NeuronalesNetz_TMN/Startcode/neuralNetworkTests.c

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include "unity.h"
#include "neuralNetwork.h"
/*
 ---------------------------------------------------------------------------------------------------------
*/
// TODO Nico
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)      //Exklusive Funktion ohne Rückgabewert mit Übergabewerten "path" für den Dateinamen und "nn" für das neuronale Netz dessen Daten abgespeichert werden soll
{
    /*---Datei zum Schreiben im Binärmodus öffnen---*/
    FILE *file = fopen(path, "wb");     //Öffnet die Datei "path" als write Binary
    if(file == NULL)                    //Wenn Datei nicht geöffnet werden kann, bringe einen Fehler und breche das Programm ab
    {
        return;
    }

    /*---Indifikationstag an den Anfang schreiben---*/
    const char *fileTag = "__info2_neural_network_file_format__";       //Der Text der laut Angabe am Anfang der Datei stehen muss
    fwrite(fileTag, sizeof(char), strlen(fileTag), file);               //Schreibbefehl mit Parametern (Vorgegebener Text soll geschrieben werden (Adresse), Größe eines Buchstaben, Länge des Textes, Zielort "file")

    if(nn.numberOfLayers > 0)       //Daten schreiben nur starten, wenn das Netz Layers besitzt
    {
        /*---Erste Eingabe-Dimension schreiben---*/
        unsigned int inputDim = nn.layers[0].weights.cols;      //Speichere den Wert des ersten Layers in "inputDim"
        fwrite(&inputDim, sizeof(int), 1, file);                //Schreibe 4 Bytes aus den Wert der Adresse "inputDim" als ganze Zahl in die Datei "file"

        /*---Schleife für jeden Layer um Gewichte und Bias zu speichern---*/
        for(int i = 0; i < nn.numberOfLayers; i++)      //Schleife von 0 bis Anzahl der Layer
        {
            Layer layer = nn.layers[i];     //Benutzt aktuellen Layer mit Nummer "i" und benutzt Hilfsvariable "layer"

            /*---Output-Dimension dieses Layers schreiben---*/
            unsigned int outputDim = layer.weights.rows;        //Speichere die Anzahl der Zeilen in "outputDim"
            fwrite(&outputDim, sizeof(int), 1, file);           //Schreibe 4 Bytes den Wert der Adresse "outputDim" als ganze Zahl in die Datei "file"

            /*---Weights Buffer speichern---*/
            int weightCount = layer.weights.rows * layer.weights.cols;                  //Berechnet die Gesamtzahl der Zahlen in der Weight Tabelle
            fwrite(layer.weights.buffer, sizeof(MatrixType), weightCount, file);        //Schreibe den Wert der Adresse "layer.weights.buffer" als Typ von "MatrixType" mit der Anzahl von "weightCount" in die Datei "file" --> Speichert alle eigentlichen Zahlenwerte der Weights auf einmal in die Datei. Buffer ist bereits intern ein Zeiger, deswegen kein "&"

            /*---Bias Buffer speichern---*/
            int biasCount = layer.biases.rows * layer.biases.cols;                  //Berechnet die Gesamtzahl der Zahlen in der Bias Tabelle
            fwrite(layer.biases.buffer, sizeof(MatrixType), biasCount, file);       //Schreibe den Wert der Adresse "layer.biases.buffer" als Typ von "MatrixType" mit der Anzahl von "biasCount" in die Datei "file" --> Speichert alle eigentlichen Zahlenwerte der Weights auf einmal in die Datei. Buffer ist bereits intern ein Zeiger, deswegen kein "&"
        }

        /*---Schreiben aller Layers beenden---*/
        unsigned int terminator = 0;                    //Speichere den Wert 0 in "terminator"
        fwrite(&terminator, sizeof(int), 1, file);      //Schreibe den Wert der Adresse "terminator" im Binärformat in die Datei "file"
    }

    else
    {
        /*---Fehlerhafte Layer---*/
        unsigned int zero = 0;                      //Speichere den Wert 0 in "zero"
        fwrite(&zero, sizeof(int), 1, file);        //0 als inputDimension
        fwrite(&zero, sizeof(int), 1, file);        //0 als outputDimension
    }

    /*---Datei wieder schließen---*/
    fclose(file);       //Schließe die Datei wieder, wenn alle Daten sauber geschrieben wurden
}
/*
 ---------------------------------------------------------------------------------------------------------
*/
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);
}
/*
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*/
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();
}