test_cifar(cfg, weights);
}

169
src/classifier.c
View File

@ -70,6 +70,11 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.min = net.w;
args.max = net.max_crop;
args.size = net.w;
args.paths = paths;
args.classes = classes;
args.n = imgs;
@ -88,6 +93,16 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
/*
int u;
for(u = 0; u < net.batch; ++u){
image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
show_image(im, "loaded");
cvWaitKey(0);
}
*/
float loss = train_network(net, train);
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
@ -99,7 +114,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
}
if(*net.seen%1000 == 0){
if(*net.seen%100 == 0){
char buff[256];
sprintf(buff, "%s/%s.backup",backup_directory,base);
save_weights(net, buff);
@ -152,13 +167,14 @@ void validate_classifier(char *datacfg, char *filename, char *weightfile)
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.paths = paths;
args.classes = classes;
args.n = num;
args.m = 0;
args.labels = labels;
args.d = &buffer;
args.type = CLASSIFICATION_DATA;
args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(i = 1; i <= splits; ++i){
@ -221,19 +237,22 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
break;
}
}
image im = load_image_color(paths[i], 256, 256);
int w = net.w;
int h = net.h;
image im = load_image_color(paths[i], w, h);
int shift = 32;
image images[10];
images[0] = crop_image(im, -16, -16, 256, 256);
images[1] = crop_image(im, 16, -16, 256, 256);
images[2] = crop_image(im, 0, 0, 256, 256);
images[3] = crop_image(im, -16, 16, 256, 256);
images[4] = crop_image(im, 16, 16, 256, 256);
images[0] = crop_image(im, -shift, -shift, w, h);
images[1] = crop_image(im, shift, -shift, w, h);
images[2] = crop_image(im, 0, 0, w, h);
images[3] = crop_image(im, -shift, shift, w, h);
images[4] = crop_image(im, shift, shift, w, h);
flip_image(im);
images[5] = crop_image(im, -16, -16, 256, 256);
images[6] = crop_image(im, 16, -16, 256, 256);
images[7] = crop_image(im, 0, 0, 256, 256);
images[8] = crop_image(im, -16, 16, 256, 256);
images[9] = crop_image(im, 16, 16, 256, 256);
images[5] = crop_image(im, -shift, -shift, w, h);
images[6] = crop_image(im, shift, -shift, w, h);
images[7] = crop_image(im, 0, 0, w, h);
images[8] = crop_image(im, -shift, shift, w, h);
images[9] = crop_image(im, shift, shift, w, h);
float *pred = calloc(classes, sizeof(float));
for(j = 0; j < 10; ++j){
float *p = network_predict(net, images[j].data);
@ -252,6 +271,122 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
}
}
void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
{
int i, j;
network net = parse_network_cfg(filename);
set_batch_network(&net, 1);
if(weightfile){
load_weights(&net, weightfile);
}
srand(time(0));
list *options = read_data_cfg(datacfg);
char *label_list = option_find_str(options, "labels", "data/labels.list");
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
char **paths = (char **)list_to_array(plist);
int m = plist->size;
free_list(plist);
float avg_acc = 0;
float avg_topk = 0;
int *indexes = calloc(topk, sizeof(int));
for(i = 0; i < m; ++i){
int class = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
class = j;
break;
}
}
image im = load_image_color(paths[i], 0, 0);
resize_network(&net, im.w, im.h);
//show_image(im, "orig");
//show_image(crop, "cropped");
//cvWaitKey(0);
float *pred = network_predict(net, im.data);
free_image(im);
top_k(pred, classes, topk, indexes);
if(indexes[0] == class) avg_acc += 1;
for(j = 0; j < topk; ++j){
if(indexes[j] == class) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
}
}
void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
{
int i, j;
network net = parse_network_cfg(filename);
set_batch_network(&net, 1);
if(weightfile){
load_weights(&net, weightfile);
}
srand(time(0));
list *options = read_data_cfg(datacfg);
char *label_list = option_find_str(options, "labels", "data/labels.list");
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
char **paths = (char **)list_to_array(plist);
int m = plist->size;
free_list(plist);
float avg_acc = 0;
float avg_topk = 0;
int *indexes = calloc(topk, sizeof(int));
for(i = 0; i < m; ++i){
int class = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
class = j;
break;
}
}
image im = load_image_color(paths[i], 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);
//show_image(im, "orig");
//show_image(crop, "cropped");
//cvWaitKey(0);
float *pred = network_predict(net, crop.data);
free_image(im);
free_image(resized);
free_image(crop);
top_k(pred, classes, topk, indexes);
if(indexes[0] == class) avg_acc += 1;
for(j = 0; j < topk; ++j){
if(indexes[j] == class) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
}
}
void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
{
int i, j;
@ -271,7 +406,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
int scales[] = {224, 256, 384, 480, 640};
int scales[] = {224, 256, 384, 480, 512};
int nscales = sizeof(scales)/sizeof(scales[0]);
char **paths = (char **)list_to_array(plist);
@ -402,7 +537,7 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_
args.m = 0;
args.labels = 0;
args.d = &buffer;
args.type = CLASSIFICATION_DATA;
args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(curr = net.batch; curr < m; curr += net.batch){
@ -420,7 +555,7 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_
time=clock();
matrix pred = network_predict_data(net, val);
int i, j;
if (target_layer >= 0){
//layer l = net.layers[target_layer];
@ -461,6 +596,8 @@ void run_classifier(int argc, char **argv)
else if(0==strcmp(argv[2], "valid")) validate_classifier(data, cfg, weights);
else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
else if(0==strcmp(argv[2], "validsingle")) validate_classifier_single(data, cfg, weights);
else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights);
}

4
src/coco.c
View File

@ -389,10 +389,10 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index, char *filename);
static void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, char* filename)
{
#if defined(OPENCV) && defined(GPU)
#if defined(OPENCV)
demo_coco(cfgfile, weightfile, thresh, cam_index, filename);
#else
fprintf(stderr, "Need to compile with GPU and OpenCV for demo.\n");
fprintf(stderr, "Need to compile with OpenCV for demo.\n");
#endif
}

152
src/coco_demo.c Normal file
View File

@ -0,0 +1,152 @@
#include "network.h"
#include "detection_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
#include "image.h"
#include <sys/time.h>
#define FRAMES 1
#ifdef OPENCV
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
void convert_coco_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
extern char *coco_classes[];
extern image coco_labels[];
static float **probs;
static box *boxes;
static network net;
static image in ;
static image in_s ;
static image det ;
static image det_s;
static image disp ;
static CvCapture * cap;
static float fps = 0;
static float demo_thresh = 0;
static float *predictions[FRAMES];
static int demo_index = 0;
static image images[FRAMES];
static float *avg;
void *fetch_in_thread_coco(void *ptr)
{
in = get_image_from_stream(cap);
in_s = resize_image(in, net.w, net.h);
return 0;
}
void *detect_in_thread_coco(void *ptr)
{
float nms = .4;
detection_layer l = net.layers[net.n-1];
float *X = det_s.data;
float *prediction = network_predict(net, X);
memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float));
mean_arrays(predictions, FRAMES, l.outputs, avg);
free_image(det_s);
convert_coco_detections(avg, l.classes, l.n, l.sqrt, l.side, 1, 1, demo_thresh, probs, boxes, 0);
if (nms > 0) do_nms(boxes, probs, l.side*l.side*l.n, l.classes, nms);
printf("\033[2J");
printf("\033[1;1H");
printf("\nFPS:%.0f\n",fps);
printf("Objects:\n\n");
images[demo_index] = det;
det = images[(demo_index + FRAMES/2 + 1)%FRAMES];
demo_index = (demo_index + 1)%FRAMES;
draw_detections(det, l.side*l.side*l.n, demo_thresh, boxes, probs, coco_classes, coco_labels, 80);
return 0;
}
void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename)
{
demo_thresh = thresh;
printf("YOLO demo\n");
net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
srand(2222222);
if(filename){
cap = cvCaptureFromFile(filename);
}else{
cap = cvCaptureFromCAM(cam_index);
}
if(!cap) error("Couldn't connect to webcam.\n");
cvNamedWindow("YOLO", CV_WINDOW_NORMAL);
cvResizeWindow("YOLO", 512, 512);
detection_layer l = net.layers[net.n-1];
int j;
avg = (float *) calloc(l.outputs, sizeof(float));
for(j = 0; j < FRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float));
for(j = 0; j < FRAMES; ++j) images[j] = make_image(1,1,3);
boxes = (box *)calloc(l.side*l.side*l.n, sizeof(box));
probs = (float **)calloc(l.side*l.side*l.n, sizeof(float *));
for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));
pthread_t fetch_thread;
pthread_t detect_thread;
fetch_in_thread_coco(0);
det = in;
det_s = in_s;
fetch_in_thread_coco(0);
detect_in_thread_coco(0);
disp = det;
det = in;
det_s = in_s;
for(j = 0; j < FRAMES/2; ++j){
fetch_in_thread_coco(0);
detect_in_thread_coco(0);
disp = det;
det = in;
det_s = in_s;
}
while(1){
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
if(pthread_create(&fetch_thread, 0, fetch_in_thread_coco, 0)) error("Thread creation failed");
if(pthread_create(&detect_thread, 0, detect_in_thread_coco, 0)) error("Thread creation failed");
show_image(disp, "YOLO");
save_image(disp, "YOLO");
free_image(disp);
cvWaitKey(10);
pthread_join(fetch_thread, 0);
pthread_join(detect_thread, 0);
disp = det;
det = in;
det_s = in_s;
gettimeofday(&tval_after, NULL);
timersub(&tval_after, &tval_before, &tval_result);
float curr = 1000000.f/((long int)tval_result.tv_usec);
fps = .9*fps + .1*curr;
}
}
#else
void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index){
fprintf(stderr, "YOLO-COCO demo needs OpenCV for webcam images.\n");
}
#endif

16
src/convolutional_kernels.cu
View File

@ -121,11 +121,11 @@ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int
check_error(cudaPeekAtLastError());
}
void swap_binary(convolutional_layer l)
void swap_binary(convolutional_layer *l)
{
float *swap = l.filters_gpu;
l.filters_gpu = l.binary_filters_gpu;
l.binary_filters_gpu = swap;
float *swap = l->filters_gpu;
l->filters_gpu = l->binary_filters_gpu;
l->binary_filters_gpu = swap;
}
void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
@ -139,7 +139,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
if(l.binary){
binarize_filters_gpu(l.filters_gpu, l.n, l.c*l.size*l.size, l.binary_filters_gpu);
swap_binary(l);
swap_binary(&l);
}
for(i = 0; i < l.batch; ++i){
@ -172,7 +172,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, n);
activate_array_ongpu(l.output_gpu, m*n*l.batch, l.activation);
if(l.binary) swap_binary(l);
if(l.binary) swap_binary(&l);
}
void backward_convolutional_layer_gpu(convolutional_layer l, network_state state)
@ -206,7 +206,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
if(state.delta){
if(l.binary) swap_binary(l);
if(l.binary) swap_binary(&l);
float * a = l.filters_gpu;
float * b = l.delta_gpu;
float * c = l.col_image_gpu;
@ -214,7 +214,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k);
col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta + i*l.c*l.h*l.w);
if(l.binary) swap_binary(l);
if(l.binary) swap_binary(&l);
}
}
}

26
src/cost_layer.c
View File

@ -41,9 +41,11 @@ cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float sca
l.outputs = inputs;
l.cost_type = cost_type;
l.delta = calloc(inputs*batch, sizeof(float));
l.output = calloc(1, sizeof(float));
l.output = calloc(inputs*batch, sizeof(float));
l.cost = calloc(1, sizeof(float));
#ifdef GPU
l.delta_gpu = cuda_make_array(l.delta, inputs*batch);
l.delta_gpu = cuda_make_array(l.output, inputs*batch);
l.output_gpu = cuda_make_array(l.delta, inputs*batch);
#endif
return l;
}
@ -53,9 +55,12 @@ void resize_cost_layer(cost_layer *l, int inputs)
l->inputs = inputs;
l->outputs = inputs;
l->delta = realloc(l->delta, inputs*l->batch*sizeof(float));
l->output = realloc(l->output, inputs*l->batch*sizeof(float));
#ifdef GPU
cuda_free(l->delta_gpu);
cuda_free(l->output_gpu);
l->delta_gpu = cuda_make_array(l->delta, inputs*l->batch);
l->output_gpu = cuda_make_array(l->output, inputs*l->batch);
#endif
}
@ -69,13 +74,11 @@ void forward_cost_layer(cost_layer l, network_state state)
}
}
if(l.cost_type == SMOOTH){
smooth_l1_cpu(l.batch*l.inputs, state.input, state.truth, l.delta);
smooth_l1_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output);
} else {
copy_cpu(l.batch*l.inputs, state.truth, 1, l.delta, 1);
axpy_cpu(l.batch*l.inputs, -1, state.input, 1, l.delta, 1);
l2_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output);
}
*(l.output) = dot_cpu(l.batch*l.inputs, l.delta, 1, l.delta, 1);
//printf("cost: %f\n", *l.output);
l.cost[0] = sum_array(l.output, l.batch*l.inputs);
}
void backward_cost_layer(const cost_layer l, network_state state)
@ -103,14 +106,13 @@ void forward_cost_layer_gpu(cost_layer l, network_state state)
}
if(l.cost_type == SMOOTH){
smooth_l1_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu);
smooth_l1_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu);
} else {
copy_ongpu(l.batch*l.inputs, state.truth, 1, l.delta_gpu, 1);
axpy_ongpu(l.batch*l.inputs, -1, state.input, 1, l.delta_gpu, 1);
l2_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu);
}
cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
*(l.output) = dot_cpu(l.batch*l.inputs, l.delta, 1, l.delta, 1);
cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs);
l.cost[0] = sum_array(l.output, l.batch*l.inputs);
}
void backward_cost_layer_gpu(const cost_layer l, network_state state)

277
src/crnn_layer.c Normal file
View File

@ -0,0 +1,277 @@
#include "crnn_layer.h"
#include "convolutional_layer.h"
#include "utils.h"
#include "cuda.h"
#include "blas.h"
#include "gemm.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
static void increment_layer(layer *l, int steps)
{
int num = l->outputs*l->batch*steps;
l->output += num;
l->delta += num;
l->x += num;
l->x_norm += num;
#ifdef GPU
l->output_gpu += num;
l->delta_gpu += num;
l->x_gpu += num;
l->x_norm_gpu += num;
#endif
}
layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize)
{
fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters);
batch = batch / steps;
layer l = {0};
l.batch = batch;
l.type = CRNN;
l.steps = steps;
l.h = h;
l.w = w;
l.c = c;
l.out_h = h;
l.out_w = w;
l.out_c = output_filters;
l.inputs = h*w*c;
l.hidden = h * w * hidden_filters;
l.outputs = l.out_h * l.out_w * l.out_c;
l.state = calloc(l.hidden*batch*(steps+1), sizeof(float));
l.input_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0);
l.input_layer->batch = batch;
l.self_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0);
l.self_layer->batch = batch;
l.output_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0);
l.output_layer->batch = batch;
l.output = l.output_layer->output;
l.delta = l.output_layer->delta;
#ifdef GPU
l.state_gpu = cuda_make_array(l.state, l.hidden*batch*(steps+1));
l.output_gpu = l.output_layer->output_gpu;
l.delta_gpu = l.output_layer->delta_gpu;
#endif
return l;
}
void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, float decay)
{
update_convolutional_layer(*(l.input_layer), batch, learning_rate, momentum, decay);
update_convolutional_layer(*(l.self_layer), batch, learning_rate, momentum, decay);
update_convolutional_layer(*(l.output_layer), batch, learning_rate, momentum, decay);
}
void forward_crnn_layer(layer l, network_state state)
{
network_state s = {0};
s.train = state.train;
int i;
layer input_layer = *(l.input_layer);
layer self_layer = *(l.self_layer);
layer output_layer = *(l.output_layer);
fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1);
fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1);
fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1);
if(state.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1);
for (i = 0; i < l.steps; ++i) {
s.input = state.input;
forward_convolutional_layer(input_layer, s);
s.input = l.state;
forward_convolutional_layer(self_layer, s);
float *old_state = l.state;
if(state.train) l.state += l.hidden*l.batch;
if(l.shortcut){
copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1);
}else{
fill_cpu(l.hidden * l.batch, 0, l.state, 1);
}
axpy_cpu(l.hidden * l.batch, 1, input_layer.output, 1, l.state, 1);
axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
s.input = l.state;
forward_convolutional_layer(output_layer, s);
state.input += l.inputs*l.batch;
increment_layer(&input_layer, 1);
increment_layer(&self_layer, 1);
increment_layer(&output_layer, 1);
}
}
void backward_crnn_layer(layer l, network_state state)
{
network_state s = {0};
s.train = state.train;
int i;
layer input_layer = *(l.input_layer);
layer self_layer = *(l.self_layer);
layer output_layer = *(l.output_layer);
increment_layer(&input_layer, l.steps-1);
increment_layer(&self_layer, l.steps-1);
increment_layer(&output_layer, l.steps-1);
l.state += l.hidden*l.batch*l.steps;
for (i = l.steps-1; i >= 0; --i) {
copy_cpu(l.hidden * l.batch, input_layer.output, 1, l.state, 1);
axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
s.input = l.state;
s.delta = self_layer.delta;
backward_convolutional_layer(output_layer, s);
l.state -= l.hidden*l.batch;
/*
if(i > 0){
copy_cpu(l.hidden * l.batch, input_layer.output - l.hidden*l.batch, 1, l.state, 1);
axpy_cpu(l.hidden * l.batch, 1, self_layer.output - l.hidden*l.batch, 1, l.state, 1);
}else{
fill_cpu(l.hidden * l.batch, 0, l.state, 1);
}
*/
s.input = l.state;
s.delta = self_layer.delta - l.hidden*l.batch;
if (i == 0) s.delta = 0;
backward_convolutional_layer(self_layer, s);
copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1);
if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1);
s.input = state.input + i*l.inputs*l.batch;
if(state.delta) s.delta = state.delta + i*l.inputs*l.batch;
else s.delta = 0;
backward_convolutional_layer(input_layer, s);
increment_layer(&input_layer, -1);
increment_layer(&self_layer, -1);
increment_layer(&output_layer, -1);
}
}
#ifdef GPU
void pull_crnn_layer(layer l)
{
pull_convolutional_layer(*(l.input_layer));
pull_convolutional_layer(*(l.self_layer));
pull_convolutional_layer(*(l.output_layer));
}
void push_crnn_layer(layer l)
{
push_convolutional_layer(*(l.input_layer));
push_convolutional_layer(*(l.self_layer));
push_convolutional_layer(*(l.output_layer));
}
void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay)
{
update_convolutional_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay);
update_convolutional_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay);
update_convolutional_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay);
}
void forward_crnn_layer_gpu(layer l, network_state state)
{
network_state s = {0};
s.train = state.train;
int i;
layer input_layer = *(l.input_layer);
layer self_layer = *(l.self_layer);
layer output_layer = *(l.output_layer);
fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1);
fill_ongpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1);
fill_ongpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1);
if(state.train) fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1);
for (i = 0; i < l.steps; ++i) {
s.input = state.input;
forward_convolutional_layer_gpu(input_layer, s);
s.input = l.state_gpu;
forward_convolutional_layer_gpu(self_layer, s);
float *old_state = l.state_gpu;
if(state.train) l.state_gpu += l.hidden*l.batch;
if(l.shortcut){
copy_ongpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1);
}else{
fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1);
}
axpy_ongpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1);
axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
s.input = l.state_gpu;
forward_convolutional_layer_gpu(output_layer, s);
state.input += l.inputs*l.batch;
increment_layer(&input_layer, 1);
increment_layer(&self_layer, 1);
increment_layer(&output_layer, 1);
}
}
void backward_crnn_layer_gpu(layer l, network_state state)
{
network_state s = {0};
s.train = state.train;
int i;
layer input_layer = *(l.input_layer);
layer self_layer = *(l.self_layer);
layer output_layer = *(l.output_layer);
increment_layer(&input_layer, l.steps - 1);
increment_layer(&self_layer, l.steps - 1);
increment_layer(&output_layer, l.steps - 1);
l.state_gpu += l.hidden*l.batch*l.steps;
for (i = l.steps-1; i >= 0; --i) {
copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1);
axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
s.input = l.state_gpu;
s.delta = self_layer.delta_gpu;
backward_convolutional_layer_gpu(output_layer, s);
l.state_gpu -= l.hidden*l.batch;
s.input = l.state_gpu;
s.delta = self_layer.delta_gpu - l.hidden*l.batch;
if (i == 0) s.delta = 0;
backward_convolutional_layer_gpu(self_layer, s);
copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1);
if (i > 0 && l.shortcut) axpy_ongpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1);
s.input = state.input + i*l.inputs*l.batch;
if(state.delta) s.delta = state.delta + i*l.inputs*l.batch;
else s.delta = 0;
backward_convolutional_layer_gpu(input_layer, s);
increment_layer(&input_layer, -1);
increment_layer(&self_layer, -1);
increment_layer(&output_layer, -1);
}
}
#endif

24
src/crnn_layer.h Normal file
View File

@ -0,0 +1,24 @@
#ifndef CRNN_LAYER_H
#define CRNN_LAYER_H
#include "activations.h"
#include "layer.h"
#include "network.h"
layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize);
void forward_crnn_layer(layer l, network_state state);
void backward_crnn_layer(layer l, network_state state);
void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, float decay);
#ifdef GPU
void forward_crnn_layer_gpu(layer l, network_state state);
void backward_crnn_layer_gpu(layer l, network_state state);
void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay);
void push_crnn_layer(layer l);
void pull_crnn_layer(layer l);
#endif
#endif

9
src/darknet.c
View File

@ -21,6 +21,9 @@ extern void run_dice(int argc, char **argv);
extern void run_compare(int argc, char **argv);
extern void run_classifier(int argc, char **argv);
extern void run_char_rnn(int argc, char **argv);
extern void run_vid_rnn(int argc, char **argv);
extern void run_tag(int argc, char **argv);
extern void run_cifar(int argc, char **argv);
void change_rate(char *filename, float scale, float add)
{
@ -223,12 +226,18 @@ int main(int argc, char **argv)
average(argc, argv);
} else if (0 == strcmp(argv[1], "yolo")){
run_yolo(argc, argv);
} else if (0 == strcmp(argv[1], "cifar")){
run_cifar(argc, argv);
} else if (0 == strcmp(argv[1], "rnn")){
run_char_rnn(argc, argv);
} else if (0 == strcmp(argv[1], "vid")){
run_vid_rnn(argc, argv);
} else if (0 == strcmp(argv[1], "coco")){
run_coco(argc, argv);
} else if (0 == strcmp(argv[1], "classifier")){
run_classifier(argc, argv);
} else if (0 == strcmp(argv[1], "tag")){
run_tag(argc, argv);
} else if (0 == strcmp(argv[1], "compare")){
run_compare(argc, argv);
} else if (0 == strcmp(argv[1], "dice")){

89
src/data.c
View File

@ -82,6 +82,27 @@ matrix load_image_paths(char **paths, int n, int w, int h)
return X;
}
matrix load_image_cropped_paths(char **paths, int n, int min, int max, int size)
{
int i;
matrix X;
X.rows = n;
X.vals = calloc(X.rows, sizeof(float*));
X.cols = 0;
for(i = 0; i < n; ++i){
image im = load_image_color(paths[i], 0, 0);
image crop = random_crop_image(im, min, max, size);
int flip = rand_r(&data_seed)%2;
if (flip) flip_image(crop);
free_image(im);
X.vals[i] = crop.data;
X.cols = crop.h*crop.w*crop.c;
}
return X;
}
box_label *read_boxes(char *filename, int *n)
{
box_label *boxes = calloc(1, sizeof(box_label));
@ -386,6 +407,33 @@ matrix load_labels_paths(char **paths, int n, char **labels, int k)
return y;
}
matrix load_tags_paths(char **paths, int n, int k)
{
matrix y = make_matrix(n, k);
int i;
int count = 0;
for(i = 0; i < n; ++i){
char *label = find_replace(paths[i], "imgs", "labels");
label = find_replace(label, "_iconl.jpeg", ".txt");
FILE *file = fopen(label, "r");
if(!file){
label = find_replace(label, "labels", "labels2");
file = fopen(label, "r");
if(!file) continue;
}
++count;
int tag;
while(fscanf(file, "%d", &tag) == 1){
if(tag < k){
y.vals[i][tag] = 1;
}
}
fclose(file);
}
printf("%d/%d\n", count, n);
return y;
}
char **get_labels(char *filename)
{
list *plist = get_paths(filename);
@ -641,8 +689,10 @@ void *load_thread(void *ptr)
//printf("Loading data: %d\n", rand_r(&data_seed));
load_args a = *(struct load_args*)ptr;
if (a.type == CLASSIFICATION_DATA){
if (a.type == OLD_CLASSIFICATION_DATA){
*a.d = load_data(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
} else if (a.type == CLASSIFICATION_DATA){
*a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size);
} else if (a.type == DETECTION_DATA){
*a.d = load_data_detection(a.n, a.paths, a.m, a.classes, a.w, a.h, a.num_boxes, a.background);
} else if (a.type == WRITING_DATA){
@ -656,6 +706,9 @@ void *load_thread(void *ptr)
} else if (a.type == IMAGE_DATA){
*(a.im) = load_image_color(a.path, 0, 0);
*(a.resized) = resize_image(*(a.im), a.w, a.h);
} else if (a.type == TAG_DATA){
*a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size);
//*a.d = load_data(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
}
free(ptr);
return 0;
@ -696,6 +749,30 @@ data load_data(char **paths, int n, int m, char **labels, int k, int w, int h)
return d;
}
data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size)
{
if(m) paths = get_random_paths(paths, n, m);
data d;
d.shallow = 0;
d.X = load_image_cropped_paths(paths, n, min, max, size);
d.y = load_labels_paths(paths, n, labels, k);
if(m) free(paths);
return d;
}
data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size)
{
if(m) paths = get_random_paths(paths, n, m);
data d = {0};
d.w = size;
d.h = size;
d.shallow = 0;
d.X = load_image_cropped_paths(paths, n, min, max, size);
d.y = load_tags_paths(paths, n, k);
if(m) free(paths);
return d;
}
matrix concat_matrix(matrix m1, matrix m2)
{
int i, count = 0;
@ -759,8 +836,8 @@ data load_cifar10_data(char *filename)
X.vals[i][j] = (double)bytes[j+1];
}
}
translate_data_rows(d, -128);
scale_data_rows(d, 1./128);
//translate_data_rows(d, -128);
scale_data_rows(d, 1./255);
//normalize_data_rows(d);
fclose(fp);
return d;
@ -800,7 +877,7 @@ data load_all_cifar10()
for(b = 0; b < 5; ++b){
char buff[256];
sprintf(buff, "data/cifar10/data_batch_%d.bin", b+1);
sprintf(buff, "data/cifar/cifar-10-batches-bin/data_batch_%d.bin", b+1);
FILE *fp = fopen(buff, "rb");
if(!fp) file_error(buff);
for(i = 0; i < 10000; ++i){
@ -815,8 +892,8 @@ data load_all_cifar10()
fclose(fp);
}
//normalize_data_rows(d);
translate_data_rows(d, -128);
scale_data_rows(d, 1./128);
//translate_data_rows(d, -128);
scale_data_rows(d, 1./255);
return d;
}

5
src/data.h
View File

@ -27,7 +27,7 @@ typedef struct{
} data;
typedef enum {
CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA
CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA
} data_type;
typedef struct load_args{
@ -43,6 +43,7 @@ typedef struct load_args{
int nh;
int nw;
int num_boxes;
int min, max, size;
int classes;
int background;
float jitter;
@ -67,6 +68,8 @@ data load_data_captcha(char **paths, int n, int m, int k, int w, int h);
data load_data_captcha_encode(char **paths, int n, int m, int w, int h);
data load_data(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 classes, int w, int h, int num_boxes, int background);
data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size);
data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size);
box_label *read_boxes(char *filename, int *n);
data load_cifar10_data(char *filename);

60
src/image.c
View File

@ -4,11 +4,6 @@
#include <stdio.h>
#include <math.h>
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#endif
#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
#define STB_IMAGE_WRITE_IMPLEMENTATION
@ -329,6 +324,16 @@ void save_image(image im, const char *name)
if(!success) fprintf(stderr, "Failed to write image %s\n", buff);
}
#ifdef OPENCV
image get_image_from_stream(CvCapture *cap)
{
IplImage* src = cvQueryFrame(cap);
image im = ipl_to_image(src);
rgbgr_image(im);
return im;
}
#endif
#ifdef OPENCV
void save_image_jpg(image p, char *name)
{
@ -459,6 +464,39 @@ image crop_image(image im, int dx, int dy, int w, int h)
return cropped;
}
image resize_min(image im, int min)
{
int w = im.w;
int h = im.h;
if(w < h){
h = (h * min) / w;
w = min;
} else {
w = (w * min) / h;
h = min;
}
image resized = resize_image(im, w, h);
return resized;
}
image random_crop_image(image im, int low, int high, int size)
{
int r = rand_int(low, high);
image resized = resize_min(im, r);
int dx = rand_int(0, resized.w - size);
int dy = rand_int(0, resized.h - size);
image crop = crop_image(resized, dx, dy, size, size);
/*
show_image(im, "orig");
show_image(crop, "cropped");
cvWaitKey(0);
*/
free_image(resized);
return crop;
}
float three_way_max(float a, float b, float c)
{
return (a > b) ? ( (a > c) ? a : c) : ( (b > c) ? b : c) ;
@ -724,7 +762,7 @@ void test_resize(char *filename)
image exp5 = copy_image(im);
exposure_image(exp5, .5);
#ifdef GPU
#ifdef GPU
image r = resize_image(im, im.w, im.h);
image black = make_image(im.w*2 + 3, im.h*2 + 3, 9);
image black2 = make_image(im.w, im.h, 3);
@ -741,7 +779,7 @@ void test_resize(char *filename)
cuda_pull_array(black2_gpu, black2.data, black2.w*black2.h*black2.c);
show_image_layers(black, "Black");
show_image(black2, "Recreate");
#endif
#endif
show_image(im, "Original");
show_image(gray, "Gray");
@ -788,8 +826,12 @@ image load_image_cv(char *filename, int channels)
if( (src = cvLoadImage(filename, flag)) == 0 )
{
printf("Cannot load image \"%s\"\n", filename);
exit(0);
fprintf(stderr, "Cannot load image \"%s\"\n", filename);
char buff[256];
sprintf(buff, "echo %s >> bad.list", filename);
system(buff);
return make_image(10,10,3);
//exit(0);
}
image out = ipl_to_image(src);
cvReleaseImage(&src);

10
src/image.h
View File

@ -8,6 +8,11 @@
#include <math.h>
#include "box.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#endif
typedef struct {
int h;
int w;
@ -25,8 +30,9 @@ 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 random_crop_image(image im, int low, int high, int size);
image resize_image(image im, int w, int h);
image resize_image2(image im, int w, int h);
image resize_min(image im, int min);
void translate_image(image m, float s);
void normalize_image(image p);
image rotate_image(image m, float rad);
@ -53,6 +59,8 @@ void show_image_collapsed(image p, char *name);
#ifdef OPENCV
void save_image_jpg(image p, char *name);
image get_image_from_stream(CvCapture *cap);
image ipl_to_image(IplImage* src);
#endif
void print_image(image m);

4
src/imagenet.c
View File

@ -39,7 +39,7 @@ void train_imagenet(char *cfgfile, char *weightfile)
args.m = N;
args.labels = labels;
args.d = &buffer;
args.type = CLASSIFICATION_DATA;
args.type = OLD_CLASSIFICATION_DATA;
load_thread = load_data_in_thread(args);
int epoch = (*net.seen)/N;
@ -115,7 +115,7 @@ void validate_imagenet(char *filename, char *weightfile)
args.m = 0;
args.labels = labels;
args.d = &buffer;
args.type = CLASSIFICATION_DATA;
args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(i = 1; i <= splits; ++i){

3
src/layer.h
View File

@ -22,7 +22,8 @@ typedef enum {
LOCAL,
SHORTCUT,
ACTIVE,
RNN
RNN,
CRNN
} LAYER_TYPE;
typedef enum{

11
src/network.c
View File

@ -9,6 +9,7 @@
#include "crop_layer.h"
#include "connected_layer.h"
#include "rnn_layer.h"
#include "crnn_layer.h"
#include "local_layer.h"
#include "convolutional_layer.h"
#include "activation_layer.h"
@ -85,6 +86,8 @@ char *get_layer_string(LAYER_TYPE a)
return "connected";
case RNN:
return "rnn";
case CRNN:
return "crnn";
case MAXPOOL:
return "maxpool";
case AVGPOOL:
@ -149,6 +152,8 @@ void forward_network(network net, network_state state)
forward_connected_layer(l, state);
} else if(l.type == RNN){
forward_rnn_layer(l, state);
} else if(l.type == CRNN){
forward_crnn_layer(l, state);
} else if(l.type == CROP){
forward_crop_layer(l, state);
} else if(l.type == COST){
@ -185,6 +190,8 @@ void update_network(network net)
update_connected_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == RNN){
update_rnn_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == CRNN){
update_crnn_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == LOCAL){
update_local_layer(l, update_batch, rate, net.momentum, net.decay);
}
@ -205,7 +212,7 @@ float get_network_cost(network net)
int count = 0;
for(i = 0; i < net.n; ++i){
if(net.layers[i].type == COST){
sum += net.layers[i].output[0];
sum += net.layers[i].cost[0];
++count;
}
if(net.layers[i].type == DETECTION){
@ -261,6 +268,8 @@ void backward_network(network net, network_state state)
backward_connected_layer(l, state);
} else if(l.type == RNN){
backward_rnn_layer(l, state);
} else if(l.type == CRNN){
backward_crnn_layer(l, state);
} else if(l.type == LOCAL){
backward_local_layer(l, state);
} else if(l.type == COST){

1
src/network.h
View File

@ -36,6 +36,7 @@ typedef struct network{
int inputs;
int h, w, c;
int max_crop;
#ifdef GPU
float **input_gpu;

7
src/network_kernels.cu
View File

@ -16,6 +16,7 @@ extern "C" {
#include "crop_layer.h"
#include "connected_layer.h"
#include "rnn_layer.h"
#include "crnn_layer.h"
#include "detection_layer.h"
#include "convolutional_layer.h"
#include "activation_layer.h"
@ -59,6 +60,8 @@ void forward_network_gpu(network net, network_state state)
forward_connected_layer_gpu(l, state);
} else if(l.type == RNN){
forward_rnn_layer_gpu(l, state);
} else if(l.type == CRNN){
forward_crnn_layer_gpu(l, state);
} else if(l.type == CROP){
forward_crop_layer_gpu(l, state);
} else if(l.type == COST){
@ -122,6 +125,8 @@ void backward_network_gpu(network net, network_state state)
backward_connected_layer_gpu(l, state);
} else if(l.type == RNN){
backward_rnn_layer_gpu(l, state);
} else if(l.type == CRNN){
backward_crnn_layer_gpu(l, state);
} else if(l.type == COST){
backward_cost_layer_gpu(l, state);
} else if(l.type == ROUTE){
@ -147,6 +152,8 @@ void update_network_gpu(network net)
update_connected_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == RNN){
update_rnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == CRNN){
update_crnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == LOCAL){
update_local_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
}

88
src/nightmare.c
View File

@ -8,6 +8,8 @@
#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)
{
int i;
@ -31,8 +33,8 @@ void calculate_loss(float *output, float *delta, int n, float thresh)
void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm)
{
scale_image(orig, 2);
translate_image(orig, -1);
//scale_image(orig, 2);
//translate_image(orig, -1);
net->n = max_layer + 1;
int dx = rand()%16 - 8;
@ -98,8 +100,8 @@ void optimize_picture(network *net, image orig, int max_layer, float scale, floa
translate_image(orig, mean);
*/
translate_image(orig, 1);
scale_image(orig, .5);
//translate_image(orig, 1);
//scale_image(orig, .5);
//normalize_image(orig);
constrain_image(orig);
@ -133,50 +135,47 @@ void smooth(image recon, image update, float lambda, int num)
}
}
void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size)
void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters)
{
scale_image(recon, 2);
translate_image(recon, -1);
int iter = 0;
for (iter = 0; iter < iters; ++iter) {
image delta = make_image(recon.w, recon.h, recon.c);
image delta = make_image(recon.w, recon.h, recon.c);
network_state state = {0};
network_state state = {0};
#ifdef GPU
state.input = cuda_make_array(recon.data, recon.w*recon.h*recon.c);
state.delta = cuda_make_array(delta.data, delta.w*delta.h*delta.c);
state.truth = cuda_make_array(features, get_network_output_size(net));
state.input = cuda_make_array(recon.data, recon.w*recon.h*recon.c);
state.delta = cuda_make_array(delta.data, delta.w*delta.h*delta.c);
state.truth = cuda_make_array(features, get_network_output_size(net));
forward_network_gpu(net, state);
backward_network_gpu(net, state);
forward_network_gpu(net, state);
backward_network_gpu(net, state);
cuda_pull_array(state.delta, delta.data, delta.w*delta.h*delta.c);
cuda_pull_array(state.delta, delta.data, delta.w*delta.h*delta.c);
cuda_free(state.input);
cuda_free(state.delta);
cuda_free(state.truth);
cuda_free(state.input);
cuda_free(state.delta);
cuda_free(state.truth);
#else
state.input = recon.data;
state.delta = delta.data;
state.truth = features;
state.input = recon.data;
state.delta = delta.data;
state.truth = features;
forward_network(net, state);
backward_network(net, state);
forward_network(net, state);
backward_network(net, state);
#endif
axpy_cpu(recon.w*recon.h*recon.c, 1, delta.data, 1, update.data, 1);
smooth(recon, update, lambda, smooth_size);
axpy_cpu(recon.w*recon.h*recon.c, 1, delta.data, 1, update.data, 1);
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);
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);
translate_image(recon, 1);
scale_image(recon, .5);
//float mag = mag_array(recon.data, recon.w*recon.h*recon.c);
//scal_cpu(recon.w*recon.h*recon.c, 600/mag, recon.data, 1);
float mag = mag_array(recon.data, recon.w*recon.h*recon.c);
scal_cpu(recon.w*recon.h*recon.c, 600/mag, recon.data, 1);
constrain_image(recon);
free_image(delta);
constrain_image(recon);
free_image(delta);
}
}
@ -226,7 +225,7 @@ void run_nightmare(int argc, char **argv)
im = resized;
}
float *features;
float *features = 0;
image update;
if (reconstruct){
resize_network(&net, im.w, im.h);
@ -241,13 +240,19 @@ void run_nightmare(int argc, char **argv)
printf("%d features\n", out_im.w*out_im.h*out_im.c);
im = resize_image(im, im.w*2, im.h);
f_im = resize_image(f_im, f_im.w*2, f_im.h);
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);
}
free_image(im);
im = make_random_image(im.w, im.h, im.c);
update = make_image(im.w, im.h, im.c);
}
int e;
@ -259,11 +264,12 @@ void run_nightmare(int argc, char **argv)
fprintf(stderr, "%d, ", n);
fflush(stderr);
if(reconstruct){
reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size);
reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1);
//if ((n+1)%30 == 0) rate *= .5;
show_image(im, "reconstruction");
#ifdef OPENCV
#ifdef OPENCV
cvWaitKey(10);
#endif
#endif
}else{
int layer = max_layer + rand()%range - range/2;
int octave = rand()%octaves;

102
src/parser.c
View File

@ -12,6 +12,7 @@
#include "deconvolutional_layer.h"
#include "connected_layer.h"
#include "rnn_layer.h"
#include "crnn_layer.h"
#include "maxpool_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
@ -36,6 +37,7 @@ int is_local(section *s);
int is_deconvolutional(section *s);
int is_connected(section *s);
int is_rnn(section *s);
int is_crnn(section *s);
int is_maxpool(section *s);
int is_avgpool(section *s);
int is_dropout(section *s);
@ -169,6 +171,21 @@ convolutional_layer parse_convolutional(list *options, size_params params)
return layer;
}
layer parse_crnn(list *options, size_params params)
{
int output_filters = option_find_int(options, "output_filters",1);
int hidden_filters = option_find_int(options, "hidden_filters",1);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, activation, batch_normalize);
l.shortcut = option_find_int_quiet(options, "shortcut", 0);
return l;
}
layer parse_rnn(list *options, size_params params)
{
int output = option_find_int(options, "output",1);
@ -419,6 +436,7 @@ void parse_net_options(list *options, network *net)
net->w = option_find_int_quiet(options, "width",0);
net->c = option_find_int_quiet(options, "channels",0);
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);
if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
@ -501,6 +519,8 @@ network parse_network_cfg(char *filename)
l = parse_deconvolutional(options, params);
}else if(is_rnn(s)){
l = parse_rnn(options, params);
}else if(is_crnn(s)){
l = parse_crnn(options, params);
}else if(is_connected(s)){
l = parse_connected(options, params);
}else if(is_crop(s)){
@ -591,6 +611,10 @@ int is_network(section *s)
return (strcmp(s->type, "[net]")==0
|| strcmp(s->type, "[network]")==0);
}
int is_crnn(section *s)
{
return (strcmp(s->type, "[crnn]")==0);
}
int is_rnn(section *s)
{
return (strcmp(s->type, "[rnn]")==0);
@ -705,6 +729,23 @@ void save_weights_double(network net, char *filename)
fclose(fp);
}
void save_convolutional_weights(layer l, FILE *fp)
{
#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(l);
}
#endif
int num = l.n*l.c*l.size*l.size;
fwrite(l.biases, sizeof(float), l.n, fp);
if (l.batch_normalize){
fwrite(l.scales, sizeof(float), l.n, fp);
fwrite(l.rolling_mean, sizeof(float), l.n, fp);
fwrite(l.rolling_variance, sizeof(float), l.n, fp);
}
fwrite(l.filters, sizeof(float), num, fp);
}
void save_connected_weights(layer l, FILE *fp)
{
#ifdef GPU
@ -739,25 +780,17 @@ void save_weights_upto(network net, char *filename, int cutoff)
for(i = 0; i < net.n && i < cutoff; ++i){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(l);
}
#endif
int num = l.n*l.c*l.size*l.size;
fwrite(l.biases, sizeof(float), l.n, fp);
if (l.batch_normalize){
fwrite(l.scales, sizeof(float), l.n, fp);
fwrite(l.rolling_mean, sizeof(float), l.n, fp);
fwrite(l.rolling_variance, sizeof(float), l.n, fp);
}
fwrite(l.filters, sizeof(float), num, fp);
save_convolutional_weights(l, fp);
} if(l.type == CONNECTED){
save_connected_weights(l, fp);
} if(l.type == RNN){
save_connected_weights(*(l.input_layer), fp);
save_connected_weights(*(l.self_layer), fp);
save_connected_weights(*(l.output_layer), fp);
} if(l.type == CRNN){
save_convolutional_weights(*(l.input_layer), fp);
save_convolutional_weights(*(l.self_layer), fp);
save_convolutional_weights(*(l.output_layer), fp);
} if(l.type == LOCAL){
#ifdef GPU
if(gpu_index >= 0){
@ -809,6 +842,27 @@ void load_connected_weights(layer l, FILE *fp, int transpose)
#endif
}
void load_convolutional_weights(layer l, FILE *fp)
{
int num = l.n*l.c*l.size*l.size;
fread(l.biases, sizeof(float), l.n, fp);
if (l.batch_normalize && (!l.dontloadscales)){
fread(l.scales, sizeof(float), l.n, fp);
fread(l.rolling_mean, sizeof(float), l.n, fp);
fread(l.rolling_variance, sizeof(float), l.n, fp);
}
fread(l.filters, sizeof(float), num, fp);
if (l.flipped) {
transpose_matrix(l.filters, l.c*l.size*l.size, l.n);
}
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(l);
}
#endif
}
void load_weights_upto(network *net, char *filename, int cutoff)
{
fprintf(stderr, "Loading weights from %s...", filename);
@ -830,22 +884,7 @@ void load_weights_upto(network *net, char *filename, int cutoff)
layer l = net->layers[i];
if (l.dontload) continue;
if(l.type == CONVOLUTIONAL){
int num = l.n*l.c*l.size*l.size;
fread(l.biases, sizeof(float), l.n, fp);
if (l.batch_normalize && (!l.dontloadscales)){
fread(l.scales, sizeof(float), l.n, fp);
fread(l.rolling_mean, sizeof(float), l.n, fp);
fread(l.rolling_variance, sizeof(float), l.n, fp);
}
fread(l.filters, sizeof(float), num, fp);
if (l.flipped) {
transpose_matrix(l.filters, l.c*l.size*l.size, l.n);
}
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(l);
}
#endif
load_convolutional_weights(l, fp);
}
if(l.type == DECONVOLUTIONAL){
int num = l.n*l.c*l.size*l.size;
@ -860,6 +899,11 @@ void load_weights_upto(network *net, char *filename, int cutoff)
if(l.type == CONNECTED){
load_connected_weights(l, fp, transpose);
}
if(l.type == CRNN){
load_convolutional_weights(*(l.input_layer), fp);
load_convolutional_weights(*(l.self_layer), fp);
load_convolutional_weights(*(l.output_layer), fp);
}
if(l.type == RNN){
load_connected_weights(*(l.input_layer), fp, transpose);
load_connected_weights(*(l.self_layer), fp, transpose);

1
src/rnn.c
View File

@ -71,6 +71,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename)
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int batch = net.batch;
int steps = net.time_steps;
//*net.seen = 0;
int i = (*net.seen)/net.batch;
clock_t time;

2
src/rnn_layer.c
View File

@ -10,7 +10,7 @@
#include <stdlib.h>
#include <string.h>
void increment_layer(layer *l, int steps)
static void increment_layer(layer *l, int steps)
{
int num = l->outputs*l->batch*steps;
l->output += num;

210
src/rnn_vid.c Normal file
View File

@ -0,0 +1,210 @@
#include "network.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "blas.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters);
typedef struct {
float *x;
float *y;
} float_pair;
float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int steps)
{
int b;
assert(net.batch == steps + 1);
image out_im = get_network_image(net);
int output_size = out_im.w*out_im.h*out_im.c;
printf("%d %d %d\n", out_im.w, out_im.h, out_im.c);
float *feats = calloc(net.batch*batch*output_size, sizeof(float));
for(b = 0; b < batch; ++b){
int input_size = net.w*net.h*net.c;
float *input = calloc(input_size*net.batch, sizeof(float));
char *filename = files[rand()%n];
CvCapture *cap = cvCaptureFromFile(filename);
int frames = cvGetCaptureProperty(cap, CV_CAP_PROP_FRAME_COUNT);
int index = rand() % (frames - steps - 2);
if (frames < (steps + 4)){
--b;
free(input);
continue;
}
printf("frames: %d, index: %d\n", frames, index);
cvSetCaptureProperty(cap, CV_CAP_PROP_POS_FRAMES, index);
int i;
for(i = 0; i < net.batch; ++i){
IplImage* src = cvQueryFrame(cap);
image im = ipl_to_image(src);
rgbgr_image(im);
image re = resize_image(im, net.w, net.h);
//show_image(re, "loaded");
//cvWaitKey(10);
memcpy(input + i*input_size, re.data, input_size*sizeof(float));
free_image(im);
free_image(re);
}
float *output = network_predict(net, input);
free(input);
for(i = 0; i < net.batch; ++i){
memcpy(feats + (b + i*batch)*output_size, output + i*output_size, output_size*sizeof(float));
}
cvReleaseCapture(&cap);
}
//printf("%d %d %d\n", out_im.w, out_im.h, out_im.c);
float_pair p = {0};
p.x = feats;
p.y = feats + output_size*batch; //+ out_im.w*out_im.h*out_im.c;
return p;
}
void train_vid_rnn(char *cfgfile, char *weightfile)
{
char *train_videos = "data/vid/train.txt";
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
data_seed = time(0);
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch*net.subdivisions;
int i = *net.seen/imgs;
list *plist = get_paths(train_videos);
int N = plist->size;
char **paths = (char **)list_to_array(plist);
clock_t time;
int steps = net.time_steps;
int batch = net.batch / net.time_steps;
network extractor = parse_network_cfg("cfg/extractor.cfg");
load_weights(&extractor, "/home/pjreddie/trained/yolo-coco.conv");
while(get_current_batch(net) < net.max_batches){
i += 1;
time=clock();
float_pair p = get_rnn_vid_data(extractor, paths, N, batch, steps);
float loss = train_network_datum(net, p.x, p.y) / (net.batch);
free(p.x);
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time));
if(i%100==0){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
if(i%10==0){
char buff[256];
sprintf(buff, "%s/%s.backup", backup_directory, base);
save_weights(net, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
}
image save_reconstruction(network net, image *init, float *feat, char *name, int i)
{
image recon;
if (init) {
recon = copy_image(*init);
} else {
recon = make_random_image(net.w, net.h, 3);
}
image update = make_image(net.w, net.h, 3);
reconstruct_picture(net, feat, recon, update, .01, .9, .1, 2, 50);
char buff[256];
sprintf(buff, "%s%d", name, i);
save_image(recon, buff);
free_image(update);
return recon;
}
void generate_vid_rnn(char *cfgfile, char *weightfile)
{
network extractor = parse_network_cfg("cfg/extractor.recon.cfg");
load_weights(&extractor, "/home/pjreddie/trained/yolo-coco.conv");
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&extractor, 1);
set_batch_network(&net, 1);
int i;
CvCapture *cap = cvCaptureFromFile("/extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4");
float *feat;
float *next;
image last;
for(i = 0; i < 25; ++i){
image im = get_image_from_stream(cap);
image re = resize_image(im, extractor.w, extractor.h);
feat = network_predict(extractor, re.data);
if(i > 0){
printf("%f %f\n", mean_array(feat, 14*14*512), variance_array(feat, 14*14*512));
printf("%f %f\n", mean_array(next, 14*14*512), variance_array(next, 14*14*512));
printf("%f\n", mse_array(feat, 14*14*512));
axpy_cpu(14*14*512, -1, feat, 1, next, 1);
printf("%f\n", mse_array(next, 14*14*512));
}
next = network_predict(net, feat);
free_image(im);
free_image(save_reconstruction(extractor, 0, feat, "feat", i));
free_image(save_reconstruction(extractor, 0, next, "next", i));
if (i==24) last = copy_image(re);
free_image(re);
}
for(i = 0; i < 30; ++i){
next = network_predict(net, next);
image new = save_reconstruction(extractor, &last, next, "new", i);
free_image(last);
last = new;
}
}
void run_vid_rnn(int argc, char **argv)
{
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
}
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
//char *filename = (argc > 5) ? argv[5]: 0;
if(0==strcmp(argv[2], "train")) train_vid_rnn(cfg, weights);
else if(0==strcmp(argv[2], "generate")) generate_vid_rnn(cfg, weights);
}
#else
void run_vid_rnn(int argc, char **argv){}
#endif

144
src/tag.c Normal file
View File

@ -0,0 +1,144 @@
#include "network.h"
#include "utils.h"
#include "parser.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
void train_tag(char *cfgfile, char *weightfile)
{
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
char *backup_directory = "/home/pjreddie/backup/";
printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1024;
list *plist = get_paths("/home/pjreddie/tag/train.list");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
int N = plist->size;
clock_t time;
pthread_t load_thread;
data train;
data buffer;
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.min = net.w;
args.max = net.max_crop;
args.size = net.w;
args.paths = paths;
args.classes = net.outputs;
args.n = imgs;
args.m = N;
args.d = &buffer;
args.type = TAG_DATA;
fprintf(stderr, "%d classes\n", net.outputs);
load_thread = load_data_in_thread(args);
int epoch = (*net.seen)/N;
while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
time=clock();
pthread_join(load_thread, 0);
train = buffer;
load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
free_data(train);
if(*net.seen/N > epoch){
epoch = *net.seen/N;
char buff[256];
sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
}
if(get_current_batch(net)%100 == 0){
char buff[256];
sprintf(buff, "%s/%s.backup",backup_directory,base);
save_weights(net, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
pthread_join(load_thread, 0);
free_data(buffer);
free_network(net);
free_ptrs((void**)paths, plist->size);
free_list(plist);
free(base);
}
void test_tag(char *cfgfile, char *weightfile, char *filename)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
srand(2222222);
int i = 0;
char **names = get_labels("data/tags.txt");
clock_t time;
int indexes[10];
char buff[256];
char *input = buff;
while(1){
if(filename){
strncpy(input, filename, 256);
}else{
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, "\n");
}
image im = load_image_color(input, net.w, net.h);
//resize_network(&net, im.w, im.h);
printf("%d %d\n", im.w, im.h);
float *X = im.data;
time=clock();
float *predictions = network_predict(net, X);
top_predictions(net, 10, indexes);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
for(i = 0; i < 10; ++i){
int index = indexes[i];
printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
}
free_image(im);
if (filename) break;
}
}
void run_tag(int argc, char **argv)
{
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
}
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
char *filename = (argc > 5) ? argv[5] : 0;
if(0==strcmp(argv[2], "train")) train_tag(cfg, weights);
else if(0==strcmp(argv[2], "test")) test_tag(cfg, weights, filename);
}

19
src/utils.c
View File

@ -2,6 +2,7 @@
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <assert.h>
#include <unistd.h>
#include <float.h>
#include <limits.h>
@ -137,15 +138,18 @@ void pm(int M, int N, float *A)
char *find_replace(char *str, char *orig, char *rep)
{
static char buffer[4096];
static char buffer2[4096];
static char buffer3[4096];
char *p;
if(!(p = strstr(str, orig))) // Is 'orig' even in 'str'?
return str;
strncpy(buffer, str, p-str); // Copy characters from 'str' start to 'orig' st$
buffer[p-str] = '\0';
strncpy(buffer2, str, p-str); // Copy characters from 'str' start to 'orig' st$
buffer2[p-str] = '\0';
sprintf(buffer+(p-str), "%s%s", rep, p+strlen(orig));
sprintf(buffer3, "%s%s%s", buffer2, rep, p+strlen(orig));
sprintf(buffer, "%s", buffer3);
return buffer;
}
@ -174,7 +178,8 @@ void top_k(float *a, int n, int k, int *index)
void error(const char *s)
{
perror(s);
exit(0);
assert(0);
exit(-1);
}
void malloc_error()
@ -450,6 +455,12 @@ int max_index(float *a, int n)
return max_i;
}
int rand_int(int min, int max)
{
int r = (rand()%(max - min + 1)) + min;
return r;
}
// From http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
#define TWO_PI 6.2831853071795864769252866
float rand_normal()

1
src/utils.h
View File

@ -35,6 +35,7 @@ float constrain(float min, float max, float a);
float mse_array(float *a, int n);
float rand_normal();
float rand_uniform(float min, float max);
int rand_int(int min, int max);
float sum_array(float *a, int n);
float mean_array(float *a, int n);
void mean_arrays(float **a, int n, int els, float *avg);