#include "connected_layer.h"
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#include "convolutional_layer.h"
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#include "maxpool_layer.h"
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#include "network.h"
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#include "image.h"
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#include <time.h>
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#include <stdlib.h>
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#include <stdio.h>
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void test_convolve()
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{
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image dog = load_image("dog.jpg");
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//show_image_layers(dog, "Dog");
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printf("dog channels %d\n", dog.c);
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image kernel = make_random_image(3,3,dog.c);
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image edge = make_image(dog.h, dog.w, 1);
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 1000; ++i){
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convolve(dog, kernel, 1, 0, edge);
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}
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end = clock();
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printf("Convolutions: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
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show_image_layers(edge, "Test Convolve");
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}
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void test_color()
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{
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image dog = load_image("test_color.png");
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show_image_layers(dog, "Test Color");
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}
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void test_convolutional_layer()
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{
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srand(0);
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image dog = load_image("dog.jpg");
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int i;
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int n = 3;
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int stride = 1;
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int size = 3;
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convolutional_layer layer = make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
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char buff[256];
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for(i = 0; i < n; ++i) {
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sprintf(buff, "Kernel %d", i);
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show_image(layer.kernels[i], buff);
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}
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run_convolutional_layer(dog, layer);
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maxpool_layer mlayer = make_maxpool_layer(layer.output.h, layer.output.w, layer.output.c, 2);
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run_maxpool_layer(layer.output,mlayer);
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show_image_layers(mlayer.output, "Test Maxpool Layer");
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}
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void test_load()
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{
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image dog = load_image("dog.jpg");
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show_image(dog, "Test Load");
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show_image_layers(dog, "Test Load");
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}
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void test_upsample()
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{
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image dog = load_image("dog.jpg");
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int n = 3;
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image up = make_image(n*dog.h, n*dog.w, dog.c);
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upsample_image(dog, n, up);
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show_image(up, "Test Upsample");
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show_image_layers(up, "Test Upsample");
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}
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void test_rotate()
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{
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int i;
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image dog = load_image("dog.jpg");
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clock_t start = clock(), end;
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for(i = 0; i < 1001; ++i){
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rotate_image(dog);
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}
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end = clock();
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printf("Rotations: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
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show_image(dog, "Test Rotate");
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image random = make_random_image(3,3,3);
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show_image(random, "Test Rotate Random");
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rotate_image(random);
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show_image(random, "Test Rotate Random");
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rotate_image(random);
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show_image(random, "Test Rotate Random");
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}
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void test_network()
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{
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network net;
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net.n = 11;
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net.layers = calloc(net.n, sizeof(void *));
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net.types = calloc(net.n, sizeof(LAYER_TYPE));
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net.types[0] = CONVOLUTIONAL;
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net.types[1] = MAXPOOL;
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net.types[2] = CONVOLUTIONAL;
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net.types[3] = MAXPOOL;
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net.types[4] = CONVOLUTIONAL;
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net.types[5] = CONVOLUTIONAL;
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net.types[6] = CONVOLUTIONAL;
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net.types[7] = MAXPOOL;
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net.types[8] = CONNECTED;
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net.types[9] = CONNECTED;
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net.types[10] = CONNECTED;
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image dog = load_image("test_hinton.jpg");
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int n = 48;
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int stride = 4;
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int size = 11;
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convolutional_layer cl = make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
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maxpool_layer ml = make_maxpool_layer(cl.output.h, cl.output.w, cl.output.c, 2);
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n = 128;
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size = 5;
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stride = 1;
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convolutional_layer cl2 = make_convolutional_layer(ml.output.h, ml.output.w, ml.output.c, n, size, stride);
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maxpool_layer ml2 = make_maxpool_layer(cl2.output.h, cl2.output.w, cl2.output.c, 2);
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n = 192;
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size = 3;
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convolutional_layer cl3 = make_convolutional_layer(ml2.output.h, ml2.output.w, ml2.output.c, n, size, stride);
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convolutional_layer cl4 = make_convolutional_layer(cl3.output.h, cl3.output.w, cl3.output.c, n, size, stride);
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n = 128;
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convolutional_layer cl5 = make_convolutional_layer(cl4.output.h, cl4.output.w, cl4.output.c, n, size, stride);
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maxpool_layer ml3 = make_maxpool_layer(cl5.output.h, cl5.output.w, cl5.output.c, 4);
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connected_layer nl = make_connected_layer(ml3.output.h*ml3.output.w*ml3.output.c, 4096, RELU);
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connected_layer nl2 = make_connected_layer(4096, 4096, RELU);
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connected_layer nl3 = make_connected_layer(4096, 1000, RELU);
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net.layers[0] = &cl;
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net.layers[1] = &ml;
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net.layers[2] = &cl2;
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net.layers[3] = &ml2;
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net.layers[4] = &cl3;
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net.layers[5] = &cl4;
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net.layers[6] = &cl5;
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net.layers[7] = &ml3;
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net.layers[8] = &nl;
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net.layers[9] = &nl2;
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net.layers[10] = &nl3;
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 10; ++i){
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run_network(dog, net);
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rotate_image(dog);
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}
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end = clock();
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printf("Ran %lf second per iteration\n", (double)(end-start)/CLOCKS_PER_SEC/10);
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show_image_layers(get_network_image(net), "Test Network Layer");
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}
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void test_backpropagate()
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{
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int n = 3;
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int size = 4;
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int stride = 10;
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image dog = load_image("dog.jpg");
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show_image(dog, "Test Backpropagate Input");
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image dog_copy = copy_image(dog);
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convolutional_layer cl = make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
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run_convolutional_layer(dog, cl);
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show_image(cl.output, "Test Backpropagate Output");
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 100; ++i){
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backpropagate_convolutional_layer(dog_copy, cl);
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}
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end = clock();
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printf("Backpropagate: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
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start = clock();
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for(i = 0; i < 100; ++i){
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backpropagate_convolutional_layer_convolve(dog, cl);
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}
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end = clock();
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printf("Backpropagate Using Convolutions: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
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show_image(dog_copy, "Test Backpropagate 1");
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show_image(dog, "Test Backpropagate 2");
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subtract_image(dog, dog_copy);
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show_image(dog, "Test Backpropagate Difference");
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}
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void test_ann()
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{
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network net;
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net.n = 3;
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net.layers = calloc(net.n, sizeof(void *));
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net.types = calloc(net.n, sizeof(LAYER_TYPE));
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net.types[0] = CONNECTED;
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net.types[1] = CONNECTED;
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net.types[2] = CONNECTED;
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connected_layer nl = make_connected_layer(1, 20, RELU);
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connected_layer nl2 = make_connected_layer(20, 20, RELU);
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connected_layer nl3 = make_connected_layer(20, 1, RELU);
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net.layers[0] = &nl;
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net.layers[1] = &nl2;
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net.layers[2] = &nl3;
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image t = make_image(1,1,1);
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int count = 0;
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double avgerr = 0;
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while(1){
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double v = ((double)rand()/RAND_MAX);
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double truth = v*v;
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set_pixel(t,0,0,0,v);
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run_network(t, net);
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double *out = get_network_output(net);
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double err = pow((out[0]-truth),2.);
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avgerr = .99 * avgerr + .01 * err;
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//if(++count % 100000 == 0) printf("%f\n", avgerr);
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if(++count % 100000 == 0) printf("%f %f :%f AVG %f \n", truth, out[0], err, avgerr);
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out[0] = truth - out[0];
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learn_network(t, net);
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update_network(net, .001);
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}
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}
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int main()
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{
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//test_backpropagate();
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test_ann();
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//test_convolve();
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//test_upsample();
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//test_rotate();
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//test_load();
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//test_network();
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//test_convolutional_layer();
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//test_color();
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cvWaitKey(0);
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return 0;
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}
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