libavfilter/dnn_backend_native.c - third_party/ffmpeg - Git at Google

 /*
  * Copyright (c) 2018 Sergey Lavrushkin
  *
  * This file is part of FFmpeg.
  *
  * FFmpeg is free software; you can redistribute it and/or
  * modify it under the terms of the GNU Lesser General Public
  * License as published by the Free Software Foundation; either
  * version 2.1 of the License, or (at your option) any later version.
  *
  * FFmpeg is distributed in the hope that it will be useful,
  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  * Lesser General Public License for more details.
  *
  * You should have received a copy of the GNU Lesser General Public
  * License along with FFmpeg; if not, write to the Free Software
  * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
  */

 /**
  * @file
  * DNN native backend implementation.
  */

 #include "dnn_backend_native.h"
 #include "libavutil/avassert.h"

 static DNNReturnType set_input_output_native(void *model, DNNInputData *input, const char *input_name, const char **output_names, uint32_t nb_output)
 {
     ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
     InputParams *input_params;
     ConvolutionalParams *conv_params;
     DepthToSpaceParams *depth_to_space_params;
     int cur_width, cur_height, cur_channels;
     int32_t layer;

     if (network->layers_num <= 0 || network->layers[0].type != INPUT){
         return DNN_ERROR;
     }
     else{
         input_params = (InputParams *)network->layers[0].params;
         input_params->width = cur_width = input->width;
         input_params->height = cur_height = input->height;
         input_params->channels = cur_channels = input->channels;
         if (input->data){
             av_freep(&input->data);
         }
         av_assert0(input->dt == DNN_FLOAT);
         network->layers[0].output = input->data = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
         if (!network->layers[0].output){
             return DNN_ERROR;
         }
     }

     for (layer = 1; layer < network->layers_num; ++layer){
         switch (network->layers[layer].type){
         case CONV:
             conv_params = (ConvolutionalParams *)network->layers[layer].params;
             if (conv_params->input_num != cur_channels){
                 return DNN_ERROR;
             }
             cur_channels = conv_params->output_num;

             if (conv_params->padding_method == VALID) {
                 int pad_size = (conv_params->kernel_size - 1) * conv_params->dilation;
                 cur_height -= pad_size;
                 cur_width -= pad_size;
             }
             break;
         case DEPTH_TO_SPACE:
             depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
             if (cur_channels % (depth_to_space_params->block_size * depth_to_space_params->block_size) != 0){
                 return DNN_ERROR;
             }
             cur_channels = cur_channels / (depth_to_space_params->block_size * depth_to_space_params->block_size);
             cur_height *= depth_to_space_params->block_size;
             cur_width *= depth_to_space_params->block_size;
             break;
         default:
             return DNN_ERROR;
         }
         if (network->layers[layer].output){
             av_freep(&network->layers[layer].output);
         }

         if (cur_height <= 0 || cur_width <= 0)
             return DNN_ERROR;

         network->layers[layer].output = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
         if (!network->layers[layer].output){
             return DNN_ERROR;
         }
     }

     return DNN_SUCCESS;
 }

 // Loads model and its parameters that are stored in a binary file with following structure:
 // layers_num,layer_type,layer_parameterss,layer_type,layer_parameters...
 // For CONV layer: activation_function, input_num, output_num, kernel_size, kernel, biases
 // For DEPTH_TO_SPACE layer: block_size
 DNNModel *ff_dnn_load_model_native(const char *model_filename)
 {
     DNNModel *model = NULL;
     ConvolutionalNetwork *network = NULL;
     AVIOContext *model_file_context;
     int file_size, dnn_size, kernel_size, i;
     int32_t layer;
     DNNLayerType layer_type;
     ConvolutionalParams *conv_params;
     DepthToSpaceParams *depth_to_space_params;

     model = av_malloc(sizeof(DNNModel));
     if (!model){
         return NULL;
     }

     if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
         av_freep(&model);
         return NULL;
     }
     file_size = avio_size(model_file_context);

     network = av_malloc(sizeof(ConvolutionalNetwork));
     if (!network){
         avio_closep(&model_file_context);
         av_freep(&model);
         return NULL;
     }
     model->model = (void *)network;

     network->layers_num = 1 + (int32_t)avio_rl32(model_file_context);
     dnn_size = 4;

     network->layers = av_malloc(network->layers_num * sizeof(Layer));
     if (!network->layers){
         av_freep(&network);
         avio_closep(&model_file_context);
         av_freep(&model);
         return NULL;
     }

     for (layer = 0; layer < network->layers_num; ++layer){
         network->layers[layer].output = NULL;
         network->layers[layer].params = NULL;
     }
     network->layers[0].type = INPUT;
     network->layers[0].params = av_malloc(sizeof(InputParams));
     if (!network->layers[0].params){
         avio_closep(&model_file_context);
         ff_dnn_free_model_native(&model);
         return NULL;
     }

     for (layer = 1; layer < network->layers_num; ++layer){
         layer_type = (int32_t)avio_rl32(model_file_context);
         dnn_size += 4;
         switch (layer_type){
         case CONV:
             conv_params = av_malloc(sizeof(ConvolutionalParams));
             if (!conv_params){
                 avio_closep(&model_file_context);
                 ff_dnn_free_model_native(&model);
                 return NULL;
             }
             conv_params->dilation = (int32_t)avio_rl32(model_file_context);
             conv_params->padding_method = (int32_t)avio_rl32(model_file_context);
             conv_params->activation = (int32_t)avio_rl32(model_file_context);
             conv_params->input_num = (int32_t)avio_rl32(model_file_context);
             conv_params->output_num = (int32_t)avio_rl32(model_file_context);
             conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
             kernel_size = conv_params->input_num * conv_params->output_num *
                           conv_params->kernel_size * conv_params->kernel_size;
             dnn_size += 24 + (kernel_size + conv_params->output_num << 2);
             if (dnn_size > file_size || conv_params->input_num <= 0 ||
                 conv_params->output_num <= 0 || conv_params->kernel_size <= 0){
                 avio_closep(&model_file_context);
                 ff_dnn_free_model_native(&model);
                 return NULL;
             }
             conv_params->kernel = av_malloc(kernel_size * sizeof(float));
             conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
             if (!conv_params->kernel || !conv_params->biases){
                 avio_closep(&model_file_context);
                 ff_dnn_free_model_native(&model);
                 return NULL;
             }
             for (i = 0; i < kernel_size; ++i){
                 conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
             }
             for (i = 0; i < conv_params->output_num; ++i){
                 conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
             }
             network->layers[layer].type = CONV;
             network->layers[layer].params = conv_params;
             break;
         case DEPTH_TO_SPACE:
             depth_to_space_params = av_malloc(sizeof(DepthToSpaceParams));
             if (!depth_to_space_params){
                 avio_closep(&model_file_context);
                 ff_dnn_free_model_native(&model);
                 return NULL;
             }
             depth_to_space_params->block_size = (int32_t)avio_rl32(model_file_context);
             dnn_size += 4;
             network->layers[layer].type = DEPTH_TO_SPACE;
             network->layers[layer].params = depth_to_space_params;
             break;
         default:
             avio_closep(&model_file_context);
             ff_dnn_free_model_native(&model);
             return NULL;
         }
     }

     avio_closep(&model_file_context);

     if (dnn_size != file_size){
         ff_dnn_free_model_native(&model);
         return NULL;
     }

     model->set_input_output = &set_input_output_native;

     return model;
 }

 #define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))

 static void convolve(const float *input, float *output, const ConvolutionalParams *conv_params, int width, int height)
 {
     int radius = conv_params->kernel_size >> 1;
     int src_linesize = width * conv_params->input_num;
     int filter_linesize = conv_params->kernel_size * conv_params->input_num;
     int filter_size = conv_params->kernel_size * filter_linesize;
     int pad_size = (conv_params->padding_method == VALID) ? (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;

     for (int y = pad_size; y < height - pad_size; ++y) {
         for (int x = pad_size; x < width - pad_size; ++x) {
             for (int n_filter = 0; n_filter < conv_params->output_num; ++n_filter) {
                 output[n_filter] = conv_params->biases[n_filter];

                 for (int ch = 0; ch < conv_params->input_num; ++ch) {
                     for (int kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y) {
                         for (int kernel_x = 0; kernel_x < conv_params->kernel_size; ++kernel_x) {
                             float input_pel;
                             if (conv_params->padding_method == SAME_CLAMP_TO_EDGE) {
                                 int y_pos = CLAMP_TO_EDGE(y + (kernel_y - radius) * conv_params->dilation, height);
                                 int x_pos = CLAMP_TO_EDGE(x + (kernel_x - radius) * conv_params->dilation, width);
                                 input_pel = input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
                             } else {
                                 int y_pos = y + (kernel_y - radius) * conv_params->dilation;
                                 int x_pos = x + (kernel_x - radius) * conv_params->dilation;
                                 input_pel = (x_pos < 0 || x_pos >= width || y_pos < 0 || y_pos >= height) ? 0.0 :
                                                    input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
                             }


                             output[n_filter] += input_pel * conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
                                                                                 kernel_x * conv_params->input_num + ch];
                         }
                     }
                 }
                 switch (conv_params->activation){
                 case RELU:
                     output[n_filter] = FFMAX(output[n_filter], 0.0);
                     break;
                 case TANH:
                     output[n_filter] = 2.0f  / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
                     break;
                 case SIGMOID:
                     output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
                     break;
                 case NONE:
                     break;
                 case LEAKY_RELU:
                     output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
                 }
             }
             output += conv_params->output_num;
         }
     }
 }

 static void depth_to_space(const float *input, float *output, int block_size, int width, int height, int channels)
 {
     int y, x, by, bx, ch;
     int new_channels = channels / (block_size * block_size);
     int output_linesize = width * channels;
     int by_linesize = output_linesize / block_size;
     int x_linesize = new_channels * block_size;

     for (y = 0; y < height; ++y){
         for (x = 0; x < width; ++x){
             for (by = 0; by < block_size; ++by){
                 for (bx = 0; bx < block_size; ++bx){
                     for (ch = 0; ch < new_channels; ++ch){
                         output[by * by_linesize + x * x_linesize + bx * new_channels + ch] = input[ch];
                     }
                     input += new_channels;
                 }
             }
         }
         output += output_linesize;
     }
 }

 DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
 {
     ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
     int cur_width, cur_height, cur_channels;
     int32_t layer;
     InputParams *input_params;
     ConvolutionalParams *conv_params;
     DepthToSpaceParams *depth_to_space_params;

     if (network->layers_num <= 0 || network->layers[0].type != INPUT || !network->layers[0].output){
         return DNN_ERROR;
     }
     else{
         input_params = (InputParams *)network->layers[0].params;
         cur_width = input_params->width;
         cur_height = input_params->height;
         cur_channels = input_params->channels;
     }

     for (layer = 1; layer < network->layers_num; ++layer){
         if (!network->layers[layer].output){
             return DNN_ERROR;
         }
         switch (network->layers[layer].type){
         case CONV:
             conv_params = (ConvolutionalParams *)network->layers[layer].params;
             convolve(network->layers[layer - 1].output, network->layers[layer].output, conv_params, cur_width, cur_height);
             cur_channels = conv_params->output_num;
             if (conv_params->padding_method == VALID) {
                 int pad_size = (conv_params->kernel_size - 1) * conv_params->dilation;
                 cur_height -= pad_size;
                 cur_width -= pad_size;
             }
             break;
         case DEPTH_TO_SPACE:
             depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
             depth_to_space(network->layers[layer - 1].output, network->layers[layer].output,
                            depth_to_space_params->block_size, cur_width, cur_height, cur_channels);
             cur_height *= depth_to_space_params->block_size;
             cur_width *= depth_to_space_params->block_size;
             cur_channels /= depth_to_space_params->block_size * depth_to_space_params->block_size;
             break;
         case INPUT:
             return DNN_ERROR;
         }
     }

     // native mode does not support multiple outputs yet
     if (nb_output > 1)
         return DNN_ERROR;
     outputs[0].data = network->layers[network->layers_num - 1].output;
     outputs[0].height = cur_height;
     outputs[0].width = cur_width;
     outputs[0].channels = cur_channels;

     return DNN_SUCCESS;
 }

 void ff_dnn_free_model_native(DNNModel **model)
 {
     ConvolutionalNetwork *network;
     ConvolutionalParams *conv_params;
     int32_t layer;

     if (*model)
     {
         network = (ConvolutionalNetwork *)(*model)->model;
         for (layer = 0; layer < network->layers_num; ++layer){
             av_freep(&network->layers[layer].output);
             if (network->layers[layer].type == CONV){
                 conv_params = (ConvolutionalParams *)network->layers[layer].params;
                 av_freep(&conv_params->kernel);
                 av_freep(&conv_params->biases);
             }
             av_freep(&network->layers[layer].params);
         }
         av_freep(&network->layers);
         av_freep(&network);
         av_freep(model);
     }
 }
	/*
	* Copyright (c) 2018 Sergey Lavrushkin
	*
	* This file is part of FFmpeg.
	*
	* FFmpeg is free software; you can redistribute it and/or
	* modify it under the terms of the GNU Lesser General Public
	* License as published by the Free Software Foundation; either
	* version 2.1 of the License, or (at your option) any later version.
	*
	* FFmpeg is distributed in the hope that it will be useful,
	* but WITHOUT ANY WARRANTY; without even the implied warranty of
	* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
	* Lesser General Public License for more details.
	*
	* You should have received a copy of the GNU Lesser General Public
	* License along with FFmpeg; if not, write to the Free Software
	* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
	*/

	/**
	* @file
	* DNN native backend implementation.
	*/

	#include "dnn_backend_native.h"
	#include "libavutil/avassert.h"

	static DNNReturnType set_input_output_native(void model, DNNInputData input, const char input_name, const char *output_names, uint32_t nb_output)
	{
	ConvolutionalNetwork network = (ConvolutionalNetwork )model;
	InputParams *input_params;
	ConvolutionalParams *conv_params;
	DepthToSpaceParams *depth_to_space_params;
	int cur_width, cur_height, cur_channels;
	int32_t layer;

	if (network->layers_num <= 0 \|\| network->layers[0].type != INPUT){
	return DNN_ERROR;
	}
	else{
	input_params = (InputParams *)network->layers[0].params;
	input_params->width = cur_width = input->width;
	input_params->height = cur_height = input->height;
	input_params->channels = cur_channels = input->channels;
	if (input->data){
	av_freep(&input->data);
	}
	av_assert0(input->dt == DNN_FLOAT);
	network->layers[0].output = input->data = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
	if (!network->layers[0].output){
	return DNN_ERROR;
	}
	}

	for (layer = 1; layer < network->layers_num; ++layer){
	switch (network->layers[layer].type){
	case CONV:
	conv_params = (ConvolutionalParams *)network->layers[layer].params;
	if (conv_params->input_num != cur_channels){
	return DNN_ERROR;
	}
	cur_channels = conv_params->output_num;

	if (conv_params->padding_method == VALID) {
	int pad_size = (conv_params->kernel_size - 1) * conv_params->dilation;
	cur_height -= pad_size;
	cur_width -= pad_size;
	}
	break;
	case DEPTH_TO_SPACE:
	depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
	if (cur_channels % (depth_to_space_params->block_size * depth_to_space_params->block_size) != 0){
	return DNN_ERROR;
	}
	cur_channels = cur_channels / (depth_to_space_params->block_size * depth_to_space_params->block_size);
	cur_height *= depth_to_space_params->block_size;
	cur_width *= depth_to_space_params->block_size;
	break;
	default:
	return DNN_ERROR;
	}
	if (network->layers[layer].output){
	av_freep(&network->layers[layer].output);
	}

	if (cur_height <= 0 \|\| cur_width <= 0)
	return DNN_ERROR;

	network->layers[layer].output = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
	if (!network->layers[layer].output){
	return DNN_ERROR;
	}
	}

	return DNN_SUCCESS;
	}

	// Loads model and its parameters that are stored in a binary file with following structure:
	// layers_num,layer_type,layer_parameterss,layer_type,layer_parameters...
	// For CONV layer: activation_function, input_num, output_num, kernel_size, kernel, biases
	// For DEPTH_TO_SPACE layer: block_size
	DNNModel ff_dnn_load_model_native(const char model_filename)
	{
	DNNModel *model = NULL;
	ConvolutionalNetwork *network = NULL;
	AVIOContext *model_file_context;
	int file_size, dnn_size, kernel_size, i;
	int32_t layer;
	DNNLayerType layer_type;
	ConvolutionalParams *conv_params;
	DepthToSpaceParams *depth_to_space_params;

	model = av_malloc(sizeof(DNNModel));
	if (!model){
	return NULL;
	}

	if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
	av_freep(&model);
	return NULL;
	}
	file_size = avio_size(model_file_context);

	network = av_malloc(sizeof(ConvolutionalNetwork));
	if (!network){
	avio_closep(&model_file_context);
	av_freep(&model);
	return NULL;
	}
	model->model = (void *)network;

	network->layers_num = 1 + (int32_t)avio_rl32(model_file_context);
	dnn_size = 4;

	network->layers = av_malloc(network->layers_num * sizeof(Layer));
	if (!network->layers){
	av_freep(&network);
	avio_closep(&model_file_context);
	av_freep(&model);
	return NULL;
	}

	for (layer = 0; layer < network->layers_num; ++layer){
	network->layers[layer].output = NULL;
	network->layers[layer].params = NULL;
	}
	network->layers[0].type = INPUT;
	network->layers[0].params = av_malloc(sizeof(InputParams));
	if (!network->layers[0].params){
	avio_closep(&model_file_context);
	ff_dnn_free_model_native(&model);
	return NULL;
	}

	for (layer = 1; layer < network->layers_num; ++layer){
	layer_type = (int32_t)avio_rl32(model_file_context);
	dnn_size += 4;
	switch (layer_type){
	case CONV:
	conv_params = av_malloc(sizeof(ConvolutionalParams));
	if (!conv_params){
	avio_closep(&model_file_context);
	ff_dnn_free_model_native(&model);
	return NULL;
	}
	conv_params->dilation = (int32_t)avio_rl32(model_file_context);
	conv_params->padding_method = (int32_t)avio_rl32(model_file_context);
	conv_params->activation = (int32_t)avio_rl32(model_file_context);
	conv_params->input_num = (int32_t)avio_rl32(model_file_context);
	conv_params->output_num = (int32_t)avio_rl32(model_file_context);
	conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
	kernel_size = conv_params->input_num * conv_params->output_num *
	conv_params->kernel_size * conv_params->kernel_size;
	dnn_size += 24 + (kernel_size + conv_params->output_num << 2);
	if (dnn_size > file_size \|\| conv_params->input_num <= 0 \|\|
	conv_params->output_num <= 0 \|\| conv_params->kernel_size <= 0){
	avio_closep(&model_file_context);
	ff_dnn_free_model_native(&model);
	return NULL;
	}
	conv_params->kernel = av_malloc(kernel_size * sizeof(float));
	conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
	if (!conv_params->kernel \|\| !conv_params->biases){
	avio_closep(&model_file_context);
	ff_dnn_free_model_native(&model);
	return NULL;
	}
	for (i = 0; i < kernel_size; ++i){
	conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
	}
	for (i = 0; i < conv_params->output_num; ++i){
	conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
	}
	network->layers[layer].type = CONV;
	network->layers[layer].params = conv_params;
	break;
	case DEPTH_TO_SPACE:
	depth_to_space_params = av_malloc(sizeof(DepthToSpaceParams));
	if (!depth_to_space_params){
	avio_closep(&model_file_context);
	ff_dnn_free_model_native(&model);
	return NULL;
	}
	depth_to_space_params->block_size = (int32_t)avio_rl32(model_file_context);
	dnn_size += 4;
	network->layers[layer].type = DEPTH_TO_SPACE;
	network->layers[layer].params = depth_to_space_params;
	break;
	default:
	avio_closep(&model_file_context);
	ff_dnn_free_model_native(&model);
	return NULL;
	}
	}

	avio_closep(&model_file_context);

	if (dnn_size != file_size){
	ff_dnn_free_model_native(&model);
	return NULL;
	}

	model->set_input_output = &set_input_output_native;

	return model;
	}

	#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))

	static void convolve(const float input, float output, const ConvolutionalParams *conv_params, int width, int height)
	{
	int radius = conv_params->kernel_size >> 1;
	int src_linesize = width * conv_params->input_num;
	int filter_linesize = conv_params->kernel_size * conv_params->input_num;
	int filter_size = conv_params->kernel_size * filter_linesize;
	int pad_size = (conv_params->padding_method == VALID) ? (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;

	for (int y = pad_size; y < height - pad_size; ++y) {
	for (int x = pad_size; x < width - pad_size; ++x) {
	for (int n_filter = 0; n_filter < conv_params->output_num; ++n_filter) {
	output[n_filter] = conv_params->biases[n_filter];

	for (int ch = 0; ch < conv_params->input_num; ++ch) {
	for (int kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y) {
	for (int kernel_x = 0; kernel_x < conv_params->kernel_size; ++kernel_x) {
	float input_pel;
	if (conv_params->padding_method == SAME_CLAMP_TO_EDGE) {
	int y_pos = CLAMP_TO_EDGE(y + (kernel_y - radius) * conv_params->dilation, height);
	int x_pos = CLAMP_TO_EDGE(x + (kernel_x - radius) * conv_params->dilation, width);
	input_pel = input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
	} else {
	int y_pos = y + (kernel_y - radius) * conv_params->dilation;
	int x_pos = x + (kernel_x - radius) * conv_params->dilation;
	input_pel = (x_pos < 0 \|\| x_pos >= width \|\| y_pos < 0 \|\| y_pos >= height) ? 0.0 :
	input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
	}


	output[n_filter] += input_pel * conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
	kernel_x * conv_params->input_num + ch];
	}
	}
	}
	switch (conv_params->activation){
	case RELU:
	output[n_filter] = FFMAX(output[n_filter], 0.0);
	break;
	case TANH:
	output[n_filter] = 2.0f / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
	break;
	case SIGMOID:
	output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
	break;
	case NONE:
	break;
	case LEAKY_RELU:
	output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
	}
	}
	output += conv_params->output_num;
	}
	}
	}

	static void depth_to_space(const float input, float output, int block_size, int width, int height, int channels)
	{
	int y, x, by, bx, ch;
	int new_channels = channels / (block_size * block_size);
	int output_linesize = width * channels;
	int by_linesize = output_linesize / block_size;
	int x_linesize = new_channels * block_size;

	for (y = 0; y < height; ++y){
	for (x = 0; x < width; ++x){
	for (by = 0; by < block_size; ++by){
	for (bx = 0; bx < block_size; ++bx){
	for (ch = 0; ch < new_channels; ++ch){
	output[by * by_linesize + x * x_linesize + bx * new_channels + ch] = input[ch];
	}
	input += new_channels;
	}
	}
	}
	output += output_linesize;
	}
	}

	DNNReturnType ff_dnn_execute_model_native(const DNNModel model, DNNData outputs, uint32_t nb_output)
	{
	ConvolutionalNetwork network = (ConvolutionalNetwork )model->model;
	int cur_width, cur_height, cur_channels;
	int32_t layer;
	InputParams *input_params;
	ConvolutionalParams *conv_params;
	DepthToSpaceParams *depth_to_space_params;

	if (network->layers_num <= 0 \|\| network->layers[0].type != INPUT \|\| !network->layers[0].output){
	return DNN_ERROR;
	}
	else{
	input_params = (InputParams *)network->layers[0].params;
	cur_width = input_params->width;
	cur_height = input_params->height;
	cur_channels = input_params->channels;
	}

	for (layer = 1; layer < network->layers_num; ++layer){
	if (!network->layers[layer].output){
	return DNN_ERROR;
	}
	switch (network->layers[layer].type){
	case CONV:
	conv_params = (ConvolutionalParams *)network->layers[layer].params;
	convolve(network->layers[layer - 1].output, network->layers[layer].output, conv_params, cur_width, cur_height);
	cur_channels = conv_params->output_num;
	if (conv_params->padding_method == VALID) {
	int pad_size = (conv_params->kernel_size - 1) * conv_params->dilation;
	cur_height -= pad_size;
	cur_width -= pad_size;
	}
	break;
	case DEPTH_TO_SPACE:
	depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
	depth_to_space(network->layers[layer - 1].output, network->layers[layer].output,
	depth_to_space_params->block_size, cur_width, cur_height, cur_channels);
	cur_height *= depth_to_space_params->block_size;
	cur_width *= depth_to_space_params->block_size;
	cur_channels /= depth_to_space_params->block_size * depth_to_space_params->block_size;
	break;
	case INPUT:
	return DNN_ERROR;
	}
	}

	// native mode does not support multiple outputs yet
	if (nb_output > 1)
	return DNN_ERROR;
	outputs[0].data = network->layers[network->layers_num - 1].output;
	outputs[0].height = cur_height;
	outputs[0].width = cur_width;
	outputs[0].channels = cur_channels;

	return DNN_SUCCESS;
	}

	void ff_dnn_free_model_native(DNNModel **model)
	{
	ConvolutionalNetwork *network;
	ConvolutionalParams *conv_params;
	int32_t layer;

	if (*model)
	{
	network = (ConvolutionalNetwork )(model)->model;
	for (layer = 0; layer < network->layers_num; ++layer){
	av_freep(&network->layers[layer].output);
	if (network->layers[layer].type == CONV){
	conv_params = (ConvolutionalParams *)network->layers[layer].params;
	av_freep(&conv_params->kernel);
	av_freep(&conv_params->biases);
	}
	av_freep(&network->layers[layer].params);
	}
	av_freep(&network->layers);
	av_freep(&network);
	av_freep(model);
	}
	}