From aa5996d58e68edfbefe51061856aecd549dd09c4 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 13 Jan 2015 01:27:08 +0000
Subject: [PATCH] Faster
---
src/cnn.c | 1126 +++++++++++++++++++---------------------------------------
1 files changed, 375 insertions(+), 751 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index 46248ed..e587a1b 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -8,6 +8,8 @@
#include "matrix.h"
#include "utils.h"
#include "mini_blas.h"
+#include "matrix.h"
+#include "server.h"
#include <time.h>
#include <stdlib.h>
@@ -16,256 +18,12 @@
#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");
-}
-
-#ifdef GPU
-
-void test_convolutional_layer()
-{
-/*
- int i;
- image dog = load_image("data/dog.jpg",224,224);
- network net = parse_network_cfg("cfg/convolutional.cfg");
- // data test = load_cifar10_data("data/cifar10/test_batch.bin");
- // float *X = calloc(net.batch*test.X.cols, sizeof(float));
- // float *y = calloc(net.batch*test.y.cols, sizeof(float));
- int in_size = get_network_input_size(net)*net.batch;
- int del_size = get_network_output_size_layer(net, 0)*net.batch;
- int size = get_network_output_size(net)*net.batch;
- float *X = calloc(in_size, sizeof(float));
- float *y = calloc(size, sizeof(float));
- for(i = 0; i < in_size; ++i){
- X[i] = dog.data[i%get_network_input_size(net)];
- }
- // get_batch(test, net.batch, X, y);
- clock_t start, end;
- cl_mem input_cl = cl_make_array(X, in_size);
- cl_mem truth_cl = cl_make_array(y, size);
-
- forward_network_gpu(net, input_cl, truth_cl, 1);
- start = clock();
- forward_network_gpu(net, input_cl, truth_cl, 1);
- end = clock();
- float gpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
- printf("forward gpu: %f sec\n", gpu_sec);
- start = clock();
- backward_network_gpu(net, input_cl);
- end = clock();
- gpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
- printf("backward gpu: %f sec\n", gpu_sec);
- //float gpu_cost = get_network_cost(net);
- float *gpu_out = calloc(size, sizeof(float));
- memcpy(gpu_out, get_network_output(net), size*sizeof(float));
-
- float *gpu_del = calloc(del_size, sizeof(float));
- memcpy(gpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
- */
-
- /*
- start = clock();
- forward_network(net, X, y, 1);
- backward_network(net, X);
- float cpu_cost = get_network_cost(net);
- end = clock();
- float cpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
- float *cpu_out = calloc(size, sizeof(float));
- memcpy(cpu_out, get_network_output(net), size*sizeof(float));
- float *cpu_del = calloc(del_size, sizeof(float));
- memcpy(cpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
-
- float sum = 0;
- float del_sum = 0;
- for(i = 0; i < size; ++i) sum += pow(gpu_out[i] - cpu_out[i], 2);
- for(i = 0; i < del_size; ++i) {
- //printf("%f %f\n", cpu_del[i], gpu_del[i]);
- del_sum += pow(cpu_del[i] - gpu_del[i], 2);
- }
- printf("GPU cost: %f, CPU cost: %f\n", gpu_cost, cpu_cost);
- printf("gpu: %f sec, cpu: %f sec, diff: %f, delta diff: %f, size: %d\n", gpu_sec, cpu_sec, sum, del_sum, size);
- */
-}
-
-/*
-void test_col2im()
-{
- float col[] = {1,2,1,2,
- 1,2,1,2,
- 1,2,1,2,
- 1,2,1,2,
- 1,2,1,2,
- 1,2,1,2,
- 1,2,1,2,
- 1,2,1,2,
- 1,2,1,2};
- float im[16] = {0};
- int batch = 1;
- int channels = 1;
- int height=4;
- int width=4;
- int ksize = 3;
- int stride = 1;
- int pad = 0;
- //col2im_gpu(col, batch,
- // channels, height, width,
- // ksize, stride, pad, im);
- int i;
- for(i = 0; i < 16; ++i)printf("%f,", im[i]);
- printf("\n");
- float data_im[] = {
- 1,2,3,4,
- 5,6,7,8,
- 9,10,11,12
- };
- float data_col[18] = {0};
- im2col_cpu(data_im, batch,
- channels, height, width,
- ksize, stride, pad, data_col) ;
- for(i = 0; i < 18; ++i)printf("%f,", data_col[i]);
- printf("\n");
-}
-*/
-
-#endif
-
-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,0,0,0);
- 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()
{
@@ -273,135 +31,216 @@
save_network(net, "cfg/trained_imagenet_smaller.cfg");
}
-void train_asirra()
+#define AMNT 3
+void draw_detection(image im, float *box, int side)
{
- network net = parse_network_cfg("cfg/imagenet.cfg");
- int imgs = 1000/net.batch+1;
- //imgs = 1;
- srand(2222222);
- int i = 0;
- char *labels[] = {"cat","dog"};
-
- list *plist = get_paths("data/assira/train.list");
- char **paths = (char **)list_to_array(plist);
- int m = plist->size;
- free_list(plist);
-
- clock_t time;
-
- while(1){
- i += 1;
- time=clock();
- data train = load_data_random(imgs*net.batch, paths, m, labels, 2, 256, 256);
- normalize_data_rows(train);
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
- //float loss = train_network_data(net, train, imgs);
- float loss = 0;
- printf("%d: %f, Time: %lf seconds\n", i*net.batch*imgs, loss, sec(clock()-time));
- free_data(train);
- if(i%10==0){
- char buff[256];
- sprintf(buff, "cfg/asirra_backup_%d.cfg", i);
- save_network(net, buff);
+ int j;
+ int r, c;
+ float amount[AMNT] = {0};
+ for(r = 0; r < side*side; ++r){
+ float val = box[r*5];
+ for(j = 0; j < AMNT; ++j){
+ if(val > amount[j]) {
+ float swap = val;
+ val = amount[j];
+ amount[j] = swap;
+ }
}
- //lr *= .99;
}
+ float smallest = amount[AMNT-1];
+
+ for(r = 0; r < side; ++r){
+ for(c = 0; c < side; ++c){
+ j = (r*side + c) * 5;
+ printf("Prob: %f\n", box[j]);
+ if(box[j] >= smallest){
+ int d = im.w/side;
+ int y = r*d+box[j+1]*d;
+ int x = c*d+box[j+2]*d;
+ int h = box[j+3]*256;
+ int w = box[j+4]*256;
+ //printf("%f %f %f %f\n", box[j+1], box[j+2], box[j+3], box[j+4]);
+ //printf("%d %d %d %d\n", x, y, w, h);
+ //printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2);
+ draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2);
+ }
+ }
+ }
+ show_image(im, "box");
+ cvWaitKey(0);
}
-void train_detection_net()
+
+void train_detection_net(char *cfgfile)
{
float avg_loss = 1;
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
- network net = parse_network_cfg("cfg/detnet.cfg");
+ network net = parse_network_cfg(cfgfile);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 1000/net.batch+1;
- //srand(time(0));
- srand(23410);
+ int imgs = 1024;
+ srand(time(0));
+ //srand(23410);
int i = 0;
list *plist = get_paths("/home/pjreddie/data/imagenet/horse.txt");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
+ data train, buffer;
+ pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, 256, 256, 7, 7, 256, &buffer);
clock_t time;
while(1){
i += 1;
time=clock();
- data train = load_data_detection_random(imgs*net.batch, paths, plist->size, 256, 256, 8, 8, 256);
- //translate_data_rows(train, -144);
- /*
- image im = float_to_image(256, 256, 3, train.X.vals[0]);
- float *truth = train.y.vals[0];
- int j;
- int r, c;
- for(r = 0; r < 8; ++r){
- for(c = 0; c < 8; ++c){
- j = (r*8 + c) * 5;
- if(truth[j]){
- int d = 256/8;
- int y = r*d+truth[j+1]*d;
- int x = c*d+truth[j+2]*d;
- int h = truth[j+3]*256;
- int w = truth[j+4]*256;
- printf("%f %f %f %f\n", truth[j+1], truth[j+2], truth[j+3], truth[j+4]);
- printf("%d %d %d %d\n", x, y, w, h);
- printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2);
- draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2);
- }
- }
- }
- show_image(im, "box");
- cvWaitKey(0);
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data_detection_thread(imgs, paths, plist->size, 256, 256, 7, 7, 256, &buffer);
+ //data train = load_data_detection_random(imgs, paths, plist->size, 224, 224, 7, 7, 256);
+
+/*
+ image im = float_to_image(224, 224, 3, train.X.vals[923]);
+ draw_detection(im, train.y.vals[923], 7);
*/
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
-#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
- free_data(train);
- if(i%10==0){
+ if(i%100==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/detnet_%d.cfg", i);
save_network(net, buff);
}
+ free_data(train);
}
}
+void validate_detection_net(char *cfgfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ srand(time(0));
-void train_imagenet()
+ list *plist = get_paths("/home/pjreddie/data/imagenet/detection.val");
+ char **paths = (char **)list_to_array(plist);
+
+ int m = plist->size;
+ int i = 0;
+ int splits = 50;
+ int num = (i+1)*m/splits - i*m/splits;
+
+ fprintf(stderr, "%d\n", m);
+ data val, buffer;
+ pthread_t load_thread = load_data_thread(paths, num, 0, 0, 245, 224, 224, &buffer);
+ clock_t time;
+ for(i = 1; i <= splits; ++i){
+ time=clock();
+ pthread_join(load_thread, 0);
+ val = buffer;
+ normalize_data_rows(val);
+
+ num = (i+1)*m/splits - i*m/splits;
+ char **part = paths+(i*m/splits);
+ if(i != splits) load_thread = load_data_thread(part, num, 0, 0, 245, 224, 224, &buffer);
+
+ fprintf(stderr, "Loaded: %lf seconds\n", sec(clock()-time));
+ matrix pred = network_predict_data(net, val);
+ int j, k;
+ for(j = 0; j < pred.rows; ++j){
+ for(k = 0; k < pred.cols; k += 5){
+ if (pred.vals[j][k] > .005){
+ int index = k/5;
+ int r = index/7;
+ int c = index%7;
+ float y = (32.*(r + pred.vals[j][k+1]))/224.;
+ float x = (32.*(c + pred.vals[j][k+2]))/224.;
+ float h = (256.*(pred.vals[j][k+3]))/224.;
+ float w = (256.*(pred.vals[j][k+4]))/224.;
+ printf("%d %f %f %f %f %f\n", (i-1)*m/splits + j + 1, pred.vals[j][k], y, x, h, w);
+ }
+ }
+ }
+
+ time=clock();
+ free_data(val);
+ }
+}
+
+void train_imagenet_distributed(char *address)
{
float avg_loss = 1;
- //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
- network net = parse_network_cfg("cfg/alexnet.part");
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 1000/net.batch+1;
srand(time(0));
+ network net = parse_network_cfg("cfg/net.cfg");
+ set_learning_network(&net, 0, 1, 0);
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ int imgs = net.batch;
int i = 0;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
clock_t time;
+ data train, buffer;
+ pthread_t load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
+ while(1){
+ i += 1;
+
+ time=clock();
+ client_update(net, address);
+ printf("Updated: %lf seconds\n", sec(clock()-time));
+
+ time=clock();
+ pthread_join(load_thread, 0);
+ train = buffer;
+ normalize_data_rows(train);
+ load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+
+ 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);
+ free_data(train);
+ }
+}
+
+void train_imagenet(char *cfgfile)
+{
+ float avg_loss = 1;
+ //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
+ srand(time(0));
+ network net = parse_network_cfg(cfgfile);
+ set_learning_network(&net, net.learning_rate, 0, net.decay);
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ int imgs = 1024;
+ int i = 77700;
+ char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
+ list *plist = get_paths("/data/imagenet/cls.train.list");
+ char **paths = (char **)list_to_array(plist);
+ printf("%d\n", plist->size);
+ clock_t time;
+ pthread_t load_thread;
+ data train;
+ data buffer;
+ load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
while(1){
i += 1;
time=clock();
- data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
- //translate_data_rows(train, -144);
- normalize_data_rows(train);
+ pthread_join(load_thread, 0);
+ train = buffer;
+ //normalize_data_rows(train);
+ translate_data_rows(train, -128);
+ scale_data_rows(train, 1./128);
+ load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
-#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){
+ if(i%100==0){
char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/alexnet_%d.cfg", i);
+ sprintf(buff, "/home/pjreddie/imagenet_backup/net_%d.cfg", i);
save_network(net, buff);
}
}
@@ -409,7 +248,7 @@
void validate_imagenet(char *filename)
{
- int i;
+ int i = 0;
network net = parse_network_cfg(filename);
srand(time(0));
@@ -422,72 +261,89 @@
clock_t time;
float avg_acc = 0;
+ float avg_top5 = 0;
int splits = 50;
+ int num = (i+1)*m/splits - i*m/splits;
- for(i = 0; i < splits; ++i){
+ data val, buffer;
+ pthread_t load_thread = load_data_thread(paths, num, 0, labels, 1000, 256, 256, &buffer);
+ for(i = 1; i <= splits; ++i){
time=clock();
- char **part = paths+(i*m/splits);
- int num = (i+1)*m/splits - i*m/splits;
- data val = load_data(part, num, labels, 1000, 256, 256);
+ pthread_join(load_thread, 0);
+ val = buffer;
normalize_data_rows(val);
+
+ num = (i+1)*m/splits - i*m/splits;
+ char **part = paths+(i*m/splits);
+ if(i != splits) load_thread = load_data_thread(part, num, 0, labels, 1000, 256, 256, &buffer);
printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
+
time=clock();
-#ifdef GPU
- float acc = network_accuracy_gpu(net, val);
- avg_acc += acc;
- printf("%d: %f, %f avg, %lf seconds, %d images\n", i, acc, avg_acc/(i+1), sec(clock()-time), val.X.rows);
-#endif
+ 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);
free_data(val);
}
}
-void draw_detection(image im, float *box)
+void test_detection(char *cfgfile)
{
- int j;
- int r, c;
- for(r = 0; r < 8; ++r){
- for(c = 0; c < 8; ++c){
- j = (r*8 + c) * 5;
- printf("Prob: %f\n", box[j]);
- if(box[j] > .05){
- int d = 256/8;
- int y = r*d+box[j+1]*d;
- int x = c*d+box[j+2]*d;
- int h = box[j+3]*256;
- int w = box[j+4]*256;
- printf("%f %f %f %f\n", box[j+1], box[j+2], box[j+3], box[j+4]);
- printf("%d %d %d %d\n", x, y, w, h);
- printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2);
- draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2);
- }
- }
- }
- show_image(im, "box");
- cvWaitKey(0);
-}
-
-void test_detection()
-{
- network net = parse_network_cfg("cfg/detnet.test");
+ network net = parse_network_cfg(cfgfile);
+ set_batch_network(&net, 1);
srand(2222222);
clock_t time;
char filename[256];
while(1){
fgets(filename, 256, stdin);
strtok(filename, "\n");
- image im = load_image_color(filename, 256, 256);
+ image im = load_image_color(filename, 224, 224);
z_normalize_image(im);
printf("%d %d %d\n", im.h, im.w, im.c);
float *X = im.data;
time=clock();
float *predictions = network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
- draw_detection(im, predictions);
+ draw_detection(im, predictions, 7);
free_image(im);
}
}
+void test_init(char *cfgfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ set_batch_network(&net, 1);
+ srand(2222222);
+ int i = 0;
+ char *filename = "data/test.jpg";
+
+ image im = load_image_color(filename, 256, 256);
+ //z_normalize_image(im);
+ translate_image(im, -128);
+ scale_image(im, 1/128.);
+ float *X = im.data;
+ forward_network(net, X, 0, 1);
+ for(i = 0; i < net.n; ++i){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ image output = get_convolutional_image(layer);
+ int size = output.h*output.w*output.c;
+ float v = variance_array(layer.output, size);
+ float m = mean_array(layer.output, size);
+ printf("%d: Convolutional, mean: %f, variance %f\n", i, m, v);
+ }
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ int size = layer.outputs;
+ float v = variance_array(layer.output, size);
+ float m = mean_array(layer.output, size);
+ printf("%d: Connected, mean: %f, variance %f\n", i, m, v);
+ }
+ }
+ free_image(im);
+}
+
void test_imagenet()
{
network net = parse_network_cfg("cfg/imagenet_test.cfg");
@@ -545,84 +401,90 @@
int iters = 10000/net.batch;
data train = load_all_cifar10();
while(++count <= 10000){
- clock_t start = clock(), end;
+ clock_t time = clock();
float loss = train_network_sgd(net, train, iters);
- end = clock();
- //visualize_network(net);
- //cvWaitKey(5000);
- //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(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);
- char buff[256];
- sprintf(buff, "/home/pjreddie/cifar/cifar10_2_%d.cfg", count);
- save_network(net, buff);
+ printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,sec(clock()-time));
+ //char buff[256];
+ //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()
+void compare_nist(char *p1,char *p2)
{
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()
-{
- srand(222222);
- network net = parse_network_cfg("cfg/nist_final.cfg");
+ network n1 = parse_network_cfg(p1);
+ network n2 = parse_network_cfg(p2);
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
- translate_data_rows(test, -144);
+ normalize_data_rows(test);
+ compare_networks(n1, n2, test);
+}
+
+void test_nist(char *path)
+{
+ srand(222222);
+ network net = parse_network_cfg(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_multi(net, test,16);
+ float test_acc = network_accuracy(net, test);
end = clock();
printf("Accuracy: %f, Time: %lf seconds\n", test_acc,(float)(end-start)/CLOCKS_PER_SEC);
}
-void train_nist()
+void train_nist(char *cfgfile)
{
srand(222222);
- network net = parse_network_cfg("cfg/nist.cfg");
+ // srand(time(0));
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);
- translate_data_rows(test, -144);
+ network net = parse_network_cfg(cfgfile);
+ int count = 0;
+ int iters = 6000/net.batch + 1;
+ while(++count <= 100){
+ clock_t start = clock(), end;
+ normalize_data_rows(train);
+ normalize_data_rows(test);
+ float loss = train_network_sgd(net, train, iters);
+ float test_acc = 0;
+ if(count%1 == 0) test_acc = network_accuracy(net, test);
+ end = clock();
+ printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC);
+ }
+ free_data(train);
+ free_data(test);
+ char buff[256];
+ sprintf(buff, "%s.trained", cfgfile);
+ save_network(net, buff);
+}
+
+void train_nist_distributed(char *address)
+{
+ srand(time(0));
+ network net = parse_network_cfg("cfg/nist.client");
+ 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);
int count = 0;
int iters = 50000/net.batch;
+ iters = 1000/net.batch + 1;
while(++count <= 2000){
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
+ client_update(net, address);
end = clock();
- float 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);
+ //float test_acc = network_accuracy_gpu(net, test);
+ //float test_acc = 0;
+ printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC);
}
}
@@ -652,96 +514,17 @@
lr /= 2;
}
matrix partial = network_predict_data(net, test);
- float acc = matrix_accuracy(test.y, partial);
+ float acc = matrix_topk_accuracy(test.y, partial,1);
printf("Model Accuracy: %lf\n", acc);
matrix_add_matrix(partial, prediction);
- acc = matrix_accuracy(test.y, prediction);
+ acc = matrix_topk_accuracy(test.y, prediction,1);
printf("Current Ensemble Accuracy: %lf\n", acc);
free_matrix(partial);
}
- float acc = matrix_accuracy(test.y, prediction);
+ float acc = matrix_topk_accuracy(test.y, prediction,1);
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");
@@ -754,8 +537,7 @@
cvWaitKey(0);
}
-
-void test_gpu_net()
+void test_correct_nist()
{
srand(222222);
network net = parse_network_cfg("cfg/nist.cfg");
@@ -765,6 +547,7 @@
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);
@@ -772,18 +555,18 @@
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()
@@ -794,298 +577,139 @@
printf("%d\n", plist->size);
clock_t time;
int count = 0;
+ network net;
srand(222222);
- network net = parse_network_cfg("cfg/alexnet.test");
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 1000/net.batch+1;
- imgs = 1;
+ net = parse_network_cfg("cfg/net.cfg");
+ int imgs = net.batch;
+ count = 0;
while(++count <= 5){
time=clock();
- data train = load_data_random(imgs*net.batch, paths, 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);
}
-#ifdef GPU
+
+ gpu_index = -1;
count = 0;
srand(222222);
- net = parse_network_cfg("cfg/alexnet.test");
+ 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_random(imgs*net.batch, paths, 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
}
-void test_server()
+void run_server()
{
- network net = parse_network_cfg("cfg/alexnet.test");
+ srand(time(0));
+ network net = parse_network_cfg("cfg/net.cfg");
+ set_batch_network(&net, 1);
server_update(net);
}
+
void test_client()
{
- network net = parse_network_cfg("cfg/alexnet.test");
- client_update(net);
+ network net = parse_network_cfg("cfg/alexnet.client");
+ clock_t time=clock();
+ client_update(net, "localhost");
+ printf("1\n");
+ client_update(net, "localhost");
+ printf("2\n");
+ client_update(net, "localhost");
+ printf("3\n");
+ printf("Transfered: %lf seconds\n", sec(clock()-time));
}
-int main(int argc, char *argv[])
+void del_arg(int argc, char **argv, int index)
+{
+ int i;
+ 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; ++i) if(0==strcmp(argv[i], arg)) {
+ del_arg(argc, argv, i);
+ return 1;
+ }
+ return 0;
+}
+
+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;
}
- if(0==strcmp(argv[1], "train")) train_imagenet();
- else 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();
+ 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], "cifar")) train_cifar10();
else if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
+ else if(0==strcmp(argv[1], "test_correct_nist")) test_correct_nist();
else if(0==strcmp(argv[1], "test")) test_imagenet();
- else if(0==strcmp(argv[1], "server")) test_server();
- else if(0==strcmp(argv[1], "client")) test_client();
- else if(0==strcmp(argv[1], "detect")) test_detection();
- else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
- else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
+ 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;
+ }
+ else if(0==strcmp(argv[1], "detection")) train_detection_net(argv[2]);
+ else if(0==strcmp(argv[1], "nist")) train_nist(argv[2]);
+ else if(0==strcmp(argv[1], "train")) train_imagenet(argv[2]);
+ else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
+ else if(0==strcmp(argv[1], "detect")) test_detection(argv[2]);
+ else if(0==strcmp(argv[1], "init")) test_init(argv[2]);
+ else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
+ else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
+ else if(0==strcmp(argv[1], "testnist")) test_nist(argv[2]);
+ else if(0==strcmp(argv[1], "validetect")) validate_detection_net(argv[2]);
+ else if(argc < 4){
+ fprintf(stderr, "usage: %s <function> <filename> <filename>\n", argv[0]);
+ return 0;
+ }
+ else if(0==strcmp(argv[1], "compare")) compare_nist(argv[2], argv[3]);
fprintf(stderr, "Success!\n");
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);
-}
-*/
--
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