darknet/src/cnn.c

#include "connected_layer.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
#include "network.h"
#include "image.h"
#include "parser.h"
#include "data.h"
#include "matrix.h"
#include "utils.h"
#include "mini_blas.h"

#include <time.h>
#include <stdlib.h>
#include <stdio.h>

#define _GNU_SOURCE
#include <fenv.h>

void test_convolve()
{
	image dog = load_image("dog.jpg",300,400);
	printf("dog channels %d\n", dog.c);
	image kernel = make_random_image(3,3,dog.c);
	image edge = make_image(dog.h, dog.w, 1);
	int i;
	clock_t start = clock(), end;
	for(i = 0; i < 1000; ++i){
		convolve(dog, kernel, 1, 0, edge, 1);
	}
	end = clock();
	printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
	show_image_layers(edge, "Test Convolve");
}

void test_convolve_matrix()
{
	image dog = load_image("dog.jpg",300,400);
	printf("dog channels %d\n", dog.c);

	int size = 11;
	int stride = 4;
	int n = 40;
	float *filters = make_random_image(size, size, dog.c*n).data;

	int mw = ((dog.h-size)/stride+1)*((dog.w-size)/stride+1);
	int mh = (size*size*dog.c);
	float *matrix = calloc(mh*mw, sizeof(float));

	image edge = make_image((dog.h-size)/stride+1, (dog.w-size)/stride+1, n);


	int i;
	clock_t start = clock(), end;
	for(i = 0; i < 1000; ++i){
		im2col_cpu(dog.data,  1, dog.c,  dog.h,  dog.w,  size,  stride, 0, matrix);
		gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
	}
	end = clock();
	printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
	show_image_layers(edge, "Test Convolve");
	cvWaitKey(0);
}

void test_color()
{
	image dog = load_image("test_color.png", 300, 400);
	show_image_layers(dog, "Test Color");
}

void verify_convolutional_layer()
{
	srand(0);
	int i;
	int n = 1;
	int stride = 1;
	int size = 3;
	float eps = .00000001;
	image test = make_random_image(5,5, 1);
	convolutional_layer layer = *make_convolutional_layer(1,test.h,test.w,test.c, n, size, stride, 0, RELU);
	image out = get_convolutional_image(layer);
	float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));

	forward_convolutional_layer(layer, test.data);
	image base = copy_image(out);

	for(i = 0; i < test.h*test.w*test.c; ++i){
		test.data[i] += eps;
		forward_convolutional_layer(layer, test.data);
		image partial = copy_image(out);
		subtract_image(partial, base);
		scale_image(partial, 1/eps);
		jacobian[i] = partial.data;
		test.data[i] -= eps;
	}
	float **jacobian2 = calloc(out.h*out.w*out.c, sizeof(float));
	image in_delta = make_image(test.h, test.w, test.c);
	image out_delta = get_convolutional_delta(layer);
	for(i = 0; i < out.h*out.w*out.c; ++i){
		out_delta.data[i] = 1;
		backward_convolutional_layer(layer, in_delta.data);
		image partial = copy_image(in_delta);
		jacobian2[i] = partial.data;
		out_delta.data[i] = 0;
	}
	int j;
	float *j1 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
	float *j2 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
	for(i = 0; i < test.h*test.w*test.c; ++i){
		for(j =0 ; j < out.h*out.w*out.c; ++j){
			j1[i*out.h*out.w*out.c + j] = jacobian[i][j];
			j2[i*out.h*out.w*out.c + j] = jacobian2[j][i];
			printf("%f %f\n", jacobian[i][j], jacobian2[j][i]);
		}
	}


	image mj1 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j1);
	image mj2 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j2);
	printf("%f %f\n", avg_image_layer(mj1,0), avg_image_layer(mj2,0));
	show_image(mj1, "forward jacobian");
	show_image(mj2, "backward jacobian");
}

void test_load()
{
	image dog = load_image("dog.jpg", 300, 400);
	show_image(dog, "Test Load");
	show_image_layers(dog, "Test Load");
}
void test_upsample()
{
	image dog = load_image("dog.jpg", 300, 400);
	int n = 3;
	image up = make_image(n*dog.h, n*dog.w, dog.c);
	upsample_image(dog, n, up);
	show_image(up, "Test Upsample");
	show_image_layers(up, "Test Upsample");
}

void test_rotate()
{
	int i;
	image dog = load_image("dog.jpg",300,400);
	clock_t start = clock(), end;
	for(i = 0; i < 1001; ++i){
		rotate_image(dog);
	}
	end = clock();
	printf("Rotations: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
	show_image(dog, "Test Rotate");

	image random = make_random_image(3,3,3);
	show_image(random, "Test Rotate Random");
	rotate_image(random);
	show_image(random, "Test Rotate Random");
	rotate_image(random);
	show_image(random, "Test Rotate Random");
}

void test_parser()
{
	network net = parse_network_cfg("test_parser.cfg");
	float input[1];
	int count = 0;

	float avgerr = 0;
	while(++count < 100000000){
		float v = ((float)rand()/RAND_MAX);
		float truth = v*v;
		input[0] = v;
		forward_network(net, input, 1);
		float *out = get_network_output(net);
		float *delta = get_network_delta(net);
		float err = pow((out[0]-truth),2.);
		avgerr = .99 * avgerr + .01 * err;
		if(count % 1000000 == 0) printf("%f %f :%f AVG %f \n", truth, out[0], err, avgerr);
		delta[0] = truth - out[0];
		backward_network(net, input, &truth);
		update_network(net, .001,0,0);
	}
}

void test_data()
{
	char *labels[] = {"cat","dog"};
	data train = load_data_image_pathfile_random("train_paths.txt", 101,labels, 2, 300, 400);
	free_data(train);
}

void train_full()
{
	network net = parse_network_cfg("cfg/imagenet.cfg");
	srand(2222222);
	int i = 0;
	char *labels[] = {"cat","dog"};
	float lr = .00001;
	float momentum = .9;
	float decay = 0.01;
	while(1){
		i += 1000;
		data train = load_data_image_pathfile_random("images/assira/train.list", 1000, labels, 2, 256, 256);
		//image im = float_to_image(256, 256, 3,train.X.vals[0]);
		//visualize_network(net);
		//cvWaitKey(100);
		//show_image(im, "input");
		//cvWaitKey(100);
		//scale_data_rows(train, 1./255.);
		normalize_data_rows(train);
		clock_t start = clock(), end;
		float loss = train_network_sgd(net, train, 1000, lr, momentum, decay);
		end = clock();
		printf("%d: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", i, loss, (float)(end-start)/CLOCKS_PER_SEC, lr, momentum, decay);
		free_data(train);
		if(i%10000==0){
			char buff[256];
			sprintf(buff, "cfg/assira_backup_%d.cfg", i);
			save_network(net, buff);
		}
		//lr *= .99;
	}
}

void test_visualize()
{
	network net = parse_network_cfg("cfg/voc_imagenet.cfg");
	srand(2222222);
	visualize_network(net);
	cvWaitKey(0);
}
void test_full()
{
	network net = parse_network_cfg("cfg/backup_1300.cfg");
	srand(2222222);
	int i,j;
	int total = 100;
	char *labels[] = {"cat","dog"};
	FILE *fp = fopen("preds.txt","w");
	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);
		image im = float_to_image(256, 256, 3,test.X.vals[0]);
		show_image(im, "input");
		cvWaitKey(100);
		normalize_data_rows(test);
		for(j = 0; j < test.X.rows; ++j){
			float *x = test.X.vals[j];
			forward_network(net, x, 0);
			int class = get_predicted_class_network(net);
			fprintf(fp, "%d\n", class);
		}
		free_data(test);
	}
	fclose(fp);
}

void test_cifar10()
{
	data test = load_cifar10_data("images/cifar10/test_batch.bin");
	scale_data_rows(test, 1./255);
	network net = parse_network_cfg("cfg/cifar10.cfg");
	int count = 0;
	float lr = .000005;
	float momentum = .99;
	float decay = 0.001;
	decay = 0;
	int batch = 10000;
	while(++count <= 10000){
		char buff[256];
		sprintf(buff, "images/cifar10/data_batch_%d.bin", rand()%5+1);
		data train = load_cifar10_data(buff);
		scale_data_rows(train, 1./255);
		train_network_sgd(net, train, batch, lr, momentum, decay);
		//printf("%5f %5f\n",(double)count*batch/train.X.rows, loss);

		float test_acc = network_accuracy(net, test);
		printf("%5f %5f\n",(double)count*batch/train.X.rows/5, 1-test_acc);
		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, lr, momentum, decay);
		printf("%5f %5f\n",(double)count*batch/train.X.rows, loss);
	}
}

void test_nist()
{
	srand(444444);
	srand(222222);
	network net = parse_network_cfg("cfg/nist.cfg");
	data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
	data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
	normalize_data_rows(train);
	normalize_data_rows(test);
	//randomize_data(train);
	int count = 0;
	float lr = .000075;
	float momentum = .9;
	float decay = 0.0001;
	decay = 0;
	//clock_t start = clock(), end;
	int iters = 100;
	while(++count <= 10){
		clock_t start = clock(), end;
		float loss = train_network_sgd(net, train, iters, lr, momentum, decay);
		end = clock();
		float test_acc = network_accuracy(net, test);
		printf("%d: %f %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, lr, momentum, decay);

		//printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*1000, loss, lr, momentum, decay);
		//end = clock();
		//printf("Time: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
		//start=end;
		//lr *= .5;
	}
	//save_network(net, "cfg/nist_basic_trained.cfg");
}

void test_ensemble()
{
	int i;
	srand(888888);
	data d = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
	normalize_data_rows(d);
	data test = load_categorical_data_csv("mnist/mnist_test.csv", 0,10);
	normalize_data_rows(test);
	data train = d;
	//   data *split = split_data(d, 1, 10);
	//   data train = split[0];
	//   data test = split[1];
	matrix prediction = make_matrix(test.y.rows, test.y.cols);
	int n = 30;
	for(i = 0; i < n; ++i){
		int count = 0;
		float lr = .0005;
		float momentum = .9;
		float decay = .01;
		network net = parse_network_cfg("nist.cfg");
		while(++count <= 15){
			float acc = train_network_sgd(net, train, train.X.rows, lr, momentum, decay);
			printf("Training Accuracy: %lf Learning Rate: %f Momentum: %f Decay: %f\n", acc, lr, momentum, decay );
			lr /= 2; 
		}
		matrix partial = network_predict_data(net, test);
		float acc = matrix_accuracy(test.y, partial);
		printf("Model Accuracy: %lf\n", acc);
		matrix_add_matrix(partial, prediction);
		acc = matrix_accuracy(test.y, prediction);
		printf("Current Ensemble Accuracy: %lf\n", acc);
		free_matrix(partial);
	}
	float acc = matrix_accuracy(test.y, prediction);
	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], 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, .00001, 0,0);
		}
		//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);
		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 tune_VOC()
{
	network net = parse_network_cfg("cfg/voc_start.cfg");
	srand(2222222);
	int i = 20;
	char *labels[] = {"aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"};
	float lr = .000005;
	float momentum = .9;
	float decay = 0.0001;
	while(i++ < 1000 || 1){
		data train = load_data_image_pathfile_random("/home/pjreddie/VOC2012/trainval_paths.txt", 10, labels, 20, 256, 256);

		image im = float_to_image(256, 256, 3,train.X.vals[0]);
		show_image(im, "input");
		visualize_network(net);
		cvWaitKey(100);

		translate_data_rows(train, -144);
		clock_t start = clock(), end;
		float loss = train_network_sgd(net, train, 10, lr, momentum, decay);
		end = clock();
		printf("%d: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", i, loss, (float)(end-start)/CLOCKS_PER_SEC, lr, momentum, decay);
		free_data(train);
        /*
		if(i%10==0){
			char buff[256];
			sprintf(buff, "/home/pjreddie/voc_cfg/voc_ramp_%d.cfg", i);
			save_network(net, buff);
		}
        */
		//lr *= .99;
	}
}

int voc_size(int x)
{
	x = x-1+3;
	x = x-1+3;
	x = x-1+3;
	x = (x-1)*2+1;
	x = x-1+5;
	x = (x-1)*2+1;
	x = (x-1)*4+11;
	return x;
}

image features_output_size(network net, IplImage *src, int outh, int outw)
{
	int h = voc_size(outh);
	int w = voc_size(outw);
	fprintf(stderr, "%d %d\n", h, w);

	IplImage *sized = cvCreateImage(cvSize(w,h), src->depth, src->nChannels);
	cvResize(src, sized, CV_INTER_LINEAR);
	image im = ipl_to_image(sized);
	//normalize_array(im.data, im.h*im.w*im.c);
	translate_image(im, -144);
	resize_network(net, im.h, im.w, im.c);
	forward_network(net, im.data, 0);
	image out = get_network_image(net);
	free_image(im);
	cvReleaseImage(&sized);
	return copy_image(out);
}

void features_VOC_image_size(char *image_path, int h, int w)
{
	int j;
	network net = parse_network_cfg("cfg/voc_imagenet.cfg");
	fprintf(stderr, "%s\n", image_path);

	IplImage* src = 0;
	if( (src = cvLoadImage(image_path,-1)) == 0 ) file_error(image_path);
	image out = features_output_size(net, src, h, w);
	for(j = 0; j < out.c*out.h*out.w; ++j){
		if(j != 0) printf(",");
		printf("%g", out.data[j]);
	}
	printf("\n");
	free_image(out);
	cvReleaseImage(&src);
}
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);
		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);
		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 visualize_cat()
{
	network net = parse_network_cfg("cfg/voc_imagenet.cfg");
	image im = load_image("data/cat.png", 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);

	visualize_network(net);
	cvWaitKey(0);
}

void features_VOC_image(char *image_file, char *image_dir, char *out_dir, int flip)
{
	int interval = 4;
	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(&copy);
			//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);
}


int main(int argc, char *argv[])
{
	//train_full();
	//test_distribution();
	//feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);

	//test_blas();
	//test_visualize();
	//test_gpu_blas();
	//test_blas();
	//test_convolve_matrix();
	//    test_im2row();
	//test_split();
	//test_ensemble();
	test_nist();
	//test_cifar10();
	//test_vince();
	//test_full();
	//tune_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]));
	//visualize_imagenet_features("data/assira/train.list");
	//visualize_imagenet_topk("data/VOC2012.list");
	//visualize_cat();
	//flip_network();
	//test_visualize();
	fprintf(stderr, "Success!\n");
	//test_random_preprocess();
	//test_random_classify();
	//test_parser();
	//test_backpropagate();
	//test_ann();
	//test_convolve();
	//test_upsample();
	//test_rotate();
	//test_load();
	//test_network();
	//test_convolutional_layer();
	//verify_convolutional_layer();
	//test_color();
	//cvWaitKey(0);
	return 0;
}