mirror of
https://github.com/augustin64/projet-tipe
synced 2025-03-14 06:45:22 +01:00
632 lines
21 KiB
C
632 lines
21 KiB
C
#include <stdio.h>
|
|
#include <float.h>
|
|
#include <math.h>
|
|
|
|
#include "include/backpropagation.h"
|
|
#include "../common/include/utils.h"
|
|
#include "include/struct.h"
|
|
|
|
#include "include/config.h"
|
|
|
|
/*
|
|
* 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 input_depth, int input_width, int size_output) {
|
|
int idx = threadIdx.x + blockDim.x*blockIdx.x; // < input_depth
|
|
int idy = threadIdx.y + blockDim.y*blockIdx.y; // < input_width
|
|
int idz = threadIdx.z + blockDim.z*blockIdx.z; // < input_width
|
|
|
|
if (idx >= input_depth || idy >= input_width || idz >= input_width) {
|
|
return;
|
|
}
|
|
|
|
int id = idx*input_width*input_width + idy*input_width + 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 input_depth, int input_width, int size_output, funcPtr d_f) {
|
|
int idx = threadIdx.x + blockDim.x*blockIdx.x; // < input_depth
|
|
int idy = threadIdx.y + blockDim.y*blockIdx.y; // < input_width
|
|
int idz = threadIdx.z + blockDim.z*blockIdx.z; // < input_width
|
|
|
|
if (idx >= input_depth || idy >= input_width || idz >= input_width) {
|
|
return;
|
|
}
|
|
int id = idx*input_width*input_width + idy*input_width + 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 input_depth, int input_width, int size_output, int activation) {
|
|
// Make computation
|
|
dim3 gridSize(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(input_width, BLOCKSIZE_y), i_div_up(input_width, BLOCKSIZE_y));
|
|
dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
|
|
|
backward_linearisation_kernel_1<<<gridSize, blockSize>>>(ker, input, output, input_depth, input_width, 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, input_depth, input_width, size_output, d_function);
|
|
|
|
gpuErrchk( cudaPeekAtLastError() );
|
|
gpuErrchk( cudaDeviceSynchronize() );
|
|
}
|
|
#endif
|
|
|
|
void backward_linearisation_cpu(Kernel_nn* ker, float*** input, float*** input_z, float* output, int input_depth, int input_width, 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 < input_depth; i++) {
|
|
for (int k=0; k < input_width; k++) {
|
|
for (int l=0; l < input_width; 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 < input_depth; i++) {
|
|
for (int k=0; k < input_width; k++) {
|
|
for (int l=0; l < input_width; 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 input_depth, int input_width, int size_output, int activation) {
|
|
#ifndef __CUDACC__
|
|
backward_linearisation_cpu(ker, input, input_z, output, input_depth, input_width, size_output, activation);
|
|
#else
|
|
backward_linearisation_device(ker, input, input_z, output, input_depth, input_width, size_output, activation);
|
|
#endif
|
|
}
|
|
|
|
/*
|
|
* Backward convolution
|
|
*/
|
|
#ifdef __CUDACC__
|
|
__global__ void backward_convolution_dbias_kernel(Kernel_cnn* ker, float*** output, int output_depth, int output_width) {
|
|
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 >= output_depth || idy >= output_width || idz >= output_width) {
|
|
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 input_depth, int output_depth, int output_width, 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 >= input_depth || idy >= output_depth || idz1 >= k_size || idz2 >= k_size) {
|
|
return;
|
|
}
|
|
|
|
float tmp = 0;
|
|
for (int l=0; l < output_width; l++) {
|
|
for (int m=0; m < output_width; 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 input_depth, int input_width, int output_depth, 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 >= input_depth || idy >= input_width || idz >= input_width) {
|
|
return;
|
|
}
|
|
|
|
int min_m, max_m, min_n, max_n;
|
|
float tmp = 0;
|
|
for (int l=0; l < output_depth; l++) {
|
|
min_m = max(0, k_size-1-idy);
|
|
max_m = min(k_size, input_width - idy);
|
|
min_n = max(0, k_size-1-idz);
|
|
max_n = min(k_size, input_width-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 input_depth, int input_width, int output_depth, int output_width, int activation, int is_first) {
|
|
// Bias Kernel
|
|
dim3 gridSize1(i_div_up(output_depth, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_y));
|
|
dim3 blockSize1(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
|
|
|
|
backward_convolution_dbias_kernel<<<gridSize1, blockSize1>>>(ker, output, output_depth, output_width);
|
|
|
|
gpuErrchk( cudaPeekAtLastError() );
|
|
gpuErrchk( cudaDeviceSynchronize() );
|
|
|
|
// Weights Kernel
|
|
int k_size = input_width - output_width +1;
|
|
|
|
dim3 gridSize2(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(output_depth, 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, input_depth, output_depth, output_width, k_size);
|
|
|
|
gpuErrchk( cudaPeekAtLastError() );
|
|
gpuErrchk( cudaDeviceSynchronize() );
|
|
|
|
// input propagation Kernel
|
|
if (is_first != 1) {
|
|
dim3 gridSize3(i_div_up(input_depth, BLOCKSIZE_x), i_div_up(input_width, BLOCKSIZE_y), i_div_up(input_width, 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, input_depth, input_width, output_depth, k_size, d_function);
|
|
|
|
gpuErrchk( cudaPeekAtLastError() );
|
|
gpuErrchk( cudaDeviceSynchronize() );
|
|
}
|
|
}
|
|
#endif
|
|
|
|
|
|
void backward_convolution_cpu(Kernel_cnn* ker, float*** input, float*** input_z, float*** output, int input_depth, int input_width, int output_depth, int output_width, int activation, int is_first) {
|
|
|
|
funcPtr d_function = get_activation_function(activation);
|
|
|
|
// Bias
|
|
for (int i=0; i < output_depth; i++) {
|
|
for (int j=0; j < output_width; j++) {
|
|
for (int k=0; k < output_width; k++) {
|
|
ker->d_bias[i][j][k] += output[i][j][k];
|
|
}
|
|
}
|
|
}
|
|
|
|
// Weights
|
|
int k_size = input_width - output_width +1;
|
|
|
|
for (int h=0; h < input_depth; h++) {
|
|
for (int i=0; i < output_depth; 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 < output_width; l++) {
|
|
for (int m=0; m < output_width; 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 < input_depth; i++) {
|
|
for (int j=0; j < input_width; j++) {
|
|
for (int k=0; k < input_width; k++) {
|
|
float tmp = 0;
|
|
for (int l=0; l < output_depth; l++) {
|
|
min_m = max(0, k_size-1-j);
|
|
max_m = min(k_size, input_width - j);
|
|
min_n = max(0, k_size-1-k);
|
|
max_n = min(k_size, input_width-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 input_depth, int input_width, int output_depth, int output_width, int activation, int is_first) {
|
|
#ifndef __CUDACC__
|
|
backward_convolution_cpu(ker, input, input_z, output, input_depth, input_width, output_depth, output_width, activation, is_first);
|
|
#else
|
|
backward_convolution_device(ker, input, input_z, output, input_depth, input_width, output_depth, output_width, activation, is_first);
|
|
#endif
|
|
} |