| /* |
| * 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" |
| |
| static DNNReturnType set_input_output_native(void *model, DNNData *input, DNNData *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); |
| } |
| 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; |
| 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); |
| } |
| network->layers[layer].output = av_malloc(cur_height * cur_width * cur_channels * sizeof(float)); |
| if (!network->layers[layer].output){ |
| return DNN_ERROR; |
| } |
| } |
| |
| output->data = network->layers[network->layers_num - 1].output; |
| output->height = cur_height; |
| output->width = cur_width; |
| output->channels = cur_channels; |
| |
| 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->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 += 16 + (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 y, x, n_filter, ch, kernel_y, kernel_x; |
| 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; |
| |
| for (y = 0; y < height; ++y){ |
| for (x = 0; x < width; ++x){ |
| for (n_filter = 0; n_filter < conv_params->output_num; ++n_filter){ |
| output[n_filter] = conv_params->biases[n_filter]; |
| for (ch = 0; ch < conv_params->input_num; ++ch){ |
| for (kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y){ |
| for (kernel_x = 0; kernel_x < conv_params->kernel_size; ++kernel_x){ |
| output[n_filter] += input[CLAMP_TO_EDGE(y + kernel_y - radius, height) * src_linesize + |
| CLAMP_TO_EDGE(x + kernel_x - radius, width) * conv_params->input_num + ch] * |
| 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])); |
| } |
| } |
| 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) |
| { |
| 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; |
| 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; |
| } |
| } |
| |
| 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); |
| } |
| } |