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Fast, needs to be faster

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This commit is contained in:
Joseph Redmon 2014-10-25 11:57:26 -07:00
parent 158bb1bee9
commit 14303717dc
19 changed files with 484 additions and 74 deletions
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4
Makefile
View File

@ -1,6 +1,6 @@
CC=gcc
GPU=1
COMMON=-Wall -Wfatal-errors `pkg-config --cflags opencv` -I/usr/local/cuda/include/
COMMON=-Wall -Wfatal-errors `pkg-config --cflags opencv` -I/usr/local/cuda/include/ -I/usr/local/clblas/include/
ifeq ($(GPU), 1)
COMMON+=-DGPU
else
@ -15,7 +15,7 @@ endif
else
OPTS+= -march=native
ifeq ($(GPU), 1)
LDFLAGS= -lOpenCL
LDFLAGS= -lOpenCL -lclBLAS
endif
endif
CFLAGS= $(COMMON) $(OPTS)

8
src/axpy.c
View File

@ -1,24 +1,24 @@
#include "mini_blas.h"
inline void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
{
int i;
for(i = 0; i < N; ++i) Y[i*INCY] += ALPHA*X[i*INCX];
}
inline void scal_cpu(int N, float ALPHA, float *X, int INCX)
void scal_cpu(int N, float ALPHA, float *X, int INCX)
{
int i;
for(i = 0; i < N; ++i) X[i*INCX] *= ALPHA;
}
inline void copy_cpu(int N, float *X, int INCX, float *Y, int INCY)
void copy_cpu(int N, float *X, int INCX, float *Y, int INCY)
{
int i;
for(i = 0; i < N; ++i) Y[i*INCY] = X[i*INCX];
}
inline float dot_cpu(int N, float *X, int INCX, float *Y, int INCY)
float dot_cpu(int N, float *X, int INCX, float *Y, int INCY)
{
int i;
float dot = 0;

82
src/cnn.c
View File

@ -286,14 +286,16 @@ void train_assira()
srand(2222222);
int i = 0;
char *labels[] = {"cat","dog"};
clock_t time;
while(1){
i += 1000;
time=clock();
data train = load_data_image_pathfile_random("data/assira/train.list", imgs*net.batch, labels, 2, 256, 256);
normalize_data_rows(train);
clock_t start = clock(), end;
float loss = train_network_sgd_gpu(net, train, imgs);
end = clock();
printf("%d: %f, Time: %lf seconds\n", i, loss, (float)(end-start)/CLOCKS_PER_SEC );
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network_sgd(net, train, imgs);
printf("%d: %f, Time: %lf seconds\n", i, loss, sec(clock()-time));
free_data(train);
if(i%10000==0){
char buff[256];
@ -304,9 +306,69 @@ void train_assira()
}
}
void train_imagenet()
{
network net = parse_network_cfg("cfg/imagenet_backup_710.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;
srand(888888);
int i = 0;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/home/pjreddie/data/imagenet/cls.cropped.list");
char **paths = (char **)list_to_array(plist);
clock_t time;
while(1){
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_sgd_gpu(net, train, imgs);
printf("%d: %f, %lf seconds, %d images\n", i, 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_backup_%d.cfg", i);
save_network(net, buff);
}
}
}
void test_imagenet()
{
network net = parse_network_cfg("cfg/imagenet_test.cfg");
//imgs=1;
srand(2222222);
int i = 0;
char **names = get_labels("cfg/shortnames.txt");
clock_t time;
char filename[256];
int indexes[10];
while(1){
gets(filename);
image im = load_image_color(filename, 256, 256);
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));
for(i = 0; i < 10; ++i){
int index = indexes[i];
printf("%s: %f\n", names[index], predictions[index]);
}
free_image(im);
}
}
void test_visualize()
{
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
network net = parse_network_cfg("cfg/assira_backup_740000.cfg");
srand(2222222);
visualize_network(net);
cvWaitKey(0);
@ -322,7 +384,7 @@ void test_full()
for(i = 0; i < total; ++i){
visualize_network(net);
cvWaitKey(100);
data test = load_data_image_pathfile_part("images/assira/test.list", i, total, labels, 2, 256, 256);
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);
@ -437,7 +499,7 @@ void train_nist()
int iters = 10000/net.batch;
while(++count <= 2000){
clock_t start = clock(), end;
float loss = train_network_sgd_gpu(net, train, iters);
float loss = train_network_sgd(net, train, iters);
end = clock();
float test_acc = network_accuracy(net, test);
//float test_acc = 0;
@ -895,10 +957,14 @@ void test_distribution()
int main(int argc, char *argv[])
{
test_gpu_blas();
//test_blas();
train_assira();
//train_assira();
//test_visualize();
//test_distribution();
//feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
//train_imagenet();
//test_imagenet();
//test_blas();
//test_visualize();

6
src/connected_layer.c
View File

@ -114,6 +114,12 @@ void pull_connected_layer(connected_layer layer)
cl_read_array(layer.biases_cl, layer.biases, layer.outputs);
}
void push_connected_layer(connected_layer layer)
{
cl_write_array(layer.weights_cl, layer.weights, layer.inputs*layer.outputs);
cl_write_array(layer.biases_cl, layer.biases, layer.outputs);
}
void update_connected_layer_gpu(connected_layer layer)
{
axpy_ongpu(layer.outputs, layer.learning_rate, layer.bias_updates_cl, 1, layer.biases_cl, 1);

1
src/connected_layer.h
View File

@ -48,6 +48,7 @@ void update_connected_layer(connected_layer layer);
void forward_connected_layer_gpu(connected_layer layer, cl_mem input);
void backward_connected_layer_gpu(connected_layer layer, cl_mem input, cl_mem delta);
void update_connected_layer_gpu(connected_layer layer);
void push_connected_layer(connected_layer layer);
#endif
#endif

8
src/convolutional_layer.c
View File

@ -212,7 +212,7 @@ void update_convolutional_layer(convolutional_layer layer)
{
int size = layer.size*layer.size*layer.c*layer.n;
axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
scal_cpu(layer.n,layer.momentum, layer.bias_updates, 1);
scal_cpu(layer.n, layer.momentum, layer.bias_updates, 1);
scal_cpu(size, 1.-layer.learning_rate*layer.decay, layer.filters, 1);
axpy_cpu(size, layer.learning_rate, layer.filter_updates, 1, layer.filters, 1);
@ -434,6 +434,12 @@ void pull_convolutional_layer(convolutional_layer layer)
cl_read_array(layer.biases_cl, layer.biases, layer.n);
}
void push_convolutional_layer(convolutional_layer layer)
{
cl_write_array(layer.filters_cl, layer.filters, layer.c*layer.n*layer.size*layer.size);
cl_write_array(layer.biases_cl, layer.biases, layer.n);
}
void update_convolutional_layer_gpu(convolutional_layer layer)
{
int size = layer.size*layer.size*layer.c*layer.n;

1
src/convolutional_layer.h
View File

@ -49,6 +49,7 @@ typedef struct {
void forward_convolutional_layer_gpu(convolutional_layer layer, cl_mem in);
void backward_convolutional_layer_gpu(convolutional_layer layer, cl_mem delta_cl);
void update_convolutional_layer_gpu(convolutional_layer layer);
void push_convolutional_layer(convolutional_layer layer);
#endif
convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, float learning_rate, float momentum, float decay);

26
src/data.c
View File

@ -41,9 +41,11 @@ data load_data_image_paths(char **paths, int n, char **labels, int k, int h, int
d.y = make_matrix(n, k);
for(i = 0; i < n; ++i){
image im = load_image(paths[i], h, w);
image im = load_image_color(paths[i], h, w);
d.X.vals[i] = im.data;
d.X.cols = im.h*im.w*im.c;
}
for(i = 0; i < n; ++i){
fill_truth(paths[i], labels, k, d.y.vals[i]);
}
return d;
@ -60,6 +62,14 @@ data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w
return d;
}
char **get_labels(char *filename)
{
list *plist = get_paths(filename);
char **labels = (char **)list_to_array(plist);
free_list(plist);
return labels;
}
void free_data(data d)
{
if(!d.shallow){
@ -84,6 +94,20 @@ data load_data_image_pathfile_part(char *filename, int part, int total, char **l
return d;
}
data load_data_random(int n, char **paths, int m, char **labels, int k, int h, int w)
{
char **random_paths = calloc(n, sizeof(char*));
int i;
for(i = 0; i < n; ++i){
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(random_paths);
return d;
}
data load_data_image_pathfile_random(char *filename, int n, char **labels, int k, int h, int w)
{
int i;

2
src/data.h
View File

@ -12,6 +12,7 @@ typedef struct{
void free_data(data d);
data load_data_random(int n, char **paths, int m, char **labels, int k, int h, int w);
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);
@ -20,6 +21,7 @@ data load_data_image_pathfile_random(char *filename, int n, char **labels,
data load_cifar10_data(char *filename);
data load_all_cifar10();
list *get_paths(char *filename);
char **get_labels(char *filename);
void get_batch(data d, int n, float *X, float *y);
data load_categorical_data_csv(char *filename, int target, int k);
void normalize_data_rows(data d);

146
src/gemm.c
View File

@ -1,5 +1,5 @@
#include "mini_blas.h"
#include <clBLAS.h>
#include "utils.h"
void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
float *A, int lda,
@ -104,6 +104,7 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA,
#include "opencl.h"
#include <math.h>
#include <clBLAS.h>
#define STR_HELPER(x) #x
#define STR(x) STR_HELPER(x)
@ -111,7 +112,7 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA,
#ifdef __APPLE__
#define BLOCK 1
#else
#define BLOCK 8
#define BLOCK 16
#endif
cl_kernel get_gemm_kernel()
@ -125,6 +126,44 @@ cl_kernel get_gemm_kernel()
return gemm_kernel;
}
cl_kernel get_gemm_nt_kernel()
{
static int init = 0;
static cl_kernel gemm_kernel;
if(!init){
gemm_kernel = get_kernel("src/gemm_new.cl", "gemm_nt", "-D BLOCK=" STR(BLOCK) );
init = 1;
}
return gemm_kernel;
}
cl_kernel get_gemm_tn_kernel()
{
static int init = 0;
static cl_kernel gemm_kernel;
if(!init){
gemm_kernel = get_kernel("src/gemm_new.cl", "gemm_tn", "-D BLOCK=" STR(BLOCK) );
init = 1;
}
return gemm_kernel;
}
cl_kernel get_gemm_nn_kernel()
{
static int init = 0;
static cl_kernel gemm_kernel;
if(!init){
gemm_kernel = get_kernel("src/gemm_new.cl", "gemm_nn", "-D BLOCK=" STR(BLOCK) );
init = 1;
}
return gemm_kernel;
}
void gemm_ongpu_new(int TA, int TB, int M, int N, int K, float ALPHA,
cl_mem A_gpu, int lda,
cl_mem B_gpu, int ldb,
float BETA,
cl_mem C_gpu, int ldc);
void gemm_ongpu_old(int TA, int TB, int M, int N, int K, float ALPHA,
cl_mem A_gpu, int lda,
cl_mem B_gpu, int ldb,
@ -137,10 +176,51 @@ void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA,
float BETA,
cl_mem C_gpu, int ldc)
{
/*
cl_setup();
//cl.error = clblasSgemm(clblasRowMajor, TA?clblasTrans:clblasNoTrans, TB?clblasTrans:clblasNoTrans,M, N, K,ALPHA, A_gpu, 0, lda,B_gpu, 0, ldb,BETA, C_gpu, 0, ldc,1, &queue, 0, NULL, &event);
//check_error(cl);
gemm_ongpu_old(TA, TB, M, N, K, ALPHA, A_gpu, lda, B_gpu, ldb, BETA, C_gpu, ldc);
cl_command_queue queue = cl.queue;
cl_event event;
cl.error = clblasSgemm(clblasRowMajor, TA?clblasTrans:clblasNoTrans, TB?clblasTrans:clblasNoTrans,M, N, K,ALPHA, A_gpu, 0, lda,B_gpu, 0, ldb,BETA, C_gpu, 0, ldc,1, &queue, 0, NULL, &event);
*/
gemm_ongpu_new(TA, TB, M, N, K, ALPHA, A_gpu, lda, B_gpu, ldb, BETA, C_gpu, ldc);
}
void gemm_ongpu_new(int TA, int TB, int M, int N, int K, float ALPHA,
cl_mem A_gpu, int lda,
cl_mem B_gpu, int ldb,
float BETA,
cl_mem C_gpu, int ldc)
{
//printf("gpu: %d %d %d %d %d\n",TA, TB, M, N, K);
cl_setup();
cl_kernel gemm_kernel = get_gemm_kernel();
if(!TA && !TB) gemm_kernel = get_gemm_nn_kernel();
if(!TA && TB) gemm_kernel = get_gemm_nt_kernel();
if(TA && !TB) gemm_kernel = get_gemm_tn_kernel();
cl_command_queue queue = cl.queue;
cl_uint i = 0;
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(TA), (void*) &TA);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(TB), (void*) &TB);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(M), (void*) &M);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(N), (void*) &N);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(K), (void*) &K);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(ALPHA), (void*) &ALPHA);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(A_gpu), (void*) &A_gpu);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(lda), (void*) &lda);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(B_gpu), (void*) &B_gpu);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(ldb), (void*) &ldb);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(BETA), (void*) &BETA);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(C_gpu), (void*) &C_gpu);
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(ldc), (void*) &ldc);
check_error(cl);
const size_t global_size[] = {ceil((float)N/BLOCK)*BLOCK, ceil((float)M/BLOCK)*BLOCK};
const size_t local_size[] = {BLOCK, BLOCK};
clEnqueueNDRangeKernel(queue, gemm_kernel, 2, 0, global_size, local_size, 0, 0, 0);
check_error(cl);
}
void gemm_ongpu_old(int TA, int TB, int M, int N, int K, float ALPHA,
@ -170,7 +250,7 @@ void gemm_ongpu_old(int TA, int TB, int M, int N, int K, float ALPHA,
cl.error = clSetKernelArg(gemm_kernel, i++, sizeof(ldc), (void*) &ldc);
check_error(cl);
const size_t global_size[] = {ceil((float)M/BLOCK)*BLOCK, ceil((float)N/BLOCK)*BLOCK};
const size_t global_size[] = {ceil((float)N/BLOCK)*BLOCK, ceil((float)M/BLOCK)*BLOCK};
const size_t local_size[] = {BLOCK, BLOCK};
clEnqueueNDRangeKernel(queue, gemm_kernel, 2, 0, global_size, local_size, 0, 0, 0);
@ -235,7 +315,7 @@ void time_gpu_random_matrix(int TA, int TB, int m, int k, int n)
float *c = random_matrix(m,n);
int i;
clock_t start = clock(), end;
for(i = 0; i<10; ++i){
for(i = 0; i<32; ++i){
gemm_gpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n);
}
end = clock();
@ -245,6 +325,39 @@ void time_gpu_random_matrix(int TA, int TB, int m, int k, int n)
free(c);
}
void time_ongpu(int TA, int TB, int m, int k, int n)
{
int iter = 100;
float *a = random_matrix(m,k);
float *b = random_matrix(k,n);
int lda = (!TA)?k:m;
int ldb = (!TB)?n:k;
float *c = random_matrix(m,n);
cl_mem a_cl = cl_make_array(a, m*k);
cl_mem b_cl = cl_make_array(b, k*n);
cl_mem c_cl = cl_make_array(c, m*n);
int i;
clock_t start = clock(), end;
for(i = 0; i<iter; ++i){
gemm_ongpu(TA,TB,m,n,k,1,a_cl,lda,b_cl,ldb,1,c_cl,n);
}
int flop = m*n*(2*k+3)*iter;
float gflop = flop/pow(10., 9);
end = clock();
float seconds = sec(end-start);
printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf s, %lf GFLOPS\n",m,k,k,n, TA, TB, seconds, gflop/seconds);
clReleaseMemObject(a_cl);
clReleaseMemObject(b_cl);
clReleaseMemObject(c_cl);
free(a);
free(b);
free(c);
}
void test_gpu_accuracy(int TA, int TB, int m, int k, int n)
{
srand(0);
@ -272,14 +385,16 @@ void test_gpu_accuracy(int TA, int TB, int m, int k, int n)
//printf("%f %f\n", c[i], c_gpu[i]);
sse += pow(c[i]-c_gpu[i], 2);
}
printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %g MSE\n",m,k,k,n, TA, TB, sse/(m*n));
printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %g SSE\n",m,k,k,n, TA, TB, sse/(m*n));
free(a);
free(b);
free(c);
free(c_gpu);
}
void test_gpu_blas()
{
/*
test_gpu_accuracy(0,0,10,576,75);
test_gpu_accuracy(0,0,17,10,10);
@ -291,6 +406,21 @@ void test_gpu_blas()
test_gpu_accuracy(1,0,1000,10,100);
test_gpu_accuracy(0,1,1000,10,100);
test_gpu_accuracy(1,1,1000,10,100);
*/
test_gpu_accuracy(0,0,131,4093,1199);
test_gpu_accuracy(0,1,131,4093,1199);
test_gpu_accuracy(1,0,131,4093,1199);
test_gpu_accuracy(1,1,131,4093,1199);
time_ongpu(0,0,1024,1024,1024);
time_ongpu(0,1,1024,1024,1024);
time_ongpu(1,0,1024,1024,1024);
time_ongpu(1,1,1024,1024,1024);
time_ongpu(0,0,128,4096,1200);
time_ongpu(0,1,128,4096,1200);
time_ongpu(1,0,128,4096,1200);
time_ongpu(1,1,128,4096,1200);
/*
time_gpu_random_matrix(0,0,1000,1000,100);

8
src/gemm.cl
View File

@ -10,11 +10,11 @@ __kernel void gemm(int TA, int TB, int M, int N, int K, float ALPHA,
float val = 0;
int row_block = get_group_id(0);
int col_block = get_group_id(1);
int row_block = get_group_id(1);
int col_block = get_group_id(0);
int sub_row = get_local_id(0);
int sub_col = get_local_id(1);
int sub_row = get_local_id(1);
int sub_col = get_local_id(0);
int row = row_block*BLOCK + sub_row;
int col = col_block*BLOCK + sub_col;

162
src/gemm_new.cl Normal file
View File

@ -0,0 +1,162 @@
__kernel void gemm_tn(int TA, int TB, int M, int N, int K, float ALPHA,
__global float *A, int lda,
__global float *B, int ldb,
float BETA,
__global float *C, int ldc)
{
__local float Asub[BLOCK][BLOCK];
__local float Bsub[BLOCK][BLOCK];
int col = get_global_id(0);
int row = get_global_id(1);
int col_block = get_group_id(0);
int row_block = get_group_id(1);
col = (col < N) ? col : N - 1;
row = (row < M) ? row : M - 1;
int x = get_local_id(0);
int y = get_local_id(1);
int i,j;
float val = 0;
float orig = C[row*ldc + col];
for(i = 0; i < K; i += BLOCK){
int arow = y + i;
int acol = x + row_block*BLOCK;
int brow = y + i;
int bcol = col;
arow = (arow < K) ? arow : K-1;
acol = (acol < M) ? acol : M-1;
brow = (brow < K) ? brow : K-1;
int aind = arow*lda + acol;
int bind = brow*ldb + bcol;
Asub[x][y] = A[aind];
Bsub[y][x] = B[bind];
barrier(CLK_LOCAL_MEM_FENCE);
for(j = 0; j < BLOCK && i+j<K; ++j){
val += Asub[y][j]*Bsub[j][x];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
C[row*ldc+col] = ALPHA*val + BETA*orig;
}
__kernel void gemm_nt(int TA, int TB, int M, int N, int K, float ALPHA,
__global float *A, int lda,
__global float *B, int ldb,
float BETA,
__global float *C, int ldc)
{
__local float Asub[BLOCK][BLOCK];
__local float Bsub[BLOCK][BLOCK];
int col = get_global_id(0);
int row = get_global_id(1);
int col_block = get_group_id(0);
int row_block = get_group_id(1);
col = (col < N) ? col : N - 1;
row = (row < M) ? row : M - 1;
int x = get_local_id(0);
int y = get_local_id(1);
int i,j;
float val = 0;
float orig = C[row*ldc + col];
for(i = 0; i < K; i += BLOCK){
int arow = row;
int acol = x + i;
int brow = col_block*BLOCK + y;
int bcol = x + i;
brow = (brow < N) ? brow : N-1;
acol = (acol < K) ? acol : K-1;
bcol = (bcol < K) ? bcol : K-1;
int aind = arow*lda + acol;
int bind = brow*ldb + bcol;
Asub[y][x] = A[aind];
Bsub[x][y] = B[bind];
barrier(CLK_LOCAL_MEM_FENCE);
for(j = 0; j < BLOCK && i+j<K; ++j){
val += Asub[y][j]*Bsub[j][x];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
C[row*ldc+col] = ALPHA*val + BETA*orig;
}
__kernel void gemm_nn(int TA, int TB, int M, int N, int K, float ALPHA,
__global float *A, int lda,
__global float *B, int ldb,
float BETA,
__global float *C, int ldc)
{
__local float Asub[BLOCK][BLOCK];
__local float Bsub[BLOCK][BLOCK];
int col = get_global_id(0);
int row = get_global_id(1);
col = (col < N) ? col : N - 1;
row = (row < M) ? row : M - 1;
int x = get_local_id(0);
int y = get_local_id(1);
int i,j;
float orig = C[row*ldc+col];
float val = 0;
for(i = 0; i < K; i += BLOCK){
int arow = row;
int acol = x + i;
int brow = y + i;
int bcol = col;
acol = (acol < K) ? acol : K-1;
brow = (brow < K) ? brow : K-1;
int aind = arow*lda + acol;
int bind = brow*ldb + bcol;
Asub[y][x] = A[aind];
Bsub[y][x] = B[bind];
barrier(CLK_LOCAL_MEM_FENCE);
for(j = 0; j < BLOCK && i+j<K; ++j){
val += Asub[y][j]*Bsub[j][x];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
C[row*ldc+col] = ALPHA*val + BETA*orig;
}

20
src/image.c
View File

@ -369,7 +369,6 @@ IplImage* resizeImage(const IplImage *origImg, int newHeight, int newWidth,
// Will do a scaled image resize with the correct aspect ratio.
outImg = resizeImage(croppedImg, newHeight, newWidth, 0);
cvReleaseImage( &croppedImg );
}
else {
@ -415,6 +414,25 @@ image ipl_to_image(IplImage* src)
return out;
}
image load_image_color(char *filename, int h, int w)
{
IplImage* src = 0;
if( (src = cvLoadImage(filename, 1)) == 0 )
{
printf("Cannot load file image %s\n", filename);
exit(0);
}
if(h && w && (src->height != h || src->width != w)){
printf("Resized!\n");
IplImage *resized = resizeImage(src, h, w, 1);
cvReleaseImage(&src);
src = resized;
}
image out = ipl_to_image(src);
cvReleaseImage(&src);
return out;
}
image load_image(char *filename, int h, int w)
{
IplImage* src = 0;

1
src/image.h
View File

@ -45,6 +45,7 @@ image make_random_kernel(int size, int c, float scale);
image float_to_image(int h, int w, int c, float *data);
image copy_image(image p);
image load_image(char *filename, int h, int w);
image load_image_color(char *filename, int h, int w);
image ipl_to_image(IplImage* src);
float get_pixel(image m, int x, int y, int c);

8
src/mini_blas.h
View File

@ -55,8 +55,8 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA,
float *B, int ldb,
float BETA,
float *C, int ldc);
inline void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY);
inline void copy_cpu(int N, float *X, int INCX, float *Y, int INCY);
inline void scal_cpu(int N, float ALPHA, float *X, int INCX);
inline float dot_cpu(int N, float *X, int INCX, float *Y, int INCY);
void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY);
void copy_cpu(int N, float *X, int INCX, float *Y, int INCY);
void scal_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();

23
src/network.c
View File

@ -621,7 +621,7 @@ void visualize_network(network net)
image *prev = 0;
int i;
char buff[256];
show_image(get_network_image_layer(net, 0), "Crop");
//show_image(get_network_image_layer(net, 0), "Crop");
for(i = 0; i < net.n; ++i){
sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){
@ -635,6 +635,27 @@ void visualize_network(network net)
}
}
void top_predictions(network net, int n, int *index)
{
int i,j;
int k = get_network_output_size(net);
float *out = get_network_output(net);
float thresh = FLT_MAX;
for(i = 0; i < n; ++i){
float max = -FLT_MAX;
int max_i = -1;
for(j = 0; j < k; ++j){
float val = out[j];
if(val > max && val < thresh){
max = val;
max_i = j;
}
}
index[i] = max_i;
thresh = max;
}
}
float *network_predict(network net, float *input)
{
forward_network(net, input, 0, 0);

2
src/network.h
View File

@ -52,8 +52,10 @@ float train_network_sgd(network net, data d, int n);
float train_network_batch(network net, data d, int n);
void train_network(network net, data d);
matrix network_predict_data(network net, data test);
float *network_predict(network net, float *input);
float network_accuracy(network net, data d);
float network_accuracy_multi(network net, data d, int n);
void top_predictions(network net, int n, int *index);
float *get_network_output(network net);
float *get_network_output_layer(network net, int i);
float *get_network_delta_layer(network net, int i);

6
src/opencl.c
View File

@ -4,7 +4,7 @@
#include <string.h>
#include <time.h>
#include <unistd.h>
//#include <clBLAS.h>
#include <clBLAS.h>
#include "opencl.h"
#include "utils.h"
@ -81,7 +81,7 @@ cl_info cl_init()
}
int index = getpid()%num_devices;
index = 0;
index = 1;
printf("%d rand, %d devices, %d index\n", getpid(), num_devices, index);
info.device = devices[index];
fprintf(stderr, "Found %d device(s)\n", num_devices);
@ -99,7 +99,7 @@ cl_info cl_init()
info.queues[i] = clCreateCommandQueue(info.context, info.device, 0, &info.error);
check_error(info);
}
//info.error = clblasSetup();
info.error = clblasSetup();
check_error(info);
info.initialized = 1;
return info;

44
src/parser.c
View File

@ -67,7 +67,6 @@ void parse_data(char *data, float *a, int n)
convolutional_layer *parse_convolutional(list *options, network *net, int count)
{
int i;
int h,w,c;
float learning_rate, momentum, decay;
int n = option_find_int(options, "filters",1);
@ -98,34 +97,19 @@ convolutional_layer *parse_convolutional(list *options, network *net, int count)
if(h == 0) error("Layer before convolutional layer must output image.");
}
convolutional_layer *layer = make_convolutional_layer(net->batch,h,w,c,n,size,stride,pad,activation,learning_rate,momentum,decay);
char *data = option_find_str(options, "data", 0);
if(data){
char *curr = data;
char *next = data;
for(i = 0; i < n; ++i){
while(*++next !='\0' && *next != ',');
*next = '\0';
sscanf(curr, "%g", &layer->biases[i]);
curr = next+1;
}
for(i = 0; i < c*n*size*size; ++i){
while(*++next !='\0' && *next != ',');
*next = '\0';
sscanf(curr, "%g", &layer->filters[i]);
curr = next+1;
}
}
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(biases, layer->biases, n);
parse_data(weights, layer->filters, c*n*size*size);
parse_data(biases, layer->biases, n);
#ifdef GPU
push_convolutional_layer(*layer);
#endif
option_unused(options);
return layer;
}
connected_layer *parse_connected(list *options, network *net, int count)
{
int i;
int input;
float learning_rate, momentum, decay;
int output = option_find_int(options, "output",1);
@ -147,27 +131,13 @@ connected_layer *parse_connected(list *options, network *net, int count)
input = get_network_output_size_layer(*net, count-1);
}
connected_layer *layer = make_connected_layer(net->batch, input, output, activation,learning_rate,momentum,decay);
char *data = option_find_str(options, "data", 0);
if(data){
char *curr = data;
char *next = data;
for(i = 0; i < output; ++i){
while(*++next !='\0' && *next != ',');
*next = '\0';
sscanf(curr, "%g", &layer->biases[i]);
curr = next+1;
}
for(i = 0; i < input*output; ++i){
while(*++next !='\0' && *next != ',');
*next = '\0';
sscanf(curr, "%g", &layer->weights[i]);
curr = next+1;
}
}
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(biases, layer->biases, output);
parse_data(weights, layer->weights, input*output);
#ifdef GPU
push_connected_layer(*layer);
#endif
option_unused(options);
return layer;
}