| | |
| | | CC=gcc |
| | | GPU=0 |
| | | COMMON=-Wall `pkg-config --cflags opencv` -I/usr/local/cuda/include/ |
| | | UNAME = $(shell uname) |
| | | OPTS=-O3 |
| | | ifeq ($(UNAME), Darwin) |
| | | COMMON+= -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include |
| | | ifeq ($(GPU), 1) |
| | | LDFLAGS= -framework OpenCL |
| | | endif |
| | | else |
| | | OPTS+= -march=native |
| | | ifeq ($(GPU), 1) |
| | | LDFLAGS= -lOpenCL |
| | | endif |
| | | endif |
| | | CFLAGS= $(COMMON) $(OPTS) |
| | | #CFLAGS= $(COMMON) -O0 -g |
| | | LDFLAGS+=`pkg-config --libs opencv` -lm |
| | | VPATH=./src/ |
| | | EXEC=cnn |
| | | |
| | | OBJ=network.o image.o tests.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o convolutional_layer.o opencl.o gpu_gemm.o cpu_gemm.o normalization_layer.o |
| | | OBJ=network.o image.o tests.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o convolutional_layer.o cpu_gemm.o normalization_layer.o |
| | | ifeq ($(GPU), 1) |
| | | OBJ+=gpu_gemm.o opencl.o |
| | | endif |
| | | |
| | | all: $(EXEC) |
| | | |
| | |
| | | |
| | | } |
| | | |
| | | void time_gpu_random_matrix(int TA, int TB, int m, int k, int n) |
| | | { |
| | | float *a; |
| | | if(!TA) a = random_matrix(m,k); |
| | | else a = random_matrix(k,m); |
| | | int lda = (!TA)?k:m; |
| | | float *b; |
| | | if(!TB) b = random_matrix(k,n); |
| | | else b = random_matrix(n,k); |
| | | int ldb = (!TB)?n:k; |
| | | |
| | | float *c = random_matrix(m,n); |
| | | int i; |
| | | clock_t start = clock(), end; |
| | | for(i = 0; i<1000; ++i){ |
| | | gpu_gemm(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); |
| | | } |
| | | end = clock(); |
| | | printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf ms\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); |
| | | free(a); |
| | | free(b); |
| | | free(c); |
| | | } |
| | | |
| | | void test_gpu_accuracy(int TA, int TB, int m, int k, int n) |
| | | { |
| | | srand(0); |
| | | float *a; |
| | | if(!TA) a = random_matrix(m,k); |
| | | else a = random_matrix(k,m); |
| | | int lda = (!TA)?k:m; |
| | | float *b; |
| | | if(!TB) b = random_matrix(k,n); |
| | | else b = random_matrix(n,k); |
| | | int ldb = (!TB)?n:k; |
| | | |
| | | float *c = random_matrix(m,n); |
| | | float *c_gpu = random_matrix(m,n); |
| | | memset(c, 0, m*n*sizeof(float)); |
| | | memset(c_gpu, 0, m*n*sizeof(float)); |
| | | int i; |
| | | //pm(m,k,b); |
| | | gpu_gemm(TA,TB,m,n,k,1,a,lda,b,ldb,1,c_gpu,n); |
| | | //pm(m, n, c_gpu); |
| | | cpu_gemm(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); |
| | | //pm(m, n, c); |
| | | double sse = 0; |
| | | for(i = 0; i < m*n; ++i) { |
| | | //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)); |
| | | free(a); |
| | | free(b); |
| | | free(c); |
| | | } |
| | | |
| | | void test_gpu_blas() |
| | | { |
| | | test_gpu_accuracy(0,0,17,10,10); |
| | | test_gpu_accuracy(1,0,17,10,10); |
| | | test_gpu_accuracy(0,1,17,10,10); |
| | | test_gpu_accuracy(1,1,17,10,10); |
| | | |
| | | test_gpu_accuracy(0,0,1000,10,100); |
| | | test_gpu_accuracy(1,0,1000,10,100); |
| | | test_gpu_accuracy(0,1,1000,10,100); |
| | | test_gpu_accuracy(1,1,1000,10,100); |
| | | |
| | | time_gpu_random_matrix(0,0,1000,1000,100); |
| | | time_random_matrix(0,0,1000,1000,100); |
| | | |
| | | time_gpu_random_matrix(0,1,1000,1000,100); |
| | | time_random_matrix(0,1,1000,1000,100); |
| | | |
| | | time_gpu_random_matrix(1,0,1000,1000,100); |
| | | time_random_matrix(1,0,1000,1000,100); |
| | | |
| | | time_gpu_random_matrix(1,1,1000,1000,100); |
| | | time_random_matrix(1,1,1000,1000,100); |
| | | |
| | | } |
| | | |
| | | /* |
| | | cl_kernel get_gemm_kernel_slow() |
| | | { |
| | |
| | | float BETA, |
| | | float *C, int ldc) |
| | | { |
| | | gpu_gemm( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc); |
| | | cpu_gemm( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc); |
| | | } |
| | | |
| | | void im2row(float *image, int h, int w, int c, int size, int stride, float *matrix) |
| | |
| | | time_random_matrix(1,0,1000,100,100); |
| | | time_random_matrix(0,1,1000,100,100); |
| | | time_random_matrix(1,1,1000,100,100); |
| | | |
| | | |
| | | } |
| | | |
| | | void time_gpu_random_matrix(int TA, int TB, int m, int k, int n) |
| | | { |
| | | float *a; |
| | | if(!TA) a = random_matrix(m,k); |
| | | else a = random_matrix(k,m); |
| | | int lda = (!TA)?k:m; |
| | | float *b; |
| | | if(!TB) b = random_matrix(k,n); |
| | | else b = random_matrix(n,k); |
| | | int ldb = (!TB)?n:k; |
| | | |
| | | float *c = random_matrix(m,n); |
| | | int i; |
| | | clock_t start = clock(), end; |
| | | for(i = 0; i<1000; ++i){ |
| | | gpu_gemm(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); |
| | | } |
| | | end = clock(); |
| | | printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf ms\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); |
| | | free(a); |
| | | free(b); |
| | | free(c); |
| | | } |
| | | |
| | | void test_gpu_accuracy(int TA, int TB, int m, int k, int n) |
| | | { |
| | | srand(0); |
| | | float *a; |
| | | if(!TA) a = random_matrix(m,k); |
| | | else a = random_matrix(k,m); |
| | | int lda = (!TA)?k:m; |
| | | float *b; |
| | | if(!TB) b = random_matrix(k,n); |
| | | else b = random_matrix(n,k); |
| | | int ldb = (!TB)?n:k; |
| | | |
| | | float *c = random_matrix(m,n); |
| | | float *c_gpu = random_matrix(m,n); |
| | | memset(c, 0, m*n*sizeof(float)); |
| | | memset(c_gpu, 0, m*n*sizeof(float)); |
| | | int i; |
| | | //pm(m,k,b); |
| | | gpu_gemm(TA,TB,m,n,k,1,a,lda,b,ldb,1,c_gpu,n); |
| | | //pm(m, n, c_gpu); |
| | | cpu_gemm(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); |
| | | //pm(m, n, c); |
| | | double sse = 0; |
| | | for(i = 0; i < m*n; ++i) { |
| | | //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)); |
| | | free(a); |
| | | free(b); |
| | | free(c); |
| | | } |
| | | |
| | | void test_gpu_blas() |
| | | { |
| | | test_gpu_accuracy(0,0,17,10,10); |
| | | test_gpu_accuracy(1,0,17,10,10); |
| | | test_gpu_accuracy(0,1,17,10,10); |
| | | test_gpu_accuracy(1,1,17,10,10); |
| | | |
| | | test_gpu_accuracy(0,0,1000,10,100); |
| | | test_gpu_accuracy(1,0,1000,10,100); |
| | | test_gpu_accuracy(0,1,1000,10,100); |
| | | test_gpu_accuracy(1,1,1000,10,100); |
| | | |
| | | time_gpu_random_matrix(0,0,1000,1000,100); |
| | | time_random_matrix(0,0,1000,1000,100); |
| | | |
| | | time_gpu_random_matrix(0,1,1000,1000,100); |
| | | time_random_matrix(0,1,1000,1000,100); |
| | | |
| | | time_gpu_random_matrix(1,0,1000,1000,100); |
| | | time_random_matrix(1,0,1000,1000,100); |
| | | |
| | | time_gpu_random_matrix(1,1,1000,1000,100); |
| | | time_random_matrix(1,1,1000,1000,100); |
| | | |
| | | } |
| | | |
| | | |
| | |
| | | //test_vince(); |
| | | //test_full(); |
| | | //train_VOC(); |
| | | features_VOC_image(argv[1], argv[2], argv[3], 0); |
| | | features_VOC_image(argv[1], argv[2], argv[3], 1); |
| | | //features_VOC_image_size(argv[1], atoi(argv[2]), atoi(argv[3])); |
| | | //features_VOC_image(argv[1], argv[2], argv[3], 0); |
| | | //features_VOC_image(argv[1], argv[2], argv[3], 1); |
| | | features_VOC_image_size(argv[1], atoi(argv[2]), atoi(argv[3])); |
| | | //visualize_imagenet_features("data/assira/train.list"); |
| | | //visualize_imagenet_topk("data/VOC2012.list"); |
| | | //visualize_cat(); |