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fixed dropout ><

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This commit is contained in:
Joseph Redmon 2014-12-13 12:01:21 -08:00
parent 79fffcce3c
commit 90d354a2a5
8 changed files with 96 additions and 48 deletions
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44
src/cnn.c
View File

@ -294,7 +294,7 @@ void train_asirra()
while(1){
i += 1;
time=clock();
data train = load_data_random(imgs*net.batch, paths, m, labels, 2, 256, 256);
data train = load_data(paths, imgs*net.batch, m, labels, 2, 256, 256);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@ -404,7 +404,7 @@ void train_imagenet_distributed(char *address)
printf("%d\n", plist->size);
clock_t time;
data train, buffer;
pthread_t load_thread = load_data_random_thread(imgs*net.batch, paths, plist->size, labels, 1000, 224, 224, &buffer);
pthread_t load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
while(1){
i += 1;
@ -416,7 +416,7 @@ void train_imagenet_distributed(char *address)
pthread_join(load_thread, 0);
train = buffer;
normalize_data_rows(train);
load_thread = load_data_random_thread(imgs*net.batch, paths, plist->size, labels, 1000, 224, 224, &buffer);
load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@ -434,11 +434,10 @@ void train_imagenet()
float avg_loss = 1;
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
srand(time(0));
network net = parse_network_cfg("cfg/net.cfg");
network net = parse_network_cfg("cfg/net.part");
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1000/net.batch+1;
//imgs=1;
int i = 0;
int i = 9540;
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);
@ -447,14 +446,14 @@ void train_imagenet()
pthread_t load_thread;
data train;
data buffer;
load_thread = load_data_random_thread(imgs*net.batch, paths, plist->size, labels, 1000, 224, 224, &buffer);
load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
while(1){
i += 1;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
normalize_data_rows(train);
load_thread = load_data_random_thread(imgs*net.batch, paths, plist->size, labels, 1000, 224, 224, &buffer);
load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
#ifdef GPU
@ -465,7 +464,7 @@ void train_imagenet()
free_data(train);
if(i%10==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/alexnet_%d.cfg", i);
sprintf(buff, "/home/pjreddie/imagenet_backup/net_%d.cfg", i);
save_network(net, buff);
}
}
@ -473,7 +472,7 @@ void train_imagenet()
void validate_imagenet(char *filename)
{
int i;
int i = 0;
network net = parse_network_cfg(filename);
srand(time(0));
@ -488,21 +487,28 @@ void validate_imagenet(char *filename)
float avg_acc = 0;
float avg_top5 = 0;
int splits = 50;
int num = (i+1)*m/splits - i*m/splits;
for(i = 0; i < splits; ++i){
data val, buffer;
pthread_t load_thread = load_data_thread(paths, num, 0, labels, 1000, 224, 224, &buffer);
for(i = 1; i <= splits; ++i){
time=clock();
char **part = paths+(i*m/splits);
int num = (i+1)*m/splits - i*m/splits;
data val = load_data(part, num, labels, 1000, 224, 224);
pthread_join(load_thread, 0);
val = buffer;
normalize_data_rows(val);
num = (i+1)*m/splits - i*m/splits;
char **part = paths+(i*m/splits);
if(i != splits) load_thread = load_data_thread(part, num, 0, labels, 1000, 224, 224, &buffer);
printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
time=clock();
#ifdef GPU
float *acc = network_accuracies_gpu(net, val);
avg_acc += acc[0];
avg_top5 += acc[1];
printf("%d: top1: %f, top5: %f, %lf seconds, %d images\n", i, avg_acc/(i+1), avg_top5/(i+1), sec(clock()-time), val.X.rows);
printf("%d: top1: %f, top5: %f, %lf seconds, %d images\n", i, avg_acc/i, avg_top5/i, sec(clock()-time), val.X.rows);
#endif
free_data(val);
}
@ -895,14 +901,14 @@ void test_correct_alexnet()
int count = 0;
srand(222222);
network net = parse_network_cfg("cfg/alexnet.test");
network net = parse_network_cfg("cfg/net.cfg");
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1000/net.batch+1;
imgs = 1;
while(++count <= 5){
time=clock();
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 224,224);
//translate_data_rows(train, -144);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
@ -914,10 +920,10 @@ void test_correct_alexnet()
#ifdef GPU
count = 0;
srand(222222);
net = parse_network_cfg("cfg/alexnet.test");
net = parse_network_cfg("cfg/net.cfg");
while(++count <= 5){
time=clock();
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224);
//translate_data_rows(train, -144);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));

30
src/data.c
View File

@ -180,16 +180,7 @@ data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh
return d;
}
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)
char **get_random_paths(char **paths, int n, int m)
{
char **random_paths = calloc(n, sizeof(char*));
int i;
@ -198,14 +189,23 @@ 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(random_paths, n, labels, k, h, w);
free(random_paths);
return random_paths;
}
data load_data(char **paths, int n, int m, char **labels, int k, int h, int w)
{
if(m) paths = get_random_paths(paths, n, m);
data d;
d.shallow = 0;
d.X = load_image_paths(paths, n, h, w);
d.y = load_labels_paths(paths, n, labels, k);
if(m) free(paths);
return d;
}
struct load_args{
int n;
char **paths;
int n;
int m;
char **labels;
int k;
@ -217,11 +217,11 @@ struct load_args{
void *load_in_thread(void *ptr)
{
struct load_args a = *(struct load_args*)ptr;
*a.d = load_data_random(a.n, a.paths, a.m, a.labels, a.k, a.h, a.w);
*a.d = load_data(a.paths, a.n, a.m, a.labels, a.k, a.h, a.w);
return 0;
}
pthread_t load_data_random_thread(int n, char **paths, int m, char **labels, int k, int h, int w, data *d)
pthread_t load_data_thread(char **paths, int n, int m, char **labels, int k, int h, int w, data *d)
{
pthread_t thread;
struct load_args *args = calloc(1, sizeof(struct load_args));

7
src/data.h
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@ -13,9 +13,10 @@ typedef struct{
void free_data(data d);
data load_data(char **paths, int n, char **labels, int k, int h, int w);
pthread_t load_data_random_thread(int n, char **paths, int m, char **labels, int k, int h, int w, data *d);
data load_data_random(int n, char **paths, int m, char **labels, int k, int h, int w);
data load_data(char **paths, int n, int m, char **labels, int k, int h, int w);
pthread_t load_data_thread(char **paths, int n, int m, char **labels, int k, int h, int w, data *d);
data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh, int nw, float scale);
data load_data_detection_jitter_random(int n, char **paths, int m, 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);

42
src/dropout_layer.c
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@ -10,8 +10,9 @@ dropout_layer *make_dropout_layer(int batch, int inputs, float probability)
layer->probability = probability;
layer->inputs = inputs;
layer->batch = batch;
#ifdef GPU
layer->rand = calloc(inputs*batch, sizeof(float));
layer->scale = 1./(1.-probability);
#ifdef GPU
layer->rand_cl = cl_make_array(layer->rand, inputs*batch);
#endif
return layer;
@ -21,13 +22,21 @@ void forward_dropout_layer(dropout_layer layer, float *input)
{
int i;
for(i = 0; i < layer.batch * layer.inputs; ++i){
if(rand_uniform() < layer.probability) input[i] = 0;
else input[i] /= (1-layer.probability);
float r = rand_uniform();
layer.rand[i] = r;
if(r < layer.probability) input[i] = 0;
else input[i] *= layer.scale;
}
}
void backward_dropout_layer(dropout_layer layer, float *input, float *delta)
void backward_dropout_layer(dropout_layer layer, float *delta)
{
// Don't do shit LULZ
int i;
for(i = 0; i < layer.batch * layer.inputs; ++i){
float r = layer.rand[i];
if(r < layer.probability) delta[i] = 0;
else delta[i] *= layer.scale;
}
}
#ifdef GPU
@ -36,7 +45,7 @@ cl_kernel get_dropout_kernel()
static int init = 0;
static cl_kernel kernel;
if(!init){
kernel = get_kernel("src/dropout_layer.cl", "forward", 0);
kernel = get_kernel("src/dropout_layer.cl", "yoloswag420blazeit360noscope", 0);
init = 1;
}
return kernel;
@ -56,6 +65,27 @@ void forward_dropout_layer_gpu(dropout_layer layer, cl_mem input)
cl.error = clSetKernelArg(kernel, i++, sizeof(input), (void*) &input);
cl.error = clSetKernelArg(kernel, i++, sizeof(layer.rand_cl), (void*) &layer.rand_cl);
cl.error = clSetKernelArg(kernel, i++, sizeof(layer.probability), (void*) &layer.probability);
cl.error = clSetKernelArg(kernel, i++, sizeof(layer.scale), (void*) &layer.scale);
check_error(cl);
const size_t global_size[] = {size};
cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
check_error(cl);
}
void backward_dropout_layer_gpu(dropout_layer layer, cl_mem delta)
{
int size = layer.inputs*layer.batch;
cl_kernel kernel = get_dropout_kernel();
cl_command_queue queue = cl.queue;
cl_uint i = 0;
cl.error = clSetKernelArg(kernel, i++, sizeof(delta), (void*) &delta);
cl.error = clSetKernelArg(kernel, i++, sizeof(layer.rand_cl), (void*) &layer.rand_cl);
cl.error = clSetKernelArg(kernel, i++, sizeof(layer.probability), (void*) &layer.probability);
cl.error = clSetKernelArg(kernel, i++, sizeof(layer.scale), (void*) &layer.scale);
check_error(cl);
const size_t global_size[] = {size};

4
src/dropout_layer.cl
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@ -1,5 +1,5 @@
__kernel void forward(__global float *input, __global float *rand, float prob)
__kernel void yoloswag420blazeit360noscope(__global float *input, __global float *rand, float prob, float scale)
{
int id = get_global_id(0);
input[id] = (rand[id] < prob) ? 0 : input[id]/(1.-prob);
input[id] = (rand[id] < prob) ? 0 : input[id]*scale;
}

9
src/dropout_layer.h
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@ -6,8 +6,9 @@ typedef struct{
int batch;
int inputs;
float probability;
#ifdef GPU
float scale;
float *rand;
#ifdef GPU
cl_mem rand_cl;
#endif
} dropout_layer;
@ -15,9 +16,11 @@ typedef struct{
dropout_layer *make_dropout_layer(int batch, int inputs, float probability);
void forward_dropout_layer(dropout_layer layer, float *input);
void backward_dropout_layer(dropout_layer layer, float *input, float *delta);
#ifdef GPU
void backward_dropout_layer(dropout_layer layer, float *delta);
#ifdef GPU
void forward_dropout_layer_gpu(dropout_layer layer, cl_mem input);
void backward_dropout_layer_gpu(dropout_layer layer, cl_mem delta);
#endif
#endif

4
src/network.c
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@ -219,6 +219,10 @@ void backward_network(network net, float *input)
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
if(i != 0) backward_maxpool_layer(layer, prev_delta);
}
else if(net.types[i] == DROPOUT){
dropout_layer layer = *(dropout_layer *)net.layers[i];
backward_dropout_layer(layer, prev_delta);
}
else if(net.types[i] == NORMALIZATION){
normalization_layer layer = *(normalization_layer *)net.layers[i];
if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);

4
src/network_gpu.c
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@ -101,6 +101,10 @@ void backward_network_gpu(network net, cl_mem input)
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
backward_maxpool_layer_gpu(layer, prev_delta);
}
else if(net.types[i] == DROPOUT){
dropout_layer layer = *(dropout_layer *)net.layers[i];
backward_dropout_layer_gpu(layer, prev_delta);
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
backward_softmax_layer_gpu(layer, prev_delta);