From d7286c273211ffeb1f56594f863d1ee9922be6d4 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 07 Nov 2013 00:09:41 +0000
Subject: [PATCH] Loading may or may not work. But probably.
---
src/tests.c | 106 +++++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 86 insertions(+), 20 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index 7e2539a..0e639be 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -3,6 +3,7 @@
#include "maxpool_layer.h"
#include "network.h"
#include "image.h"
+#include "parser.h"
#include <time.h>
#include <stdlib.h>
@@ -34,12 +35,12 @@
void test_convolutional_layer()
{
srand(0);
- image dog = load_image("test_dog.jpg");
+ image dog = load_image("dog.jpg");
int i;
- int n = 5;
+ int n = 3;
int stride = 1;
- int size = 8;
- convolutional_layer layer = make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
+ int size = 3;
+ convolutional_layer layer = *make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
char buff[256];
for(i = 0; i < n; ++i) {
sprintf(buff, "Kernel %d", i);
@@ -47,7 +48,7 @@
}
run_convolutional_layer(dog, layer);
- maxpool_layer mlayer = make_maxpool_layer(layer.output.h, layer.output.w, layer.output.c, 3);
+ maxpool_layer mlayer = *make_maxpool_layer(layer.output.h, layer.output.w, layer.output.c, 2);
run_maxpool_layer(layer.output,mlayer);
show_image_layers(mlayer.output, "Test Maxpool Layer");
@@ -112,25 +113,25 @@
int n = 48;
int stride = 4;
int size = 11;
- convolutional_layer cl = make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
- maxpool_layer ml = make_maxpool_layer(cl.output.h, cl.output.w, cl.output.c, 2);
+ convolutional_layer cl = *make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
+ maxpool_layer ml = *make_maxpool_layer(cl.output.h, cl.output.w, cl.output.c, 2);
n = 128;
size = 5;
stride = 1;
- convolutional_layer cl2 = make_convolutional_layer(ml.output.h, ml.output.w, ml.output.c, n, size, stride);
- maxpool_layer ml2 = make_maxpool_layer(cl2.output.h, cl2.output.w, cl2.output.c, 2);
+ convolutional_layer cl2 = *make_convolutional_layer(ml.output.h, ml.output.w, ml.output.c, n, size, stride);
+ maxpool_layer ml2 = *make_maxpool_layer(cl2.output.h, cl2.output.w, cl2.output.c, 2);
n = 192;
size = 3;
- convolutional_layer cl3 = make_convolutional_layer(ml2.output.h, ml2.output.w, ml2.output.c, n, size, stride);
- convolutional_layer cl4 = make_convolutional_layer(cl3.output.h, cl3.output.w, cl3.output.c, n, size, stride);
+ convolutional_layer cl3 = *make_convolutional_layer(ml2.output.h, ml2.output.w, ml2.output.c, n, size, stride);
+ convolutional_layer cl4 = *make_convolutional_layer(cl3.output.h, cl3.output.w, cl3.output.c, n, size, stride);
n = 128;
- convolutional_layer cl5 = make_convolutional_layer(cl4.output.h, cl4.output.w, cl4.output.c, n, size, stride);
- maxpool_layer ml3 = make_maxpool_layer(cl5.output.h, cl5.output.w, cl5.output.c, 4);
- connected_layer nl = make_connected_layer(ml3.output.h*ml3.output.w*ml3.output.c, 4096);
- connected_layer nl2 = make_connected_layer(4096, 4096);
- connected_layer nl3 = make_connected_layer(4096, 1000);
+ convolutional_layer cl5 = *make_convolutional_layer(cl4.output.h, cl4.output.w, cl4.output.c, n, size, stride);
+ maxpool_layer ml3 = *make_maxpool_layer(cl5.output.h, cl5.output.w, cl5.output.c, 4);
+ connected_layer nl = *make_connected_layer(ml3.output.h*ml3.output.w*ml3.output.c, 4096, RELU);
+ connected_layer nl2 = *make_connected_layer(4096, 4096, RELU);
+ connected_layer nl3 = *make_connected_layer(4096, 1000, RELU);
net.layers[0] = &cl;
net.layers[1] = &ml;
@@ -155,6 +156,7 @@
show_image_layers(get_network_image(net), "Test Network Layer");
}
+
void test_backpropagate()
{
int n = 3;
@@ -163,19 +165,19 @@
image dog = load_image("dog.jpg");
show_image(dog, "Test Backpropagate Input");
image dog_copy = copy_image(dog);
- convolutional_layer cl = make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
+ convolutional_layer cl = *make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
run_convolutional_layer(dog, cl);
show_image(cl.output, "Test Backpropagate Output");
int i;
clock_t start = clock(), end;
for(i = 0; i < 100; ++i){
- backpropagate_layer(dog_copy, cl);
+ backpropagate_convolutional_layer(dog_copy, cl);
}
end = clock();
printf("Backpropagate: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
start = clock();
for(i = 0; i < 100; ++i){
- backpropagate_layer_convolve(dog, cl);
+ backpropagate_convolutional_layer_convolve(dog, cl);
}
end = clock();
printf("Backpropagate Using Convolutions: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
@@ -185,14 +187,78 @@
show_image(dog, "Test Backpropagate Difference");
}
+void test_ann()
+{
+ network net;
+ net.n = 3;
+ net.layers = calloc(net.n, sizeof(void *));
+ net.types = calloc(net.n, sizeof(LAYER_TYPE));
+ net.types[0] = CONNECTED;
+ net.types[1] = CONNECTED;
+ net.types[2] = CONNECTED;
+
+ connected_layer nl = *make_connected_layer(1, 20, RELU);
+ connected_layer nl2 = *make_connected_layer(20, 20, RELU);
+ connected_layer nl3 = *make_connected_layer(20, 1, RELU);
+
+ net.layers[0] = &nl;
+ net.layers[1] = &nl2;
+ net.layers[2] = &nl3;
+
+ image t = make_image(1,1,1);
+ int count = 0;
+
+ double avgerr = 0;
+ while(1){
+ double v = ((double)rand()/RAND_MAX);
+ double truth = v*v;
+ set_pixel(t,0,0,0,v);
+ run_network(t, net);
+ double *out = get_network_output(net);
+ double err = pow((out[0]-truth),2.);
+ avgerr = .99 * avgerr + .01 * err;
+ //if(++count % 100000 == 0) printf("%f\n", avgerr);
+ if(++count % 100000 == 0) printf("%f %f :%f AVG %f \n", truth, out[0], err, avgerr);
+ out[0] = truth - out[0];
+ learn_network(t, net);
+ update_network(net, .001);
+ }
+
+}
+
+void test_parser()
+{
+ network net = parse_network_cfg("test.cfg");
+ image t = make_image(1,1,1);
+ int count = 0;
+
+ double avgerr = 0;
+ while(1){
+ double v = ((double)rand()/RAND_MAX);
+ double truth = v*v;
+ set_pixel(t,0,0,0,v);
+ run_network(t, net);
+ double *out = get_network_output(net);
+ double err = pow((out[0]-truth),2.);
+ avgerr = .99 * avgerr + .01 * err;
+ //if(++count % 100000 == 0) printf("%f\n", avgerr);
+ if(++count % 100000 == 0) printf("%f %f :%f AVG %f \n", truth, out[0], err, avgerr);
+ out[0] = truth - out[0];
+ learn_network(t, net);
+ update_network(net, .001);
+ }
+}
+
int main()
{
+ test_parser();
//test_backpropagate();
+ //test_ann();
//test_convolve();
//test_upsample();
//test_rotate();
//test_load();
- test_network();
+ //test_network();
//test_convolutional_layer();
//test_color();
cvWaitKey(0);
--
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