void backward_detection_layer_gpu(detection_layer l, network_state state)
{
int outputs = get_detection_layer_output_size(l);
float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
float *delta_cpu = calloc(l.batch*l.inputs, sizeof(float));
float *truth_cpu = 0;
if(state.truth){
truth_cpu = calloc(l.batch*outputs, sizeof(float));
cuda_pull_array(state.truth, truth_cpu, l.batch*outputs);
}
network_state cpu_state;
cpu_state.train = state.train;
cpu_state.input = in_cpu;
cpu_state.truth = truth_cpu;
cpu_state.delta = delta_cpu;
cuda_pull_array(state.input, in_cpu, l.batch*l.inputs);
cuda_pull_array(state.delta, delta_cpu, l.batch*l.inputs);
cuda_pull_array(l.delta_gpu, l.delta, l.batch*outputs);
backward_detection_layer(l, cpu_state);
cuda_push_array(state.delta, delta_cpu, l.batch*l.inputs);
if (truth_cpu) free(truth_cpu);
free(in_cpu);
free(delta_cpu);
}
void backward_network(network net, network_state state)
{
int i;
float *original_input = state.input;
for(i = net.n-1; i >= 0; --i){
if(i == 0){
state.input = original_input;
state.delta = 0;
}else{
layer prev = net.layers[i-1];
state.input = prev.output;
state.delta = prev.delta;
}
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
backward_convolutional_layer(l, state);
} else if(l.type == DECONVOLUTIONAL){
backward_deconvolutional_layer(l, state);
} else if(l.type == MAXPOOL){
if(i != 0) backward_maxpool_layer(l, state);
} else if(l.type == DROPOUT){
backward_dropout_layer(l, state);
} else if(l.type == DETECTION){
backward_detection_layer(l, state);
} else if(l.type == SOFTMAX){
if(i != 0) backward_softmax_layer(l, state);
} else if(l.type == CONNECTED){
backward_connected_layer(l, state);
} else if(l.type == COST){
backward_cost_layer(l, state);
} else if(l.type == ROUTE){
backward_route_layer(l, net);
}
}
}
void backward_network(network net, network_state state)
{
int i;
float *original_input = state.input;
float *original_delta = state.delta;
state.workspace = net.workspace;
for(i = net.n-1; i >= 0; --i){
state.index = i;
if(i == 0){
state.input = original_input;
state.delta = original_delta;
}else{
layer prev = net.layers[i-1];
state.input = prev.output;
state.delta = prev.delta;
}
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
backward_convolutional_layer(l, state);
} else if(l.type == DECONVOLUTIONAL){
backward_deconvolutional_layer(l, state);
} else if(l.type == ACTIVE){
backward_activation_layer(l, state);
} else if(l.type == NORMALIZATION){
backward_normalization_layer(l, state);
} else if(l.type == BATCHNORM){
backward_batchnorm_layer(l, state);
} else if(l.type == MAXPOOL){
if(i != 0) backward_maxpool_layer(l, state);
} else if(l.type == AVGPOOL){
backward_avgpool_layer(l, state);
} else if(l.type == DROPOUT){
backward_dropout_layer(l, state);
} else if(l.type == DETECTION){
backward_detection_layer(l, state);
} else if(l.type == SOFTMAX){
if(i != 0) backward_softmax_layer(l, state);
} else if(l.type == CONNECTED){
backward_connected_layer(l, state);
} else if(l.type == RNN){
backward_rnn_layer(l, state);
} else if(l.type == GRU){
backward_gru_layer(l, state);
} else if(l.type == CRNN){
backward_crnn_layer(l, state);
} else if(l.type == LOCAL){
backward_local_layer(l, state);
} else if(l.type == COST){
backward_cost_layer(l, state);
} else if(l.type == ROUTE){
backward_route_layer(l, net);
} else if(l.type == SHORTCUT){
backward_shortcut_layer(l, state);
}
}
}
