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cleaned up data parsing a lot. probably nothing broken?

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pull/5299/head
Joseph Redmon 11 years ago
parent 7c120aef23
commit e36182cd8c
3 changed files with 390 additions and 451 deletions
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  1. 725
      src/cnn.c
  2. 110
      src/data.c
  3. 6
      src/data.h

725
src/cnn.c
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@ -18,18 +18,18 @@
void test_convolve()
{
image dog = load_image("dog.jpg",300,400);
printf("dog channels %d\n", dog.c);
image kernel = make_random_image(3,3,dog.c);
image edge = make_image(dog.h, dog.w, 1);
int i;
clock_t start = clock(), end;
for(i = 0; i < 1000; ++i){
convolve(dog, kernel, 1, 0, edge, 1);
}
end = clock();
printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
show_image_layers(edge, "Test Convolve");
image dog = load_image("dog.jpg",300,400);
printf("dog channels %d\n", dog.c);
image kernel = make_random_image(3,3,dog.c);
image edge = make_image(dog.h, dog.w, 1);
int i;
clock_t start = clock(), end;
for(i = 0; i < 1000; ++i){
convolve(dog, kernel, 1, 0, edge, 1);
}
end = clock();
printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
show_image_layers(edge, "Test Convolve");
}
#ifdef GPU
@ -37,11 +37,11 @@ void test_convolve()
void test_convolutional_layer()
{
int i;
image dog = load_image("data/dog.jpg",224,224);
network net = parse_network_cfg("cfg/convolutional.cfg");
// data test = load_cifar10_data("data/cifar10/test_batch.bin");
// float *X = calloc(net.batch*test.X.cols, sizeof(float));
// float *y = calloc(net.batch*test.y.cols, sizeof(float));
image dog = load_image("data/dog.jpg",224,224);
network net = parse_network_cfg("cfg/convolutional.cfg");
// data test = load_cifar10_data("data/cifar10/test_batch.bin");
// float *X = calloc(net.batch*test.X.cols, sizeof(float));
// float *y = calloc(net.batch*test.y.cols, sizeof(float));
int in_size = get_network_input_size(net)*net.batch;
int del_size = get_network_output_size_layer(net, 0)*net.batch;
int size = get_network_output_size(net)*net.batch;
@ -50,7 +50,7 @@ void test_convolutional_layer()
for(i = 0; i < in_size; ++i){
X[i] = dog.data[i%get_network_input_size(net)];
}
// get_batch(test, net.batch, X, y);
// get_batch(test, net.batch, X, y);
clock_t start, end;
cl_mem input_cl = cl_make_array(X, in_size);
cl_mem truth_cl = cl_make_array(y, size);
@ -73,41 +73,41 @@ void test_convolutional_layer()
float *gpu_del = calloc(del_size, sizeof(float));
memcpy(gpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
/*
start = clock();
forward_network(net, X, y, 1);
backward_network(net, X);
float cpu_cost = get_network_cost(net);
end = clock();
float cpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
float *cpu_out = calloc(size, sizeof(float));
memcpy(cpu_out, get_network_output(net), size*sizeof(float));
float *cpu_del = calloc(del_size, sizeof(float));
memcpy(cpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
float sum = 0;
float del_sum = 0;
for(i = 0; i < size; ++i) sum += pow(gpu_out[i] - cpu_out[i], 2);
for(i = 0; i < del_size; ++i) {
//printf("%f %f\n", cpu_del[i], gpu_del[i]);
del_sum += pow(cpu_del[i] - gpu_del[i], 2);
/*
start = clock();
forward_network(net, X, y, 1);
backward_network(net, X);
float cpu_cost = get_network_cost(net);
end = clock();
float cpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
float *cpu_out = calloc(size, sizeof(float));
memcpy(cpu_out, get_network_output(net), size*sizeof(float));
float *cpu_del = calloc(del_size, sizeof(float));
memcpy(cpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
float sum = 0;
float del_sum = 0;
for(i = 0; i < size; ++i) sum += pow(gpu_out[i] - cpu_out[i], 2);
for(i = 0; i < del_size; ++i) {
//printf("%f %f\n", cpu_del[i], gpu_del[i]);
del_sum += pow(cpu_del[i] - gpu_del[i], 2);
}
printf("GPU cost: %f, CPU cost: %f\n", gpu_cost, cpu_cost);
printf("gpu: %f sec, cpu: %f sec, diff: %f, delta diff: %f, size: %d\n", gpu_sec, cpu_sec, sum, del_sum, size);
*/
*/
}
void test_col2im()
{
float col[] = {1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2};
1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2,
1,2,1,2};
float im[16] = {0};
int batch = 1;
int channels = 1;
@ -117,289 +117,304 @@ void test_col2im()
int stride = 1;
int pad = 0;
col2im_gpu(col, batch,
channels, height, width,
ksize, stride, pad, im);
channels, height, width,
ksize, stride, pad, im);
int i;
for(i = 0; i < 16; ++i)printf("%f,", im[i]);
printf("\n");
/*
float data_im[] = {
1,2,3,4,
5,6,7,8,
9,10,11,12
};
float data_col[18] = {0};
im2col_cpu(data_im, batch,
channels, height, width,
ksize, stride, pad, data_col) ;
for(i = 0; i < 18; ++i)printf("%f,", data_col[i]);
printf("\n");
*/
float data_im[] = {
1,2,3,4,
5,6,7,8,
9,10,11,12
};
float data_col[18] = {0};
im2col_cpu(data_im, batch,
channels, height, width,
ksize, stride, pad, data_col) ;
for(i = 0; i < 18; ++i)printf("%f,", data_col[i]);
printf("\n");
*/
}
#endif
void test_convolve_matrix()
{
image dog = load_image("dog.jpg",300,400);
printf("dog channels %d\n", dog.c);
int size = 11;
int stride = 4;
int n = 40;
float *filters = make_random_image(size, size, dog.c*n).data;
int mw = ((dog.h-size)/stride+1)*((dog.w-size)/stride+1);
int mh = (size*size*dog.c);
float *matrix = calloc(mh*mw, sizeof(float));
image edge = make_image((dog.h-size)/stride+1, (dog.w-size)/stride+1, n);
int i;
clock_t start = clock(), end;
for(i = 0; i < 1000; ++i){
im2col_cpu(dog.data,1, dog.c, dog.h, dog.w, size, stride, 0, matrix);
gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
}
end = clock();
printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
show_image_layers(edge, "Test Convolve");
cvWaitKey(0);
image dog = load_image("dog.jpg",300,400);
printf("dog channels %d\n", dog.c);
int size = 11;
int stride = 4;
int n = 40;
float *filters = make_random_image(size, size, dog.c*n).data;
int mw = ((dog.h-size)/stride+1)*((dog.w-size)/stride+1);
int mh = (size*size*dog.c);
float *matrix = calloc(mh*mw, sizeof(float));
image edge = make_image((dog.h-size)/stride+1, (dog.w-size)/stride+1, n);
int i;
clock_t start = clock(), end;
for(i = 0; i < 1000; ++i){
im2col_cpu(dog.data,1, dog.c, dog.h, dog.w, size, stride, 0, matrix);
gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
}
end = clock();
printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
show_image_layers(edge, "Test Convolve");
cvWaitKey(0);
}
void test_color()
{
image dog = load_image("test_color.png", 300, 400);
show_image_layers(dog, "Test Color");
image dog = load_image("test_color.png", 300, 400);
show_image_layers(dog, "Test Color");
}
void verify_convolutional_layer()
{
srand(0);
int i;
int n = 1;
int stride = 1;
int size = 3;
float eps = .00000001;
image test = make_random_image(5,5, 1);
convolutional_layer layer = *make_convolutional_layer(1,test.h,test.w,test.c, n, size, stride, 0, RELU,0,0,0);
image out = get_convolutional_image(layer);
float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));
forward_convolutional_layer(layer, test.data);
image base = copy_image(out);
for(i = 0; i < test.h*test.w*test.c; ++i){
test.data[i] += eps;
forward_convolutional_layer(layer, test.data);
image partial = copy_image(out);
subtract_image(partial, base);
scale_image(partial, 1/eps);
jacobian[i] = partial.data;
test.data[i] -= eps;
}
float **jacobian2 = calloc(out.h*out.w*out.c, sizeof(float));
image in_delta = make_image(test.h, test.w, test.c);
image out_delta = get_convolutional_delta(layer);
for(i = 0; i < out.h*out.w*out.c; ++i){
out_delta.data[i] = 1;
backward_convolutional_layer(layer, in_delta.data);
image partial = copy_image(in_delta);
jacobian2[i] = partial.data;
out_delta.data[i] = 0;
}
int j;
float *j1 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
float *j2 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
for(i = 0; i < test.h*test.w*test.c; ++i){
for(j =0 ; j < out.h*out.w*out.c; ++j){
j1[i*out.h*out.w*out.c + j] = jacobian[i][j];
j2[i*out.h*out.w*out.c + j] = jacobian2[j][i];
printf("%f %f\n", jacobian[i][j], jacobian2[j][i]);
}
}
image mj1 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j1);
image mj2 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j2);
printf("%f %f\n", avg_image_layer(mj1,0), avg_image_layer(mj2,0));
show_image(mj1, "forward jacobian");
show_image(mj2, "backward jacobian");
srand(0);
int i;
int n = 1;
int stride = 1;
int size = 3;
float eps = .00000001;
image test = make_random_image(5,5, 1);
convolutional_layer layer = *make_convolutional_layer(1,test.h,test.w,test.c, n, size, stride, 0, RELU,0,0,0);
image out = get_convolutional_image(layer);
float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));
forward_convolutional_layer(layer, test.data);
image base = copy_image(out);
for(i = 0; i < test.h*test.w*test.c; ++i){
test.data[i] += eps;
forward_convolutional_layer(layer, test.data);
image partial = copy_image(out);
subtract_image(partial, base);
scale_image(partial, 1/eps);
jacobian[i] = partial.data;
test.data[i] -= eps;
}
float **jacobian2 = calloc(out.h*out.w*out.c, sizeof(float));
image in_delta = make_image(test.h, test.w, test.c);
image out_delta = get_convolutional_delta(layer);
for(i = 0; i < out.h*out.w*out.c; ++i){
out_delta.data[i] = 1;
backward_convolutional_layer(layer, in_delta.data);
image partial = copy_image(in_delta);
jacobian2[i] = partial.data;
out_delta.data[i] = 0;
}
int j;
float *j1 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
float *j2 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
for(i = 0; i < test.h*test.w*test.c; ++i){
for(j =0 ; j < out.h*out.w*out.c; ++j){
j1[i*out.h*out.w*out.c + j] = jacobian[i][j];
j2[i*out.h*out.w*out.c + j] = jacobian2[j][i];
printf("%f %f\n", jacobian[i][j], jacobian2[j][i]);
}
}
image mj1 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j1);
image mj2 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j2);
printf("%f %f\n", avg_image_layer(mj1,0), avg_image_layer(mj2,0));
show_image(mj1, "forward jacobian");
show_image(mj2, "backward jacobian");
}
void test_load()
{
image dog = load_image("dog.jpg", 300, 400);
show_image(dog, "Test Load");
show_image_layers(dog, "Test Load");
image dog = load_image("dog.jpg", 300, 400);
show_image(dog, "Test Load");
show_image_layers(dog, "Test Load");
}
void test_upsample()
{
image dog = load_image("dog.jpg", 300, 400);
int n = 3;
image up = make_image(n*dog.h, n*dog.w, dog.c);
upsample_image(dog, n, up);
show_image(up, "Test Upsample");
show_image_layers(up, "Test Upsample");
image dog = load_image("dog.jpg", 300, 400);
int n = 3;
image up = make_image(n*dog.h, n*dog.w, dog.c);
upsample_image(dog, n, up);
show_image(up, "Test Upsample");
show_image_layers(up, "Test Upsample");
}
void test_rotate()
{
int i;
image dog = load_image("dog.jpg",300,400);
clock_t start = clock(), end;
for(i = 0; i < 1001; ++i){
rotate_image(dog);
}
end = clock();
printf("Rotations: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
show_image(dog, "Test Rotate");
image random = make_random_image(3,3,3);
show_image(random, "Test Rotate Random");
rotate_image(random);
show_image(random, "Test Rotate Random");
rotate_image(random);
show_image(random, "Test Rotate Random");
int i;
image dog = load_image("dog.jpg",300,400);
clock_t start = clock(), end;
for(i = 0; i < 1001; ++i){
rotate_image(dog);
}
end = clock();
printf("Rotations: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
show_image(dog, "Test Rotate");
image random = make_random_image(3,3,3);
show_image(random, "Test Rotate Random");
rotate_image(random);
show_image(random, "Test Rotate Random");
rotate_image(random);
show_image(random, "Test Rotate Random");
}
void test_parser()
{
network net = parse_network_cfg("cfg/trained_imagenet.cfg");
network net = parse_network_cfg("cfg/trained_imagenet.cfg");
save_network(net, "cfg/trained_imagenet_smaller.cfg");
}
void test_data()
{
char *labels[] = {"cat","dog"};
data train = load_data_image_pathfile_random("train_paths.txt", 101,labels, 2, 300, 400);
free_data(train);
}
void train_asirra()
{
network net = parse_network_cfg("cfg/imagenet.cfg");
network net = parse_network_cfg("cfg/imagenet.cfg");
int imgs = 1000/net.batch+1;
//imgs = 1;
srand(2222222);
int i = 0;
char *labels[] = {"cat","dog"};
srand(2222222);
int i = 0;
char *labels[] = {"cat","dog"};
list *plist = get_paths("data/assira/train.list");
char **paths = (char **)list_to_array(plist);
int m = plist->size;
free_list(plist);
clock_t time;
while(1){
i += 1;
while(1){
i += 1;
time=clock();
data train = load_data_image_pathfile_random("data/assira/train.list", imgs*net.batch, labels, 2, 256, 256);
normalize_data_rows(train);
data train = load_data_random(imgs*net.batch, paths, m, labels, 2, 256, 256);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
//float loss = train_network_data(net, train, imgs);
//float loss = train_network_data(net, train, imgs);
float loss = 0;
printf("%d: %f, Time: %lf seconds\n", i*net.batch*imgs, loss, sec(clock()-time));
free_data(train);
if(i%10==0){
char buff[256];
sprintf(buff, "cfg/asirra_backup_%d.cfg", i);
save_network(net, buff);
}
//lr *= .99;
}
printf("%d: %f, Time: %lf seconds\n", i*net.batch*imgs, loss, sec(clock()-time));
free_data(train);
if(i%10==0){
char buff[256];
sprintf(buff, "cfg/asirra_backup_%d.cfg", i);
save_network(net, buff);
}
//lr *= .99;
}
}
void train_detection_net()
{
float avg_loss = 1;
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
network net = parse_network_cfg("cfg/detnet.cfg");
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1000/net.batch+1;
srand(time(0));
int i = 0;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
clock_t time;
while(1){
i += 1;
time=clock();
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
//translate_data_rows(train, -144);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
#ifdef GPU
float loss = train_network_data_gpu(net, train, imgs);
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs*net.batch);
#endif
free_data(train);
if(i%10==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i);
save_network(net, buff);
}
}
}
void train_imagenet()
{
float avg_loss = 1;
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
network net = parse_network_cfg("cfg/imagenet.cfg");
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
network net = parse_network_cfg("cfg/alexnet.cfg");
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1000/net.batch+1;
srand(time(0));
int i = 0;
srand(time(0));
int i = 0;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
clock_t time;
while(1){
i += 1;
while(1){
i += 1;
time=clock();
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
//translate_data_rows(train, -144);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
#ifdef GPU
float loss = train_network_data_gpu(net, train, imgs);
#ifdef GPU
float loss = train_network_data_gpu(net, train, imgs);
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs*net.batch);
#endif
free_data(train);
if(i%10==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i);
save_network(net, buff);
}
}
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs*net.batch);
#endif
free_data(train);
if(i%10==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i);
save_network(net, buff);
}
}
}
void validate_imagenet(char *filename)
{
int i;
network net = parse_network_cfg(filename);
srand(time(0));
network net = parse_network_cfg(filename);
srand(time(0));
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.val.labels.list");
char *path = "/home/pjreddie/data/imagenet/cls.val.list";
list *plist = get_paths("/home/pjreddie/data/imagenet/cls.val.list");
char **paths = (char **)list_to_array(plist);
int m = plist->size;
free_list(plist);
clock_t time;
float avg_acc = 0;
int splits = 50;
for(i = 0; i < splits; ++i){
time=clock();
data val = load_data_image_pathfile_part(path, i, splits, labels, 1000, 256, 256);
char **part = paths+(i*m/splits);
int num = (i+1)*m/splits - i*m/splits;
data val = load_data(part, num, labels, 1000, 256, 256);
normalize_data_rows(val);
printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
time=clock();
#ifdef GPU
float acc = network_accuracy_gpu(net, val);
avg_acc += acc;
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, acc, avg_acc/(i+1), sec(clock()-time), val.X.rows);
#endif
free_data(val);
}
}
void train_imagenet_small()
{
network net = parse_network_cfg("cfg/imagenet_small.cfg");
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs=1;
srand(111222);
int i = 0;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
clock_t time;
i += 1;
time=clock();
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
#ifdef GPU
float loss = train_network_data_gpu(net, train, imgs);
printf("%d: %f, %lf seconds, %d images\n", i, loss, sec(clock()-time), i*imgs*net.batch);
float acc = network_accuracy_gpu(net, val);
avg_acc += acc;
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, acc, avg_acc/(i+1), sec(clock()-time), val.X.rows);
#endif
free_data(train);
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_backup_slower_larger_%d.cfg", i);
save_network(net, buff);
free_data(val);
}
}
void test_imagenet()
{
network net = parse_network_cfg("cfg/imagenet_test.cfg");
network net = parse_network_cfg("cfg/imagenet_test.cfg");
//imgs=1;
srand(2222222);
int i = 0;
@ -431,32 +446,6 @@ void test_visualize(char *filename)
visualize_network(net);
cvWaitKey(0);
}
void test_full()
{
network net = parse_network_cfg("cfg/backup_1300.cfg");
srand(2222222);
int i,j;
int total = 100;
char *labels[] = {"cat","dog"};
FILE *fp = fopen("preds.txt","w");
for(i = 0; i < total; ++i){
visualize_network(net);
cvWaitKey(100);
data test = load_data_image_pathfile_part("data/assira/test.list", i, total, labels, 2, 256, 256);
image im = float_to_image(256, 256, 3,test.X.vals[0]);
show_image(im, "input");
cvWaitKey(100);
normalize_data_rows(test);
for(j = 0; j < test.X.rows; ++j){
float *x = test.X.vals[j];
forward_network(net, x, 0, 0);
int class = get_predicted_class_network(net);
fprintf(fp, "%d\n", class);
}
free_data(test);
}
fclose(fp);
}
void test_cifar10()
{
@ -675,88 +664,74 @@ void flip_network()
save_network(net, "cfg/voc_imagenet_rev.cfg");
}
void tune_VOC()
{
network net = parse_network_cfg("cfg/voc_start.cfg");
srand(2222222);
int i = 20;
char *labels[] = {"aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"};
float lr = .000005;
float momentum = .9;
float decay = 0.0001;
while(i++ < 1000 || 1){
data train = load_data_image_pathfile_random("/home/pjreddie/VOC2012/trainval_paths.txt", 10, labels, 20, 256, 256);
image im = float_to_image(256, 256, 3,train.X.vals[0]);
show_image(im, "input");
visualize_network(net);
cvWaitKey(100);
translate_data_rows(train, -144);
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, 10);
end = clock();
printf("%d: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", i, loss, (float)(end-start)/CLOCKS_PER_SEC, lr, momentum, decay);
free_data(train);
/*
if(i%10==0){
char buff[256];
sprintf(buff, "/home/pjreddie/voc_cfg/voc_ramp_%d.cfg", i);
save_network(net, buff);
}
*/
//lr *= .99;
}
}
int voc_size(int x)
{
x = x-1+3;
x = x-1+3;
x = x-1+3;
x = (x-1)*2+1;
x = x-1+5;
x = (x-1)*2+1;
x = (x-1)*4+11;
return x;
}
image features_output_size(network net, IplImage *src, int outh, int outw)
void visualize_cat()
{
int h = voc_size(outh);
int w = voc_size(outw);
fprintf(stderr, "%d %d\n", h, w);
IplImage *sized = cvCreateImage(cvSize(w,h), src->depth, src->nChannels);
cvResize(src, sized, CV_INTER_LINEAR);
image im = ipl_to_image(sized);
//normalize_array(im.data, im.h*im.w*im.c);
translate_image(im, -144);
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
image im = load_image("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);
image out = get_network_image(net);
free_image(im);
cvReleaseImage(&sized);
return copy_image(out);
visualize_network(net);
cvWaitKey(0);
}
void features_VOC_image_size(char *image_path, int h, int w)
void test_gpu_net()
{
int j;
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
fprintf(stderr, "%s\n", image_path);
srand(222222);
network net = parse_network_cfg("cfg/nist.cfg");
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);
translate_data_rows(train, -144);
translate_data_rows(test, -144);
int count = 0;
int iters = 1000/net.batch;
while(++count <= 5){
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
end = clock();
float test_acc = network_accuracy(net, test);
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
}
#ifdef GPU
count = 0;
srand(222222);
net = parse_network_cfg("cfg/nist.cfg");
while(++count <= 5){
clock_t start = clock(), end;
float loss = train_network_sgd_gpu(net, train, iters);
end = clock();
float test_acc = network_accuracy(net, test);
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
}
#endif
}
IplImage* src = 0;
if( (src = cvLoadImage(image_path,-1)) == 0 ) file_error(image_path);
image out = features_output_size(net, src, h, w);
for(j = 0; j < out.c*out.h*out.w; ++j){
if(j != 0) printf(",");
printf("%g", out.data[j]);
int main(int argc, char *argv[])
{
if(argc < 2){
fprintf(stderr, "usage: %s <function>\n", argv[0]);
return 0;
}
printf("\n");
free_image(out);
cvReleaseImage(&src);
if(0==strcmp(argv[1], "train")) train_imagenet();
else if(0==strcmp(argv[1], "asirra")) train_asirra();
else if(0==strcmp(argv[1], "nist")) train_nist();
else if(0==strcmp(argv[1], "test_correct")) test_gpu_net();
else if(0==strcmp(argv[1], "test")) test_imagenet();
else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
#ifdef GPU
else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
#endif
test_parser();
fprintf(stderr, "Success!\n");
return 0;
}
/*
void visualize_imagenet_topk(char *filename)
{
int i,j,k,l;
@ -873,19 +848,6 @@ void visualize_imagenet_features(char *filename)
}
cvWaitKey(0);
}
void visualize_cat()
{
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
image im = load_image("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);
visualize_network(net);
cvWaitKey(0);
}
void features_VOC_image(char *image_file, char *image_dir, char *out_dir, int flip, int interval)
{
int i,j;
@ -992,57 +954,4 @@ void test_distribution()
cvWaitKey(0);
cvWaitKey(0);
}
void test_gpu_net()
{
srand(222222);
network net = parse_network_cfg("cfg/nist.cfg");
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);
translate_data_rows(train, -144);
translate_data_rows(test, -144);
int count = 0;
int iters = 1000/net.batch;
while(++count <= 5){
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
end = clock();
float test_acc = network_accuracy(net, test);
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
}
#ifdef GPU
count = 0;
srand(222222);
net = parse_network_cfg("cfg/nist.cfg");
while(++count <= 5){
clock_t start = clock(), end;
float loss = train_network_sgd_gpu(net, train, iters);
end = clock();
float test_acc = network_accuracy(net, test);
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
}
#endif
}
int main(int argc, char *argv[])
{
if(argc < 2){
fprintf(stderr, "usage: %s <function>\n", argv[0]);
return 0;
}
if(0==strcmp(argv[1], "train")) train_imagenet();
else if(0==strcmp(argv[1], "asirra")) train_asirra();
else if(0==strcmp(argv[1], "nist")) train_nist();
else if(0==strcmp(argv[1], "train_small")) train_imagenet_small();
else if(0==strcmp(argv[1], "test_correct")) test_gpu_net();
else if(0==strcmp(argv[1], "test")) test_imagenet();
else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
#ifdef GPU
else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
#endif
test_parser();
fprintf(stderr, "Success!\n");
return 0;
}
*/

110
src/data.c
Unescape View File

@ -19,10 +19,28 @@ list *get_paths(char *filename)
return lines;
}
void fill_truth_det(char *path, float *truth)
void fill_truth_detection(char *path, float *truth, int height, int width, int num_height, int num_width, float scale)
{
find_replace(path, "imgs", "det");
find_replace(path, ".JPEG", ".txt");
int box_height = height/num_height;
int box_width = width/num_width;
char *labelpath = find_replace(path, "imgs", "det");
labelpath = find_replace(labelpath, ".JPEG", ".txt");
FILE *file = fopen(labelpath, "r");
int x, y, h, w;
while(fscanf(file, "%d %d %d %d", &x, &y, &w, &h) == 4){
int i = x/box_width;
int j = y/box_height;
float dh = (float)(x%box_width)/box_height;
float dw = (float)(y%box_width)/box_width;
float sh = h/scale;
float sw = w/scale;
int index = (i+j*num_width)*5;
truth[index++] = 1;
truth[index++] = dh;
truth[index++] = dw;
truth[index++] = sh;
truth[index++] = sw;
}
}
void fill_truth(char *path, char **labels, int k, float *truth)
@ -36,32 +54,52 @@ void fill_truth(char *path, char **labels, int k, float *truth)
}
}
data load_data_image_paths(char **paths, int n, char **labels, int k, int h, int w)
matrix load_image_paths(char **paths, int n, int h, int w)
{
int i;
data d;
d.shallow = 0;
d.X.rows = n;
d.X.vals = calloc(d.X.rows, sizeof(float*));
d.X.cols = 0;
d.y = make_matrix(n, k);
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], h, w);
d.X.vals[i] = im.data;
d.X.cols = im.h*im.w*im.c;
X.vals[i] = im.data;
X.cols = im.h*im.w*im.c;
}
return X;
}
matrix load_labels_paths(char **paths, int n, char **labels, int k)
{
matrix y = make_matrix(n, k);
int i;
for(i = 0; i < n; ++i){
fill_truth(paths[i], labels, k, d.y.vals[i]);
fill_truth(paths[i], labels, k, y.vals[i]);
}
return d;
return y;
}
matrix load_labels_detection(char **paths, int n, int height, int width, int num_height, int num_width, float scale)
{
int k = num_height*num_width*5;
matrix y = make_matrix(n, k);
int i;
for(i = 0; i < n; ++i){
fill_truth_detection(paths[i], y.vals[i], height, width, num_height, num_width, scale);
}
return y;
}
data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w)
{
list *plist = get_paths(filename);
char **paths = (char **)list_to_array(plist);
data d = load_data_image_paths(paths, plist->size, labels, k, h, w);
int n = plist->size;
data d;
d.shallow = 0;
d.X = load_image_paths(paths, n, h, w);
d.y = load_labels_paths(paths, n, labels, k);
free_list_contents(plist);
free_list(plist);
free(paths);
@ -87,20 +125,7 @@ void free_data(data d)
}
}
data load_data_image_pathfile_part(char *filename, int part, int total, char **labels, int k, int h, int w)
{
list *plist = get_paths(filename);
char **paths = (char **)list_to_array(plist);
int start = part*plist->size/total;
int end = (part+1)*plist->size/total;
data d = load_data_image_paths(paths+start, end-start, labels, k, h, w);
free_list_contents(plist);
free_list(plist);
free(paths);
return d;
}
data load_data_random(int n, char **paths, int m, char **labels, int k, int h, int w)
data load_data_detection_random(int n, char **paths, int m, char **labels, int h, int w, int nh, int nw, float scale)
{
char **random_paths = calloc(n, sizeof(char*));
int i;
@ -109,26 +134,33 @@ data load_data_random(int n, char **paths, int m, char **labels, int k, int h, i
random_paths[i] = paths[index];
if(i == 0) printf("%s\n", paths[index]);
}
data d = load_data_image_paths(random_paths, n, labels, k, h, w);
data d;
d.shallow = 0;
d.X = load_image_paths(random_paths, n, h, w);
d.y = load_labels_detection(random_paths, n, h, w, nh, nw, scale);
free(random_paths);
return d;
}
data load_data_image_pathfile_random(char *filename, int n, char **labels, int k, int h, int w)
data load_data(char **paths, int n, char **labels, int k, int h, int w)
{
data d;
d.shallow = 0;
d.X = load_image_paths(paths, n, h, w);
d.y = load_labels_paths(paths, n, labels, k);
return d;
}
data load_data_random(int n, char **paths, int m, char **labels, int k, int h, int w)
{
int i;
list *plist = get_paths(filename);
char **paths = (char **)list_to_array(plist);
char **random_paths = calloc(n, sizeof(char*));
int i;
for(i = 0; i < n; ++i){
int index = rand()%plist->size;
int index = rand()%m;
random_paths[i] = paths[index];
if(i == 0) printf("%s\n", paths[index]);
}
data d = load_data_image_paths(random_paths, n, labels, k, h, w);
free_list_contents(plist);
free_list(plist);
free(paths);
data d = load_data(random_paths, n, labels, k, h, w);
free(random_paths);
return d;
}

6
src/data.h
Unescape View File

@ -12,12 +12,10 @@ typedef struct{
void free_data(data d);
data load_data(char **paths, int n, char **labels, int k, int h, int w);
data load_data_random(int n, char **paths, int m, char **labels, int k, int h, int w);
data load_data_detection_random(int n, char **paths, int m, char **labels, int h, int w, int nh, int nw, float scale);
data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w);
data load_data_image_pathfile_part(char *filename, int part, int total,
char **labels, int k, int h, int w);
data load_data_image_pathfile_random(char *filename, int n, char **labels,
int k, int h, int w);
data load_cifar10_data(char *filename);
data load_all_cifar10();
list *get_paths(char *filename);

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