void local_contrast_subtractive_layer_updater_plain::test(
const_additional_buffer_smart_ptr input_buffer,
additional_buffer_smart_ptr output_buffer,
std::vector<additional_buffer_smart_ptr>& additional_buffers,
plain_running_configuration_const_smart_ptr plain_config,
const_layer_smart_ptr layer_schema,
const_layer_data_smart_ptr data,
const_layer_data_custom_smart_ptr data_custom,
const layer_configuration_specific& input_configuration_specific,
const layer_configuration_specific& output_configuration_specific,
unsigned int updater_count,
unsigned int offset_input_entry_id) const
{
if (offset_input_entry_id > 0)
throw neural_network_exception("local_contrast_subtractive_layer_updater_plain is not able to run using offset");
const unsigned int input_neuron_count = input_configuration_specific.get_neuron_count();
const unsigned int input_neuron_count_per_feature_map = input_configuration_specific.get_neuron_count_per_feature_map();
const unsigned int output_neuron_count = output_configuration_specific.get_neuron_count();
const unsigned int output_neuron_count_per_feature_map = output_configuration_specific.get_neuron_count_per_feature_map();
nnforge_shared_ptr<const local_contrast_subtractive_layer> layer_derived = nnforge_dynamic_pointer_cast<const local_contrast_subtractive_layer>(layer_schema);
const std::vector<std::vector<float> >& window_weights_list = layer_derived->window_weights_list;
const std::vector<unsigned int>& feature_maps_affected = layer_derived->feature_maps_affected;
const std::vector<unsigned int>& feature_maps_unaffected = layer_derived->feature_maps_unaffected;
const unsigned int dimension_count = static_cast<unsigned int>(window_weights_list.size());
std::vector<unsigned int> input_slices(input_configuration_specific.dimension_sizes.size());
input_slices[0] = 1;
for(unsigned int i = 0; i < dimension_count - 1; ++i)
input_slices[i + 1] = input_slices[i] * input_configuration_specific.dimension_sizes[i];
const std::vector<unsigned int>::const_iterator dimension_sizes_it = output_configuration_specific.dimension_sizes.begin();
const unsigned int feature_maps_affected_count = static_cast<unsigned int>(feature_maps_affected.size());
const unsigned int feature_maps_unaffected_count = static_cast<unsigned int>(feature_maps_affected.size());
const std::vector<unsigned int>::const_iterator input_slices_it = input_slices.begin();
const std::vector<unsigned int>::const_iterator feature_maps_affected_it = feature_maps_affected.begin();
const std::vector<float>::const_iterator input_buffer_it = input_buffer->begin();
const std::vector<float>::iterator output_buffer_it = output_buffer->begin();
const std::vector<std::vector<float> >::const_iterator window_weights_list_it = window_weights_list.begin();
const int total_workload = updater_count * feature_maps_affected_count;
const int openmp_thread_count = plain_config->openmp_thread_count;
#pragma omp parallel default(none) shared(additional_buffers) num_threads(openmp_thread_count)
{
std::vector<additional_buffer_smart_ptr> local_additional_buffers;
int thread_id = 0;
#ifdef _OPENMP
thread_id = omp_get_thread_num();
#endif
local_additional_buffers.push_back(additional_buffers[thread_id]);
if (dimension_count > 1)
local_additional_buffers.push_back(additional_buffers[openmp_thread_count + thread_id]);
#pragma omp for schedule(guided)
for(int workload_id = 0; workload_id < total_workload; ++workload_id)
{
int entry_id = workload_id / feature_maps_affected_count;
int affected_feature_map_id = workload_id - (entry_id * feature_maps_affected_count);
unsigned int current_output_buffer_index = 0;
unsigned int feature_map_id = *(feature_maps_affected_it + affected_feature_map_id);
for(unsigned int dimension_id = 0; dimension_id < dimension_count; ++dimension_id)
{
std::vector<float>::iterator out_it_base = local_additional_buffers[current_output_buffer_index]->begin();
std::vector<float>::const_iterator in_it;
if (dimension_id > 0)
in_it = local_additional_buffers[1 - current_output_buffer_index]->begin();
else
in_it = input_buffer_it + (entry_id * input_neuron_count) + (feature_map_id * input_neuron_count_per_feature_map);
int max_output_size = *(dimension_sizes_it + dimension_id);
int input_slice_size = *(input_slices_it + dimension_id);
std::vector<unsigned int> current_output_position(dimension_count, 0);
for(std::vector<float>::iterator out_it = out_it_base; out_it != out_it_base + output_neuron_count_per_feature_map; ++out_it, ++in_it)
{
const std::vector<float>& current_window_weights_list = *(window_weights_list_it + dimension_id);
float sum = *in_it * current_window_weights_list[0];
int current_position = static_cast<int>(current_output_position[dimension_id]);
int dest_forward = current_position;
int dest_backward = dest_forward;
for (std::vector<float>::const_iterator it = current_window_weights_list.begin() + 1; it != current_window_weights_list.end(); ++it)
{
dest_forward++;
dest_backward--;
int dest_forward_actual = (dest_forward < max_output_size) ? dest_forward : (((max_output_size << 1) - 1) - dest_forward);
int dest_backward_actual = (dest_backward >= 0) ? dest_backward : (-1 - dest_backward);
int offset_forward = ((dest_forward_actual - current_position) * input_slice_size);
int offset_backward = ((dest_backward_actual - current_position) * input_slice_size);
sum += (*(in_it + offset_forward) + *(in_it + offset_backward)) * (*it);
}
*out_it = sum;
// Go to the next output element
for(unsigned int i = 0; i < dimension_count; ++i)
{
if ((++current_output_position[i]) < *(dimension_sizes_it + i))
break;
current_output_position[i] = 0;
}
}
current_output_buffer_index = 1 - current_output_buffer_index;
} // for(unsigned int dimension_id
// Subtract the gaussian blur
{
std::vector<float>::const_iterator original_in_it = input_buffer_it + (entry_id * input_neuron_count) + (feature_map_id * input_neuron_count_per_feature_map);
std::vector<float>::iterator out_it = output_buffer_it + (entry_id * input_neuron_count) + (feature_map_id * input_neuron_count_per_feature_map);
std::vector<float>::const_iterator in_it = local_additional_buffers[1 - current_output_buffer_index]->begin();
for(int i = 0; i < static_cast<int>(input_neuron_count_per_feature_map); ++i)
*(out_it + i) = *(original_in_it + i) - *(in_it + i);
}
}
} // #pragma parallel
if (feature_maps_unaffected_count > 0)
{
for(unsigned int entry_id = 0; entry_id < updater_count; ++entry_id)
{
for(std::vector<unsigned int>::const_iterator it = feature_maps_unaffected.begin(); it != feature_maps_unaffected.end(); ++it)
{
unsigned int feature_map_id = *it;
std::vector<float>::const_iterator original_in_it = input_buffer_it + (entry_id * input_neuron_count) + (feature_map_id * input_neuron_count_per_feature_map);
std::vector<float>::iterator out_it = output_buffer_it + (entry_id * input_neuron_count) + (feature_map_id * input_neuron_count_per_feature_map);
std::copy(original_in_it, original_in_it + input_neuron_count_per_feature_map, out_it);
}
}
}
}
void local_contrast_subtractive_layer_updater_plain::run_backward_data_propagation(
unsigned int input_index,
plain_buffer::ptr input_errors_buffer,
plain_buffer::const_ptr output_errors_buffer,
const std::vector<plain_buffer::const_ptr>& input_neurons_buffers,
plain_buffer::const_ptr output_neurons_buffer,
plain_buffer::ptr temporary_working_fixed_buffer,
plain_buffer::ptr temporary_working_per_entry_buffer,
plain_buffer::ptr temporary_per_entry_buffer,
plain_running_configuration::const_ptr plain_config,
layer::const_ptr layer_schema,
layer_data::const_ptr data,
layer_data_custom::const_ptr data_custom,
const std::vector<layer_configuration_specific>& input_configuration_specific_list,
const layer_configuration_specific& output_configuration_specific,
const bool add_update_to_destination,
const std::set<layer_action>& actions,
unsigned int entry_count) const
{
const unsigned int neuron_count = output_configuration_specific.get_neuron_count();
const unsigned int neuron_count_per_feature_map = output_configuration_specific.get_neuron_count_per_feature_map();
nnforge_shared_ptr<const local_contrast_subtractive_layer> layer_derived = nnforge_dynamic_pointer_cast<const local_contrast_subtractive_layer>(layer_schema);
const std::vector<std::vector<float> >& window_weights_list = layer_derived->window_weights_list;
const std::vector<unsigned int>& feature_maps_affected = layer_derived->feature_maps_affected;
const std::vector<unsigned int>& feature_maps_unaffected = layer_derived->feature_maps_unaffected;
const unsigned int dimension_count = static_cast<unsigned int>(window_weights_list.size());
std::vector<unsigned int> input_slices(input_configuration_specific_list[0].dimension_sizes.size());
input_slices[0] = 1;
for(unsigned int i = 0; i < dimension_count - 1; ++i)
input_slices[i + 1] = input_slices[i] * input_configuration_specific_list[0].dimension_sizes[i];
const std::vector<unsigned int>::const_iterator dimension_sizes_it = output_configuration_specific.dimension_sizes.begin();
const unsigned int feature_maps_affected_count = static_cast<unsigned int>(feature_maps_affected.size());
const unsigned int feature_maps_unaffected_count = static_cast<unsigned int>(feature_maps_affected.size());
const std::vector<unsigned int>::const_iterator input_slices_it = input_slices.begin();
const std::vector<unsigned int>::const_iterator feature_maps_affected_it = feature_maps_affected.begin();
float * const input_errors_it = *input_errors_buffer;
const float * const output_errors_it = *output_errors_buffer;
const std::vector<std::vector<float> >::const_iterator window_weights_list_it = window_weights_list.begin();
float * const working_buffer_it = *temporary_working_fixed_buffer;
const int total_workload = entry_count * feature_maps_affected_count;
const int openmp_thread_count = plain_config->openmp_thread_count;
#pragma omp parallel default(none) num_threads(openmp_thread_count)
{
std::vector<float *> local_additional_buffers;
int thread_id = 0;
#ifdef _OPENMP
thread_id = omp_get_thread_num();
#endif
local_additional_buffers.push_back(working_buffer_it + thread_id * neuron_count_per_feature_map);
if (dimension_count > 1)
local_additional_buffers.push_back(working_buffer_it + (openmp_thread_count + thread_id) * neuron_count_per_feature_map);
#pragma omp for schedule(guided)
for(int workload_id = 0; workload_id < total_workload; ++workload_id)
{
int entry_id = workload_id / feature_maps_affected_count;
int affected_feature_map_id = workload_id - (entry_id * feature_maps_affected_count);
unsigned int current_output_buffer_index = 0;
unsigned int feature_map_id = *(feature_maps_affected_it + affected_feature_map_id);
for(unsigned int dimension_id = 0; dimension_id < dimension_count; ++dimension_id)
{
float * out_it_base = local_additional_buffers[current_output_buffer_index];
const float * in_it;
if (dimension_id > 0)
in_it = local_additional_buffers[1 - current_output_buffer_index];
else
in_it = output_errors_it + (entry_id * neuron_count) + (feature_map_id * neuron_count_per_feature_map);
int max_output_size = *(dimension_sizes_it + dimension_id);
int input_slice_size = *(input_slices_it + dimension_id);
std::vector<unsigned int> current_output_position(dimension_count, 0);
for(float * out_it = out_it_base; out_it != out_it_base + neuron_count_per_feature_map; ++out_it, ++in_it)
{
const std::vector<float>& current_window_weights_list = *(window_weights_list_it + dimension_id);
float sum = *in_it * current_window_weights_list[0];
int current_position = static_cast<int>(current_output_position[dimension_id]);
int dest_forward = current_position;
int dest_backward = dest_forward;
for (std::vector<float>::const_iterator it = current_window_weights_list.begin() + 1; it != current_window_weights_list.end(); ++it)
{
dest_forward++;
dest_backward--;
int dest_forward_actual = (dest_forward < max_output_size) ? dest_forward : (((max_output_size << 1) - 1) - dest_forward);
int dest_backward_actual = (dest_backward >= 0) ? dest_backward : (-1 - dest_backward);
int offset_forward = ((dest_forward_actual - current_position) * input_slice_size);
int offset_backward = ((dest_backward_actual - current_position) * input_slice_size);
sum += (*(in_it + offset_forward) + *(in_it + offset_backward)) * (*it);
}
*out_it = sum;
// Go to the next output element
for(unsigned int i = 0; i < dimension_count; ++i)
{
if ((++current_output_position[i]) < *(dimension_sizes_it + i))
break;
current_output_position[i] = 0;
}
}
current_output_buffer_index = 1 - current_output_buffer_index;
} // for(unsigned int dimension_id
{
float * out_it = input_errors_it + (entry_id * neuron_count) + (feature_map_id * neuron_count_per_feature_map);
const float * orig_it = output_errors_it + (entry_id * neuron_count) + (feature_map_id * neuron_count_per_feature_map);
const float * in_it = local_additional_buffers[1 - current_output_buffer_index];
if (add_update_to_destination)
{
for(int i = 0; i < static_cast<int>(neuron_count_per_feature_map); ++i)
*(out_it + i) += *(orig_it + i) - *(in_it + i);
}
else
{
for(int i = 0; i < static_cast<int>(neuron_count_per_feature_map); ++i)
*(out_it + i) = *(orig_it + i) - *(in_it + i);
}
}
}
} // #pragma parallel
if ((!add_update_to_destination) && (feature_maps_unaffected_count > 0) && (input_errors_it != output_errors_it))
{
for(unsigned int entry_id = 0; entry_id < entry_count; ++entry_id)
{
for(std::vector<unsigned int>::const_iterator it = feature_maps_unaffected.begin(); it != feature_maps_unaffected.end(); ++it)
{
unsigned int feature_map_id = *it;
float * out_it = input_errors_it + (entry_id * neuron_count) + (feature_map_id * neuron_count_per_feature_map);
for(unsigned int i = 0; i < neuron_count_per_feature_map; ++i)
*(out_it + i) = 0.0F;
}
}
}
}
