From b2b7137b6f185ce2f01664d782a09b08d50d5a07 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 28 Jan 2014 07:16:56 +0000
Subject: [PATCH] About to do something stupid...
---
src/tests.c | 139 +++++++++++++++++++++++++++++-----------------
1 files changed, 87 insertions(+), 52 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index 2a50bac..af22ddb 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -1,4 +1,5 @@
#include "connected_layer.h"
+//#include "old_conv.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
#include "network.h"
@@ -7,6 +8,7 @@
#include "data.h"
#include "matrix.h"
#include "utils.h"
+#include "mini_blas.h"
#include <time.h>
#include <stdlib.h>
@@ -28,35 +30,41 @@
show_image_layers(edge, "Test Convolve");
}
+void test_convolve_matrix()
+{
+ image dog = load_image("dog.jpg");
+ printf("dog channels %d\n", dog.c);
+
+ int size = 11;
+ int stride = 4;
+ int n = 40;
+ double *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);
+ double *matrix = calloc(mh*mw, sizeof(double));
+
+ 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, dog.c, dog.h, dog.w, size, stride, matrix);
+ gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
+ }
+ end = clock();
+ printf("Convolutions: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+ show_image_layers(edge, "Test Convolve");
+ cvWaitKey(0);
+}
+
void test_color()
{
image dog = load_image("test_color.png");
show_image_layers(dog, "Test Color");
}
-void test_convolutional_layer()
-{
- srand(0);
- image dog = load_image("dog.jpg");
- int i;
- int n = 3;
- int stride = 1;
- int size = 3;
- convolutional_layer layer = *make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride, RELU);
- char buff[256];
- for(i = 0; i < n; ++i) {
- sprintf(buff, "Kernel %d", i);
- show_image(layer.kernels[i], buff);
- }
- forward_convolutional_layer(layer, dog.data);
-
- image output = get_convolutional_image(layer);
- maxpool_layer mlayer = *make_maxpool_layer(output.h, output.w, output.c, 2);
- forward_maxpool_layer(mlayer, layer.output);
-
- show_image_layers(get_maxpool_image(mlayer), "Test Maxpool Layer");
-}
-
void verify_convolutional_layer()
{
srand(0);
@@ -87,7 +95,7 @@
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, test.data, in_delta.data);
+ //backward_convolutional_layer(layer, test.data, in_delta.data);
image partial = copy_image(in_delta);
jacobian2[i] = partial.data;
out_delta.data[i] = 0;
@@ -199,7 +207,7 @@
{
srand(444444);
srand(888888);
- network net = parse_network_cfg("nist.cfg");
+ network net = parse_network_cfg("nist_basic.cfg");
data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
data test = load_categorical_data_csv("mnist/mnist_test.csv",0,10);
normalize_data_rows(train);
@@ -210,16 +218,16 @@
double momentum = .9;
double decay = 0.01;
clock_t start = clock(), end;
- while(++count <= 1000){
- double acc = train_network_sgd(net, train, 6400, lr, momentum, decay);
- printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, 1.-acc, lr, momentum, decay);
+ while(++count <= 100){
+ visualize_network(net);
+ double loss = train_network_sgd(net, train, 10000, lr, momentum, decay);
+ printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, loss, lr, momentum, decay);
end = clock();
printf("Time: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
start=end;
- visualize_network(net);
cvWaitKey(100);
//lr /= 2;
- if(count%5 == 0 && 0){
+ if(count%5 == 0){
double train_acc = network_accuracy(net, train);
fprintf(stderr, "\nTRAIN: %f\n", train_acc);
double test_acc = network_accuracy(net, test);
@@ -238,11 +246,9 @@
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];
- */
+ // 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){
@@ -268,22 +274,6 @@
printf("Full Ensemble Accuracy: %lf\n", acc);
}
-void test_kernel_update()
-{
- srand(0);
- double delta[] = {.1};
- double input[] = {.3, .5, .3, .5, .5, .5, .5, .0, .5};
- double kernel[] = {1,2,3,4,5,6,7,8,9};
- convolutional_layer layer = *make_convolutional_layer(3, 3, 1, 1, 3, 1, LINEAR);
- layer.kernels[0].data = kernel;
- layer.delta = delta;
- learn_convolutional_layer(layer, input);
- print_image(layer.kernels[0]);
- print_image(get_convolutional_delta(layer));
- print_image(layer.kernel_updates[0]);
-
-}
-
void test_random_classify()
{
network net = parse_network_cfg("connected.cfg");
@@ -336,10 +326,55 @@
printf("%d, %d, %d\n", train.X.rows, split[0].X.rows, split[1].X.rows);
}
+double *random_matrix(int rows, int cols)
+{
+ int i, j;
+ double *m = calloc(rows*cols, sizeof(double));
+ for(i = 0; i < rows; ++i){
+ for(j = 0; j < cols; ++j){
+ m[i*cols+j] = (double)rand()/RAND_MAX;
+ }
+ }
+ return m;
+}
+
+void test_blas()
+{
+ int m = 1000, n = 1000, k = 1000;
+ double *a = random_matrix(m,k);
+ double *b = random_matrix(k,n);
+ double *c = random_matrix(m,n);
+ int i;
+ for(i = 0; i<1000; ++i){
+ gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+ }
+}
+
+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;
+ double *matrix = calloc(msize, sizeof(double));
+ int i;
+ for(i = 0; i < 1000; ++i){
+ im2col_cpu(test.data, c, h, w, size, stride, matrix);
+ image render = double_to_image(mh, mw, mc, matrix);
+ }
+}
int main()
{
- //test_kernel_update();
+ //test_blas();
+ //test_convolve_matrix();
+ // test_im2row();
//test_split();
//test_ensemble();
test_nist();
--
Gitblit v1.10.0