void backward_connected_layer_gpu(connected_layer l, network_state state)
{
int i;
constrain_ongpu(l.outputs*l.batch, 5, l.delta_gpu, 1);
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);
}
if(l.batch_normalize){
backward_batchnorm_layer_gpu(l, state);
}
int m = l.outputs;
int k = l.batch;
int n = l.inputs;
float * a = l.delta_gpu;
float * b = state.input;
float * c = l.weight_updates_gpu;
gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
m = l.batch;
k = l.outputs;
n = l.inputs;
a = l.delta_gpu;
b = l.weights_gpu;
c = state.delta;
if(c) gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
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);
}
}
}
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_compact_layer_gpu(const layer l, network_state state)
{
gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
int i, b;
for (b=0;b<l.batch;b++)
{
if (l.method==0) // add
{
for (i=0;i<l.index;i++)
{
axpy_ongpu(l.outputs, 1, l.delta_gpu+b*l.outputs, 1, state.delta+b*l.inputs+i*l.outputs, 1);
}
} else if (l.method==1) // sub
{
axpy_ongpu(l.outputs, 1, l.delta_gpu+b*l.outputs, 1, state.delta+b*l.inputs, 1);
for (i=1;i<l.index;i++)
{
axpy_ongpu(l.outputs, -1, l.delta_gpu+b*l.outputs, 1, state.delta+b*l.inputs+i*l.outputs, 1);
}
} else if (l.method==2) // max
{
compact_backward_max_gpu(l.w, l.h, l.c, l.index,
l.delta_gpu+b*l.outputs,
state.delta+b*l.inputs,
l.indexes_gpu);
} else if (l.method==10)
{
compact_backward_padd_gpu(l.w, l.h, l.c,
l.delta_gpu+b*l.outputs,
state.delta+b*l.inputs);
} else if (l.method==12)
{
compact_backward_pmax_gpu(l.w, l.h, l.c,
l.delta_gpu+b*l.outputs,
state.delta+b*l.inputs,
l.indexes_gpu);
}
}
}
void backward_shortcut_layer_gpu(const layer l, network_state state)
{
gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
axpy_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1, state.delta, 1);
shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, state.net.layers[l.index].delta_gpu);
}
