void forward_local_layer_gpu(const local_layer l, network_state state)
{
int out_h = local_out_height(l);
int out_w = local_out_width(l);
int i, j;
int locations = out_h * out_w;
for(i = 0; i < l.batch; ++i) {
copy_ongpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1, state.st_handle.stream);
}
for(i = 0; i < l.batch; ++i) {
float *input = state.input + i*l.w*l.h*l.c;
im2col_ongpu(input, l.c, l.h, l.w,
l.size, l.stride, l.pad, l.col_image_gpu, state.st_handle.stream);
float *output = l.output_gpu + i*l.outputs;
for(j = 0; j < locations; ++j) {
float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n;
float *b = l.col_image_gpu + j;
float *c = output + j;
int m = l.n;
int n = 1;
int k = l.size*l.size*l.c;
//printf("passou no local layer forward_local_layer_gpu\n");
gemm_ongpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations, state.st_handle);
}
}
activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, state.st_handle.stream);
}
void backward_local_layer_gpu(local_layer l, network_state state)
{
int i, j;
int locations = l.out_w*l.out_h;
gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu, state.st_handle.stream);
for(i = 0; i < l.batch; ++i) {
axpy_ongpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1, state.st_handle.stream);
}
for(i = 0; i < l.batch; ++i) {
float *input = state.input + i*l.w*l.h*l.c;
im2col_ongpu(input, l.c, l.h, l.w,
l.size, l.stride, l.pad, l.col_image_gpu, state.st_handle.stream);
for(j = 0; j < locations; ++j) {
float *a = l.delta_gpu + i*l.outputs + j;
float *b = l.col_image_gpu + j;
float *c = l.weight_updates_gpu + j*l.size*l.size*l.c*l.n;
int m = l.n;
int n = l.size*l.size*l.c;
int k = 1;
//printf("passou no backward_local_layer_gpu first call\n");
gemm_ongpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n, state.st_handle);
}
if(state.delta) {
for(j = 0; j < locations; ++j) {
float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n;
float *b = l.delta_gpu + i*l.outputs + j;
float *c = l.col_image_gpu + j;
int m = l.size*l.size*l.c;
int n = 1;
int k = l.n;
//printf("passou no backward_local_layer_gpu second call\n");
gemm_ongpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations, state.st_handle);
}
col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w, state.st_handle.stream);
}
}
}
void backward_local_layer_gpu(local_layer l, network_state state)
{
int i, j;
int locations = l.out_w*l.out_h;
gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
for(i = 0; i < l.batch; ++i){
axpy_ongpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1);
}
for(i = 0; i < l.batch; ++i){
float *input = state.input + i*l.w*l.h*l.c;
im2col_ongpu(input, l.c, l.h, l.w,
l.size, l.stride, l.pad, l.col_image_gpu);
for(j = 0; j < locations; ++j){
float *a = l.delta_gpu + i*l.outputs + j;
float *b = l.col_image_gpu + j;
float *c = l.filter_updates_gpu + j*l.size*l.size*l.c*l.n;
int m = l.n;
int n = l.size*l.size*l.c;
int k = 1;
gemm_ongpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
}
if(state.delta){
for(j = 0; j < locations; ++j){
float *a = l.filters_gpu + j*l.size*l.size*l.c*l.n;
float *b = l.delta_gpu + i*l.outputs + j;
float *c = l.col_image_gpu + j;
int m = l.size*l.size*l.c;
int n = 1;
int k = l.n;
gemm_ongpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
}
col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
}
}
}
