Fertisch

imageInputTests.c

@@ -1,11 +1,9 @@
-
 #include <stdlib.h>
 #include <stdio.h>
 #include <string.h>
 #include "unity.h"
 #include "imageInput.h"
 
-
 static void prepareImageFile(const char *path, unsigned short int width, unsigned short int height, unsigned int short numberOfImages, unsigned char label)
 {
     FILE *file = fopen(path, "wb");
@@ -13,29 +11,33 @@ static void prepareImageFile(const char *path, unsigned short int width, unsigne
     if (file != NULL)
     {
         const char *fileTag = "__info2_image_file_format__";
-        GrayScalePixelType *zeroBuffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
+        GrayScalePixelType *buffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
 
-        if(zeroBuffer != NULL)
+        if (buffer != NULL)
         {
-            fwrite(fileTag, sizeof(fileTag[0]), strlen(fileTag), file);
+            for (int i = 0; i < width * height; i++)
+            {    
+            buffer[i] = (GrayScalePixelType)i;                      // füllen des buffers mit Graustufen des Pixel für Test                              
+            }
+
+            fwrite(fileTag, 1, strlen(fileTag), file);
             fwrite(&numberOfImages, sizeof(numberOfImages), 1, file);
             fwrite(&width, sizeof(width), 1, file);
             fwrite(&height, sizeof(height), 1, file);
 
             for (int i = 0; i < numberOfImages; i++)
             {
-                fwrite(zeroBuffer, sizeof(GrayScalePixelType), width * height, file);
+                fwrite(buffer, sizeof(GrayScalePixelType), width * height, file);
                 fwrite(&label, sizeof(unsigned char), 1, file);
             }
 
-            free(zeroBuffer);
+            free(buffer);
         }
 
         fclose(file);
     }
 }
 
-
 void test_readImagesReturnsCorrectNumberOfImages(void)
 {
     GrayScaleImageSeries *series = NULL;
@@ -92,7 +94,8 @@ void test_readImagesReturnsCorrectLabels(void)
     TEST_ASSERT_NOT_NULL(series);
     TEST_ASSERT_NOT_NULL(series->labels);
     TEST_ASSERT_EQUAL_UINT16(2, series->count);
-    for (int i = 0; i < 2; i++) {
+    for (int i = 0; i < 2; i++)
+    {
         TEST_ASSERT_EQUAL_UINT8(expectedLabel, series->labels[i]);
     }
     clearSeries(series);
@@ -119,11 +122,36 @@ void test_readImagesFailsOnWrongFileTag(void)
     remove(path);
 }
 
-void setUp(void) {
+// Test der Hilfsfunktionen
+
+void test_read_GrayScale_Pixel(void)
+{
+    GrayScaleImageSeries *series = NULL;
+    const char *path = "testFile.info2";
+
+    prepareImageFile(path, 8, 8, 1, 1);
+    series = readImages(path);
+
+    TEST_ASSERT_NOT_NULL(series);
+    TEST_ASSERT_NOT_NULL(series->images);
+    TEST_ASSERT_EQUAL_UINT16(1, series->count);
+
+    for (int i = 0; i < (8 * 8); i++)
+    {
+        TEST_ASSERT_EQUAL_UINT8((GrayScalePixelType)i, series->images->buffer[i]);
+    }
+
+    clearSeries(series);
+    remove(path);
+}
+
+void setUp(void)
+{
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) {
+void tearDown(void)
+{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }
 
@@ -138,6 +166,7 @@ int main()
     RUN_TEST(test_readImagesReturnsCorrectLabels);
     RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
     RUN_TEST(test_readImagesFailsOnWrongFileTag);
+    RUN_TEST(test_read_GrayScale_Pixel);
 
     return UNITY_END();
 }

matrix.h

@@ -11,8 +11,8 @@ typedef struct Matrix
 {
     unsigned int rows;
     unsigned int cols;
-    MatrixType *data;
+    MatrixType *buffer;
-    #define buffer data    
+  
 } Matrix;
 
 

neuralNetwork.c

@@ -170,7 +170,7 @@ NeuralNetwork loadModel(const char *path)
 
 static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
 {
-    Matrix matrix = {NULL, 0, 0};
+    Matrix matrix = {0, 0, NULL};
 
     if(count > 0 && images != NULL)
     {

neuralNetworkTests.c

@@ -5,10 +5,49 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
-
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    FILE *f = fopen(path, "wb");
+    if (!f) return;
+
+    const char *tag = "__info2_neural_network_file_format__";
+    fwrite(tag, 1, strlen(tag), f);
+
+    if (nn.numberOfLayers == 0) {
+        fclose(f);
+        return;
+    }                                                               // In localmodel Struktur Testdateu aufruf:
+                                                                    // Header --> Input Dim --> Output Dim
+                                                                    // i. Layer weights --> biases --> nächste Dim
+
+  
+    int input  = nn.layers[0].weights.cols;
+    int output = nn.layers[0].weights.rows;
+
+    fwrite(&input,  sizeof(int), 1, f);
+    fwrite(&output, sizeof(int), 1, f);
+
+    for (int i = 0; i < nn.numberOfLayers; i++)
+    {
+        const Layer *layer = &nn.layers[i];
+        int out = layer->weights.rows;
+        int in  = layer->weights.cols;
+
+        
+        fwrite(layer->weights.buffer, sizeof(MatrixType), out * in, f);
+
+        
+        fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, f);
+
+        
+        if (i + 1 < nn.numberOfLayers)
+        {
+            int nextOut = nn.layers[i + 1].weights.rows;
+            fwrite(&nextOut, sizeof(int), 1, f);
+        }
+    }
+
+    fclose(f);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -205,8 +244,8 @@ void test_predictReturnsCorrectLabels(void)
     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}, \
+    Layer layers[] = {{.weights = weights1, .biases = biases1, .activation = someActivation},
-                      {.weights=weights2, .biases=biases2, .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);
@@ -216,11 +255,13 @@ void test_predictReturnsCorrectLabels(void)
     free(predictedLabels);
 }
 
-void setUp(void) {
+void setUp(void)
+{
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) {
+void tearDown(void)
+{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }