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

idk, probably something changed

Browse Source
This commit is contained in:
Joseph Redmon 2015-01-30 22:05:23 -08:00
parent c592fc7491
commit 0f1a31648c
11 changed files with 121 additions and 72 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
src/crop_layer.c
View File

@ -28,14 +28,19 @@ crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int
return layer;
}
void forward_crop_layer(const crop_layer layer, float *input)
void forward_crop_layer(const crop_layer layer, int train, float *input)
{
int i,j,c,b,row,col;
int index;
int count = 0;
int flip = (layer.flip && rand()%2);
int dh = rand()%(layer.h - layer.crop_height);
int dw = rand()%(layer.w - layer.crop_width);
int dh = rand()%(layer.h - layer.crop_height + 1);
int dw = rand()%(layer.w - layer.crop_width + 1);
if(!train){
flip = 0;
dh = (layer.h - layer.crop_height)/2;
dw = (layer.w - layer.crop_width)/2;
}
for(b = 0; b < layer.batch; ++b){
for(c = 0; c < layer.c; ++c){
for(i = 0; i < layer.crop_height; ++i){

4
src/crop_layer.h
View File

@ -17,10 +17,10 @@ typedef struct {
image get_crop_image(crop_layer layer);
crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip);
void forward_crop_layer(const crop_layer layer, float *input);
void forward_crop_layer(const crop_layer layer, int train, float *input);
#ifdef GPU
void forward_crop_layer_gpu(crop_layer layer, float *input);
void forward_crop_layer_gpu(crop_layer layer, int train, float *input);
#endif
#endif

11
src/crop_layer_kernels.cu
View File

@ -24,11 +24,16 @@ __global__ void forward_crop_layer_kernel(float *input, int size, int c, int h,
output[count] = input[index];
}
extern "C" void forward_crop_layer_gpu(crop_layer layer, float *input)
extern "C" void forward_crop_layer_gpu(crop_layer layer, int train, float *input)
{
int flip = (layer.flip && rand()%2);
int dh = rand()%(layer.h - layer.crop_height);
int dw = rand()%(layer.w - layer.crop_width);
int dh = rand()%(layer.h - layer.crop_height + 1);
int dw = rand()%(layer.w - layer.crop_width + 1);
if(!train){
flip = 0;
dh = (layer.h - layer.crop_height)/2;
dw = (layer.w - layer.crop_width)/2;
}
int size = layer.batch*layer.c*layer.crop_width*layer.crop_height;
dim3 dimBlock(BLOCK, 1, 1);

7
src/cuda.c
View File

@ -1,9 +1,12 @@
int gpu_index = 0;
#ifdef GPU
#include "cuda.h"
#include "utils.h"
#include "blas.h"
#include <stdlib.h>
int gpu_index = 0;
void check_error(cudaError_t status)
{
@ -96,4 +99,4 @@ void cuda_pull_array(float *x_gpu, float *x, int n)
check_error(status);
}
#endif

7
src/cuda.h
View File

@ -1,13 +1,15 @@
#ifndef CUDA_H
#define CUDA_H
extern int gpu_index;
#ifdef GPU
#define BLOCK 256
#include "cuda_runtime.h"
#include "cublas_v2.h"
extern int gpu_index;
void check_error(cudaError_t status);
cublasHandle_t blas_handle();
float *cuda_make_array(float *x, int n);
@ -19,3 +21,4 @@ float cuda_compare(float *x_gpu, float *x, int n, char *s);
dim3 cuda_gridsize(size_t n);
#endif
#endif

126
src/darknet.c
View File

@ -209,13 +209,12 @@ void train_imagenet_distributed(char *address)
void train_imagenet(char *cfgfile)
{
float avg_loss = 1;
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
srand(time(0));
network net = parse_network_cfg(cfgfile);
//test_learn_bias(*(convolutional_layer *)net.layers[1]);
//set_learning_network(&net, net.learning_rate, 0, net.decay);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 3072;
int imgs = 1024;
int i = net.seen/imgs;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
@ -231,9 +230,6 @@ void train_imagenet(char *cfgfile)
time=clock();
pthread_join(load_thread, 0);
train = buffer;
//normalize_data_rows(train);
//translate_data_rows(train, -128);
//scale_data_rows(train, 1./128);
load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@ -244,7 +240,7 @@ void train_imagenet(char *cfgfile)
free_data(train);
if(i%100==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/alexnet_%d.cfg", i);
sprintf(buff, "/home/pjreddie/imagenet_backup/vgg_%d.cfg", i);
save_network(net, buff);
}
}
@ -347,10 +343,28 @@ void test_init(char *cfgfile)
}
free_image(im);
}
void test_imagenet()
void test_dog(char *cfgfile)
{
network net = parse_network_cfg("cfg/imagenet_test.cfg");
image im = load_image_color("data/dog.jpg", 224, 224);
translate_image(im, -128);
print_image(im);
float *X = im.data;
network net = parse_network_cfg(cfgfile);
set_batch_network(&net, 1);
float *predictions = network_predict(net, X);
image crop = get_network_image_layer(net, 0);
//show_image(crop, "cropped");
// print_image(crop);
//show_image(im, "orig");
float * inter = get_network_output(net);
pm(1000, 1, inter);
//cvWaitKey(0);
}
void test_imagenet(char *cfgfile)
{
network net = parse_network_cfg(cfgfile);
set_batch_network(&net, 1);
//imgs=1;
srand(2222222);
int i = 0;
@ -362,7 +376,8 @@ void test_imagenet()
fgets(filename, 256, stdin);
strtok(filename, "\n");
image im = load_image_color(filename, 256, 256);
z_normalize_image(im);
translate_image(im, -128);
//scale_image(im, 1/128.);
printf("%d %d %d\n", im.h, im.w, im.c);
float *X = im.data;
time=clock();
@ -472,28 +487,28 @@ void train_nist(char *cfgfile)
}
/*
void train_nist_distributed(char *address)
{
srand(time(0));
network net = parse_network_cfg("cfg/nist.client");
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
//data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
normalize_data_rows(train);
//normalize_data_rows(test);
int count = 0;
int iters = 50000/net.batch;
iters = 1000/net.batch + 1;
while(++count <= 2000){
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
client_update(net, address);
end = clock();
//float test_acc = network_accuracy_gpu(net, test);
//float test_acc = 0;
printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC);
}
void train_nist_distributed(char *address)
{
srand(time(0));
network net = parse_network_cfg("cfg/nist.client");
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
//data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
normalize_data_rows(train);
//normalize_data_rows(test);
int count = 0;
int iters = 50000/net.batch;
iters = 1000/net.batch + 1;
while(++count <= 2000){
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
client_update(net, address);
end = clock();
//float test_acc = network_accuracy_gpu(net, test);
//float test_acc = 0;
printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC);
}
*/
}
*/
void test_ensemble()
{
@ -535,7 +550,7 @@ void test_ensemble()
void visualize_cat()
{
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
image im = load_image("data/cat.png", 0, 0);
image im = load_image_color("data/cat.png", 0, 0);
printf("Processing %dx%d image\n", im.h, im.w);
resize_network(net, im.h, im.w, im.c);
forward_network(net, im.data, 0, 0);
@ -544,6 +559,7 @@ void visualize_cat()
cvWaitKey(0);
}
#ifdef GPU
void test_convolutional_layer()
{
network net = parse_network_cfg("cfg/nist_conv.cfg");
@ -561,6 +577,7 @@ void test_convolutional_layer()
bias_output_gpu(layer);
cuda_compare(layer.output_gpu, layer.output, out_size, "biased output");
}
#endif
void test_correct_nist()
{
@ -586,7 +603,7 @@ void test_correct_nist()
gpu_index = -1;
count = 0;
srand(222222);
net = parse_network_cfg("cfg/nist_conv.cfg");
net = parse_network_cfg("cfg/nist_conv.cfg");
while(++count <= 5){
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
@ -641,27 +658,27 @@ void test_correct_alexnet()
}
/*
void run_server()
{
srand(time(0));
network net = parse_network_cfg("cfg/net.cfg");
set_batch_network(&net, 1);
server_update(net);
}
void run_server()
{
srand(time(0));
network net = parse_network_cfg("cfg/net.cfg");
set_batch_network(&net, 1);
server_update(net);
}
void test_client()
{
network net = parse_network_cfg("cfg/alexnet.client");
clock_t time=clock();
client_update(net, "localhost");
printf("1\n");
client_update(net, "localhost");
printf("2\n");
client_update(net, "localhost");
printf("3\n");
printf("Transfered: %lf seconds\n", sec(clock()-time));
}
*/
void test_client()
{
network net = parse_network_cfg("cfg/alexnet.client");
clock_t time=clock();
client_update(net, "localhost");
printf("1\n");
client_update(net, "localhost");
printf("2\n");
client_update(net, "localhost");
printf("3\n");
printf("Transfered: %lf seconds\n", sec(clock()-time));
}
*/
void del_arg(int argc, char **argv, int index)
{
@ -713,7 +730,6 @@ int main(int argc, char **argv)
if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
else if(0==strcmp(argv[1], "test_correct_nist")) test_correct_nist();
else if(0==strcmp(argv[1], "test")) test_imagenet();
//else if(0==strcmp(argv[1], "server")) run_server();
#ifdef GPU
@ -725,6 +741,8 @@ int main(int argc, char **argv)
return 0;
}
else if(0==strcmp(argv[1], "detection")) train_detection_net(argv[2]);
else if(0==strcmp(argv[1], "test")) test_imagenet(argv[2]);
else if(0==strcmp(argv[1], "dog")) test_dog(argv[2]);
else if(0==strcmp(argv[1], "ctrain")) train_cifar10(argv[2]);
else if(0==strcmp(argv[1], "nist")) train_nist(argv[2]);
else if(0==strcmp(argv[1], "ctest")) test_cifar10(argv[2]);

2
src/data.c
View File

@ -239,7 +239,7 @@ void *load_in_thread(void *ptr)
{
struct load_args a = *(struct load_args*)ptr;
*a.d = load_data(a.paths, a.n, a.m, a.labels, a.k, a.h, a.w);
translate_data_rows(*a.d, -144);
translate_data_rows(*a.d, -128);
scale_data_rows(*a.d, 1./128);
free(ptr);
return 0;

17
src/image.c
View File

@ -484,7 +484,7 @@ image load_image(char *filename, int h, int w)
exit(0);
}
if(h && w ){
IplImage *resized = resizeImage(src, h, w, 1);
IplImage *resized = resizeImage(src, h, w, 0);
cvReleaseImage(&src);
src = resized;
}
@ -702,10 +702,21 @@ void back_convolve(image m, image kernel, int stride, int channel, image out, in
void print_image(image m)
{
int i;
for(i =0 ; i < m.h*m.w*m.c; ++i) printf("%lf, ", m.data[i]);
int i, j, k;
for(i =0 ; i < m.c; ++i){
for(j =0 ; j < m.h; ++j){
for(k = 0; k < m.w; ++k){
printf("%.2lf, ", m.data[i*m.h*m.w + j*m.w + k]);
if(k > 30) break;
}
printf("\n");
if(j > 30) break;
}
printf("\n");
}
printf("\n");
}
image collapse_images_vert(image *ims, int n)
{
int color = 1;

5
src/network.c
View File

@ -75,7 +75,7 @@ void forward_network(network net, float *input, float *truth, int train)
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
forward_crop_layer(layer, input);
forward_crop_layer(layer, train, input);
input = layer.output;
}
else if(net.types[i] == COST){
@ -536,6 +536,9 @@ image get_network_image_layer(network net, int i)
normalization_layer layer = *(normalization_layer *)net.layers[i];
return get_normalization_image(layer);
}
else if(net.types[i] == DROPOUT){
return get_network_image_layer(net, i-1);
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
return get_crop_image(layer);

2
src/network_kernels.cu
View File

@ -58,7 +58,7 @@ void forward_network_gpu(network net, float * input, float * truth, int train)
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
forward_crop_layer_gpu(layer, input);
forward_crop_layer_gpu(layer, train, input);
input = layer.output_gpu;
}
//printf("Forward %d %s %f\n", i, get_layer_string(net.types[i]), sec(clock() - time));

1
src/utils.c
View File

@ -11,6 +11,7 @@ void pm(int M, int N, float *A)
{
int i,j;
for(i =0 ; i < M; ++i){
printf("%d ", i+1);
for(j = 0; j < N; ++j){
printf("%10.6f, ", A[i*N+j]);
}