mem management: remove warning

Makefile

@@ -41,7 +41,12 @@ NVCCFLAGS = -g
 # -fsanitize=address -lasan
 #! WARNING: test/cnn-neuron_io fails with this option enabled
 
+
+
 all: dense cnn;
+
+
+
 #
 # Build dense
 #
@@ -61,82 +66,95 @@ $(BUILDDIR)/dense_%.o: $(DENSE_SRCDIR)/%.c $(DENSE_SRCDIR)/include/%.h
 	$(CC)  -c $< -o $@  $(CFLAGS)
 
 
+
 #
 # Build cnn
 #
 cnn: $(BUILDDIR)/cnn-main $(BUILDDIR)/cnn-main-cuda $(BUILDDIR)/cnn-preview $(BUILDDIR)/cnn-export;
 
 $(BUILDDIR)/cnn-main: $(CNN_SRCDIR)/main.c \
-		$(BUILDDIR)/cnn_train.o \
+		$(BUILDDIR)/cnn_backpropagation.o \
-		$(BUILDDIR)/cnn_test_network.o \
-		$(BUILDDIR)/cnn_cnn.o \
-		$(BUILDDIR)/cnn_creation.o \
 		$(BUILDDIR)/cnn_initialisation.o \
-		$(BUILDDIR)/cnn_make.o \
+		$(BUILDDIR)/cnn_test_network.o \
+		$(BUILDDIR)/cnn_convolution.o \
 		$(BUILDDIR)/cnn_neuron_io.o \
 		$(BUILDDIR)/cnn_function.o  \
-		$(BUILDDIR)/cnn_utils.o \
+		$(BUILDDIR)/cnn_creation.o \
+		$(BUILDDIR)/cnn_models.o \
 		$(BUILDDIR)/cnn_update.o \
+		$(BUILDDIR)/cnn_train.o \
+		$(BUILDDIR)/cnn_utils.o \
+		$(BUILDDIR)/cnn_make.o \
 		$(BUILDDIR)/cnn_free.o \
 		$(BUILDDIR)/cnn_jpeg.o \
-		$(BUILDDIR)/cnn_convolution.o \
+		$(BUILDDIR)/cnn_cnn.o \
-		$(BUILDDIR)/cnn_backpropagation.o \
+		\
 		$(BUILDDIR)/memory_management.o \
 		$(BUILDDIR)/colors.o \
 		$(BUILDDIR)/mnist.o \
 		$(BUILDDIR)/utils.o
 	$(CC)  $^ -o $@  $(CFLAGS) $(LD_CFLAGS)
 
+
 ifdef NVCC_INSTALLED
 $(BUILDDIR)/cnn-main-cuda: $(BUILDDIR)/cnn_main.cuda.o \
-		$(BUILDDIR)/cnn_train.cuda.o \
-		$(BUILDDIR)/cnn_test_network.cuda.o \
-		$(BUILDDIR)/cnn_cnn.cuda.o \
-		$(BUILDDIR)/cnn_creation.cuda.o \
 		$(BUILDDIR)/cnn_initialisation.cuda.o \
-		$(BUILDDIR)/cnn_cuda_make.o \
+		$(BUILDDIR)/cnn_test_network.cuda.o \
 		$(BUILDDIR)/cnn_neuron_io.cuda.o \
-		$(BUILDDIR)/cnn_cuda_function.o  \
+		$(BUILDDIR)/cnn_creation.cuda.o \
-		$(BUILDDIR)/cnn_utils.cuda.o \
+		$(BUILDDIR)/cnn_models.cuda.o \
 		$(BUILDDIR)/cnn_update.cuda.o \
+		$(BUILDDIR)/cnn_train.cuda.o \
+		$(BUILDDIR)/cnn_utils.cuda.o \
 		$(BUILDDIR)/cnn_free.cuda.o \
 		$(BUILDDIR)/cnn_jpeg.cuda.o \
-		$(BUILDDIR)/cnn_cuda_convolution.o \
+		$(BUILDDIR)/cnn_cnn.cuda.o \
+		\
 		$(BUILDDIR)/cnn_cuda_backpropagation.o \
-		$(BUILDDIR)/colors.cuda.o \
+		$(BUILDDIR)/cnn_cuda_convolution.o \
+		$(BUILDDIR)/cnn_cuda_function.o  \
+		$(BUILDDIR)/cnn_cuda_make.o \
+		\
 		$(BUILDDIR)/cuda_memory_management.o \
+		$(BUILDDIR)/colors.cuda.o \
 		$(BUILDDIR)/mnist.cuda.o \
-		$(BUILDDIR)/cuda_utils.o
+		$(BUILDDIR)/cuda_utils.o		
 	$(NVCC)  $(LD_NVCCFLAGS) $(NVCCFLAGS)  $^ -o $@
 else
 $(BUILDDIR)/cnn-main-cuda:
 	@echo "$(NVCC) not found, skipping"
 endif
 
+
 $(BUILDDIR)/cnn-preview: $(CNN_SRCDIR)/preview.c $(BUILDDIR)/cnn_jpeg.o $(BUILDDIR)/colors.o $(BUILDDIR)/utils.o
 	$(CC)  $^ -o $@  $(CFLAGS) $(LD_CFLAGS)
 
+
 $(BUILDDIR)/cnn-export: $(CNN_SRCDIR)/export.c \
-		$(BUILDDIR)/cnn_free.o \
-		$(BUILDDIR)/cnn_neuron_io.o \
-		$(BUILDDIR)/utils.o \
-		$(BUILDDIR)/memory_management.o \
-		$(BUILDDIR)/cnn_cnn.o \
-		$(BUILDDIR)/cnn_make.o \
 		$(BUILDDIR)/cnn_backpropagation.o \
+		$(BUILDDIR)/cnn_convolution.o \
+		$(BUILDDIR)/cnn_neuron_io.o \
 		$(BUILDDIR)/cnn_function.o \
-		$(BUILDDIR)/cnn_convolution.o  \
+		$(BUILDDIR)/cnn_free.o \
+		$(BUILDDIR)/cnn_make.o \
+		$(BUILDDIR)/cnn_cnn.o \
+		$(BUILDDIR)/cnn_jpeg.o \
+		\
+		$(BUILDDIR)/memory_management.o \
 		$(BUILDDIR)/colors.o \
 		$(BUILDDIR)/mnist.o \
-		$(BUILDDIR)/cnn_jpeg.o
+		$(BUILDDIR)/utils.o
 	$(CC)  $^ -o $@  $(CFLAGS) $(LD_CFLAGS)
 
+
 $(BUILDDIR)/cnn_%.o: $(CNN_SRCDIR)/%.c $(CNN_SRCDIR)/include/%.h
 	$(CC)  -c $< -o $@  $(CFLAGS)
 
+
 $(BUILDDIR)/cnn_%.cuda.o: $(CNN_SRCDIR)/%.c $(CNN_SRCDIR)/include/%.h
 	$(CC)  -c $< -o $@  $(CFLAGS) -DUSE_CUDA -lcuda -I$(CUDA_INCLUDE)
 
+
 ifdef NVCC_INSTALLED
 $(BUILDDIR)/cnn_cuda_%.o: $(CNN_SRCDIR)/%.cu $(CNN_SRCDIR)/include/%.h
 	$(NVCC)  $(NVCCFLAGS)  -c -dc $< -o $@
@@ -144,15 +162,20 @@ else
 $(BUILDDIR)/cnn_cuda_%.o: $(CNN_SRCDIR)/%.cu $(CNN_SRCDIR)/include/%.h
 	@echo "$(NVCC) not found, skipping"
 endif
+
+
+
 #
 # Build general files
 #
 $(BUILDDIR)/%.o: $(COMMON_SRCDIR)/%.c $(COMMON_SRCDIR)/include/%.h
 	$(CC)  -c $< -o $@  $(CFLAGS)
 
+
 $(BUILDDIR)/%.cuda.o: $(COMMON_SRCDIR)/%.c $(COMMON_SRCDIR)/include/%.h
 	$(CC)  -c $< -o $@  $(CFLAGS) -DUSE_CUDA -lcuda -I$(CUDA_INCLUDE)
 
+
 ifdef NVCC_INSTALLED
 $(BUILDDIR)/cuda_%.o: $(COMMON_SRCDIR)/%.cu $(COMMON_SRCDIR)/include/%.h
 	$(NVCC)  $(NVCCFLAGS)  -c -dc $< -o $@
@@ -160,6 +183,8 @@ else
 	@echo "$(NVCC) not found, skipping"
 endif
 
+
+
 #
 # Tests
 #
@@ -207,6 +232,8 @@ $(BUILDDIR)/test-cnn_%: $(TEST_SRCDIR)/cnn_%.cu
 	@echo "$(NVCC) not found, skipping"
 endif
 
+
+
 #
 # Utils
 #
@@ -231,6 +258,7 @@ $(CACHE_DIR)/mnist-reseau-cnn.bin: $(BUILDDIR)/cnn-main
 		--out $(CACHE_DIR)/mnist-reseau-cnn.bin 
 
 
+
 #
 # Clean project
 #

doc/cnn/neuron_io.md

@@ -38,6 +38,8 @@ uint32_t|linearisation|
 uint32_t|k_size|
 uint32_t|rows|
 uint32_t|columns|
+uint32_t|stride|
+uint32_t|padding|
 
 #### Si la couche est un nn:
 type | nom de la variable | commentaire
@@ -52,6 +54,8 @@ type | nom de la variable | commentaire
 :---:|:---:|:---:
 uint32_t|linearisation|
 uint32_t|pooling|
+uint32_t|stride|
+uint32_t|padding|
 
 
 ### Corps

src/cnn/creation.c

@@ -34,73 +34,6 @@ Network* create_network(int max_size, float learning_rate, int dropout, int init
     return network;
 }
 
-Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth) {
-    Network* network = create_network(8, learning_rate, dropout, initialisation, input_width, input_depth);
-    add_convolution(network, 5, 6, 1, 0, activation);
-    add_average_pooling(network, 2, 2, 0);
-    add_convolution(network, 5, 16, 1, 0, activation);
-    add_average_pooling(network, 2, 2, 0);
-    add_dense_linearisation(network, 120, activation);
-    add_dense(network, 84, activation);
-    add_dense(network, 10, SOFTMAX);
-    return network;
-}
-
-Network* create_network_alexnet(float learning_rate, int dropout, int activation, int initialisation, int size_output) {
-    Network* network = create_network(12, learning_rate, dropout, initialisation, 227, 3);
-    add_convolution(network, 11, 96, 4, 0, activation);
-    add_average_pooling(network, 3, 2, 0);
-    add_convolution(network, 5, 256, 1, 2, activation);
-    add_average_pooling(network, 3, 2, 0);
-    add_convolution(network, 3, 384, 1, 1, activation);
-    add_convolution(network, 3, 384, 1, 1, activation);
-    add_convolution(network, 3, 256, 1, 1, activation);
-    add_average_pooling(network, 3, 2, 0);
-    add_dense_linearisation(network, 4096, activation);
-    add_dense(network, 4096, activation);
-    add_dense(network, size_output, SOFTMAX);
-    return network;
-}
-
-Network* create_network_VGG16(float learning_rate, int dropout, int activation, int initialisation, int size_output) {
-    Network* network = create_network(23, learning_rate, dropout, initialisation, 256, 3);
-    add_convolution(network, 3, 64, 1, 0, activation); // Conv3-64
-    add_convolution(network, 3, 64, 1, 0, activation); // Conv3-64
-    add_average_pooling(network, 2, 2, 0); // Max Pool
-
-    add_convolution(network, 3, 128, 1, 0, activation); // Conv3-128
-    add_convolution(network, 1, 128, 1, 0, activation); // Conv1-128
-    add_average_pooling(network, 2, 2, 0); // Max Pool
-
-    add_convolution(network, 3, 256, 1, 0, activation); // Conv3-256
-    add_convolution(network, 3, 256, 1, 0, activation); // Conv3-256
-    add_convolution(network, 1, 256, 1, 0, activation); // Conv1-256
-    add_average_pooling(network, 2, 2, 0); // Max Pool
-
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
-    add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
-    add_average_pooling(network, 2, 2, 0); // Max Pool
-
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
-    add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
-    add_average_pooling(network, 2, 2, 0); // Max Pool
-
-    add_dense_linearisation(network, 2048, activation);
-    add_dense(network, 2048, activation);
-    add_dense(network, 256, activation);
-    add_dense(network, size_output, SOFTMAX);
-    return network;
-}
-
-Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth) {
-    Network* network = create_network(3, learning_rate, dropout, initialisation, input_width, input_depth);
-    add_dense_linearisation(network, 80, activation);
-    add_dense(network, 10, SOFTMAX);
-    return network;
-}
-
 void create_a_cube_input_layer(Network* network, int pos, int depth, int dim) {
     network->input[pos] = (float***)nalloc(depth, sizeof(float**));
     for (int i=0; i < depth; i++) {

src/cnn/include/creation.h

@@ -9,28 +9,6 @@
 */
 Network* create_network(int max_size, float learning_rate, int dropout, int initialisation, int input_width, int input_depth);
 
-/*
-* Renvoie un réseau suivant l'architecture LeNet5
-*/
-Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth);
-
-/*
-* Renvoie un réseau suivant l'architecture AlexNet
-* C'est à dire en entrée 3x227x227 et une sortie de taille 'size_output'
-*/
-Network* create_network_alexnet(float learning_rate, int dropout, int activation, int initialisation, int size_output);
-
-/*
-* Renvoie un réseau suivant l'architecture VGG16 modifiée pour prendre en entrée 3x256x256
-* et une sortie de taille 'size_output'
-*/
-Network* create_network_VGG16(float learning_rate, int dropout, int activation, int initialisation, int size_output);
-
-/*
-* Renvoie un réseau sans convolution, similaire à celui utilisé dans src/dense
-*/
-Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth);
-
 /*
 * Créé et alloue de la mémoire à une couche de type input cube
 */

src/cnn/include/models.h

@@ -0,0 +1,29 @@
+#include <stdlib.h>
+#include <stdio.h>
+
+#include "struct.h"
+
+#ifndef DEF_MODELS_H
+#define DEF_MODELS_H
+/*
+* Renvoie un réseau suivant l'architecture LeNet5
+*/
+Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth);
+
+/*
+* Renvoie un réseau suivant l'architecture AlexNet
+* C'est à dire en entrée 3x227x227 et une sortie de taille 'size_output'
+*/
+Network* create_network_alexnet(float learning_rate, int dropout, int activation, int initialisation, int size_output);
+
+/*
+* Renvoie un réseau suivant l'architecture VGG16 modifiée pour prendre en entrée 3x256x256
+* et une sortie de taille 'size_output'
+*/
+Network* create_network_VGG16(float learning_rate, int dropout, int activation, int initialisation, int size_output);
+
+/*
+* Renvoie un réseau sans convolution, similaire à celui utilisé dans src/dense
+*/
+Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth);
+#endif

src/cnn/models.c

@@ -0,0 +1,75 @@
+#include <stdlib.h>
+#include <stdio.h>
+
+#include "include/creation.h"
+#include "include/function.h"
+#include "include/struct.h"
+
+#include "include/models.h"
+
+Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth) {
+    Network* network = create_network(8, learning_rate, dropout, initialisation, input_width, input_depth);
+    add_convolution(network, 5, 6, 1, 0, activation);
+    add_average_pooling(network, 2, 2, 0);
+    add_convolution(network, 5, 16, 1, 0, activation);
+    add_average_pooling(network, 2, 2, 0);
+    add_dense_linearisation(network, 120, activation);
+    add_dense(network, 84, activation);
+    add_dense(network, 10, SOFTMAX);
+    return network;
+}
+
+Network* create_network_alexnet(float learning_rate, int dropout, int activation, int initialisation, int size_output) {
+    Network* network = create_network(12, learning_rate, dropout, initialisation, 227, 3);
+    add_convolution(network, 11, 96, 4, 0, activation);
+    add_average_pooling(network, 3, 2, 0);
+    add_convolution(network, 5, 256, 1, 2, activation);
+    add_average_pooling(network, 3, 2, 0);
+    add_convolution(network, 3, 384, 1, 1, activation);
+    add_convolution(network, 3, 384, 1, 1, activation);
+    add_convolution(network, 3, 256, 1, 1, activation);
+    add_average_pooling(network, 3, 2, 0);
+    add_dense_linearisation(network, 4096, activation);
+    add_dense(network, 4096, activation);
+    add_dense(network, size_output, SOFTMAX);
+    return network;
+}
+
+Network* create_network_VGG16(float learning_rate, int dropout, int activation, int initialisation, int size_output) {
+    Network* network = create_network(23, learning_rate, dropout, initialisation, 256, 3);
+    add_convolution(network, 3, 64, 1, 0, activation); // Conv3-64
+    add_convolution(network, 3, 64, 1, 0, activation); // Conv3-64
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 128, 1, 0, activation); // Conv3-128
+    add_convolution(network, 1, 128, 1, 0, activation); // Conv1-128
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 256, 1, 0, activation); // Conv3-256
+    add_convolution(network, 3, 256, 1, 0, activation); // Conv3-256
+    add_convolution(network, 1, 256, 1, 0, activation); // Conv1-256
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
+    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
+    add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
+    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
+    add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_dense_linearisation(network, 2048, activation);
+    add_dense(network, 2048, activation);
+    add_dense(network, 256, activation);
+    add_dense(network, size_output, SOFTMAX);
+    return network;
+}
+
+Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth) {
+    Network* network = create_network(3, learning_rate, dropout, initialisation, input_width, input_depth);
+    add_dense_linearisation(network, 80, activation);
+    add_dense(network, 10, SOFTMAX);
+    return network;
+}

src/cnn/neuron_io.c

@@ -10,7 +10,8 @@
 
 #include "include/neuron_io.h"
 
-#define MAGIC_NUMBER 1012
+#define INITIAL_MAGIC_NUMBER 1010
+#define MAGIC_NUMBER 1013 // Increment this whenever you change the code
 
 #define CNN 0
 #define NN 1
@@ -114,13 +115,11 @@ void write_couche(Network* network, int indice_couche, int type_couche, FILE* pt
         Kernel_nn* nn = kernel->nn;
 
         // Écriture du pré-corps
-        uint32_t pre_buffer[6];
+        uint32_t pre_buffer[4];
         pre_buffer[0] = kernel->activation;
         pre_buffer[1] = kernel->linearisation;
         pre_buffer[2] = nn->size_input;
         pre_buffer[3] = nn->size_output;
-        pre_buffer[4] = kernel->stride;
-        pre_buffer[5] = kernel->padding;
         fwrite(pre_buffer, sizeof(pre_buffer), 1, ptr);
 
         // Écriture du corps
@@ -163,7 +162,12 @@ Network* read_network(char* filename) {
 
     (void) !fread(&magic, sizeof(uint32_t), 1, ptr);
     if (magic != MAGIC_NUMBER) {
-        printf_error("Incorrect magic number !\n");
+        printf_error((char*)"Incorrect magic number !\n");
+        if (INITIAL_MAGIC_NUMBER < magic && magic >= INITIAL_MAGIC_NUMBER) {
+            printf("\tThis backup is no longer supported\n");
+            printf("\tnPlease update it manually or re-train the network.\n");
+            printf("\t(You can update it with a script or manually with a Hex Editor)\n");
+        }
         exit(1);
     }
 
@@ -330,15 +334,15 @@ Kernel* read_kernel(int type_couche, int output_width, FILE* ptr) {
         // Lecture du "Pré-corps"
         kernel->nn = (Kernel_nn*)nalloc(1, sizeof(Kernel_nn));
         kernel->cnn = NULL;
-        uint32_t buffer[6];
+        uint32_t buffer[4];
         (void) !fread(&buffer, sizeof(buffer), 1, ptr);
 
         kernel->activation = buffer[0];
         kernel->linearisation = buffer[1];
         kernel->nn->size_input = buffer[2];
         kernel->nn->size_output = buffer[3];
-        kernel->stride = buffer[4];
+        kernel->padding = -1;
-        kernel->padding = buffer[5];
+        kernel->stride = -1;
 
         // Lecture du corps
         Kernel_nn* nn = kernel->nn;

src/cnn/train.c

@@ -8,15 +8,15 @@
 #include <omp.h>
 
 #include "../common/include/memory_management.h"
+#include "../common/include/colors.h"
+#include "../common/include/utils.h"
 #include "../common/include/mnist.h"
 #include "include/initialisation.h"
 #include "include/test_network.h"
 #include "include/neuron_io.h"
-#include "../common/include/colors.h"
-#include "../common/include/utils.h"
 #include "include/function.h"
-#include "include/creation.h"
 #include "include/update.h"
+#include "include/models.h"
 #include "include/utils.h"
 #include "include/free.h"
 #include "include/jpeg.h"

src/common/memory_management.c

@@ -214,6 +214,7 @@ void gree(void* ptr, bool already_freed) {
         memory = free_memory(ptr, memory, already_freed);
         pthread_mutex_unlock(&memory_lock);
     #else
+        (void)already_freed;
         free(ptr);
     #endif
 }

src/common/memory_management.cu

@@ -214,6 +214,7 @@ void gree(void* ptr, bool already_freed) {
         memory = free_memory(ptr, memory, already_freed);
         pthread_mutex_unlock(&memory_lock);
     #else
+        (void)already_freed;
         free(ptr);
     #endif
 }

test/cnn_neuron_io.c

@@ -6,6 +6,7 @@
 #include "../src/common/include/colors.h"
 #include "../src/cnn/include/neuron_io.h"
 #include "../src/cnn/include/creation.h"
+#include "../src/cnn/include/models.h"
 #include "../src/cnn/include/utils.h"
 #include "../src/cnn/include/free.h"
 

test/cnn_structure.c

@@ -5,6 +5,7 @@
 
 #include "../src/common/include/colors.h"
 #include "../src/cnn/include/creation.h"
+#include "../src/cnn/include/models.h"
 #include "../src/cnn/include/utils.h"
 #include "../src/cnn/include/free.h"
 

test/cnn_utils.c

@@ -3,6 +3,7 @@
 
 #include "../src/common/include/colors.h"
 #include "../src/cnn/include/creation.h"
+#include "../src/cnn/include/models.h"
 #include "../src/cnn/include/utils.h"
 #include "../src/cnn/include/free.h"