Update Makefile

Makefile

@@ -90,8 +90,8 @@ $(BUILDDIR)/%.o: $(SRCDIR)/%.c $(SRCDIR)/include/%.h
 # Tests
 #
 run-tests: build-tests
-	$(foreach file, $(wildcard $(BUILDDIR)/test-*), $(file);)
 	$(foreach file, $(wildcard $(TEST_SRCDIR)/*.sh), $(file);)
+	@echo "$$(for file in build/test-*; do echo $$file; $$file; done)"
 
 build-tests: prepare-tests $(TESTS_OBJ) $(BUILDDIR)/test-cnn_matrix_multiplication $(BUILDDIR)/test-cnn_convolution
 

src/cnn/convolution.c

@@ -21,6 +21,33 @@ inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=t
 }
 #endif
 
+bool check_cuda_compatibility() {
+    #ifdef __CUDACC__
+    int nDevices;
+    cudaDeviceProp prop;
+
+    cudaGetDeviceCount(&nDevices);
+    if (nDevices == 0) {
+        printf("Pas d'utilisation du GPU\n\n");
+        return false;
+    }
+
+    printf("GPUs disponibles:\n");
+
+    for (int i=0; i < nDevices; i++) {
+        cudaGetDeviceProperties(&prop, i);
+        printf(" - %s\n", prop.name);
+    }
+
+    cudaGetDeviceProperties(&prop, 0);
+    printf("Utilisation du GPU: %s (Compute capability: %d.%d)\n\n", prop.name, prop.major, prop.minor);
+    return true;
+    #else
+    printf("Pas d'utilisation du GPU\n\n");
+    return false;
+    #endif
+}
+
 void make_convolution_cpu(Kernel_cnn* kernel, float*** input, float*** output, int output_dim) {
     // c'est le kernel de input
     // input[kernel->rows][kernel_k_size + output_dim-1][kernel_k_size + output_dim-1]

src/cnn/function.c

@@ -112,10 +112,10 @@ ptr get_function_activation(int activation) {
     } else if (activation == -SIGMOID) {
         return &sigmoid_derivative;
     } else if (activation == SOFTMAX) {
-        printf("Erreur, impossible de renvoyer la fonction softmax");
+        printf("Erreur, impossible de renvoyer la fonction softmax\n");
         return NULL;
     } else if (activation == -SOFTMAX) {
-        printf("Erreur, impossible de renvoyer la dérivée de la fonction softmax");
+        printf("Erreur, impossible de renvoyer la dérivée de la fonction softmax\n");
         return NULL;
     } else if (activation == TANH) {
         return &tanh_;

src/cnn/neuron_io.c

@@ -59,7 +59,7 @@ void write_couche(Network* network, int indice_couche, int type_couche, FILE* pt
     int indice_buffer = 0;
     if (type_couche == 0) { // Cas du CNN
         Kernel_cnn* cnn = kernel->cnn;
-        int output_dim = network->width[indice_couche];
+        int output_dim = network->width[indice_couche+1];
 
         // Écriture du pré-corps
         uint32_t pre_buffer[4];
@@ -75,7 +75,6 @@ void write_couche(Network* network, int indice_couche, int type_couche, FILE* pt
         for (int i=0; i < cnn->columns; i++) {
             for (int j=0; j < output_dim; j++) {
                 for (int k=0; k < output_dim; k++) {
-                    printf("%f\n", cnn->bias[i][j][k]);
                     bufferAdd(cnn->bias[i][j][k]);
                 }
             }
@@ -169,7 +168,7 @@ Network* read_network(char* filename) {
     network->kernel = (Kernel**)malloc(sizeof(Kernel*)*size);
 
     for (int i=0; i < (int)size; i++) {
-        network->kernel[i] = read_kernel(type_couche[i], network->width[i], ptr);
+        network->kernel[i] = read_kernel(type_couche[i], network->width[i+1], ptr);
     }
 
     network->input = (float****)malloc(sizeof(float***)*size);

src/cnn/utils.c

@@ -51,7 +51,7 @@ bool equals_networks(Network* network1, Network* network2) {
             }
         } else {
             // Type CNN
-            output_dim = network1->width[i];
+            output_dim = network1->width[i+1];
             checkEquals(kernel[i]->cnn->k_size, "kernel[i]->k_size", i);
             checkEquals(kernel[i]->cnn->rows, "kernel[i]->rows", i);
             checkEquals(kernel[i]->cnn->columns, "kernel[i]->columns", i);
@@ -152,7 +152,7 @@ Network* copy_network(Network* network) {
             rows = network->kernel[i]->cnn->rows;
             k_size = network->kernel[i]->cnn->k_size;
             columns = network->kernel[i]->cnn->columns;
-            output_dim = network->width[i];
+            output_dim = network->width[i+1];
             
 
             network_cp->kernel[i]->nn = NULL;

test/cnn_convolution.cu

@@ -75,6 +75,32 @@ void free_matrix(float*** matrix, int n, int p) {
     free(matrix);
 }
 
+bool check_cuda_compatibility() {
+    #ifdef __CUDACC__
+    int nDevices;
+    cudaDeviceProp prop;
+
+    cudaGetDeviceCount(&nDevices);
+    if (nDevices == 0) {
+        printf("Pas d'utilisation du GPU\n\n");
+        return false;
+    }
+
+    printf("GPUs disponibles:\n");
+
+    for (int i=0; i < nDevices; i++) {
+        cudaGetDeviceProperties(&prop, i);
+        printf(" - %s\n", prop.name);
+    }
+
+    cudaGetDeviceProperties(&prop, 0);
+    printf("Utilisation du GPU: %s (Compute capability: %d.%d)\n\n", prop.name, prop.major, prop.minor);
+    return true;
+    #else
+    printf("Pas d'utilisation du GPU\n\n");
+    return false;
+    #endif
+}
 
 bool check_matrices_equality(float*** m1, float*** m2, int n, int p, int q, int acceptation) {
     for (int i=0; i < n; i++) {
@@ -163,6 +189,14 @@ void run_convolution_test(int input_dim, int output_dim, int rows, int columns)
 
 
 int main() {
+    printf("Checking CUDA compatibility.\n");
+    bool cuda_compatible = check_cuda_compatibility();
+    if (!cuda_compatible) {
+        printf("CUDA not compatible, skipping tests.\n");
+        return 0;
+    }
+    printf("OK\n");
+    
     srand(time(NULL));
 
     run_convolution_test(20, 15, 30, 40);