forked from freudenreichan/info2Praktikum-NeuronalesNetz
243 lines
9.0 KiB
C
243 lines
9.0 KiB
C
#include <stdio.h>
|
|
#include <stdlib.h>
|
|
#include <string.h>
|
|
#include <math.h>
|
|
#include "unity.h"
|
|
#include "neuralNetwork.h"
|
|
|
|
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
|
|
{
|
|
|
|
}
|
|
|
|
void test_loadModelReturnsCorrectNumberOfLayers(void)
|
|
{
|
|
const char *path = "some__nn_test_file.info2";
|
|
MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
|
|
MatrixType buffer2[] = {1, 2, 3, 4, 5, 6};
|
|
Matrix weights1 = {.buffer = buffer1, .rows = 3, .cols = 2};
|
|
Matrix weights2 = {.buffer = buffer2, .rows = 2, .cols = 3};
|
|
MatrixType buffer3[] = {1, 2, 3};
|
|
MatrixType buffer4[] = {1, 2};
|
|
Matrix biases1 = {.buffer = buffer3, .rows = 3, .cols = 1};
|
|
Matrix biases2 = {.buffer = buffer4, .rows = 2, .cols = 1};
|
|
Layer layers[] = {{.weights = weights1, .biases = biases1}, {.weights = weights2, .biases = biases2}};
|
|
|
|
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2};
|
|
NeuralNetwork netUnderTest;
|
|
|
|
prepareNeuralNetworkFile(path, expectedNet);
|
|
|
|
netUnderTest = loadModel(path);
|
|
remove(path);
|
|
|
|
TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, netUnderTest.numberOfLayers);
|
|
clearModel(&netUnderTest);
|
|
}
|
|
|
|
void test_loadModelReturnsCorrectWeightDimensions(void)
|
|
{
|
|
const char *path = "some__nn_test_file.info2";
|
|
MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
|
|
Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
|
|
MatrixType biasBuffer[] = {7, 8, 9};
|
|
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
|
|
Layer layers[] = {{.weights = weights, .biases = biases}};
|
|
|
|
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
|
|
NeuralNetwork netUnderTest;
|
|
|
|
prepareNeuralNetworkFile(path, expectedNet);
|
|
|
|
netUnderTest = loadModel(path);
|
|
remove(path);
|
|
|
|
TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
|
|
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
|
|
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
|
|
clearModel(&netUnderTest);
|
|
}
|
|
|
|
void test_loadModelReturnsCorrectBiasDimensions(void)
|
|
{
|
|
const char *path = "some__nn_test_file.info2";
|
|
MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
|
|
Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
|
|
MatrixType biasBuffer[] = {7, 8, 9};
|
|
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
|
|
Layer layers[] = {{.weights = weights, .biases = biases}};
|
|
|
|
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
|
|
NeuralNetwork netUnderTest;
|
|
|
|
prepareNeuralNetworkFile(path, expectedNet);
|
|
|
|
netUnderTest = loadModel(path);
|
|
remove(path);
|
|
|
|
TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
|
|
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.rows, netUnderTest.layers[0].biases.rows);
|
|
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.cols, netUnderTest.layers[0].biases.cols);
|
|
clearModel(&netUnderTest);
|
|
}
|
|
|
|
void test_loadModelReturnsCorrectWeights(void)
|
|
{
|
|
const char *path = "some__nn_test_file.info2";
|
|
MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
|
|
Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
|
|
MatrixType biasBuffer[] = {7, 8, 9};
|
|
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
|
|
Layer layers[] = {{.weights = weights, .biases = biases}};
|
|
|
|
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
|
|
NeuralNetwork netUnderTest;
|
|
|
|
prepareNeuralNetworkFile(path, expectedNet);
|
|
|
|
netUnderTest = loadModel(path);
|
|
remove(path);
|
|
|
|
TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
|
|
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
|
|
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
|
|
int n = netUnderTest.layers[0].weights.rows * netUnderTest.layers[0].weights.cols;
|
|
TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].weights.buffer, netUnderTest.layers[0].weights.buffer, n);
|
|
clearModel(&netUnderTest);
|
|
}
|
|
|
|
void test_loadModelReturnsCorrectBiases(void)
|
|
{
|
|
const char *path = "some__nn_test_file.info2";
|
|
MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
|
|
Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
|
|
MatrixType biasBuffer[] = {7, 8, 9};
|
|
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
|
|
Layer layers[] = {{.weights = weights, .biases = biases}};
|
|
|
|
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
|
|
NeuralNetwork netUnderTest;
|
|
|
|
prepareNeuralNetworkFile(path, expectedNet);
|
|
|
|
netUnderTest = loadModel(path);
|
|
remove(path);
|
|
|
|
TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
|
|
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
|
|
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
|
|
int n = netUnderTest.layers[0].biases.rows * netUnderTest.layers[0].biases.cols;
|
|
TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].biases.buffer, netUnderTest.layers[0].biases.buffer, n);
|
|
clearModel(&netUnderTest);
|
|
}
|
|
|
|
void test_loadModelFailsOnWrongFileTag(void)
|
|
{
|
|
const char *path = "some_nn_test_file.info2";
|
|
NeuralNetwork netUnderTest;
|
|
FILE *file = fopen(path, "wb");
|
|
|
|
if (file != NULL)
|
|
{
|
|
const char *fileTag = "info2_neural_network_file_format";
|
|
|
|
fwrite(fileTag, sizeof(char), strlen(fileTag), file);
|
|
|
|
fclose(file);
|
|
}
|
|
|
|
netUnderTest = loadModel(path);
|
|
|
|
remove(path);
|
|
|
|
TEST_ASSERT_NULL(netUnderTest.layers);
|
|
TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
|
|
}
|
|
|
|
void test_clearModelSetsMembersToNull(void)
|
|
{
|
|
const char *path = "some__nn_test_file.info2";
|
|
MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
|
|
Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
|
|
MatrixType biasBuffer[] = {7, 8, 9};
|
|
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
|
|
Layer layers[] = {{.weights = weights, .biases = biases}};
|
|
|
|
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
|
|
NeuralNetwork netUnderTest;
|
|
|
|
prepareNeuralNetworkFile(path, expectedNet);
|
|
|
|
netUnderTest = loadModel(path);
|
|
remove(path);
|
|
|
|
TEST_ASSERT_NOT_NULL(netUnderTest.layers);
|
|
TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
|
|
clearModel(&netUnderTest);
|
|
TEST_ASSERT_NULL(netUnderTest.layers);
|
|
TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
|
|
}
|
|
|
|
static void someActivation(Matrix *matrix)
|
|
{
|
|
for (int i = 0; i < matrix->rows * matrix->cols; i++)
|
|
{
|
|
matrix->buffer[i] = fabs(matrix->buffer[i]);
|
|
}
|
|
}
|
|
|
|
void test_predictReturnsCorrectLabels(void)
|
|
{
|
|
const unsigned char expectedLabels[] = {4, 2};
|
|
GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
|
|
GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
|
|
GrayScaleImage inputImages[] = {{.buffer = imageBuffer1, .width = 2, .height = 2}, {.buffer = imageBuffer2, .width = 2, .height = 2}};
|
|
MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
|
|
MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
|
|
MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22, 23, -24, 25, 26, 27, -28, -29};
|
|
Matrix weights1 = {.buffer = weightsBuffer1, .rows = 2, .cols = 4};
|
|
Matrix weights2 = {.buffer = weightsBuffer2, .rows = 3, .cols = 2};
|
|
Matrix weights3 = {.buffer = weightsBuffer3, .rows = 5, .cols = 3};
|
|
MatrixType biasBuffer1[] = {200, 0};
|
|
MatrixType biasBuffer2[] = {0, -100, 0};
|
|
MatrixType biasBuffer3[] = {0, -1000, 0, 2000, 0};
|
|
Matrix biases1 = {.buffer = biasBuffer1, .rows = 2, .cols = 1};
|
|
Matrix biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
|
|
Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
|
|
Layer layers[] = {{.weights = weights1, .biases = biases1, .activation = someActivation},
|
|
{.weights = weights2, .biases = biases2, .activation = someActivation},
|
|
{.weights = weights3, .biases = biases3, .activation = someActivation}};
|
|
NeuralNetwork netUnderTest = {.layers = layers, .numberOfLayers = 3};
|
|
unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
|
|
TEST_ASSERT_NOT_NULL(predictedLabels);
|
|
int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0]));
|
|
TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n);
|
|
free(predictedLabels);
|
|
}
|
|
|
|
void setUp(void)
|
|
{
|
|
// Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
|
|
}
|
|
|
|
void tearDown(void)
|
|
{
|
|
// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
|
|
}
|
|
|
|
int main()
|
|
{
|
|
UNITY_BEGIN();
|
|
|
|
printf("\n============================\nNeural network tests\n============================\n");
|
|
RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers);
|
|
RUN_TEST(test_loadModelReturnsCorrectWeightDimensions);
|
|
RUN_TEST(test_loadModelReturnsCorrectBiasDimensions);
|
|
RUN_TEST(test_loadModelReturnsCorrectWeights);
|
|
RUN_TEST(test_loadModelReturnsCorrectBiases);
|
|
RUN_TEST(test_loadModelFailsOnWrongFileTag);
|
|
RUN_TEST(test_clearModelSetsMembersToNull);
|
|
RUN_TEST(test_predictReturnsCorrectLabels);
|
|
|
|
return UNITY_END();
|
|
} |