mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
lots of cleaning
This commit is contained in:
Joseph Redmon
parent
d6fbe86e7a
commit
884045091b
753
src/cnn.c
753
src/cnn.c
@ -18,256 +18,12 @@
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#define _GNU_SOURCE
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#include <fenv.h>
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void test_convolve()
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{
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image dog = load_image("dog.jpg",300,400);
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printf("dog channels %d\n", dog.c);
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image kernel = make_random_image(3,3,dog.c);
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image edge = make_image(dog.h, dog.w, 1);
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 1000; ++i){
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convolve(dog, kernel, 1, 0, edge, 1);
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}
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end = clock();
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printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image_layers(edge, "Test Convolve");
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}
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#ifdef GPU
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void test_convolutional_layer()
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{
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/*
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int i;
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image dog = load_image("data/dog.jpg",224,224);
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network net = parse_network_cfg("cfg/convolutional.cfg");
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// data test = load_cifar10_data("data/cifar10/test_batch.bin");
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// float *X = calloc(net.batch*test.X.cols, sizeof(float));
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// float *y = calloc(net.batch*test.y.cols, sizeof(float));
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int in_size = get_network_input_size(net)*net.batch;
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int del_size = get_network_output_size_layer(net, 0)*net.batch;
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int size = get_network_output_size(net)*net.batch;
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float *X = calloc(in_size, sizeof(float));
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float *y = calloc(size, sizeof(float));
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for(i = 0; i < in_size; ++i){
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X[i] = dog.data[i%get_network_input_size(net)];
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}
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// get_batch(test, net.batch, X, y);
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clock_t start, end;
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cl_mem input_cl = cl_make_array(X, in_size);
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cl_mem truth_cl = cl_make_array(y, size);
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forward_network_gpu(net, input_cl, truth_cl, 1);
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start = clock();
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forward_network_gpu(net, input_cl, truth_cl, 1);
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end = clock();
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float gpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
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printf("forward gpu: %f sec\n", gpu_sec);
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start = clock();
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backward_network_gpu(net, input_cl);
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end = clock();
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gpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
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printf("backward gpu: %f sec\n", gpu_sec);
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//float gpu_cost = get_network_cost(net);
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float *gpu_out = calloc(size, sizeof(float));
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memcpy(gpu_out, get_network_output(net), size*sizeof(float));
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float *gpu_del = calloc(del_size, sizeof(float));
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memcpy(gpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
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*/
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/*
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start = clock();
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forward_network(net, X, y, 1);
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backward_network(net, X);
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float cpu_cost = get_network_cost(net);
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end = clock();
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float cpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
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float *cpu_out = calloc(size, sizeof(float));
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memcpy(cpu_out, get_network_output(net), size*sizeof(float));
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float *cpu_del = calloc(del_size, sizeof(float));
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memcpy(cpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
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float sum = 0;
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float del_sum = 0;
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for(i = 0; i < size; ++i) sum += pow(gpu_out[i] - cpu_out[i], 2);
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for(i = 0; i < del_size; ++i) {
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//printf("%f %f\n", cpu_del[i], gpu_del[i]);
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del_sum += pow(cpu_del[i] - gpu_del[i], 2);
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}
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printf("GPU cost: %f, CPU cost: %f\n", gpu_cost, cpu_cost);
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printf("gpu: %f sec, cpu: %f sec, diff: %f, delta diff: %f, size: %d\n", gpu_sec, cpu_sec, sum, del_sum, size);
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*/
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}
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/*
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void test_col2im()
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{
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float col[] = {1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2};
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float im[16] = {0};
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int batch = 1;
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int channels = 1;
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int height=4;
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int width=4;
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int ksize = 3;
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int stride = 1;
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int pad = 0;
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//col2im_gpu(col, batch,
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// channels, height, width,
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// ksize, stride, pad, im);
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int i;
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for(i = 0; i < 16; ++i)printf("%f,", im[i]);
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printf("\n");
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float data_im[] = {
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1,2,3,4,
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5,6,7,8,
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9,10,11,12
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};
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float data_col[18] = {0};
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im2col_cpu(data_im, batch,
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channels, height, width,
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ksize, stride, pad, data_col) ;
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for(i = 0; i < 18; ++i)printf("%f,", data_col[i]);
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printf("\n");
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}
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*/
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#endif
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void test_convolve_matrix()
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{
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image dog = load_image("dog.jpg",300,400);
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printf("dog channels %d\n", dog.c);
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int size = 11;
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int stride = 4;
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int n = 40;
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float *filters = make_random_image(size, size, dog.c*n).data;
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int mw = ((dog.h-size)/stride+1)*((dog.w-size)/stride+1);
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int mh = (size*size*dog.c);
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float *matrix = calloc(mh*mw, sizeof(float));
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image edge = make_image((dog.h-size)/stride+1, (dog.w-size)/stride+1, n);
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 1000; ++i){
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//im2col_cpu(dog.data,1, dog.c, dog.h, dog.w, size, stride, 0, matrix);
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gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
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}
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end = clock();
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printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image_layers(edge, "Test Convolve");
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cvWaitKey(0);
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}
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void test_color()
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{
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image dog = load_image("test_color.png", 300, 400);
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show_image_layers(dog, "Test Color");
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}
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void verify_convolutional_layer()
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{
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/*
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srand(0);
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int i;
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int n = 1;
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int stride = 1;
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int size = 3;
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float eps = .00000001;
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image test = make_random_image(5,5, 1);
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convolutional_layer layer = *make_convolutional_layer(1,test.h,test.w,test.c, n, size, stride, 0, RELU,0,0,0);
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image out = get_convolutional_image(layer);
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float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));
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forward_convolutional_layer(layer, test.data);
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image base = copy_image(out);
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for(i = 0; i < test.h*test.w*test.c; ++i){
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test.data[i] += eps;
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forward_convolutional_layer(layer, test.data);
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image partial = copy_image(out);
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subtract_image(partial, base);
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scale_image(partial, 1/eps);
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jacobian[i] = partial.data;
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test.data[i] -= eps;
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}
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float **jacobian2 = calloc(out.h*out.w*out.c, sizeof(float));
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image in_delta = make_image(test.h, test.w, test.c);
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image out_delta = get_convolutional_delta(layer);
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for(i = 0; i < out.h*out.w*out.c; ++i){
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out_delta.data[i] = 1;
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backward_convolutional_layer(layer, in_delta.data);
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image partial = copy_image(in_delta);
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jacobian2[i] = partial.data;
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out_delta.data[i] = 0;
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}
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int j;
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float *j1 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
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float *j2 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
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for(i = 0; i < test.h*test.w*test.c; ++i){
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for(j =0 ; j < out.h*out.w*out.c; ++j){
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j1[i*out.h*out.w*out.c + j] = jacobian[i][j];
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j2[i*out.h*out.w*out.c + j] = jacobian2[j][i];
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printf("%f %f\n", jacobian[i][j], jacobian2[j][i]);
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}
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}
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image mj1 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j1);
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image mj2 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j2);
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printf("%f %f\n", avg_image_layer(mj1,0), avg_image_layer(mj2,0));
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show_image(mj1, "forward jacobian");
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show_image(mj2, "backward jacobian");
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*/
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}
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void test_load()
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{
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image dog = load_image("dog.jpg", 300, 400);
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show_image(dog, "Test Load");
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show_image_layers(dog, "Test Load");
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}
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void test_upsample()
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{
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image dog = load_image("dog.jpg", 300, 400);
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int n = 3;
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image up = make_image(n*dog.h, n*dog.w, dog.c);
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upsample_image(dog, n, up);
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show_image(up, "Test Upsample");
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show_image_layers(up, "Test Upsample");
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}
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void test_rotate()
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{
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int i;
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image dog = load_image("dog.jpg",300,400);
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clock_t start = clock(), end;
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for(i = 0; i < 1001; ++i){
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rotate_image(dog);
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}
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end = clock();
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printf("Rotations: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image(dog, "Test Rotate");
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image random = make_random_image(3,3,3);
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show_image(random, "Test Rotate Random");
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rotate_image(random);
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show_image(random, "Test Rotate Random");
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rotate_image(random);
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show_image(random, "Test Rotate Random");
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}
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void test_parser()
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{
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@ -275,47 +31,11 @@ void test_parser()
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save_network(net, "cfg/trained_imagenet_smaller.cfg");
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}
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void train_asirra()
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{
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network net = parse_network_cfg("cfg/imagenet.cfg");
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int imgs = 1000/net.batch+1;
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//imgs = 1;
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srand(2222222);
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int i = 0;
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char *labels[] = {"cat","dog"};
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list *plist = get_paths("data/assira/train.list");
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char **paths = (char **)list_to_array(plist);
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int m = plist->size;
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free_list(plist);
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clock_t time;
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while(1){
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i += 1;
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time=clock();
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data train = load_data(paths, imgs*net.batch, m, labels, 2, 256, 256);
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normalize_data_rows(train);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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//float loss = train_network_data(net, train, imgs);
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float loss = 0;
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printf("%d: %f, Time: %lf seconds\n", i*net.batch*imgs, loss, sec(clock()-time));
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free_data(train);
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if(i%10==0){
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char buff[256];
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sprintf(buff, "cfg/asirra_backup_%d.cfg", i);
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save_network(net, buff);
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}
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//lr *= .99;
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}
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}
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void draw_detection(image im, float *box, int side)
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{
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int j;
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int r, c;
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float amount[5];
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float amount[5] = {0,0,0,0,0};
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for(r = 0; r < side*side; ++r){
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for(j = 0; j < 5; ++j){
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if(box[r*5] > amount[j]) {
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@ -355,7 +75,7 @@ void train_detection_net()
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//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
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network net = parse_network_cfg("cfg/detnet.cfg");
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 1000/net.batch+1;
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int imgs = 1024;
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srand(time(0));
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//srand(23410);
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int i = 0;
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@ -366,7 +86,7 @@ void train_detection_net()
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while(1){
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i += 1;
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time=clock();
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data train = load_data_detection_jitter_random(imgs*net.batch, paths, plist->size, 256, 256, 7, 7, 256);
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data train = load_data_detection_jitter_random(imgs, paths, plist->size, 256, 256, 7, 7, 256);
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/*
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image im = float_to_image(224, 224, 3, train.X.vals[0]);
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draw_detection(im, train.y.vals[0], 7);
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@ -375,11 +95,9 @@ void train_detection_net()
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normalize_data_rows(train);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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#ifdef GPU
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float loss = train_network_data_gpu(net, train, imgs);
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float loss = train_network(net, train);
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs*net.batch);
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#endif
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if(i%10==0){
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char buff[256];
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sprintf(buff, "/home/pjreddie/imagenet_backup/detnet_%d.cfg", i);
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@ -396,7 +114,7 @@ void train_imagenet_distributed(char *address)
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network net = parse_network_cfg("cfg/net.cfg");
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set_learning_network(&net, 0, 1, 0);
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 1;
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int imgs = net.batch;
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int i = 0;
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char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
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list *plist = get_paths("/data/imagenet/cls.train.list");
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@ -404,7 +122,7 @@ void train_imagenet_distributed(char *address)
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printf("%d\n", plist->size);
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clock_t time;
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data train, buffer;
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pthread_t load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
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pthread_t load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
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while(1){
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i += 1;
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@ -416,15 +134,13 @@ void train_imagenet_distributed(char *address)
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pthread_join(load_thread, 0);
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train = buffer;
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normalize_data_rows(train);
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load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
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load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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#ifdef GPU
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float loss = train_network_data_gpu(net, train, imgs);
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float loss = train_network(net, train);
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs*net.batch);
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#endif
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
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free_data(train);
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}
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}
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@ -437,7 +153,7 @@ void train_imagenet(char *cfgfile)
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network net = parse_network_cfg(cfgfile);
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set_learning_network(&net, .000001, .9, .0005);
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 1000/net.batch+1;
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int imgs = 1024;
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int i = 20590;
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char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
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list *plist = get_paths("/data/imagenet/cls.train.list");
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@ -447,21 +163,19 @@ void train_imagenet(char *cfgfile)
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pthread_t load_thread;
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data train;
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data buffer;
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load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 256, 256, &buffer);
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load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
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while(1){
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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normalize_data_rows(train);
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load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 256, 256, &buffer);
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load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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#ifdef GPU
|
||||
float loss = train_network_data_gpu(net, train, imgs);
|
||||
float loss = train_network(net, train);
|
||||
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
|
||||
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
|
||||
free_data(train);
|
||||
if(i%10==0){
|
||||
char buff[256];
|
||||
@ -505,12 +219,10 @@ void validate_imagenet(char *filename)
|
||||
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);
|
||||
float *acc = network_accuracies(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, avg_top5/i, sec(clock()-time), val.X.rows);
|
||||
#endif
|
||||
free_data(val);
|
||||
}
|
||||
}
|
||||
@ -620,62 +332,29 @@ void test_cifar10()
|
||||
void train_cifar10()
|
||||
{
|
||||
srand(555555);
|
||||
network net = parse_network_cfg("cfg/cifar_ramp.part");
|
||||
network net = parse_network_cfg("cfg/cifar10.cfg");
|
||||
data test = load_cifar10_data("data/cifar10/test_batch.bin");
|
||||
int count = 0;
|
||||
int iters = 10000/net.batch;
|
||||
data train = load_all_cifar10();
|
||||
while(++count <= 10000){
|
||||
clock_t start = clock(), end;
|
||||
float loss = train_network_sgd_gpu(net, train, iters);
|
||||
end = clock();
|
||||
//visualize_network(net);
|
||||
//cvWaitKey(5000);
|
||||
clock_t time = clock();
|
||||
float loss = train_network_sgd(net, train, iters);
|
||||
|
||||
//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);
|
||||
if(count%10 == 0){
|
||||
float test_acc = network_accuracy_gpu(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);
|
||||
float test_acc = network_accuracy(net, test);
|
||||
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,sec(clock()-time));
|
||||
char buff[256];
|
||||
sprintf(buff, "/home/pjreddie/cifar/cifar10_%d.cfg", count);
|
||||
sprintf(buff, "unikitty/cifar10_%d.cfg", count);
|
||||
save_network(net, buff);
|
||||
}else{
|
||||
printf("%d: Loss: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
|
||||
printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, sec(clock()-time));
|
||||
}
|
||||
|
||||
}
|
||||
free_data(train);
|
||||
}
|
||||
|
||||
void test_vince()
|
||||
{
|
||||
network net = parse_network_cfg("cfg/vince.cfg");
|
||||
data train = load_categorical_data_csv("images/vince.txt", 144, 2);
|
||||
normalize_data_rows(train);
|
||||
|
||||
int count = 0;
|
||||
//float lr = .00005;
|
||||
//float momentum = .9;
|
||||
//float decay = 0.0001;
|
||||
//decay = 0;
|
||||
int batch = 10000;
|
||||
while(++count <= 10000){
|
||||
float loss = train_network_sgd(net, train, batch);
|
||||
printf("%5f %5f\n",(double)count*batch/train.X.rows, loss);
|
||||
}
|
||||
}
|
||||
|
||||
void test_nist_single()
|
||||
{
|
||||
srand(222222);
|
||||
network net = parse_network_cfg("cfg/nist_single.cfg");
|
||||
data train = load_categorical_data_csv("data/mnist/mnist_tiny.csv", 0, 10);
|
||||
normalize_data_rows(train);
|
||||
float loss = train_network_sgd(net, train, 1);
|
||||
printf("Loss: %f, LR: %f, Momentum: %f, Decay: %f\n", loss, net.learning_rate, net.momentum, net.decay);
|
||||
|
||||
}
|
||||
|
||||
void test_nist(char *path)
|
||||
{
|
||||
srand(222222);
|
||||
@ -683,7 +362,7 @@ void test_nist(char *path)
|
||||
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
|
||||
normalize_data_rows(test);
|
||||
clock_t start = clock(), end;
|
||||
float test_acc = network_accuracy_gpu(net, test);
|
||||
float test_acc = network_accuracy(net, test);
|
||||
end = clock();
|
||||
printf("Accuracy: %f, Time: %lf seconds\n", test_acc,(float)(end-start)/CLOCKS_PER_SEC);
|
||||
}
|
||||
@ -698,13 +377,12 @@ void train_nist()
|
||||
normalize_data_rows(test);
|
||||
int count = 0;
|
||||
int iters = 60000/net.batch + 1;
|
||||
//iters = 6000/net.batch + 1;
|
||||
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 = 0;
|
||||
if(count%1 == 0) test_acc = network_accuracy_gpu(net, test);
|
||||
if(count%1 == 0) test_acc = network_accuracy(net, test);
|
||||
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC);
|
||||
}
|
||||
}
|
||||
@ -722,7 +400,7 @@ void train_nist_distributed(char *address)
|
||||
iters = 1000/net.batch + 1;
|
||||
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);
|
||||
client_update(net, address);
|
||||
end = clock();
|
||||
//float test_acc = network_accuracy_gpu(net, test);
|
||||
@ -768,87 +446,6 @@ void test_ensemble()
|
||||
printf("Full Ensemble Accuracy: %lf\n", acc);
|
||||
}
|
||||
|
||||
void test_random_classify()
|
||||
{
|
||||
network net = parse_network_cfg("connected.cfg");
|
||||
matrix m = csv_to_matrix("train.csv");
|
||||
//matrix ho = hold_out_matrix(&m, 2500);
|
||||
float *truth = pop_column(&m, 0);
|
||||
//float *ho_truth = pop_column(&ho, 0);
|
||||
int i;
|
||||
clock_t start = clock(), end;
|
||||
int count = 0;
|
||||
while(++count <= 300){
|
||||
for(i = 0; i < m.rows; ++i){
|
||||
int index = rand()%m.rows;
|
||||
//image p = float_to_image(1690,1,1,m.vals[index]);
|
||||
//normalize_image(p);
|
||||
forward_network(net, m.vals[index], 0, 1);
|
||||
float *out = get_network_output(net);
|
||||
float *delta = get_network_delta(net);
|
||||
//printf("%f\n", out[0]);
|
||||
delta[0] = truth[index] - out[0];
|
||||
// printf("%f\n", delta[0]);
|
||||
//printf("%f %f\n", truth[index], out[0]);
|
||||
//backward_network(net, m.vals[index], );
|
||||
update_network(net);
|
||||
}
|
||||
//float test_acc = error_network(net, m, truth);
|
||||
//float valid_acc = error_network(net, ho, ho_truth);
|
||||
//printf("%f, %f\n", test_acc, valid_acc);
|
||||
//fprintf(stderr, "%5d: %f Valid: %f\n",count, test_acc, valid_acc);
|
||||
//if(valid_acc > .70) break;
|
||||
}
|
||||
end = clock();
|
||||
FILE *fp = fopen("submission/out.txt", "w");
|
||||
matrix test = csv_to_matrix("test.csv");
|
||||
truth = pop_column(&test, 0);
|
||||
for(i = 0; i < test.rows; ++i){
|
||||
forward_network(net, test.vals[i],0, 0);
|
||||
float *out = get_network_output(net);
|
||||
if(fabs(out[0]) < .5) fprintf(fp, "0\n");
|
||||
else fprintf(fp, "1\n");
|
||||
}
|
||||
fclose(fp);
|
||||
printf("Neural Net Learning: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
|
||||
}
|
||||
|
||||
void test_split()
|
||||
{
|
||||
data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
|
||||
data *split = split_data(train, 0, 13);
|
||||
printf("%d, %d, %d\n", train.X.rows, split[0].X.rows, split[1].X.rows);
|
||||
}
|
||||
|
||||
/*
|
||||
void test_im2row()
|
||||
{
|
||||
int h = 20;
|
||||
int w = 20;
|
||||
int c = 3;
|
||||
int stride = 1;
|
||||
int size = 11;
|
||||
image test = make_random_image(h,w,c);
|
||||
int mc = 1;
|
||||
int mw = ((h-size)/stride+1)*((w-size)/stride+1);
|
||||
int mh = (size*size*c);
|
||||
int msize = mc*mw*mh;
|
||||
float *matrix = calloc(msize, sizeof(float));
|
||||
int i;
|
||||
for(i = 0; i < 1000; ++i){
|
||||
//im2col_cpu(test.data,1, c, h, w, size, stride, 0, matrix);
|
||||
//image render = float_to_image(mh, mw, mc, matrix);
|
||||
}
|
||||
}
|
||||
*/
|
||||
|
||||
void flip_network()
|
||||
{
|
||||
network net = parse_network_cfg("cfg/voc_imagenet_orig.cfg");
|
||||
save_network(net, "cfg/voc_imagenet_rev.cfg");
|
||||
}
|
||||
|
||||
|
||||
void visualize_cat()
|
||||
{
|
||||
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
|
||||
@ -861,7 +458,6 @@ void visualize_cat()
|
||||
cvWaitKey(0);
|
||||
}
|
||||
|
||||
|
||||
void test_gpu_net()
|
||||
{
|
||||
srand(222222);
|
||||
@ -872,6 +468,7 @@ void test_gpu_net()
|
||||
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);
|
||||
@ -879,18 +476,18 @@ void test_gpu_net()
|
||||
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
|
||||
|
||||
gpu_index = -1;
|
||||
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);
|
||||
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);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
void test_correct_alexnet()
|
||||
@ -902,36 +499,34 @@ void test_correct_alexnet()
|
||||
clock_t time;
|
||||
int count = 0;
|
||||
network net;
|
||||
int imgs = 1000/net.batch+1;
|
||||
imgs = 1;
|
||||
#ifdef GPU
|
||||
int imgs = net.batch;
|
||||
|
||||
count = 0;
|
||||
srand(222222);
|
||||
net = parse_network_cfg("cfg/net.cfg");
|
||||
while(++count <= 5){
|
||||
time=clock();
|
||||
data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 256, 256);
|
||||
//translate_data_rows(train, -144);
|
||||
data train = load_data(paths, imgs, plist->size, labels, 1000, 256, 256);
|
||||
normalize_data_rows(train);
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
time=clock();
|
||||
float loss = train_network_data_gpu(net, train, imgs);
|
||||
float loss = train_network(net, train);
|
||||
printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
|
||||
free_data(train);
|
||||
}
|
||||
#endif
|
||||
|
||||
gpu_index = -1;
|
||||
count = 0;
|
||||
srand(222222);
|
||||
net = parse_network_cfg("cfg/net.cfg");
|
||||
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
|
||||
while(++count <= 5){
|
||||
time=clock();
|
||||
data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 256,256);
|
||||
//translate_data_rows(train, -144);
|
||||
data train = load_data(paths, imgs, plist->size, labels, 1000, 256,256);
|
||||
normalize_data_rows(train);
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
time=clock();
|
||||
float loss = train_network_data_cpu(net, train, imgs);
|
||||
float loss = train_network(net, train);
|
||||
printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
|
||||
free_data(train);
|
||||
}
|
||||
@ -944,6 +539,7 @@ void run_server()
|
||||
set_batch_network(&net, 1);
|
||||
server_update(net);
|
||||
}
|
||||
|
||||
void test_client()
|
||||
{
|
||||
network net = parse_network_cfg("cfg/alexnet.client");
|
||||
@ -957,33 +553,64 @@ void test_client()
|
||||
printf("Transfered: %lf seconds\n", sec(clock()-time));
|
||||
}
|
||||
|
||||
int find_int_arg(int argc, char* argv[], char *arg)
|
||||
void del_arg(int argc, char **argv, int index)
|
||||
{
|
||||
int i;
|
||||
for(i = 0; i < argc-1; ++i) if(0==strcmp(argv[i], arg)) return atoi(argv[i+1]);
|
||||
for(i = index; i < argc-1; ++i) argv[i] = argv[i+1];
|
||||
}
|
||||
|
||||
int find_arg(int argc, char* argv[], char *arg)
|
||||
{
|
||||
int i;
|
||||
for(i = 0; i < argc-1; ++i) if(0==strcmp(argv[i], arg)) {
|
||||
del_arg(argc, argv, i);
|
||||
return 1;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
int main(int argc, char *argv[])
|
||||
int find_int_arg(int argc, char **argv, char *arg, int def)
|
||||
{
|
||||
int i;
|
||||
for(i = 0; i < argc-1; ++i){
|
||||
if(0==strcmp(argv[i], arg)){
|
||||
def = atoi(argv[i+1]);
|
||||
del_arg(argc, argv, i);
|
||||
del_arg(argc, argv, i);
|
||||
break;
|
||||
}
|
||||
}
|
||||
return def;
|
||||
}
|
||||
|
||||
int main(int argc, char **argv)
|
||||
{
|
||||
if(argc < 2){
|
||||
fprintf(stderr, "usage: %s <function>\n", argv[0]);
|
||||
return 0;
|
||||
}
|
||||
int index = find_int_arg(argc, argv, "-i");
|
||||
#ifdef GPU
|
||||
cl_setup(index);
|
||||
gpu_index = find_int_arg(argc, argv, "-i", 0);
|
||||
if(find_arg(argc, argv, "-nogpu")) gpu_index = -1;
|
||||
|
||||
#ifndef GPU
|
||||
gpu_index = -1;
|
||||
#else
|
||||
if(gpu_index >= 0){
|
||||
cl_setup();
|
||||
}
|
||||
#endif
|
||||
|
||||
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], "cifar")) train_cifar10();
|
||||
else if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
|
||||
else if(0==strcmp(argv[1], "test")) test_imagenet();
|
||||
else if(0==strcmp(argv[1], "server")) run_server();
|
||||
|
||||
#ifdef GPU
|
||||
else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
|
||||
#endif
|
||||
|
||||
else if(argc < 3){
|
||||
fprintf(stderr, "usage: %s <function> <filename>\n", argv[0]);
|
||||
return 0;
|
||||
@ -999,227 +626,3 @@ int main(int argc, char *argv[])
|
||||
return 0;
|
||||
}
|
||||
|
||||
/*
|
||||
void visualize_imagenet_topk(char *filename)
|
||||
{
|
||||
int i,j,k,l;
|
||||
int topk = 10;
|
||||
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
|
||||
list *plist = get_paths(filename);
|
||||
node *n = plist->front;
|
||||
int h = voc_size(1), w = voc_size(1);
|
||||
int num = get_network_image(net).c;
|
||||
image **vizs = calloc(num, sizeof(image*));
|
||||
float **score = calloc(num, sizeof(float *));
|
||||
for(i = 0; i < num; ++i){
|
||||
vizs[i] = calloc(topk, sizeof(image));
|
||||
for(j = 0; j < topk; ++j) vizs[i][j] = make_image(h,w,3);
|
||||
score[i] = calloc(topk, sizeof(float));
|
||||
}
|
||||
|
||||
int count = 0;
|
||||
while(n){
|
||||
++count;
|
||||
char *image_path = (char *)n->val;
|
||||
image im = load_image(image_path, 0, 0);
|
||||
n = n->next;
|
||||
if(im.h < 200 || im.w < 200) continue;
|
||||
printf("Processing %dx%d image\n", im.h, im.w);
|
||||
resize_network(net, im.h, im.w, im.c);
|
||||
//scale_image(im, 1./255);
|
||||
translate_image(im, -144);
|
||||
forward_network(net, im.data, 0, 0);
|
||||
image out = get_network_image(net);
|
||||
|
||||
int dh = (im.h - h)/(out.h-1);
|
||||
int dw = (im.w - w)/(out.w-1);
|
||||
//printf("%d %d\n", dh, dw);
|
||||
for(k = 0; k < out.c; ++k){
|
||||
float topv = 0;
|
||||
int topi = -1;
|
||||
int topj = -1;
|
||||
for(i = 0; i < out.h; ++i){
|
||||
for(j = 0; j < out.w; ++j){
|
||||
float val = get_pixel(out, i, j, k);
|
||||
if(val > topv){
|
||||
topv = val;
|
||||
topi = i;
|
||||
topj = j;
|
||||
}
|
||||
}
|
||||
}
|
||||
if(topv){
|
||||
image sub = get_sub_image(im, dh*topi, dw*topj, h, w);
|
||||
for(l = 0; l < topk; ++l){
|
||||
if(topv > score[k][l]){
|
||||
float swap = score[k][l];
|
||||
score[k][l] = topv;
|
||||
topv = swap;
|
||||
|
||||
image swapi = vizs[k][l];
|
||||
vizs[k][l] = sub;
|
||||
sub = swapi;
|
||||
}
|
||||
}
|
||||
free_image(sub);
|
||||
}
|
||||
}
|
||||
free_image(im);
|
||||
if(count%50 == 0){
|
||||
image grid = grid_images(vizs, num, topk);
|
||||
//show_image(grid, "IMAGENET Visualization");
|
||||
save_image(grid, "IMAGENET Grid Single Nonorm");
|
||||
free_image(grid);
|
||||
}
|
||||
}
|
||||
//cvWaitKey(0);
|
||||
}
|
||||
|
||||
void visualize_imagenet_features(char *filename)
|
||||
{
|
||||
int i,j,k;
|
||||
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
|
||||
list *plist = get_paths(filename);
|
||||
node *n = plist->front;
|
||||
int h = voc_size(1), w = voc_size(1);
|
||||
int num = get_network_image(net).c;
|
||||
image *vizs = calloc(num, sizeof(image));
|
||||
for(i = 0; i < num; ++i) vizs[i] = make_image(h, w, 3);
|
||||
while(n){
|
||||
char *image_path = (char *)n->val;
|
||||
image im = load_image(image_path, 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);
|
||||
|
||||
int dh = (im.h - h)/h;
|
||||
int dw = (im.w - w)/w;
|
||||
for(i = 0; i < out.h; ++i){
|
||||
for(j = 0; j < out.w; ++j){
|
||||
image sub = get_sub_image(im, dh*i, dw*j, h, w);
|
||||
for(k = 0; k < out.c; ++k){
|
||||
float val = get_pixel(out, i, j, k);
|
||||
//printf("%f, ", val);
|
||||
image sub_c = copy_image(sub);
|
||||
scale_image(sub_c, val);
|
||||
add_into_image(sub_c, vizs[k], 0, 0);
|
||||
free_image(sub_c);
|
||||
}
|
||||
free_image(sub);
|
||||
}
|
||||
}
|
||||
//printf("\n");
|
||||
show_images(vizs, 10, "IMAGENET Visualization");
|
||||
cvWaitKey(1000);
|
||||
n = n->next;
|
||||
}
|
||||
cvWaitKey(0);
|
||||
}
|
||||
void features_VOC_image(char *image_file, char *image_dir, char *out_dir, int flip, int interval)
|
||||
{
|
||||
int i,j;
|
||||
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
|
||||
char image_path[1024];
|
||||
sprintf(image_path, "%s/%s",image_dir, image_file);
|
||||
char out_path[1024];
|
||||
if (flip)sprintf(out_path, "%s%d/%s_r.txt",out_dir, interval, image_file);
|
||||
else sprintf(out_path, "%s%d/%s.txt",out_dir, interval, image_file);
|
||||
printf("%s\n", image_file);
|
||||
|
||||
IplImage* src = 0;
|
||||
if( (src = cvLoadImage(image_path,-1)) == 0 ) file_error(image_path);
|
||||
if(flip)cvFlip(src, 0, 1);
|
||||
int w = src->width;
|
||||
int h = src->height;
|
||||
int sbin = 8;
|
||||
double scale = pow(2., 1./interval);
|
||||
int m = (w<h)?w:h;
|
||||
int max_scale = 1+floor((double)log((double)m/(5.*sbin))/log(scale));
|
||||
if(max_scale < interval) error("max_scale must be >= interval");
|
||||
image *ims = calloc(max_scale+interval, sizeof(image));
|
||||
|
||||
for(i = 0; i < interval; ++i){
|
||||
double factor = 1./pow(scale, i);
|
||||
double ih = round(h*factor);
|
||||
double iw = round(w*factor);
|
||||
int ex_h = round(ih/4.) - 2;
|
||||
int ex_w = round(iw/4.) - 2;
|
||||
ims[i] = features_output_size(net, src, ex_h, ex_w);
|
||||
|
||||
ih = round(h*factor);
|
||||
iw = round(w*factor);
|
||||
ex_h = round(ih/8.) - 2;
|
||||
ex_w = round(iw/8.) - 2;
|
||||
ims[i+interval] = features_output_size(net, src, ex_h, ex_w);
|
||||
for(j = i+interval; j < max_scale; j += interval){
|
||||
factor /= 2.;
|
||||
ih = round(h*factor);
|
||||
iw = round(w*factor);
|
||||
ex_h = round(ih/8.) - 2;
|
||||
ex_w = round(iw/8.) - 2;
|
||||
ims[j+interval] = features_output_size(net, src, ex_h, ex_w);
|
||||
}
|
||||
}
|
||||
FILE *fp = fopen(out_path, "w");
|
||||
if(fp == 0) file_error(out_path);
|
||||
for(i = 0; i < max_scale+interval; ++i){
|
||||
image out = ims[i];
|
||||
fprintf(fp, "%d, %d, %d\n",out.c, out.h, out.w);
|
||||
for(j = 0; j < out.c*out.h*out.w; ++j){
|
||||
if(j != 0)fprintf(fp, ",");
|
||||
float o = out.data[j];
|
||||
if(o < 0) o = 0;
|
||||
fprintf(fp, "%g", o);
|
||||
}
|
||||
fprintf(fp, "\n");
|
||||
free_image(out);
|
||||
}
|
||||
free(ims);
|
||||
fclose(fp);
|
||||
cvReleaseImage(&src);
|
||||
}
|
||||
|
||||
void test_distribution()
|
||||
{
|
||||
IplImage* img = 0;
|
||||
if( (img = cvLoadImage("im_small.jpg",-1)) == 0 ) file_error("im_small.jpg");
|
||||
network net = parse_network_cfg("cfg/voc_features.cfg");
|
||||
int h = img->height/8-2;
|
||||
int w = img->width/8-2;
|
||||
image out = features_output_size(net, img, h, w);
|
||||
int c = out.c;
|
||||
out.c = 1;
|
||||
show_image(out, "output");
|
||||
out.c = c;
|
||||
image input = ipl_to_image(img);
|
||||
show_image(input, "input");
|
||||
CvScalar s;
|
||||
int i,j;
|
||||
image affects = make_image(input.h, input.w, 1);
|
||||
int count = 0;
|
||||
for(i = 0; i<img->height; i += 1){
|
||||
for(j = 0; j < img->width; j += 1){
|
||||
IplImage *copy = cvCloneImage(img);
|
||||
s=cvGet2D(copy,i,j); // get the (i,j) pixel value
|
||||
printf("%d/%d\n", count++, img->height*img->width);
|
||||
s.val[0]=0;
|
||||
s.val[1]=0;
|
||||
s.val[2]=0;
|
||||
cvSet2D(copy,i,j,s); // set the (i,j) pixel value
|
||||
image mod = features_output_size(net, copy, h, w);
|
||||
image dist = image_distance(out, mod);
|
||||
show_image(affects, "affects");
|
||||
cvWaitKey(1);
|
||||
cvReleaseImage(©);
|
||||
//affects.data[i*affects.w + j] += dist.data[3*dist.w+5];
|
||||
affects.data[i*affects.w + j] += dist.data[1*dist.w+1];
|
||||
free_image(mod);
|
||||
free_image(dist);
|
||||
}
|
||||
}
|
||||
show_image(affects, "Origins");
|
||||
cvWaitKey(0);
|
||||
cvWaitKey(0);
|
||||
}
|
||||
*/
|
||||
|
||||
@ -245,17 +245,15 @@ void backward_network(network net, float *input)
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
float train_network_datum(network net, float *x, float *y)
|
||||
{
|
||||
#ifdef GPU
|
||||
if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
|
||||
#endif
|
||||
forward_network(net, x, y, 1);
|
||||
//int class = get_predicted_class_network(net);
|
||||
backward_network(net, x);
|
||||
float error = get_network_cost(net);
|
||||
update_network(net);
|
||||
//return (y[class]?1:0);
|
||||
return error;
|
||||
}
|
||||
|
||||
@ -277,6 +275,25 @@ float train_network_sgd(network net, data d, int n)
|
||||
return (float)sum/(n*batch);
|
||||
}
|
||||
|
||||
float train_network(network net, data d)
|
||||
{
|
||||
int batch = net.batch;
|
||||
int n = d.X.rows / batch;
|
||||
float *X = calloc(batch*d.X.cols, sizeof(float));
|
||||
float *y = calloc(batch*d.y.cols, sizeof(float));
|
||||
|
||||
int i;
|
||||
float sum = 0;
|
||||
for(i = 0; i < n; ++i){
|
||||
get_next_batch(d, batch, i*batch, X, y);
|
||||
float err = train_network_datum(net, X, y);
|
||||
sum += err;
|
||||
}
|
||||
free(X);
|
||||
free(y);
|
||||
return (float)sum/(n*batch);
|
||||
}
|
||||
|
||||
float train_network_batch(network net, data d, int n)
|
||||
{
|
||||
int i,j;
|
||||
@ -296,40 +313,6 @@ float train_network_batch(network net, data d, int n)
|
||||
return (float)sum/(n*batch);
|
||||
}
|
||||
|
||||
float train_network_data_cpu(network net, data d, int n)
|
||||
{
|
||||
int batch = net.batch;
|
||||
float *X = calloc(batch*d.X.cols, sizeof(float));
|
||||
float *y = calloc(batch*d.y.cols, sizeof(float));
|
||||
|
||||
int i;
|
||||
float sum = 0;
|
||||
for(i = 0; i < n; ++i){
|
||||
get_next_batch(d, batch, i*batch, X, y);
|
||||
float err = train_network_datum(net, X, y);
|
||||
sum += err;
|
||||
}
|
||||
free(X);
|
||||
free(y);
|
||||
return (float)sum/(n*batch);
|
||||
}
|
||||
|
||||
void train_network(network net, data d)
|
||||
{
|
||||
int i;
|
||||
int correct = 0;
|
||||
for(i = 0; i < d.X.rows; ++i){
|
||||
correct += train_network_datum(net, d.X.vals[i], d.y.vals[i]);
|
||||
if(i%100 == 0){
|
||||
visualize_network(net);
|
||||
cvWaitKey(10);
|
||||
}
|
||||
}
|
||||
visualize_network(net);
|
||||
cvWaitKey(100);
|
||||
fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
|
||||
}
|
||||
|
||||
void set_learning_network(network *net, float rate, float momentum, float decay)
|
||||
{
|
||||
int i;
|
||||
@ -561,6 +544,10 @@ void top_predictions(network net, int k, int *index)
|
||||
|
||||
float *network_predict(network net, float *input)
|
||||
{
|
||||
#ifdef GPU
|
||||
if(gpu_index >= 0) return network_predict_gpu(net, input);
|
||||
#endif
|
||||
|
||||
forward_network(net, input, 0, 0);
|
||||
float *out = get_network_output(net);
|
||||
return out;
|
||||
|
||||
@ -35,34 +35,24 @@ typedef struct {
|
||||
#endif
|
||||
} network;
|
||||
|
||||
#ifndef GPU
|
||||
typedef int cl_mem;
|
||||
#endif
|
||||
|
||||
cl_mem get_network_output_cl_layer(network net, int i);
|
||||
cl_mem get_network_delta_cl_layer(network net, int i);
|
||||
void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train);
|
||||
void backward_network_gpu(network net, cl_mem input);
|
||||
void update_network_gpu(network net);
|
||||
float train_network_sgd_gpu(network net, data d, int n);
|
||||
float train_network_data_gpu(network net, data d, int n);
|
||||
#ifdef GPU
|
||||
float train_network_datum_gpu(network net, float *x, float *y);
|
||||
float *network_predict_gpu(network net, float *input);
|
||||
float network_accuracy_gpu(network net, data d);
|
||||
float *network_accuracies_gpu(network net, data d);
|
||||
|
||||
float *network_accuracies(network net, data d);
|
||||
#endif
|
||||
|
||||
network make_network(int n, int batch);
|
||||
void forward_network(network net, float *input, float *truth, int train);
|
||||
void backward_network(network net, float *input);
|
||||
void update_network(network net);
|
||||
float train_network_sgd(network net, data d, int n);
|
||||
|
||||
float train_network(network net, data d);
|
||||
float train_network_batch(network net, data d, int n);
|
||||
float train_network_data_cpu(network net, data d, int n);
|
||||
void train_network(network net, data d);
|
||||
float train_network_sgd(network net, data d, int n);
|
||||
|
||||
matrix network_predict_data(network net, data test);
|
||||
float *network_predict(network net, float *input);
|
||||
float network_accuracy(network net, data d);
|
||||
float *network_accuracies(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);
|
||||
|
||||
@ -17,15 +17,13 @@
|
||||
#include "dropout_layer.h"
|
||||
|
||||
#ifdef GPU
|
||||
cl_mem get_network_output_cl_layer(network net, int i);
|
||||
cl_mem get_network_delta_cl_layer(network net, int i);
|
||||
|
||||
void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
|
||||
{
|
||||
//printf("start\n");
|
||||
int i;
|
||||
// printf("Truth: %f\n", cl_checksum(truth, 1000*net.batch));
|
||||
for(i = 0; i < net.n; ++i){
|
||||
//printf("Truth %i: %f\n", i, cl_checksum(truth, 1000*net.batch));
|
||||
//clock_t time = clock();
|
||||
if(net.types[i] == CONVOLUTIONAL){
|
||||
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
|
||||
forward_convolutional_layer_gpu(layer, input);
|
||||
@ -60,19 +58,6 @@ void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
|
||||
forward_crop_layer_gpu(layer, input);
|
||||
input = layer.output_cl;
|
||||
}
|
||||
//printf("%d %f\n", i, sec(clock()-time));
|
||||
/*
|
||||
else if(net.types[i] == CROP){
|
||||
crop_layer layer = *(crop_layer *)net.layers[i];
|
||||
forward_crop_layer(layer, input);
|
||||
input = layer.output;
|
||||
}
|
||||
else if(net.types[i] == NORMALIZATION){
|
||||
normalization_layer layer = *(normalization_layer *)net.layers[i];
|
||||
forward_normalization_layer(layer, input);
|
||||
input = layer.output;
|
||||
}
|
||||
*/
|
||||
}
|
||||
}
|
||||
|
||||
@ -82,7 +67,6 @@ void backward_network_gpu(network net, cl_mem input)
|
||||
cl_mem prev_input;
|
||||
cl_mem prev_delta;
|
||||
for(i = net.n-1; i >= 0; --i){
|
||||
//clock_t time = clock();
|
||||
if(i == 0){
|
||||
prev_input = input;
|
||||
prev_delta = 0;
|
||||
@ -114,7 +98,6 @@ void backward_network_gpu(network net, cl_mem input)
|
||||
softmax_layer layer = *(softmax_layer *)net.layers[i];
|
||||
backward_softmax_layer_gpu(layer, prev_delta);
|
||||
}
|
||||
//printf("back: %d %f\n", i, sec(clock()-time));
|
||||
}
|
||||
}
|
||||
|
||||
@ -187,7 +170,6 @@ float train_network_datum_gpu(network net, float *x, float *y)
|
||||
{
|
||||
int x_size = get_network_input_size(net)*net.batch;
|
||||
int y_size = get_network_output_size(net)*net.batch;
|
||||
//clock_t time = clock();
|
||||
if(!*net.input_cl){
|
||||
*net.input_cl = cl_make_array(x, x_size);
|
||||
*net.truth_cl = cl_make_array(y, y_size);
|
||||
@ -202,42 +184,6 @@ float train_network_datum_gpu(network net, float *x, float *y)
|
||||
return error;
|
||||
}
|
||||
|
||||
float train_network_sgd_gpu(network net, data d, int n)
|
||||
{
|
||||
int batch = net.batch;
|
||||
float *X = calloc(batch*d.X.cols, sizeof(float));
|
||||
float *y = calloc(batch*d.y.cols, sizeof(float));
|
||||
|
||||
int i;
|
||||
float sum = 0;
|
||||
for(i = 0; i < n; ++i){
|
||||
get_random_batch(d, batch, X, y);
|
||||
float err = train_network_datum_gpu(net, X, y);
|
||||
sum += err;
|
||||
}
|
||||
free(X);
|
||||
free(y);
|
||||
return (float)sum/(n*batch);
|
||||
}
|
||||
|
||||
float train_network_data_gpu(network net, data d, int n)
|
||||
{
|
||||
int batch = net.batch;
|
||||
float *X = calloc(batch*d.X.cols, sizeof(float));
|
||||
float *y = calloc(batch*d.y.cols, sizeof(float));
|
||||
|
||||
int i;
|
||||
float sum = 0;
|
||||
for(i = 0; i < n; ++i){
|
||||
get_next_batch(d, batch, i*batch, X, y);
|
||||
float err = train_network_datum_gpu(net, X, y);
|
||||
sum += err;
|
||||
}
|
||||
free(X);
|
||||
free(y);
|
||||
return (float)sum/(n*batch);
|
||||
}
|
||||
|
||||
float *get_network_output_layer_gpu(network net, int i)
|
||||
{
|
||||
if(net.types[i] == CONVOLUTIONAL){
|
||||
@ -278,54 +224,4 @@ float *network_predict_gpu(network net, float *input)
|
||||
return out;
|
||||
}
|
||||
|
||||
matrix network_predict_data_gpu(network net, data test)
|
||||
{
|
||||
int i,j,b;
|
||||
int k = get_network_output_size(net);
|
||||
matrix pred = make_matrix(test.X.rows, k);
|
||||
float *X = calloc(net.batch*test.X.cols, sizeof(float));
|
||||
for(i = 0; i < test.X.rows; i += net.batch){
|
||||
for(b = 0; b < net.batch; ++b){
|
||||
if(i+b == test.X.rows) break;
|
||||
memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
|
||||
}
|
||||
float *out = network_predict_gpu(net, X);
|
||||
for(b = 0; b < net.batch; ++b){
|
||||
if(i+b == test.X.rows) break;
|
||||
for(j = 0; j < k; ++j){
|
||||
pred.vals[i+b][j] = out[j+b*k];
|
||||
}
|
||||
}
|
||||
}
|
||||
free(X);
|
||||
return pred;
|
||||
}
|
||||
float network_accuracy_gpu(network net, data d)
|
||||
{
|
||||
matrix guess = network_predict_data_gpu(net, d);
|
||||
float acc = matrix_topk_accuracy(d.y, guess,1);
|
||||
free_matrix(guess);
|
||||
return acc;
|
||||
}
|
||||
|
||||
float *network_accuracies_gpu(network net, data d)
|
||||
{
|
||||
static float acc[2];
|
||||
matrix guess = network_predict_data_gpu(net, d);
|
||||
acc[0] = matrix_topk_accuracy(d.y, guess,1);
|
||||
acc[1] = matrix_topk_accuracy(d.y, guess,5);
|
||||
free_matrix(guess);
|
||||
return acc;
|
||||
}
|
||||
|
||||
|
||||
#else
|
||||
void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train){}
|
||||
void backward_network_gpu(network net, cl_mem input){}
|
||||
void update_network_gpu(network net){}
|
||||
float train_network_sgd_gpu(network net, data d, int n){return 0;}
|
||||
float train_network_data_gpu(network net, data d, int n){return 0;}
|
||||
float *network_predict_gpu(network net, float *input){return 0;}
|
||||
float network_accuracy_gpu(network net, data d){return 0;}
|
||||
|
||||
#endif
|
||||
|
||||
114
src/opencl.c
114
src/opencl.c
@ -1,3 +1,4 @@
|
||||
int gpu_index;
|
||||
#ifdef GPU
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
@ -31,11 +32,36 @@ cl_info cl_init(int index)
|
||||
{
|
||||
cl_info info;
|
||||
info.initialized = 0;
|
||||
if(index < 0) error("Won't initialize negative gpu id\n");
|
||||
cl_uint num_platforms, num_devices;
|
||||
// Fetch the Platform and Device IDs; we only want one.
|
||||
cl_device_id devices[MAX_DEVICES];
|
||||
info.error=clGetPlatformIDs(1, &info.platform, &num_platforms);
|
||||
check_error(info);
|
||||
|
||||
info.error=clGetDeviceIDs(info.platform, CL_DEVICE_TYPE_ALL, MAX_DEVICES, devices, &num_devices);
|
||||
check_error(info);
|
||||
|
||||
index = index%num_devices;
|
||||
info.device = devices[index];
|
||||
check_error(info);
|
||||
|
||||
cl_context_properties properties[]={
|
||||
CL_CONTEXT_PLATFORM, (cl_context_properties)info.platform, 0};
|
||||
|
||||
// Note that nVidia's OpenCL requires the platform property
|
||||
info.context=clCreateContext(properties, 1, &info.device, 0, 0, &info.error);
|
||||
check_error(info);
|
||||
|
||||
info.queue = clCreateCommandQueue(info.context, info.device, 0, &info.error);
|
||||
check_error(info);
|
||||
#ifdef CLBLAS
|
||||
info.error = clblasSetup();
|
||||
#endif
|
||||
check_error(info);
|
||||
info.initialized = 1;
|
||||
|
||||
#ifdef VERBOSE
|
||||
printf("=== %d OpenCL platform(s) found: ===\n", num_platforms);
|
||||
char buffer[10240];
|
||||
clGetPlatformInfo(info.platform, CL_PLATFORM_PROFILE, 10240, buffer, NULL);
|
||||
@ -48,14 +74,12 @@ cl_info cl_init(int index)
|
||||
printf(" VENDOR = %s\n", buffer);
|
||||
clGetPlatformInfo(info.platform, CL_PLATFORM_EXTENSIONS, 10240, buffer, NULL);
|
||||
printf(" EXTENSIONS = %s\n", buffer);
|
||||
|
||||
check_error(info);
|
||||
info.error=clGetDeviceIDs(info.platform, CL_DEVICE_TYPE_ALL, MAX_DEVICES, devices, &num_devices);
|
||||
|
||||
if(num_devices > MAX_DEVICES) num_devices = MAX_DEVICES;
|
||||
printf("=== %d OpenCL device(s) found on platform:\n", num_devices);
|
||||
int i;
|
||||
for (i=0; i<num_devices; i++)
|
||||
{
|
||||
for (i=0; i<num_devices; i++){
|
||||
char buffer[10240];
|
||||
cl_uint buf_uint;
|
||||
cl_ulong buf_ulong;
|
||||
@ -80,74 +104,57 @@ cl_info cl_init(int index)
|
||||
printf(" DEVICE_MAX_WORK_GROUP_SIZE = %llu\n", (unsigned long long)buf_ulong);
|
||||
cl_uint items;
|
||||
clGetDeviceInfo( devices[i], CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS, sizeof(cl_uint),
|
||||
&items, NULL);
|
||||
&items, NULL);
|
||||
printf(" DEVICE_MAX_WORK_ITEM_DIMENSIONS = %u\n", (unsigned int)items);
|
||||
size_t workitem_size[10];
|
||||
clGetDeviceInfo(devices[i], CL_DEVICE_MAX_WORK_ITEM_SIZES, 10*sizeof(workitem_size), workitem_size, NULL);
|
||||
printf(" DEVICE_MAX_WORK_ITEM_SIZES = %u / %u / %u \n", (unsigned int)workitem_size[0], (unsigned int)workitem_size[1], (unsigned int)workitem_size[2]);
|
||||
printf("%d devices, %d index\n", num_devices, index);
|
||||
|
||||
}
|
||||
index = index%num_devices;
|
||||
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);
|
||||
check_error(info);
|
||||
|
||||
cl_context_properties properties[]={
|
||||
CL_CONTEXT_PLATFORM, (cl_context_properties)info.platform,
|
||||
0};
|
||||
// Note that nVidia's OpenCL requires the platform property
|
||||
info.context=clCreateContext(properties, 1, &info.device, 0, 0, &info.error);
|
||||
check_error(info);
|
||||
info.queue = clCreateCommandQueue(info.context, info.device, 0, &info.error);
|
||||
check_error(info);
|
||||
#ifdef CLBLAS
|
||||
info.error = clblasSetup();
|
||||
#endif
|
||||
check_error(info);
|
||||
info.initialized = 1;
|
||||
#endif
|
||||
return info;
|
||||
}
|
||||
|
||||
cl_program cl_fprog(char *filename, char *options, cl_info info)
|
||||
{
|
||||
size_t srcsize;
|
||||
char src[64*1024];
|
||||
memset(src, 0, 64*1024);
|
||||
FILE *fil=fopen(filename,"r");
|
||||
size_t srcsize;
|
||||
char src[64*1024];
|
||||
memset(src, 0, 64*1024);
|
||||
FILE *fil=fopen(filename,"r");
|
||||
if(fil == 0) file_error(filename);
|
||||
srcsize=fread(src, sizeof src, 1, fil);
|
||||
fclose(fil);
|
||||
const char *srcptr[]={src};
|
||||
// Submit the source code of the example kernel to OpenCL
|
||||
cl_program prog=clCreateProgramWithSource(info.context,1, srcptr, &srcsize, &info.error);
|
||||
check_error(info);
|
||||
char build_c[1024*64];
|
||||
// and compile it (after this we could extract the compiled version)
|
||||
info.error=clBuildProgram(prog, 0, 0, options, 0, 0);
|
||||
if ( info.error != CL_SUCCESS ) {
|
||||
fprintf(stderr, "Error Building Program: %d\n", info.error);
|
||||
clGetProgramBuildInfo( prog, info.device, CL_PROGRAM_BUILD_LOG, 1024*64, build_c, 0);
|
||||
fprintf(stderr, "Build Log for %s program:\n%s\n", filename, build_c);
|
||||
}
|
||||
check_error(info);
|
||||
return prog;
|
||||
srcsize=fread(src, sizeof src, 1, fil);
|
||||
fclose(fil);
|
||||
const char *srcptr[]={src};
|
||||
// Submit the source code of the example kernel to OpenCL
|
||||
cl_program prog=clCreateProgramWithSource(info.context,1, srcptr, &srcsize, &info.error);
|
||||
check_error(info);
|
||||
char build_c[1024*64];
|
||||
// and compile it (after this we could extract the compiled version)
|
||||
info.error=clBuildProgram(prog, 0, 0, options, 0, 0);
|
||||
if ( info.error != CL_SUCCESS ) {
|
||||
fprintf(stderr, "Error Building Program: %d\n", info.error);
|
||||
clGetProgramBuildInfo( prog, info.device, CL_PROGRAM_BUILD_LOG, 1024*64, build_c, 0);
|
||||
fprintf(stderr, "Build Log for %s program:\n%s\n", filename, build_c);
|
||||
}
|
||||
check_error(info);
|
||||
return prog;
|
||||
}
|
||||
|
||||
void cl_setup(int index)
|
||||
void cl_setup()
|
||||
{
|
||||
if(!cl.initialized){
|
||||
if(!cl.initialized){
|
||||
printf("initializing\n");
|
||||
cl = cl_init(index);
|
||||
}
|
||||
cl = cl_init(gpu_index);
|
||||
}
|
||||
}
|
||||
|
||||
cl_kernel get_kernel(char *filename, char *kernelname, char *options)
|
||||
{
|
||||
cl_program prog = cl_fprog(filename, options, cl);
|
||||
cl_kernel kernel=clCreateKernel(prog, kernelname, &cl.error);
|
||||
check_error(cl);
|
||||
return kernel;
|
||||
cl_program prog = cl_fprog(filename, options, cl);
|
||||
cl_kernel kernel=clCreateKernel(prog, kernelname, &cl.error);
|
||||
check_error(cl);
|
||||
return kernel;
|
||||
}
|
||||
|
||||
void cl_read_array(cl_mem mem, float *x, int n)
|
||||
@ -158,7 +165,7 @@ void cl_read_array(cl_mem mem, float *x, int n)
|
||||
|
||||
float cl_checksum(cl_mem mem, int n)
|
||||
{
|
||||
|
||||
|
||||
float *x = calloc(n, sizeof(float));
|
||||
cl_read_array(mem, x, n);
|
||||
float sum = sum_array(x, n);
|
||||
@ -191,6 +198,7 @@ cl_mem cl_sub_array(cl_mem src, int offset, int size)
|
||||
|
||||
cl_mem cl_make_array(float *x, int n)
|
||||
{
|
||||
if(gpu_index < 0) return 0;
|
||||
cl_mem mem = clCreateBuffer(cl.context,
|
||||
CL_MEM_READ_WRITE|CL_MEM_COPY_HOST_PTR,
|
||||
sizeof(float)*n, x, &cl.error);
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
#ifdef GPU
|
||||
#ifndef OPENCL_H
|
||||
#define OPENCL_H
|
||||
extern int gpu_index;
|
||||
#ifdef GPU
|
||||
#ifdef __APPLE__
|
||||
#include <OpenCL/opencl.h>
|
||||
#else
|
||||
@ -19,7 +20,7 @@ typedef struct {
|
||||
|
||||
extern cl_info cl;
|
||||
|
||||
void cl_setup(int index);
|
||||
void cl_setup();
|
||||
void check_error(cl_info info);
|
||||
cl_kernel get_kernel(char *filename, char *kernelname, char *options);
|
||||
void cl_read_array(cl_mem mem, float *x, int n);
|
||||
|
||||
@ -87,7 +87,7 @@ void handle_connection(void *pointer)
|
||||
//printf("New Connection\n");
|
||||
if(info.counter%100==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "/home/pjreddie/net_%d.part", info.counter);
|
||||
sprintf(buff, "unikitty/net_%d.part", info.counter);
|
||||
save_network(info.net, buff);
|
||||
}
|
||||
int fd = info.fd;
|
||||
@ -131,8 +131,8 @@ void handle_connection(void *pointer)
|
||||
void server_update(network net)
|
||||
{
|
||||
int fd = socket_setup(1);
|
||||
int counter = 0;
|
||||
listen(fd, 10);
|
||||
int counter = 18000;
|
||||
listen(fd, 64);
|
||||
struct sockaddr_in client; /* remote address */
|
||||
socklen_t client_size = sizeof(client); /* length of addresses */
|
||||
time_t t=0;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user