mirror of
https://github.com/augustin64/projet-tipe
synced 2025-04-16 11:13:54 +02:00
Translate backward convolution to CUDA
Not working yet, CUDA kernels in `backpropagation.cu` don't have access to activation functions declared in `function.cu` using `get_activation_function_cuda`. Temporary workaround: copy `backpropagation.cu` parts that don't work to `function.cu` (all the parts using function pointers in kernels
This commit is contained in:
parent
5088c415d6
commit
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2
Makefile
2
Makefile
@ -101,7 +101,7 @@ $(BUILDDIR)/cnn-main-cuda: $(BUILDDIR)/cnn_main.cuda.o \
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$(BUILDDIR)/cnn_free.cuda.o \
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$(BUILDDIR)/cnn_jpeg.cuda.o \
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$(BUILDDIR)/cnn_cuda_convolution.o \
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$(BUILDDIR)/cnn_backpropagation.cuda.o \
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$(BUILDDIR)/cnn_cuda_backpropagation.o \
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$(BUILDDIR)/colors.cuda.o \
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$(BUILDDIR)/cuda_memory_management.o \
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$(BUILDDIR)/mnist.cuda.o \
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@ -3,8 +3,12 @@
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#include <math.h>
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#include "include/backpropagation.h"
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#include "../include/utils.h"
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#include "include/struct.h"
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#include "include/config.h"
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#ifndef __CUDACC__
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int min(int a, int b) {
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return a<b?a:b;
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}
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@ -12,8 +16,38 @@ int min(int a, int b) {
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int max(int a, int b) {
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return a > b ? a : b;
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}
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#endif
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void softmax_backward_mse(float* input, float* output, int size) {
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/*
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* Softmax backward MSE
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*/
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#ifdef __CUDACC__
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__global__ void softmax_backward_mse_kernel(float* input, float* output, int size) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x;
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if (idx >= size) {
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return;
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}
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int input_val = input[idx];
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int output_val = output[idx];
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input[idx] = (output_val-input_val)*input_val*(1-input_val);
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}
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void softmax_backward_mse_device(float* input, float* output, int size) {
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// Make computation
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dim3 gridSize(i_div_up(size, BLOCKSIZE_x));
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dim3 blockSize(BLOCKSIZE_x);
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softmax_backward_mse_kernel<<<gridSize, blockSize>>>(input, output, size);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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}
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#endif
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void softmax_backward_mse_cpu(float* input, float* output, int size) {
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/* Input et output ont la même taille */
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for (int i=0; i < size; i++){
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@ -21,7 +55,42 @@ void softmax_backward_mse(float* input, float* output, int size) {
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}
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}
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void softmax_backward_cross_entropy(float* input, float* output, int size) {
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void softmax_backward_mse(float* input, float* output, int size) {
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#ifdef __CUDACC__
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softmax_backward_mse_device(input, output, size);
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#else
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softmax_backward_mse_cpu(input, output, size);
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#endif
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}
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/*
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* Softmax backward Cross entropy
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*/
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#ifdef __CUDACC__
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__global__ void softmax_backward_cross_entropy_kernel(float* input, float* output, int size) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x;
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if (idx >= size) {
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return;
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}
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input[idx] = output[idx] - input[idx];
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}
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void softmax_backward_cross_entropy_device(float* input, float* output, int size) {
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// Make computation
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dim3 gridSize(i_div_up(size, BLOCKSIZE_x));
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dim3 blockSize(BLOCKSIZE_x);
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softmax_backward_cross_entropy_kernel<<<gridSize, blockSize>>>(input, output, size);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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}
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#endif
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void softmax_backward_cross_entropy_cpu(float* input, float* output, int size) {
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/* Input et output ont la même taille */
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for (int i=0; i < size; i++){
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@ -29,16 +98,60 @@ void softmax_backward_cross_entropy(float* input, float* output, int size) {
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}
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}
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void backward_average_pooling(float*** input, float*** output, int input_width, int output_width, int depth) {
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void softmax_backward_cross_entropy(float* input, float* output, int size) {
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#ifdef __CUDACC__
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softmax_backward_cross_entropy_device(input, output, size);
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#else
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softmax_backward_cross_entropy_cpu(input, output, size);
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#endif
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}
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/*
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* Backward average pooling
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*/
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#ifdef __CUDACC__
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__global__ void backward_average_pooling_kernel(float*** input, float*** output, int input_width, int output_width, int depth, int n, int size) {
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// Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < depth
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < output_width
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int idz = threadIdx.z + blockDim.z*blockIdx.z; // < output_width
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if (idx >= depth || idy >= output_width || idz >= output_width) {
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return;
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}
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for (int a=0; a < size; a++) {
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for (int b=0; b < size; b++) {
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input[idx][size*idy +a][size*idz +b] += output[idx][idy][idz]/n;
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}
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}
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}
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void backward_average_pooling_device(float*** input, float*** output, int input_width, int output_width, int depth) {
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// Make computation
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dim3 gridSize(i_div_up(depth, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_z));
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dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
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int size = input_width/output_width; // Taille du pooling
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reset_3d_array(input, depth, input_width, input_width);
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backward_average_pooling_kernel<<<gridSize, blockSize>>>(input, output, input_width, output_width, depth, size*size, size);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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}
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#endif
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void backward_average_pooling_cpu(float*** input, float*** output, int input_width, int output_width, int depth) {
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/* Input et output ont la même profondeur (depth) */
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int size = input_width/output_width; // Taille du pooling
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int n = size*size; // Nombre d'éléments dans le pooling
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for (int a=0; a < depth; a++)
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for (int b=0; b < input_width; b++)
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for (int c=0; c < input_width; c++)
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input[a][b][c] = 0;
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reset_3d_array(input, depth, input_width, input_width);
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for (int i=0; i < depth; i++) {
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for (int j=0; j < output_width; j++) {
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@ -53,7 +166,65 @@ void backward_average_pooling(float*** input, float*** output, int input_width,
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}
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}
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void backward_max_pooling(float*** input, float*** output, int input_width, int output_width, int depth) {
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#ifdef __CUDACC__
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extern "C"
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#endif
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void backward_average_pooling(float*** input, float*** output, int input_width, int output_width, int depth) {
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#ifndef __CUDACC__
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backward_average_pooling_cpu(input, output, input_width, output_width, depth);
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#else
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backward_average_pooling_device(input, output, input_width, output_width, depth);
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#endif
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}
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/*
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* Backward max pooling
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*/
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#ifdef __CUDACC__
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__global__ void backward_max_pooling_kernel(float*** input, float*** output, int input_width, int output_width, int depth, int n, int size) {
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// Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < depth
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < output_width
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int idz = threadIdx.z + blockDim.z*blockIdx.z; // < output_width
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if (idx >= depth || idy >= output_width || idz >= output_width) {
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return;
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}
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float m = -FLT_MAX;
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int a_max = -1;
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int b_max = -1;
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for (int a=0; a < size; a++) {
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for (int b=0; b < size; b++) {
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if (input[idx][size*idy +a][size*idz +b] > m) {
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m = input[idx][size*idy +a][size*idz +b];
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a_max = a;
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b_max = b;
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}
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input[idx][size*idy +a][size*idz +b] = 0;
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}
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}
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input[idx][size*idy +a_max][size*idz +b_max] = output[idx][idy][idz]/n;
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}
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void backward_max_pooling_device(float*** input, float*** output, int input_width, int output_width, int depth) {
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// Make computation
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dim3 gridSize(i_div_up(depth, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_z));
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dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
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int size = input_width/output_width; // Taille du pooling
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backward_max_pooling_kernel<<<gridSize, blockSize>>>(input, output, input_width, output_width, depth, size*size, size);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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}
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#endif
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void backward_max_pooling_cpu(float*** input, float*** output, int input_width, int output_width, int depth) {
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int size = input_width/output_width;
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float m; // Maximum
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@ -82,7 +253,78 @@ void backward_max_pooling(float*** input, float*** output, int input_width, int
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}
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}
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void backward_dense(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, funcPtr d_function, int is_first) {
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#ifdef __CUDACC__
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extern "C"
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#endif
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void backward_max_pooling(float*** input, float*** output, int input_width, int output_width, int depth) {
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#ifndef __CUDACC__
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backward_max_pooling_cpu(input, output, input_width, output_width, depth);
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#else
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backward_max_pooling_device(input, output, input_width, output_width, depth);
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#endif
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}
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/*
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* Backward Dense
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*/
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#ifdef __CUDACC__
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__global__ void backward_dense_kernel_1(Kernel_nn* ker, float* input, float* output, int size_input, int size_output) {
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// Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < size_input
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < size_output
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if (idx >= size_input || idy >= size_output) {
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return;
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}
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if (idx == 0) {
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ker->d_bias[idy] += output[idy];
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}
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ker->d_weights[idx][idy] += input[idx]*output[idy];
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}
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__global__ void backward_dense_kernel_2(float** weights, float* input, float* input_z, float* output, int size_input, int size_output, funcPtr d_f) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < size_input
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if (idx >= size_input) {
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return;
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}
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float tmp=0;
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for (int j=0; j < size_output; j++) {
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tmp += output[j]*weights[idx][j];
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}
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input[idx] = tmp*( (*d_f)(input_z[idx]) );
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}
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void backward_dense_device(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, int activation, int is_first) {
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// Make computation
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dim3 gridSize1(i_div_up(size_input, BLOCKSIZE_x), i_div_up(size_output, BLOCKSIZE_y));
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dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y);
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backward_dense_kernel_1<<<gridSize1, blockSize1>>>(ker, input, output, size_input, size_output);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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// Second kernel
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if (is_first != 1) {
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dim3 gridSize1(i_div_up(size_input, BLOCKSIZE_x));
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dim3 blockSize1(BLOCKSIZE_x);
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funcPtr d_function = get_activation_function_cuda(activation);
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backward_dense_kernel_2<<<gridSize1, blockSize1>>>(ker->weights, input, input_z, output, size_input, size_output, d_function);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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}
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}
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#endif
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void backward_dense_cpu(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, int activation, int is_first) {
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funcPtr d_function = get_activation_function(activation);
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// Bias
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for (int j=0; j < size_output; j++) {
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ker->d_bias[j] += output[j];
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@ -109,7 +351,85 @@ void backward_dense(Kernel_nn* ker, float* input, float* input_z, float* output,
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}
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}
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void backward_linearisation(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output,funcPtr d_function) {
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#ifdef __CUDACC__
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extern "C"
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#endif
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void backward_dense(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, int activation, int is_first) {
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#ifndef __CUDACC__
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backward_dense_cpu(ker, input, input_z, output, size_input, size_output, activation, is_first);
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#else
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backward_dense_device(ker, input, input_z, output, size_input, size_output, activation, is_first);
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#endif
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}
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/*
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* Backward linearisation
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*/
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#ifdef __CUDACC__
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__global__ void backward_linearisation_kernel_1(Kernel_nn* ker, float*** input, float* output, int depth_input, int dim_input, int size_output) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < depth_input
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < dim_input
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int idz = threadIdx.z + blockDim.z*blockIdx.z; // < dim_input
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if (idx >= depth_input || idy >= dim_input || idz >= dim_input) {
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return;
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}
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int id = idx*dim_input*dim_input + idy*dim_input + idz;
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for (int j=0; j < size_output; j++) {
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ker->d_weights[id][j] += input[idx][idy][idz]*output[j];
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}
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if (id == 0) {
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for (int j=0; j < size_output; j++) {
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ker->d_bias[j] += output[j];
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}
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}
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}
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__global__ void backward_linearisation_kernel_2(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output, funcPtr d_f) {
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int idx = threadIdx.x + blockDim.x*blockIdx.x; // < depth_input
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int idy = threadIdx.y + blockDim.y*blockIdx.y; // < dim_input
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int idz = threadIdx.z + blockDim.z*blockIdx.z; // < dim_input
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if (idx >= depth_input || idy >= dim_input || idz >= dim_input) {
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return;
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}
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int id = idx*dim_input*dim_input + idy*dim_input + idz;
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float tmp=0;
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for (int j=0; j < size_output; j++) {
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tmp += output[j]*ker->weights[id][j];
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}
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input[idx][idy][idz] = tmp*( (*d_f)(input_z[idx][idy][idz]) );
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}
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void backward_linearisation_device(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output, int activation) {
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// Make computation
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dim3 gridSize(i_div_up(depth_input, BLOCKSIZE_x), i_div_up(dim_input, BLOCKSIZE_y), i_div_up(dim_input, BLOCKSIZE_y));
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dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
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backward_linearisation_kernel_1<<<gridSize, blockSize>>>(ker, input, output, depth_input, dim_input, size_output);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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// Second kernel
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funcPtr d_function = get_activation_function_cuda(activation);
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backward_linearisation_kernel_2<<<gridSize, blockSize>>>(ker, input, input_z, output, depth_input, dim_input, size_output, d_function);
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gpuErrchk( cudaPeekAtLastError() );
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gpuErrchk( cudaDeviceSynchronize() );
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}
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#endif
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void backward_linearisation_cpu(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output, int activation) {
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funcPtr d_function = get_activation_function(activation);
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// Bias
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for (int j=0; j < size_output; j++) {
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ker->d_bias[j] += output[j];
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@ -144,7 +464,119 @@ void backward_linearisation(Kernel_nn* ker, float*** input, float*** input_z, fl
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}
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}
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|
||||
void backward_convolution(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int dim_output, funcPtr d_function, int is_first) {
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
void backward_linearisation(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output, int activation) {
|
||||
#ifndef __CUDACC__
|
||||
backward_linearisation_cpu(ker, input, input_z, output, depth_input, dim_input, size_output, activation);
|
||||
#else
|
||||
backward_linearisation_device(ker, input, input_z, output, depth_input, dim_input, size_output, activation);
|
||||
#endif
|
||||
}
|
||||
|
||||
/*
|
||||
* Backward convolution
|
||||
*/
|
||||
#ifdef __CUDACC__
|
||||
__global__ void backward_convolution_dbias_kernel(Kernel_cnn* ker, float*** output, int depth_output, int dim_output) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x;
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y;
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z;
|
||||
|
||||
if (idx >= depth_output || idy >= dim_output || idz >= dim_output) {
|
||||
return;
|
||||
}
|
||||
ker->d_bias[idx][idy][idz] += output[idx][idy][idz];
|
||||
}
|
||||
|
||||
__global__ void backward_convolution_dweight_kernel(Kernel_cnn* ker, float*** input, float*** output, int depth_input, int depth_output, int dim_output, int k_size) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x;
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y;
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z;
|
||||
|
||||
int idz1 = idz / k_size;
|
||||
int idz2 = idz % k_size;
|
||||
|
||||
if (idx >= depth_input || idy >= depth_output || idz1 >= k_size || idz2 >= k_size) {
|
||||
return;
|
||||
}
|
||||
|
||||
float tmp = 0;
|
||||
for (int l=0; l < dim_output; l++) {
|
||||
for (int m=0; m < dim_output; m++) {
|
||||
tmp += input[idx][l+idz1][m+idz2]*output[idy][l][m];
|
||||
}
|
||||
}
|
||||
ker->d_weights[idx][idy][idz1][idz2] += tmp;
|
||||
}
|
||||
|
||||
__global__ void backward_convolution_propagate_kernel(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int k_size, funcPtr d_f) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x;
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y;
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z;
|
||||
|
||||
if (idx >= depth_input || idy >= dim_input || idz >= dim_input) {
|
||||
return;
|
||||
}
|
||||
|
||||
int min_m, max_m, min_n, max_n;
|
||||
float tmp = 0;
|
||||
for (int l=0; l < depth_output; l++) {
|
||||
min_m = max(0, k_size-1-idy);
|
||||
max_m = min(k_size, dim_input - idy);
|
||||
min_n = max(0, k_size-1-idz);
|
||||
max_n = min(k_size, dim_input-idz);
|
||||
for (int m=min_m; m < max_m; m++) {
|
||||
for (int n=min_n; n < max_n; n++) {
|
||||
tmp += output[l][idy-k_size+m+1][idz-k_size+n+1]*ker->weights[idx][l][m][n];
|
||||
}
|
||||
}
|
||||
}
|
||||
input[idx][idy][idz] = tmp*( (*d_f)(input_z[idx][idy][idz]) );
|
||||
}
|
||||
|
||||
void backward_convolution_device(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int dim_output, int activation, int is_first) {
|
||||
// Bias Kernel
|
||||
dim3 gridSize1(i_div_up(depth_output, BLOCKSIZE_x), i_div_up(dim_output, BLOCKSIZE_y), i_div_up(dim_output, BLOCKSIZE_y));
|
||||
dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
|
||||
backward_convolution_dbias_kernel<<<gridSize1, blockSize1>>>(ker, output, depth_output, dim_output);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
|
||||
// Weights Kernel
|
||||
int k_size = dim_input - dim_output +1;
|
||||
|
||||
dim3 gridSize2(i_div_up(depth_input, BLOCKSIZE_x), i_div_up(depth_output, BLOCKSIZE_y), i_div_up(k_size*k_size, BLOCKSIZE_y));
|
||||
dim3 blockSize2(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
|
||||
backward_convolution_dweight_kernel<<<gridSize2, blockSize2>>>(ker, input, output, depth_input, depth_output, dim_output, k_size);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
|
||||
// input propagation Kernel
|
||||
if (is_first != 1) {
|
||||
dim3 gridSize3(i_div_up(depth_input, BLOCKSIZE_x), i_div_up(dim_input, BLOCKSIZE_y), i_div_up(dim_input, BLOCKSIZE_y));
|
||||
dim3 blockSize3(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
|
||||
funcPtr d_function = get_activation_function_cuda(activation);
|
||||
|
||||
backward_convolution_propagate_kernel<<<gridSize3, blockSize3>>>(ker, input, input_z, output, depth_input, dim_input, depth_output, k_size, d_function);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
|
||||
void backward_convolution_cpu(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int dim_output, int activation, int is_first) {
|
||||
|
||||
funcPtr d_function = get_activation_function(activation);
|
||||
|
||||
// Bias
|
||||
for (int i=0; i < depth_output; i++) {
|
||||
for (int j=0; j < dim_output; j++) {
|
||||
@ -197,3 +629,14 @@ void backward_convolution(Kernel_cnn* ker, float*** input, float*** input_z, flo
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
void backward_convolution(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int dim_output, int activation, int is_first) {
|
||||
#ifndef __CUDACC__
|
||||
backward_convolution_cpu(ker, input, input_z, output, depth_input, dim_input, depth_output, dim_output, activation, is_first);
|
||||
#else
|
||||
backward_convolution_device(ker, input, input_z, output, depth_input, dim_input, depth_output, dim_output, activation, is_first);
|
||||
#endif
|
||||
}
|
||||
642
src/cnn/backpropagation.cu
Normal file
642
src/cnn/backpropagation.cu
Normal file
@ -0,0 +1,642 @@
|
||||
#include <stdio.h>
|
||||
#include <float.h>
|
||||
#include <math.h>
|
||||
|
||||
#include "include/backpropagation.h"
|
||||
#include "../include/utils.h"
|
||||
#include "include/struct.h"
|
||||
|
||||
#include "include/config.h"
|
||||
|
||||
#ifndef __CUDACC__
|
||||
int min(int a, int b) {
|
||||
return a<b?a:b;
|
||||
}
|
||||
|
||||
int max(int a, int b) {
|
||||
return a > b ? a : b;
|
||||
}
|
||||
#endif
|
||||
|
||||
/*
|
||||
* Softmax backward MSE
|
||||
*/
|
||||
#ifdef __CUDACC__
|
||||
__global__ void softmax_backward_mse_kernel(float* input, float* output, int size) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x;
|
||||
|
||||
if (idx >= size) {
|
||||
return;
|
||||
}
|
||||
|
||||
int input_val = input[idx];
|
||||
int output_val = output[idx];
|
||||
|
||||
input[idx] = (output_val-input_val)*input_val*(1-input_val);
|
||||
}
|
||||
|
||||
void softmax_backward_mse_device(float* input, float* output, int size) {
|
||||
// Make computation
|
||||
dim3 gridSize(i_div_up(size, BLOCKSIZE_x));
|
||||
dim3 blockSize(BLOCKSIZE_x);
|
||||
|
||||
softmax_backward_mse_kernel<<<gridSize, blockSize>>>(input, output, size);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
}
|
||||
#endif
|
||||
|
||||
void softmax_backward_mse_cpu(float* input, float* output, int size) {
|
||||
/* Input et output ont la même taille */
|
||||
|
||||
for (int i=0; i < size; i++){
|
||||
input[i] = (output[i]-input[i])*input[i]*(1-input[i]);
|
||||
}
|
||||
}
|
||||
|
||||
void softmax_backward_mse(float* input, float* output, int size) {
|
||||
#ifdef __CUDACC__
|
||||
softmax_backward_mse_device(input, output, size);
|
||||
#else
|
||||
softmax_backward_mse_cpu(input, output, size);
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
/*
|
||||
* Softmax backward Cross entropy
|
||||
*/
|
||||
#ifdef __CUDACC__
|
||||
__global__ void softmax_backward_cross_entropy_kernel(float* input, float* output, int size) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x;
|
||||
|
||||
if (idx >= size) {
|
||||
return;
|
||||
}
|
||||
|
||||
input[idx] = output[idx] - input[idx];
|
||||
}
|
||||
|
||||
void softmax_backward_cross_entropy_device(float* input, float* output, int size) {
|
||||
// Make computation
|
||||
dim3 gridSize(i_div_up(size, BLOCKSIZE_x));
|
||||
dim3 blockSize(BLOCKSIZE_x);
|
||||
|
||||
softmax_backward_cross_entropy_kernel<<<gridSize, blockSize>>>(input, output, size);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
}
|
||||
#endif
|
||||
|
||||
void softmax_backward_cross_entropy_cpu(float* input, float* output, int size) {
|
||||
/* Input et output ont la même taille */
|
||||
|
||||
for (int i=0; i < size; i++){
|
||||
input[i] = output[i] - input[i];
|
||||
}
|
||||
}
|
||||
|
||||
void softmax_backward_cross_entropy(float* input, float* output, int size) {
|
||||
#ifdef __CUDACC__
|
||||
softmax_backward_cross_entropy_device(input, output, size);
|
||||
#else
|
||||
softmax_backward_cross_entropy_cpu(input, output, size);
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
/*
|
||||
* Backward average pooling
|
||||
*/
|
||||
#ifdef __CUDACC__
|
||||
__global__ void backward_average_pooling_kernel(float*** input, float*** output, int input_width, int output_width, int depth, int n, int size) {
|
||||
// Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x; // < depth
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y; // < output_width
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z; // < output_width
|
||||
|
||||
if (idx >= depth || idy >= output_width || idz >= output_width) {
|
||||
return;
|
||||
}
|
||||
|
||||
for (int a=0; a < size; a++) {
|
||||
for (int b=0; b < size; b++) {
|
||||
input[idx][size*idy +a][size*idz +b] += output[idx][idy][idz]/n;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void backward_average_pooling_device(float*** input, float*** output, int input_width, int output_width, int depth) {
|
||||
// Make computation
|
||||
dim3 gridSize(i_div_up(depth, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_z));
|
||||
dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
|
||||
int size = input_width/output_width; // Taille du pooling
|
||||
|
||||
reset_3d_array(input, depth, input_width, input_width);
|
||||
|
||||
backward_average_pooling_kernel<<<gridSize, blockSize>>>(input, output, input_width, output_width, depth, size*size, size);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
}
|
||||
#endif
|
||||
|
||||
void backward_average_pooling_cpu(float*** input, float*** output, int input_width, int output_width, int depth) {
|
||||
/* Input et output ont la même profondeur (depth) */
|
||||
|
||||
int size = input_width/output_width; // Taille du pooling
|
||||
int n = size*size; // Nombre d'éléments dans le pooling
|
||||
|
||||
reset_3d_array(input, depth, input_width, input_width);
|
||||
|
||||
for (int i=0; i < depth; i++) {
|
||||
for (int j=0; j < output_width; j++) {
|
||||
for (int k=0; k < output_width; k++) {
|
||||
for (int a=0; a < size; a++) {
|
||||
for (int b=0; b < size; b++) {
|
||||
input[i][size*j +a][size*k +b] += output[i][j][k]/n;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
void backward_average_pooling(float*** input, float*** output, int input_width, int output_width, int depth) {
|
||||
#ifndef __CUDACC__
|
||||
backward_average_pooling_cpu(input, output, input_width, output_width, depth);
|
||||
#else
|
||||
backward_average_pooling_device(input, output, input_width, output_width, depth);
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
/*
|
||||
* Backward max pooling
|
||||
*/
|
||||
#ifdef __CUDACC__
|
||||
__global__ void backward_max_pooling_kernel(float*** input, float*** output, int input_width, int output_width, int depth, int n, int size) {
|
||||
// Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x; // < depth
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y; // < output_width
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z; // < output_width
|
||||
|
||||
if (idx >= depth || idy >= output_width || idz >= output_width) {
|
||||
return;
|
||||
}
|
||||
|
||||
float m = -FLT_MAX;
|
||||
int a_max = -1;
|
||||
int b_max = -1;
|
||||
|
||||
for (int a=0; a < size; a++) {
|
||||
for (int b=0; b < size; b++) {
|
||||
if (input[idx][size*idy +a][size*idz +b] > m) {
|
||||
m = input[idx][size*idy +a][size*idz +b];
|
||||
a_max = a;
|
||||
b_max = b;
|
||||
}
|
||||
input[idx][size*idy +a][size*idz +b] = 0;
|
||||
}
|
||||
}
|
||||
input[idx][size*idy +a_max][size*idz +b_max] = output[idx][idy][idz]/n;
|
||||
}
|
||||
|
||||
|
||||
void backward_max_pooling_device(float*** input, float*** output, int input_width, int output_width, int depth) {
|
||||
// Make computation
|
||||
dim3 gridSize(i_div_up(depth, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_z));
|
||||
dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
|
||||
int size = input_width/output_width; // Taille du pooling
|
||||
|
||||
backward_max_pooling_kernel<<<gridSize, blockSize>>>(input, output, input_width, output_width, depth, size*size, size);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
}
|
||||
#endif
|
||||
|
||||
void backward_max_pooling_cpu(float*** input, float*** output, int input_width, int output_width, int depth) {
|
||||
int size = input_width/output_width;
|
||||
|
||||
float m; // Maximum
|
||||
int a_max, b_max; // Indices du maximum
|
||||
|
||||
for (int i=0; i < depth; i++) {
|
||||
for (int j=0; j < output_width; j++) {
|
||||
for (int k=0; k < output_width; k++) {
|
||||
m = -FLT_MAX;
|
||||
a_max = -1;
|
||||
b_max = -1;
|
||||
|
||||
for (int a=0; a < size; a++) {
|
||||
for (int b=0; b < size; b++) {
|
||||
if (input[i][size*j +a][size*k +b] > m) {
|
||||
m = input[i][size*j +a][size*k +b];
|
||||
a_max = a;
|
||||
b_max = b;
|
||||
}
|
||||
input[i][size*j +a][size*k +b] = 0;
|
||||
}
|
||||
}
|
||||
input[i][size*j +a_max][size*k +b_max] = output[i][j][k]/(size*size);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
void backward_max_pooling(float*** input, float*** output, int input_width, int output_width, int depth) {
|
||||
#ifndef __CUDACC__
|
||||
backward_max_pooling_cpu(input, output, input_width, output_width, depth);
|
||||
#else
|
||||
backward_max_pooling_device(input, output, input_width, output_width, depth);
|
||||
#endif
|
||||
}
|
||||
|
||||
/*
|
||||
* Backward Dense
|
||||
*/
|
||||
#ifdef __CUDACC__
|
||||
__global__ void backward_dense_kernel_1(Kernel_nn* ker, float* input, float* output, int size_input, int size_output) {
|
||||
// Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x; // < size_input
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y; // < size_output
|
||||
|
||||
if (idx >= size_input || idy >= size_output) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (idx == 0) {
|
||||
ker->d_bias[idy] += output[idy];
|
||||
}
|
||||
ker->d_weights[idx][idy] += input[idx]*output[idy];
|
||||
}
|
||||
|
||||
__global__ void backward_dense_kernel_2(float** weights, float* input, float* input_z, float* output, int size_input, int size_output, funcPtr d_f) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x; // < size_input
|
||||
|
||||
if (idx >= size_input) {
|
||||
return;
|
||||
}
|
||||
|
||||
float tmp=0;
|
||||
for (int j=0; j < size_output; j++) {
|
||||
tmp += output[j]*weights[idx][j];
|
||||
}
|
||||
input[idx] = tmp*( (*d_f)(input_z[idx]) );
|
||||
}
|
||||
|
||||
void backward_dense_device(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, int activation, int is_first) {
|
||||
// Make computation
|
||||
dim3 gridSize1(i_div_up(size_input, BLOCKSIZE_x), i_div_up(size_output, BLOCKSIZE_y));
|
||||
dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y);
|
||||
|
||||
backward_dense_kernel_1<<<gridSize1, blockSize1>>>(ker, input, output, size_input, size_output);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
|
||||
// Second kernel
|
||||
if (is_first != 1) {
|
||||
dim3 gridSize1(i_div_up(size_input, BLOCKSIZE_x));
|
||||
dim3 blockSize1(BLOCKSIZE_x);
|
||||
|
||||
funcPtr d_function = get_activation_function_cuda(activation);
|
||||
|
||||
backward_dense_kernel_2<<<gridSize1, blockSize1>>>(ker->weights, input, input_z, output, size_input, size_output, d_function);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
void backward_dense_cpu(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, int activation, int is_first) {
|
||||
|
||||
funcPtr d_function = get_activation_function(activation);
|
||||
// Bias
|
||||
for (int j=0; j < size_output; j++) {
|
||||
ker->d_bias[j] += output[j];
|
||||
}
|
||||
|
||||
// Weights
|
||||
for (int i=0; i < size_input; i++) {
|
||||
for (int j=0; j < size_output; j++) {
|
||||
ker->d_weights[i][j] += input[i]*output[j];
|
||||
}
|
||||
}
|
||||
|
||||
// Input
|
||||
if (is_first==1) {// Pas besoin de backpropager dans l'input
|
||||
return;
|
||||
}
|
||||
|
||||
for (int i=0; i < size_input; i++) {
|
||||
float tmp=0;
|
||||
for (int j=0; j < size_output; j++) {
|
||||
tmp += output[j]*ker->weights[i][j];
|
||||
}
|
||||
input[i] = tmp*d_function(input_z[i]);
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
void backward_dense(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, int activation, int is_first) {
|
||||
#ifndef __CUDACC__
|
||||
backward_dense_cpu(ker, input, input_z, output, size_input, size_output, activation, is_first);
|
||||
#else
|
||||
backward_dense_device(ker, input, input_z, output, size_input, size_output, activation, is_first);
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
|
||||
/*
|
||||
* Backward linearisation
|
||||
*/
|
||||
#ifdef __CUDACC__
|
||||
__global__ void backward_linearisation_kernel_1(Kernel_nn* ker, float*** input, float* output, int depth_input, int dim_input, int size_output) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x; // < depth_input
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y; // < dim_input
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z; // < dim_input
|
||||
|
||||
if (idx >= depth_input || idy >= dim_input || idz >= dim_input) {
|
||||
return;
|
||||
}
|
||||
|
||||
int id = idx*dim_input*dim_input + idy*dim_input + idz;
|
||||
|
||||
for (int j=0; j < size_output; j++) {
|
||||
ker->d_weights[id][j] += input[idx][idy][idz]*output[j];
|
||||
}
|
||||
if (id == 0) {
|
||||
for (int j=0; j < size_output; j++) {
|
||||
ker->d_bias[j] += output[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void backward_linearisation_kernel_2(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output, funcPtr d_f) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x; // < depth_input
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y; // < dim_input
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z; // < dim_input
|
||||
|
||||
if (idx >= depth_input || idy >= dim_input || idz >= dim_input) {
|
||||
return;
|
||||
}
|
||||
int id = idx*dim_input*dim_input + idy*dim_input + idz;
|
||||
|
||||
float tmp=0;
|
||||
for (int j=0; j < size_output; j++) {
|
||||
tmp += output[j]*ker->weights[id][j];
|
||||
}
|
||||
input[idx][idy][idz] = tmp*( (*d_f)(input_z[idx][idy][idz]) );
|
||||
}
|
||||
|
||||
void backward_linearisation_device(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output, int activation) {
|
||||
// Make computation
|
||||
dim3 gridSize(i_div_up(depth_input, BLOCKSIZE_x), i_div_up(dim_input, BLOCKSIZE_y), i_div_up(dim_input, BLOCKSIZE_y));
|
||||
dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
|
||||
backward_linearisation_kernel_1<<<gridSize, blockSize>>>(ker, input, output, depth_input, dim_input, size_output);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
|
||||
// Second kernel
|
||||
funcPtr d_function = get_activation_function_cuda(activation);
|
||||
|
||||
backward_linearisation_kernel_2<<<gridSize, blockSize>>>(ker, input, input_z, output, depth_input, dim_input, size_output, d_function);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
}
|
||||
#endif
|
||||
|
||||
void backward_linearisation_cpu(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output, int activation) {
|
||||
|
||||
funcPtr d_function = get_activation_function(activation);
|
||||
|
||||
// Bias
|
||||
for (int j=0; j < size_output; j++) {
|
||||
ker->d_bias[j] += output[j];
|
||||
}
|
||||
|
||||
// Weights
|
||||
int cpt = 0;
|
||||
for (int i=0; i < depth_input; i++) {
|
||||
for (int k=0; k < dim_input; k++) {
|
||||
for (int l=0; l < dim_input; l++) {
|
||||
for (int j=0; j < size_output; j++) {
|
||||
ker->d_weights[cpt][j] += input[i][k][l]*output[j];
|
||||
}
|
||||
cpt++;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Input
|
||||
cpt = 0;
|
||||
for (int i=0; i < depth_input; i++) {
|
||||
for (int k=0; k < dim_input; k++) {
|
||||
for (int l=0; l < dim_input; l++) {
|
||||
float tmp=0;
|
||||
for (int j=0; j < size_output; j++) {
|
||||
tmp += output[j]*ker->weights[cpt][j];
|
||||
}
|
||||
input[i][k][l] = tmp*d_function(input_z[i][k][l]);
|
||||
cpt++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
void backward_linearisation(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output, int activation) {
|
||||
#ifndef __CUDACC__
|
||||
backward_linearisation_cpu(ker, input, input_z, output, depth_input, dim_input, size_output, activation);
|
||||
#else
|
||||
backward_linearisation_device(ker, input, input_z, output, depth_input, dim_input, size_output, activation);
|
||||
#endif
|
||||
}
|
||||
|
||||
/*
|
||||
* Backward convolution
|
||||
*/
|
||||
#ifdef __CUDACC__
|
||||
__global__ void backward_convolution_dbias_kernel(Kernel_cnn* ker, float*** output, int depth_output, int dim_output) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x;
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y;
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z;
|
||||
|
||||
if (idx >= depth_output || idy >= dim_output || idz >= dim_output) {
|
||||
return;
|
||||
}
|
||||
ker->d_bias[idx][idy][idz] += output[idx][idy][idz];
|
||||
}
|
||||
|
||||
__global__ void backward_convolution_dweight_kernel(Kernel_cnn* ker, float*** input, float*** output, int depth_input, int depth_output, int dim_output, int k_size) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x;
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y;
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z;
|
||||
|
||||
int idz1 = idz / k_size;
|
||||
int idz2 = idz % k_size;
|
||||
|
||||
if (idx >= depth_input || idy >= depth_output || idz1 >= k_size || idz2 >= k_size) {
|
||||
return;
|
||||
}
|
||||
|
||||
float tmp = 0;
|
||||
for (int l=0; l < dim_output; l++) {
|
||||
for (int m=0; m < dim_output; m++) {
|
||||
tmp += input[idx][l+idz1][m+idz2]*output[idy][l][m];
|
||||
}
|
||||
}
|
||||
ker->d_weights[idx][idy][idz1][idz2] += tmp;
|
||||
}
|
||||
|
||||
__global__ void backward_convolution_propagate_kernel(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int k_size, funcPtr d_f) {
|
||||
int idx = threadIdx.x + blockDim.x*blockIdx.x;
|
||||
int idy = threadIdx.y + blockDim.y*blockIdx.y;
|
||||
int idz = threadIdx.z + blockDim.z*blockIdx.z;
|
||||
|
||||
if (idx >= depth_input || idy >= dim_input || idz >= dim_input) {
|
||||
return;
|
||||
}
|
||||
|
||||
int min_m, max_m, min_n, max_n;
|
||||
float tmp = 0;
|
||||
for (int l=0; l < depth_output; l++) {
|
||||
min_m = max(0, k_size-1-idy);
|
||||
max_m = min(k_size, dim_input - idy);
|
||||
min_n = max(0, k_size-1-idz);
|
||||
max_n = min(k_size, dim_input-idz);
|
||||
for (int m=min_m; m < max_m; m++) {
|
||||
for (int n=min_n; n < max_n; n++) {
|
||||
tmp += output[l][idy-k_size+m+1][idz-k_size+n+1]*ker->weights[idx][l][m][n];
|
||||
}
|
||||
}
|
||||
}
|
||||
input[idx][idy][idz] = tmp*( (*d_f)(input_z[idx][idy][idz]) );
|
||||
}
|
||||
|
||||
void backward_convolution_device(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int dim_output, int activation, int is_first) {
|
||||
// Bias Kernel
|
||||
dim3 gridSize1(i_div_up(depth_output, BLOCKSIZE_x), i_div_up(dim_output, BLOCKSIZE_y), i_div_up(dim_output, BLOCKSIZE_y));
|
||||
dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
|
||||
backward_convolution_dbias_kernel<<<gridSize1, blockSize1>>>(ker, output, depth_output, dim_output);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
|
||||
// Weights Kernel
|
||||
int k_size = dim_input - dim_output +1;
|
||||
|
||||
dim3 gridSize2(i_div_up(depth_input, BLOCKSIZE_x), i_div_up(depth_output, BLOCKSIZE_y), i_div_up(k_size*k_size, BLOCKSIZE_y));
|
||||
dim3 blockSize2(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
|
||||
backward_convolution_dweight_kernel<<<gridSize2, blockSize2>>>(ker, input, output, depth_input, depth_output, dim_output, k_size);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
|
||||
// input propagation Kernel
|
||||
if (is_first != 1) {
|
||||
dim3 gridSize3(i_div_up(depth_input, BLOCKSIZE_x), i_div_up(dim_input, BLOCKSIZE_y), i_div_up(dim_input, BLOCKSIZE_y));
|
||||
dim3 blockSize3(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
||||
|
||||
funcPtr d_function = get_activation_function_cuda(activation);
|
||||
|
||||
backward_convolution_propagate_kernel<<<gridSize3, blockSize3>>>(ker, input, input_z, output, depth_input, dim_input, depth_output, k_size, d_function);
|
||||
|
||||
gpuErrchk( cudaPeekAtLastError() );
|
||||
gpuErrchk( cudaDeviceSynchronize() );
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
|
||||
void backward_convolution_cpu(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int dim_output, int activation, int is_first) {
|
||||
|
||||
funcPtr d_function = get_activation_function(activation);
|
||||
|
||||
// Bias
|
||||
for (int i=0; i < depth_output; i++) {
|
||||
for (int j=0; j < dim_output; j++) {
|
||||
for (int k=0; k < dim_output; k++) {
|
||||
ker->d_bias[i][j][k] += output[i][j][k];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Weights
|
||||
int k_size = dim_input - dim_output +1;
|
||||
|
||||
for (int h=0; h < depth_input; h++) {
|
||||
for (int i=0; i < depth_output; i++) {
|
||||
for (int j=0; j < k_size; j++) {
|
||||
for (int k=0; k < k_size; k++) {
|
||||
float tmp = 0;
|
||||
for (int l=0; l < dim_output; l++) {
|
||||
for (int m=0; m < dim_output; m++) {
|
||||
tmp += input[h][l+j][m+k]*output[i][l][m];
|
||||
}
|
||||
}
|
||||
ker->d_weights[h][i][j][k] += tmp;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Input
|
||||
if (is_first==1) // Pas besoin de backpropager dans l'input
|
||||
return;
|
||||
int min_m, max_m, min_n, max_n;
|
||||
for (int i=0; i < depth_input; i++) {
|
||||
for (int j=0; j < dim_input; j++) {
|
||||
for (int k=0; k < dim_input; k++) {
|
||||
float tmp = 0;
|
||||
for (int l=0; l < depth_output; l++) {
|
||||
min_m = max(0, k_size-1-j);
|
||||
max_m = min(k_size, dim_input - j);
|
||||
min_n = max(0, k_size-1-k);
|
||||
max_n = min(k_size, dim_input-k);
|
||||
for (int m=min_m; m < max_m; m++) {
|
||||
for (int n=min_n; n < max_n; n++) {
|
||||
tmp += output[l][j-k_size+m+1][k-k_size+n+1]*ker->weights[i][l][m][n];
|
||||
}
|
||||
}
|
||||
}
|
||||
input[i][j][k] = tmp*d_function(input_z[i][j][k]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
void backward_convolution(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int dim_output, int activation, int is_first) {
|
||||
#ifndef __CUDACC__
|
||||
backward_convolution_cpu(ker, input, input_z, output, depth_input, dim_input, depth_output, dim_output, activation, is_first);
|
||||
#else
|
||||
backward_convolution_device(ker, input, input_z, output, depth_input, dim_input, depth_output, dim_output, activation, is_first);
|
||||
#endif
|
||||
}
|
||||
@ -4,6 +4,7 @@
|
||||
#include <float.h> // Is it used ?
|
||||
#include <math.h>
|
||||
|
||||
#include "../include/memory_management.h"
|
||||
#include "include/backpropagation.h"
|
||||
#include "include/initialisation.h"
|
||||
#include "include/function.h"
|
||||
@ -226,7 +227,7 @@ void backward_propagation(Network* network, int wanted_number) {
|
||||
// Backward sur la dernière couche qui utilise toujours SOFTMAX
|
||||
float* wanted_output = generate_wanted_output(wanted_number, network->width[network->size -1]); // Sortie désirée, permet d'initialiser une erreur
|
||||
softmax_backward_cross_entropy(network->input[n-1][0][0], wanted_output, network->width[n-1]);
|
||||
free(wanted_output);
|
||||
gree(wanted_output);
|
||||
|
||||
/*
|
||||
* On propage à chaque étape:
|
||||
@ -252,14 +253,12 @@ void backward_propagation(Network* network, int wanted_number) {
|
||||
|
||||
|
||||
if (k_i->cnn) { // Convolution
|
||||
funcPtr d_f = get_activation_function(-activation);
|
||||
backward_convolution(k_i->cnn, input, input_z, output, input_depth, input_width, output_depth, output_width, d_f, i==0);
|
||||
backward_convolution(k_i->cnn, input, input_z, output, input_depth, input_width, output_depth, output_width, -activation, i==0);
|
||||
} else if (k_i->nn) { // Full connection
|
||||
funcPtr d_f = get_activation_function(-activation);
|
||||
if (k_i->linearisation == DOESNT_LINEARISE) { // Vecteur -> Vecteur
|
||||
backward_dense(k_i->nn, input[0][0], input_z[0][0], output[0][0], input_width, output_width, d_f, i==0);
|
||||
backward_dense(k_i->nn, input[0][0], input_z[0][0], output[0][0], input_width, output_width, -activation, i==0);
|
||||
} else { // Matrice -> vecteur
|
||||
backward_linearisation(k_i->nn, input, input_z, output[0][0], input_depth, input_width, output_width, d_f);
|
||||
backward_linearisation(k_i->nn, input, input_z, output[0][0], input_depth, input_width, output_width, -activation);
|
||||
}
|
||||
} else { // Pooling
|
||||
if (k_i->pooling == AVG_POOLING) {
|
||||
@ -313,7 +312,7 @@ float compute_cross_entropy_loss(float* output, float* wanted_output, int len) {
|
||||
}
|
||||
|
||||
float* generate_wanted_output(int wanted_number, int size_output) {
|
||||
float* wanted_output = (float*)malloc(sizeof(float)*size_output);
|
||||
float* wanted_output = (float*)nalloc(size_output, sizeof(float));
|
||||
for (int i=0; i < size_output; i++) {
|
||||
if (i==wanted_number) {
|
||||
wanted_output[i]=1;
|
||||
|
||||
@ -14,42 +14,70 @@ int min(int a, int b);
|
||||
*/
|
||||
int max(int a, int b);
|
||||
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
/*
|
||||
* Transfert les informations d'erreur de la sortie voulue à la sortie réelle
|
||||
*/
|
||||
void softmax_backward_mse(float* input, float* output, int size);
|
||||
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
/*
|
||||
* Transfert les informations d'erreur de la sortie voulue à la sortie réelle
|
||||
* en considérant MSE (Mean Squared Error) comme fonction d'erreur
|
||||
*/
|
||||
void softmax_backward_cross_entropy(float* input, float* output, int size);
|
||||
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
/*
|
||||
* Transfert les informations d'erreur à travers une couche d'average pooling
|
||||
* en considérant cross_entropy comme fonction d'erreur
|
||||
*/
|
||||
void backward_average_pooling(float*** input, float*** output, int input_width, int output_width, int depth);
|
||||
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
/*
|
||||
* Transfert les informations d'erreur à travers une couche de max pooling
|
||||
* en considérant cross_entropy comme fonction d'erreur
|
||||
*/
|
||||
void backward_max_pooling(float*** input, float*** output, int input_width, int output_width, int depth);
|
||||
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
/*
|
||||
* Transfert les informations d'erreur à travers une couche fully connected
|
||||
*/
|
||||
void backward_dense(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, funcPtr d_function, int is_first);
|
||||
void backward_dense(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, int activation, int is_first);
|
||||
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
/*
|
||||
* Transfert les informations d'erreur à travers une couche de linéarisation
|
||||
*/
|
||||
void backward_linearisation(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output, funcPtr d_function);
|
||||
void backward_linearisation(Kernel_nn* ker, float*** input, float*** input_z, float* output, int depth_input, int dim_input, int size_output, int activation);
|
||||
|
||||
|
||||
#ifdef __CUDACC__
|
||||
extern "C"
|
||||
#endif
|
||||
/*
|
||||
* Transfert les informations d'erreur à travers un couche de convolution
|
||||
*/
|
||||
void backward_convolution(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int dim_output, funcPtr d_function, int is_first);
|
||||
void backward_convolution(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int depth_input, int dim_input, int depth_output, int dim_output, int activation, int is_first);
|
||||
|
||||
#endif
|
||||
|
||||
@ -51,7 +51,7 @@ float* test_network_mnist(Network* network, char* images_file, char* labels_file
|
||||
// Compute loss
|
||||
wanted_output = generate_wanted_output(labels[i], 10);
|
||||
loss += compute_mean_squared_error(network->input[network->size-1][0][0], wanted_output, 10);
|
||||
free(wanted_output);
|
||||
gree(wanted_output);
|
||||
|
||||
for (int j=0; j < height; j++) {
|
||||
free(images[i][j]);
|
||||
@ -60,7 +60,7 @@ float* test_network_mnist(Network* network, char* images_file, char* labels_file
|
||||
}
|
||||
free(images);
|
||||
|
||||
float* results = malloc(sizeof(float)*2);
|
||||
float* results = (float*)malloc(sizeof(float)*2);
|
||||
results[0] = 100*accuracy/(float)nb_elem;
|
||||
results[1] = loss/(float)nb_elem;
|
||||
return results;
|
||||
@ -90,7 +90,7 @@ float* test_network_jpg(Network* network, char* data_dir, bool preview_fails, bo
|
||||
free(dataset->images[i]);
|
||||
}
|
||||
|
||||
float* results = malloc(sizeof(float)*2);
|
||||
float* results = (float*)malloc(sizeof(float)*2);
|
||||
results[0] = 100*accuracy/(float)dataset->numImages;
|
||||
results[1] = 0;
|
||||
|
||||
|
||||
@ -62,7 +62,7 @@ void* train_thread(void* parameters) {
|
||||
|
||||
wanted_output = generate_wanted_output(labels[index[i]], 10);
|
||||
loss += compute_mean_squared_error(network->input[network->size-1][0][0], wanted_output, 10);
|
||||
free(wanted_output);
|
||||
gree(wanted_output);
|
||||
|
||||
backward_propagation(network, labels[index[i]]);
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user