mirror of https://git.ffmpeg.org/ffmpeg.git
383 lines
13 KiB
C
383 lines
13 KiB
C
/*
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* Copyright (c) 2018 Sergey Lavrushkin
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*
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* This file is part of FFmpeg.
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*
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* FFmpeg is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* FFmpeg is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with FFmpeg; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*/
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/**
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* @file
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* DNN native backend implementation.
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*/
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#include "dnn_backend_native.h"
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#include "dnn_srcnn.h"
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#include "libavformat/avio.h"
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typedef enum {INPUT, CONV} LayerType;
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typedef struct Layer{
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LayerType type;
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float* output;
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void* params;
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} Layer;
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typedef struct ConvolutionalParams{
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int32_t input_num, output_num, kernel_size;
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float* kernel;
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float* biases;
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} ConvolutionalParams;
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typedef struct InputParams{
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int height, width, channels;
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} InputParams;
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// Represents simple feed-forward convolutional network.
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typedef struct ConvolutionalNetwork{
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Layer* layers;
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int32_t layers_num;
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} ConvolutionalNetwork;
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static DNNReturnType set_input_output_native(void* model, const DNNData* input, const DNNData* output)
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{
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ConvolutionalNetwork* network = (ConvolutionalNetwork*)model;
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InputParams* input_params;
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ConvolutionalParams* conv_params;
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int cur_width, cur_height, cur_channels;
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int32_t layer;
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if (network->layers_num <= 0 || network->layers[0].type != INPUT){
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return DNN_ERROR;
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}
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else{
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network->layers[0].output = input->data;
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input_params = (InputParams*)network->layers[0].params;
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input_params->width = cur_width = input->width;
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input_params->height = cur_height = input->height;
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input_params->channels = cur_channels = input->channels;
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}
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for (layer = 1; layer < network->layers_num; ++layer){
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switch (network->layers[layer].type){
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case CONV:
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conv_params = (ConvolutionalParams*)network->layers[layer].params;
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if (conv_params->input_num != cur_channels){
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return DNN_ERROR;
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}
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cur_channels = conv_params->output_num;
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if (layer < network->layers_num - 1){
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if (!network->layers[layer].output){
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av_freep(&network->layers[layer].output);
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}
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network->layers[layer].output = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
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if (!network->layers[layer].output){
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return DNN_ERROR;
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}
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}
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else{
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network->layers[layer].output = output->data;
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if (output->width != cur_width || output->height != cur_height || output->channels != cur_channels){
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return DNN_ERROR;
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}
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}
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break;
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default:
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return DNN_ERROR;
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}
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}
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return DNN_SUCCESS;
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}
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// Loads model and its parameters that are stored in a binary file with following structure:
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// layers_num,conv_input_num,conv_output_num,conv_kernel_size,conv_kernel,conv_biases,conv_input_num...
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DNNModel* ff_dnn_load_model_native(const char* model_filename)
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{
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DNNModel* model = NULL;
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ConvolutionalNetwork* network = NULL;
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AVIOContext* model_file_context;
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int file_size, dnn_size, kernel_size, i;
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int32_t layer;
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ConvolutionalParams* conv_params;
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model = av_malloc(sizeof(DNNModel));
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if (!model){
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return NULL;
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}
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if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
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av_freep(&model);
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return NULL;
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}
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file_size = avio_size(model_file_context);
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network = av_malloc(sizeof(ConvolutionalNetwork));
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if (!network){
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avio_closep(&model_file_context);
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av_freep(&model);
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return NULL;
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}
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model->model = (void*)network;
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network->layers_num = 1 + (int32_t)avio_rl32(model_file_context);
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dnn_size = 4;
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network->layers = av_malloc(network->layers_num * sizeof(Layer));
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if (!network->layers){
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av_freep(&network);
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avio_closep(&model_file_context);
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av_freep(&model);
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return NULL;
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}
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for (layer = 0; layer < network->layers_num; ++layer){
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network->layers[layer].output = NULL;
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network->layers[layer].params = NULL;
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}
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network->layers[0].type = INPUT;
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network->layers[0].params = av_malloc(sizeof(InputParams));
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if (!network->layers[0].params){
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avio_closep(&model_file_context);
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ff_dnn_free_model_native(&model);
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return NULL;
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}
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for (layer = 1; layer < network->layers_num; ++layer){
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conv_params = av_malloc(sizeof(ConvolutionalParams));
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if (!conv_params){
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avio_closep(&model_file_context);
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ff_dnn_free_model_native(&model);
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return NULL;
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}
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conv_params->input_num = (int32_t)avio_rl32(model_file_context);
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conv_params->output_num = (int32_t)avio_rl32(model_file_context);
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conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
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kernel_size = conv_params->input_num * conv_params->output_num *
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conv_params->kernel_size * conv_params->kernel_size;
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dnn_size += 12 + (kernel_size + conv_params->output_num << 2);
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if (dnn_size > file_size || conv_params->input_num <= 0 ||
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conv_params->output_num <= 0 || conv_params->kernel_size <= 0){
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avio_closep(&model_file_context);
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ff_dnn_free_model_native(&model);
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return NULL;
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}
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conv_params->kernel = av_malloc(kernel_size * sizeof(float));
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conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
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if (!conv_params->kernel || !conv_params->biases){
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avio_closep(&model_file_context);
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ff_dnn_free_model_native(&model);
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return NULL;
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}
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for (i = 0; i < kernel_size; ++i){
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conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
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}
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for (i = 0; i < conv_params->output_num; ++i){
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conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
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}
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network->layers[layer].type = CONV;
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network->layers[layer].params = conv_params;
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}
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avio_closep(&model_file_context);
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if (dnn_size != file_size){
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ff_dnn_free_model_native(&model);
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return NULL;
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}
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model->set_input_output = &set_input_output_native;
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return model;
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}
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static int set_up_conv_layer(Layer* layer, const float* kernel, const float* biases, int32_t input_num, int32_t output_num, int32_t size)
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{
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ConvolutionalParams* conv_params;
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int kernel_size;
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conv_params = av_malloc(sizeof(ConvolutionalParams));
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if (!conv_params){
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return DNN_ERROR;
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}
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conv_params->input_num = input_num;
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conv_params->output_num = output_num;
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conv_params->kernel_size = size;
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kernel_size = input_num * output_num * size * size;
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conv_params->kernel = av_malloc(kernel_size * sizeof(float));
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conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
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if (!conv_params->kernel || !conv_params->biases){
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av_freep(&conv_params->kernel);
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av_freep(&conv_params->biases);
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av_freep(&conv_params);
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return DNN_ERROR;
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}
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memcpy(conv_params->kernel, kernel, kernel_size * sizeof(float));
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memcpy(conv_params->biases, biases, output_num * sizeof(float));
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layer->type = CONV;
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layer->params = conv_params;
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return DNN_SUCCESS;
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}
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DNNModel* ff_dnn_load_default_model_native(DNNDefaultModel model_type)
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{
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DNNModel* model = NULL;
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ConvolutionalNetwork* network = NULL;
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int32_t layer;
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model = av_malloc(sizeof(DNNModel));
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if (!model){
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return NULL;
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}
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network = av_malloc(sizeof(ConvolutionalNetwork));
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if (!network){
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av_freep(&model);
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return NULL;
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}
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model->model = (void*)network;
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switch (model_type){
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case DNN_SRCNN:
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network->layers_num = 4;
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network->layers = av_malloc(network->layers_num * sizeof(Layer));
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if (!network->layers){
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av_freep(&network);
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av_freep(&model);
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return NULL;
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}
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for (layer = 0; layer < network->layers_num; ++layer){
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network->layers[layer].output = NULL;
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network->layers[layer].params = NULL;
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}
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network->layers[0].type = INPUT;
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network->layers[0].params = av_malloc(sizeof(InputParams));
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if (!network->layers[0].params){
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ff_dnn_free_model_native(&model);
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return NULL;
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}
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if (set_up_conv_layer(network->layers + 1, conv1_kernel, conv1_biases, 1, 64, 9) != DNN_SUCCESS ||
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set_up_conv_layer(network->layers + 2, conv2_kernel, conv2_biases, 64, 32, 1) != DNN_SUCCESS ||
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set_up_conv_layer(network->layers + 3, conv3_kernel, conv3_biases, 32, 1, 5) != DNN_SUCCESS){
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ff_dnn_free_model_native(&model);
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return NULL;
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}
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model->set_input_output = &set_input_output_native;
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return model;
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default:
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av_freep(&network);
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av_freep(&model);
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return NULL;
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}
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}
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#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
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static void convolve(const float* input, float* output, const ConvolutionalParams* conv_params, int32_t width, int32_t height)
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{
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int y, x, n_filter, ch, kernel_y, kernel_x;
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int radius = conv_params->kernel_size >> 1;
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int src_linesize = width * conv_params->input_num;
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int filter_linesize = conv_params->kernel_size * conv_params->input_num;
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int filter_size = conv_params->kernel_size * filter_linesize;
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for (y = 0; y < height; ++y){
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for (x = 0; x < width; ++x){
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for (n_filter = 0; n_filter < conv_params->output_num; ++n_filter){
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output[n_filter] = conv_params->biases[n_filter];
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for (ch = 0; ch < conv_params->input_num; ++ch){
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for (kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y){
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for (kernel_x = 0; kernel_x < conv_params->kernel_size; ++kernel_x){
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output[n_filter] += input[CLAMP_TO_EDGE(y + kernel_y - radius, height) * src_linesize +
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CLAMP_TO_EDGE(x + kernel_x - radius, width) * conv_params->input_num + ch] *
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conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
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kernel_x * conv_params->input_num + ch];
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}
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}
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}
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output[n_filter] = FFMAX(output[n_filter], 0.0);
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}
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output += conv_params->output_num;
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}
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}
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}
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DNNReturnType ff_dnn_execute_model_native(const DNNModel* model)
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{
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ConvolutionalNetwork* network = (ConvolutionalNetwork*)model->model;
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InputParams* input_params;
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int cur_width, cur_height;
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int32_t layer;
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if (network->layers_num <= 0 || network->layers[0].type != INPUT || !network->layers[0].output){
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return DNN_ERROR;
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}
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else{
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input_params = (InputParams*)network->layers[0].params;
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cur_width = input_params->width;
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cur_height = input_params->height;
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}
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for (layer = 1; layer < network->layers_num; ++layer){
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if (!network->layers[layer].output){
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return DNN_ERROR;
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}
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switch (network->layers[layer].type){
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case CONV:
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convolve(network->layers[layer - 1].output, network->layers[layer].output, (ConvolutionalParams*)network->layers[layer].params, cur_width, cur_height);
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break;
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case INPUT:
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return DNN_ERROR;
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}
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}
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return DNN_SUCCESS;
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}
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void ff_dnn_free_model_native(DNNModel** model)
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{
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ConvolutionalNetwork* network;
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ConvolutionalParams* conv_params;
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int32_t layer;
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if (*model)
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{
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network = (ConvolutionalNetwork*)(*model)->model;
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for (layer = 0; layer < network->layers_num; ++layer){
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switch (network->layers[layer].type){
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case CONV:
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if (layer < network->layers_num - 1){
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av_freep(&network->layers[layer].output);
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}
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conv_params = (ConvolutionalParams*)network->layers[layer].params;
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av_freep(&conv_params->kernel);
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av_freep(&conv_params->biases);
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av_freep(&conv_params);
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break;
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case INPUT:
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av_freep(&network->layers[layer].params);
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}
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}
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av_freep(network);
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av_freep(model);
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}
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}
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