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Merge remote-tracking branch 'upstream/main' into Finetuning_deepening

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augustin64 2023-05-28 09:30:52 +02:00
commit 7e90b15671
4 changed files with 32 additions and 10 deletions
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24
Makefile
View File

@ -70,7 +70,7 @@ $(BUILDDIR)/dense_%.o: $(DENSE_SRCDIR)/%.c $(DENSE_SRCDIR)/include/%.h
# #
# Build cnn # Build cnn
# #
cnn: $(BUILDDIR)/cnn-main $(BUILDDIR)/cnn-main-cuda $(BUILDDIR)/cnn-preview $(BUILDDIR)/cnn-export; cnn: $(BUILDDIR)/cnn-main $(BUILDDIR)/cnn-main-cuda $(BUILDDIR)/cnn-preview $(BUILDDIR)/cnn-export $(BUILDDIR)/cnn-export-cuda;
$(BUILDDIR)/cnn-main: $(CNN_SRCDIR)/main.c \ $(BUILDDIR)/cnn-main: $(CNN_SRCDIR)/main.c \
$(BUILDDIR)/cnn_backpropagation.o \ $(BUILDDIR)/cnn_backpropagation.o \
@ -147,6 +147,28 @@ $(BUILDDIR)/cnn-export: $(CNN_SRCDIR)/export.c \
$(CC) $^ -o $@ $(CFLAGS) $(LD_CFLAGS) $(CC) $^ -o $@ $(CFLAGS) $(LD_CFLAGS)
ifdef NVCC_INSTALLED
$(BUILDDIR)/cnn-export-cuda: $(CNN_SRCDIR)/export.c \
$(BUILDDIR)/cnn_cuda_backpropagation.o \
$(BUILDDIR)/cnn_cuda_convolution.o \
$(BUILDDIR)/cnn_neuron_io.cuda.o \
$(BUILDDIR)/cnn_cuda_function.o \
$(BUILDDIR)/cnn_free.cuda.o \
$(BUILDDIR)/cnn_cuda_make.o \
$(BUILDDIR)/cnn_cnn.cuda.o \
$(BUILDDIR)/cnn_utils.cuda.o \
$(BUILDDIR)/cnn_jpeg.cuda.o \
\
$(BUILDDIR)/cuda_memory_management.o \
$(BUILDDIR)/colors.cuda.o \
$(BUILDDIR)/mnist.o \
$(BUILDDIR)/cuda_utils.o
$(NVCC) $^ -o $@ $(NVCCFLAGS) $(LD_NVCCFLAGS)
else
$(BUILDDIR)/cnn-export-cuda: $(CNN_SRCDIR)/export.c
@echo "$(NVCC) not found, skipping"
endif
$(BUILDDIR)/cnn_%.o: $(CNN_SRCDIR)/%.c $(CNN_SRCDIR)/include/%.h $(BUILDDIR)/cnn_%.o: $(CNN_SRCDIR)/%.c $(CNN_SRCDIR)/include/%.h
$(CC) -c $< -o $@ $(CFLAGS) $(CC) -c $< -o $@ $(CFLAGS)

2
src/cnn/export.c
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@ -82,7 +82,7 @@ void print_poids_ker_cnn(char* modele) {
void write_image(float** data, int width, int height, char* base_filename, int layer_id, int kernel_id) { void write_image(float** data, int width, int height, char* base_filename, int layer_id, int kernel_id) {
int filename_length = strlen(base_filename) + (int)log10(layer_id+1)+1 + (int)log10(kernel_id+1)+1 + 21; int filename_length = strlen(base_filename) + (int)log10(layer_id+1)+1 + (int)log10(kernel_id+1)+1 + 21 +1;
char* filename = (char*)malloc(sizeof(char)*filename_length); char* filename = (char*)malloc(sizeof(char)*filename_length);
sprintf(filename, "%s_layer-%d_feature-%d.jpeg", base_filename, layer_id, kernel_id); sprintf(filename, "%s_layer-%d_feature-%d.jpeg", base_filename, layer_id, kernel_id);

8
src/cnn/make.c
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@ -183,17 +183,17 @@ void make_max_pooling(float*** input, float*** output, int size, int output_dept
* Dense * Dense
*/ */
#ifdef __CUDACC__ #ifdef __CUDACC__
__global__ void make_dense_kernel(Kernel_nn* kernel, float* input, float* output, int size_input, int size_output) { __global__ void make_dense_kernel(float** weights, float* bias, float* input, float* output, int size_input, int size_output) {
// Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu // Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
int idx = threadIdx.x + blockDim.x*blockIdx.x; // < size_output int idx = threadIdx.x + blockDim.x*blockIdx.x; // < size_output
if (idx >= size_output) { if (idx >= size_output) {
return; return;
} }
float f = kernel->bias[idx]; float f = bias[idx];
for (int j=0; j < size_input; j++) { for (int j=0; j < size_input; j++) {
f += kernel->weights[j][idx]*input[j]; f += weights[j][idx]*input[j];
} }
output[idx] = f; output[idx] = f;
} }
@ -203,7 +203,7 @@ void make_dense_device(Kernel_nn* kernel, float* input, float* output, int size_
dim3 gridSize(i_div_up(size_output, BLOCKSIZE_x*BLOCKSIZE_y), 1, 1); dim3 gridSize(i_div_up(size_output, BLOCKSIZE_x*BLOCKSIZE_y), 1, 1);
dim3 blockSize(BLOCKSIZE_x*BLOCKSIZE_y, 1, BLOCKSIZE_z); dim3 blockSize(BLOCKSIZE_x*BLOCKSIZE_y, 1, BLOCKSIZE_z);
make_dense_kernel<<<gridSize, blockSize>>>(kernel, input, output, size_input, size_output); make_dense_kernel<<<gridSize, blockSize>>>(kernel->weights, kernel->bias, input, output, size_input, size_output);
gpuErrchk( cudaPeekAtLastError() ); gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() ); gpuErrchk( cudaDeviceSynchronize() );
} }

8
src/cnn/make.cu
View File

@ -183,17 +183,17 @@ void make_max_pooling(float*** input, float*** output, int size, int output_dept
* Dense * Dense
*/ */
#ifdef __CUDACC__ #ifdef __CUDACC__
__global__ void make_dense_kernel(Kernel_nn* kernel, float* input, float* output, int size_input, int size_output) { __global__ void make_dense_kernel(float** weights, float* bias, float* input, float* output, int size_input, int size_output) {
// Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu // Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
int idx = threadIdx.x + blockDim.x*blockIdx.x; // < size_output int idx = threadIdx.x + blockDim.x*blockIdx.x; // < size_output
if (idx >= size_output) { if (idx >= size_output) {
return; return;
} }
float f = kernel->bias[idx]; float f = bias[idx];
for (int j=0; j < size_input; j++) { for (int j=0; j < size_input; j++) {
f += kernel->weights[j][idx]*input[j]; f += weights[j][idx]*input[j];
} }
output[idx] = f; output[idx] = f;
} }
@ -203,7 +203,7 @@ void make_dense_device(Kernel_nn* kernel, float* input, float* output, int size_
dim3 gridSize(i_div_up(size_output, BLOCKSIZE_x*BLOCKSIZE_y), 1, 1); dim3 gridSize(i_div_up(size_output, BLOCKSIZE_x*BLOCKSIZE_y), 1, 1);
dim3 blockSize(BLOCKSIZE_x*BLOCKSIZE_y, 1, BLOCKSIZE_z); dim3 blockSize(BLOCKSIZE_x*BLOCKSIZE_y, 1, BLOCKSIZE_z);
make_dense_kernel<<<gridSize, blockSize>>>(kernel, input, output, size_input, size_output); make_dense_kernel<<<gridSize, blockSize>>>(kernel->weights, kernel->bias, input, output, size_input, size_output);
gpuErrchk( cudaPeekAtLastError() ); gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() ); gpuErrchk( cudaDeviceSynchronize() );
} }