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This commit is contained in:
Joseph Redmon 2014-11-28 10:38:26 -08:00
parent e36182cd8c
commit b77a8f3987
7 changed files with 114 additions and 11 deletions
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6
src/axpy.cl
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@ -10,6 +10,12 @@ __kernel void scal(int N, float ALPHA, __global float *X, int INCX)
X[i*INCX] *= ALPHA;
}
__kernel void mask(int n, __global float *x, __global float *mask, int mod)
{
int i = get_global_id(0);
x[i] = (mask[(i/mod)*mod]) ? x[i] : 0;
}
__kernel void copy(int N, __global float *X, int OFFX, int INCX, __global float *Y, int OFFY, int INCY)
{
int i = get_global_id(0);

32
src/cnn.c
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@ -314,15 +314,14 @@ void train_detection_net()
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");
list *plist = get_paths("/home/pjreddie/data/imagenet/horse.txt");
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);
data train = load_data_detection_random(imgs*net.batch, paths, plist->size, 256, 256, 8, 8, 256);
//translate_data_rows(train, -144);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
@ -346,7 +345,7 @@ 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/alexnet.cfg");
network net = parse_network_cfg("cfg/trained_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));
@ -412,6 +411,29 @@ void validate_imagenet(char *filename)
}
}
void test_detection()
{
network net = parse_network_cfg("cfg/detnet_test.cfg");
//imgs=1;
srand(2222222);
int i = 0;
clock_t time;
char filename[256];
int indexes[10];
while(1){
fgets(filename, 256, stdin);
image im = load_image_color(filename, 256, 256);
z_normalize_image(im);
printf("%d %d %d\n", im.h, im.w, im.c);
float *X = im.data;
time=clock();
float *predictions = network_predict(net, X);
top_predictions(net, 10, indexes);
printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
free_image(im);
}
}
void test_imagenet()
{
network net = parse_network_cfg("cfg/imagenet_test.cfg");
@ -717,6 +739,7 @@ int main(int argc, char *argv[])
return 0;
}
if(0==strcmp(argv[1], "train")) train_imagenet();
else if(0==strcmp(argv[1], "detection")) train_detection_net();
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();
@ -726,7 +749,6 @@ int main(int argc, char *argv[])
#ifdef GPU
else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
#endif
test_parser();
fprintf(stderr, "Success!\n");
return 0;
}

65
src/cost_layer.c
View File

@ -2,15 +2,36 @@
#include "utils.h"
#include "mini_blas.h"
#include <math.h>
#include <string.h>
#include <stdlib.h>
#include <stdio.h>
cost_layer *make_cost_layer(int batch, int inputs)
COST_TYPE get_cost_type(char *s)
{
if (strcmp(s, "sse")==0) return SSE;
if (strcmp(s, "detection")==0) return DETECTION;
fprintf(stderr, "Couldn't find activation function %s, going with SSE\n", s);
return SSE;
}
char *get_cost_string(COST_TYPE a)
{
switch(a){
case SSE:
return "sse";
case DETECTION:
return "detection";
}
return "sse";
}
cost_layer *make_cost_layer(int batch, int inputs, COST_TYPE type)
{
fprintf(stderr, "Cost Layer: %d inputs\n", inputs);
cost_layer *layer = calloc(1, sizeof(cost_layer));
layer->batch = batch;
layer->inputs = inputs;
layer->type = type;
layer->delta = calloc(inputs*batch, sizeof(float));
layer->output = calloc(1, sizeof(float));
#ifdef GPU
@ -24,6 +45,12 @@ void forward_cost_layer(cost_layer layer, float *input, float *truth)
if (!truth) return;
copy_cpu(layer.batch*layer.inputs, truth, 1, layer.delta, 1);
axpy_cpu(layer.batch*layer.inputs, -1, input, 1, layer.delta, 1);
if(layer.type == DETECTION){
int i;
for(i = 0; i < layer.batch*layer.inputs; ++i){
if((i%5) && !truth[(i/5)*5]) layer.delta[i] = 0;
}
}
*(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
}
@ -33,6 +60,38 @@ void backward_cost_layer(const cost_layer layer, float *input, float *delta)
}
#ifdef GPU
cl_kernel get_mask_kernel()
{
static int init = 0;
static cl_kernel kernel;
if(!init){
kernel = get_kernel("src/axpy.cl", "mask", 0);
init = 1;
}
return kernel;
}
void mask_ongpu(int n, cl_mem x, cl_mem mask, int mod)
{
cl_setup();
cl_kernel kernel = get_mask_kernel();
cl_command_queue queue = cl.queue;
cl_uint i = 0;
cl.error = clSetKernelArg(kernel, i++, sizeof(n), (void*) &n);
cl.error = clSetKernelArg(kernel, i++, sizeof(x), (void*) &x);
cl.error = clSetKernelArg(kernel, i++, sizeof(mask), (void*) &mask);
cl.error = clSetKernelArg(kernel, i++, sizeof(mod), (void*) &mod);
check_error(cl);
const size_t global_size[] = {n};
cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
check_error(cl);
}
void forward_cost_layer_gpu(cost_layer layer, cl_mem input, cl_mem truth)
{
if (!truth) return;
@ -40,6 +99,10 @@ void forward_cost_layer_gpu(cost_layer layer, cl_mem input, cl_mem truth)
copy_ongpu(layer.batch*layer.inputs, truth, 1, layer.delta_cl, 1);
axpy_ongpu(layer.batch*layer.inputs, -1, input, 1, layer.delta_cl, 1);
if(layer.type==DETECTION){
mask_ongpu(layer.inputs*layer.batch, layer.delta_cl, truth, 5);
}
cl_read_array(layer.delta_cl, layer.delta, layer.batch*layer.inputs);
*(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
//printf("%f\n", *layer.output);

9
src/cost_layer.h
View File

@ -2,17 +2,24 @@
#define COST_LAYER_H
#include "opencl.h"
typedef enum{
SSE, DETECTION
} COST_TYPE;
typedef struct {
int inputs;
int batch;
float *delta;
float *output;
COST_TYPE type;
#ifdef GPU
cl_mem delta_cl;
#endif
} cost_layer;
cost_layer *make_cost_layer(int batch, int inputs);
COST_TYPE get_cost_type(char *s);
char *get_cost_string(COST_TYPE a);
cost_layer *make_cost_layer(int batch, int inputs, COST_TYPE type);
void forward_cost_layer(const cost_layer layer, float *input, float *truth);
void backward_cost_layer(const cost_layer layer, float *input, float *delta);

5
src/data.c
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@ -26,6 +26,7 @@ void fill_truth_detection(char *path, float *truth, int height, int width, int n
char *labelpath = find_replace(path, "imgs", "det");
labelpath = find_replace(labelpath, ".JPEG", ".txt");
FILE *file = fopen(labelpath, "r");
if(!file) file_error(labelpath);
int x, y, h, w;
while(fscanf(file, "%d %d %d %d", &x, &y, &w, &h) == 4){
int i = x/box_width;
@ -34,6 +35,7 @@ void fill_truth_detection(char *path, float *truth, int height, int width, int n
float dw = (float)(y%box_width)/box_width;
float sh = h/scale;
float sw = w/scale;
//printf("%d %d %f %f\n", i, j, dh, dw);
int index = (i+j*num_width)*5;
truth[index++] = 1;
truth[index++] = dh;
@ -41,6 +43,7 @@ void fill_truth_detection(char *path, float *truth, int height, int width, int n
truth[index++] = sh;
truth[index++] = sw;
}
fclose(file);
}
void fill_truth(char *path, char **labels, int k, float *truth)
@ -125,7 +128,7 @@ void free_data(data d)
}
}
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_detection_random(int n, char **paths, int m, int h, int w, int nh, int nw, float scale)
{
char **random_paths = calloc(n, sizeof(char*));
int i;

2
src/data.h
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@ -14,7 +14,7 @@ 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_detection_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);
data load_cifar10_data(char *filename);
data load_all_cifar10();

6
src/parser.c
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@ -165,7 +165,9 @@ cost_layer *parse_cost(list *options, network *net, int count)
}else{
input = get_network_output_size_layer(*net, count-1);
}
cost_layer *layer = make_cost_layer(net->batch, input);
char *type_s = option_find_str(options, "type", "sse");
COST_TYPE type = get_cost_type(type_s);
cost_layer *layer = make_cost_layer(net->batch, input, type);
option_unused(options);
return layer;
}
@ -565,7 +567,7 @@ void print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count)
void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
{
fprintf(fp, "[cost]\n");
fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type));
if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "\n");
}