From d9f1b0b16edeb59281355a855e18a8be343fc33c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 08 Aug 2014 19:04:15 +0000
Subject: [PATCH] probably how maxpool layers should be
---
src/cnn.c | 974 ++++++++++++++++++++++++++++----------------------------
1 files changed, 484 insertions(+), 490 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index cac1149..f866194 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -51,7 +51,7 @@
int i;
clock_t start = clock(), end;
for(i = 0; i < 1000; ++i){
- im2col_cpu(dog.data, dog.c, dog.h, dog.w, size, stride, 0, matrix);
+ 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();
@@ -75,7 +75,7 @@
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);
+ convolutional_layer layer = *make_convolutional_layer(1,test.h,test.w,test.c, n, size, stride, 0, RELU,0,0,0);
image out = get_convolutional_image(layer);
float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));
@@ -158,25 +158,10 @@
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);
- }
+ network net = parse_network_cfg("cfg/test_parser.cfg");
+ save_network(net, "cfg/test_parser_1.cfg");
+ network net2 = parse_network_cfg("cfg/test_parser_1.cfg");
+ save_network(net2, "cfg/test_parser_2.cfg");
}
void test_data()
@@ -206,7 +191,7 @@
//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);
+ float loss = train_network_sgd(net, train, 1000);
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);
@@ -255,558 +240,567 @@
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);
+ srand(222222);
+ 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(net, train, iters);
+ end = clock();
+ //visualize_network(net);
+ //cvWaitKey(1000);
- float test_acc = network_accuracy(net, test);
- printf("%5f %5f\n",(double)count*batch/train.X.rows/5, 1-test_acc);
- free_data(train);
- }
-
+ //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);
+ 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);
+ }
+ 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);
+ 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);
- }
+ 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.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, 5);
+ printf("Loss: %f, LR: %f, Momentum: %f, Decay: %f\n", loss, net.learning_rate, net.momentum, net.decay);
+
}
void test_nist()
{
- 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 = .0001;
- float momentum = .9;
- float decay = 0.0001;
- //clock_t start = clock(), end;
- int iters = 1000;
- 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);
+ 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);
+ translate_data_rows(train, -144);
+ scale_data_rows(train, 1./128);
+ translate_data_rows(test, -144);
+ scale_data_rows(test, 1./128);
+ //randomize_data(train);
+ int count = 0;
+ //clock_t start = clock(), end;
+ int iters = 10000/net.batch;
+ while(++count <= 100){
+ clock_t start = clock(), end;
+ float loss = train_network_sgd(net, train, iters);
+ end = clock();
+ float test_acc = network_accuracy(net, test);
//float test_acc = 0;
- 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, lr, momentum, decay);
+ 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);
+ //save_network(net, "cfg/nist_basic_trained.cfg");
- //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");
+ //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);
+ 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);
+ 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);
+ 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);
+ }
+ //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);
+ 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, c, h, w, size, stride, 0, matrix);
- //image render = float_to_image(mh, mw, mc, matrix);
- }
+ 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");
+ 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);
+ 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);
+ 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);
+ translate_data_rows(train, -144);
+ clock_t start = clock(), end;
+ float loss = train_network_sgd(net, train, 10);
+ 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;
- }
+ 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;
+ 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);
+ 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);
+ 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);
+ 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);
+ 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 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 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;
+ 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);
+ 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 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);
+ 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);
+ 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);
+ visualize_network(net);
+ 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);
+ 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));
+ 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);
+ 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);
+ 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);
+ 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);
}
int main(int argc, char *argv[])
{
- //train_full();
- //test_distribution();
- //feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
+ //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);
- //train_VOC();
- //features_VOC_image(argv[1], argv[2], argv[3], 0, 4);
- //features_VOC_image(argv[1], argv[2], argv[3], 1, 4);
- //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;
+ //test_blas();
+ //test_visualize();
+ //test_gpu_blas();
+ //test_blas();
+ //test_convolve_matrix();
+ // test_im2row();
+ //test_split();
+ //test_ensemble();
+ //test_nist_single();
+ 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);
+ //train_VOC();
+ //features_VOC_image(argv[1], argv[2], argv[3], 0, 4);
+ //features_VOC_image(argv[1], argv[2], argv[3], 1, 4);
+ //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();
+ //test_parser();
+ 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;
}
--
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