From d6fbe86e7a8c1bc389902c90c57ee7e80f5475b9 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 16 Dec 2014 19:40:05 +0000
Subject: [PATCH] updates?
---
src/cnn.c | 570 ++++++++++++++++++++++++++++++++------------------------
1 files changed, 328 insertions(+), 242 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index 7971b95..8c56bda 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -8,6 +8,7 @@
#include "matrix.h"
#include "utils.h"
#include "mini_blas.h"
+#include "matrix.h"
#include "server.h"
#include <time.h>
@@ -293,7 +294,7 @@
while(1){
i += 1;
time=clock();
- data train = load_data_random(imgs*net.batch, paths, m, labels, 2, 256, 256);
+ data train = load_data(paths, imgs*net.batch, m, labels, 2, 256, 256);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@@ -310,186 +311,28 @@
}
}
-void train_detection_net()
-{
- float avg_loss = 1;
- //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
- network net = parse_network_cfg("cfg/detnet.cfg");
- 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 i = 0;
- list *plist = get_paths("/home/pjreddie/data/imagenet/horse.txt");
- char **paths = (char **)list_to_array(plist);
- printf("%d\n", plist->size);
- 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);
- */
-
- 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);
- 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){
- char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/detnet_%d.cfg", i);
- save_network(net, buff);
- }
- }
-}
-
-void train_imagenet_distributed(char *address)
-{
- float avg_loss = 1;
- srand(0);
- network net = parse_network_cfg("cfg/alexnet.client");
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 1000/net.batch+1;
- imgs = 1;
- 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;
- 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);
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
-#ifdef GPU
- float loss = train_network_data_gpu(net, train, imgs);
- client_update(net, address);
- 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){
- char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/alexnet_%d.cfg", i);
- save_network(net, buff);
- }
- }
-}
-
-void train_imagenet()
-{
- float avg_loss = 1;
- //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
- srand(0);
- network net = parse_network_cfg("cfg/alexnet.cfg");
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 1000/net.batch+1;
- imgs=1;
- 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;
- 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);
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
-#ifdef GPU
- float loss = train_network_data_gpu(net, train, imgs);
- 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){
- char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/alexnet_%d.cfg", i);
- save_network(net, buff);
- }
- }
-}
-
-void validate_imagenet(char *filename)
-{
- int i;
- network net = parse_network_cfg(filename);
- srand(time(0));
-
- char **labels = get_labels("/home/pjreddie/data/imagenet/cls.val.labels.list");
-
- list *plist = get_paths("/home/pjreddie/data/imagenet/cls.val.list");
- char **paths = (char **)list_to_array(plist);
- int m = plist->size;
- free_list(plist);
-
- clock_t time;
- float avg_acc = 0;
- int splits = 50;
-
- for(i = 0; 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);
-
- normalize_data_rows(val);
- 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
- free_data(val);
- }
-}
-
-void draw_detection(image im, float *box)
+void draw_detection(image im, float *box, int side)
{
int j;
int r, c;
- for(r = 0; r < 8; ++r){
- for(c = 0; c < 8; ++c){
- j = (r*8 + c) * 5;
+ float amount[5];
+ for(r = 0; r < side*side; ++r){
+ for(j = 0; j < 5; ++j){
+ if(box[r*5] > amount[j]) {
+ amount[j] = box[r*5];
+ break;
+ }
+ }
+ }
+ float smallest = amount[0];
+ for(j = 1; j < 5; ++j) if(amount[j] < smallest) smallest = amount[j];
+
+ 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] > .01){
- int d = 256/8;
+ 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;
@@ -505,27 +348,227 @@
cvWaitKey(0);
}
-void test_detection()
+
+void train_detection_net()
{
- network net = parse_network_cfg("cfg/detnet.test");
+ float avg_loss = 1;
+ //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
+ network net = parse_network_cfg("cfg/detnet.cfg");
+ 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 i = 0;
+ list *plist = get_paths("/home/pjreddie/data/imagenet/horse.txt");
+ char **paths = (char **)list_to_array(plist);
+ printf("%d\n", plist->size);
+ clock_t time;
+ while(1){
+ i += 1;
+ time=clock();
+ data train = load_data_detection_jitter_random(imgs*net.batch, paths, plist->size, 256, 256, 7, 7, 256);
+ /*
+ image im = float_to_image(224, 224, 3, train.X.vals[0]);
+ draw_detection(im, train.y.vals[0], 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);
+ 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
+ if(i%10==0){
+ char buff[256];
+ sprintf(buff, "/home/pjreddie/imagenet_backup/detnet_%d.cfg", i);
+ save_network(net, buff);
+ }
+ free_data(train);
+ }
+}
+
+void train_imagenet_distributed(char *address)
+{
+ float avg_loss = 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 = 1;
+ 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*net.batch, 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*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+
+#ifdef GPU
+ float loss = train_network_data_gpu(net, train, imgs);
+ 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);
+ }
+}
+
+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, .000001, .9, .0005);
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ int imgs = 1000/net.batch+1;
+ int i = 20590;
+ 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*net.batch, plist->size, labels, 1000, 256, 256, &buffer);
+ while(1){
+ i += 1;
+ time=clock();
+ pthread_join(load_thread, 0);
+ train = buffer;
+ normalize_data_rows(train);
+ load_thread = load_data_thread(paths, imgs*net.batch, 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);
+ 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){
+ char buff[256];
+ sprintf(buff, "/home/pjreddie/imagenet_backup/net_%d.cfg", i);
+ save_network(net, buff);
+ }
+ }
+}
+
+void validate_imagenet(char *filename)
+{
+ int i = 0;
+ network net = parse_network_cfg(filename);
+ srand(time(0));
+
+ char **labels = get_labels("/home/pjreddie/data/imagenet/cls.val.labels.list");
+
+ list *plist = get_paths("/home/pjreddie/data/imagenet/cls.val.list");
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ clock_t time;
+ float avg_acc = 0;
+ float avg_top5 = 0;
+ int splits = 50;
+ int num = (i+1)*m/splits - i*m/splits;
+
+ 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();
+
+ 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_accuracies_gpu(net, val);
+ avg_acc += acc[0];
+ avg_top5 += acc[1];
+ printf("%d: top1: %f, top5: %f, %lf seconds, %d images\n", i, avg_acc/i, avg_top5/i, sec(clock()-time), val.X.rows);
+#endif
+ free_data(val);
+ }
+}
+
+void test_detection(char *cfgfile)
+{
+ 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, 224, 224);
+ z_normalize_image(im);
+ 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");
@@ -577,14 +620,14 @@
void train_cifar10()
{
srand(555555);
- network net = parse_network_cfg("cfg/cifar10.cfg");
+ network net = parse_network_cfg("cfg/cifar_ramp.part");
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);
+ float loss = train_network_sgd_gpu(net, train, iters);
end = clock();
//visualize_network(net);
//cvWaitKey(5000);
@@ -592,10 +635,10 @@
//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);
+ float test_acc = network_accuracy_gpu(net, test);
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
char buff[256];
- sprintf(buff, "/home/pjreddie/cifar/cifar10_2_%d.cfg", count);
+ sprintf(buff, "/home/pjreddie/cifar/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);
@@ -633,14 +676,14 @@
}
-void test_nist()
+void test_nist(char *path)
{
srand(222222);
- network net = parse_network_cfg("cfg/nist_final.cfg");
+ network net = parse_network_cfg(path);
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
- translate_data_rows(test, -144);
+ normalize_data_rows(test);
clock_t start = clock(), end;
- float test_acc = network_accuracy_multi(net, test,16);
+ float test_acc = network_accuracy_gpu(net, test);
end = clock();
printf("Accuracy: %f, Time: %lf seconds\n", test_acc,(float)(end-start)/CLOCKS_PER_SEC);
}
@@ -651,19 +694,43 @@
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);
- translate_data_rows(test, -144);
+ normalize_data_rows(train);
+ normalize_data_rows(test);
int count = 0;
- int iters = 50000/net.batch;
+ int iters = 60000/net.batch + 1;
+ //iters = 6000/net.batch + 1;
while(++count <= 2000){
clock_t start = clock(), end;
- float loss = train_network_sgd(net, train, iters);
+ float loss = train_network_sgd_gpu(net, train, iters);
end = clock();
- float test_acc = network_accuracy(net, test);
+ float test_acc = 0;
+ if(count%1 == 0) test_acc = network_accuracy_gpu(net, test);
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC);
}
}
+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_gpu(net, train, iters);
+ client_update(net, address);
+ end = clock();
+ //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);
+ }
+}
+
void test_ensemble()
{
int i;
@@ -690,14 +757,14 @@
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);
}
@@ -754,26 +821,26 @@
}
/*
-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 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()
{
@@ -834,31 +901,16 @@
printf("%d\n", plist->size);
clock_t time;
int count = 0;
-
- 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);
+ network net;
int imgs = 1000/net.batch+1;
imgs = 1;
-
- 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);
- normalize_data_rows(train);
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
- float loss = train_network_data_cpu(net, train, imgs);
- printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
- free_data(train);
- }
#ifdef GPU
count = 0;
srand(222222);
- net = parse_network_cfg("cfg/alexnet.test");
+ net = parse_network_cfg("cfg/net.cfg");
while(++count <= 5){
time=clock();
- data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
+ data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 256, 256);
//translate_data_rows(train, -144);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
@@ -868,12 +920,28 @@
free_data(train);
}
#endif
+ count = 0;
+ srand(222222);
+ net = parse_network_cfg("cfg/net.cfg");
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ while(++count <= 5){
+ time=clock();
+ data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 256,256);
+ //translate_data_rows(train, -144);
+ normalize_data_rows(train);
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+ float loss = train_network_data_cpu(net, train, imgs);
+ printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
+ free_data(train);
+ }
}
void run_server()
{
- srand(0);
- network net = parse_network_cfg("cfg/alexnet.server");
+ srand(time(0));
+ network net = parse_network_cfg("cfg/net.cfg");
+ set_batch_network(&net, 1);
server_update(net);
}
void test_client()
@@ -889,26 +957,44 @@
printf("Transfered: %lf seconds\n", sec(clock()-time));
}
+int find_int_arg(int argc, char* argv[], char *arg)
+{
+ int i;
+ for(i = 0; i < argc-1; ++i) if(0==strcmp(argv[i], arg)) return atoi(argv[i+1]);
+ return 0;
+}
+
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();
+ int index = find_int_arg(argc, argv, "-i");
+#ifdef GPU
+ cl_setup(index);
+#endif
+ if(0==strcmp(argv[1], "detection")) train_detection_net();
else if(0==strcmp(argv[1], "asirra")) train_asirra();
else if(0==strcmp(argv[1], "nist")) train_nist();
+ else if(0==strcmp(argv[1], "cifar")) train_cifar10();
else if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
else if(0==strcmp(argv[1], "test")) test_imagenet();
else if(0==strcmp(argv[1], "server")) run_server();
- else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
- 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]);
#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], "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]);
fprintf(stderr, "Success!\n");
return 0;
}
--
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