Em sex, 26 de jul de 2019 às 13:02, Pedro Arthur <bygran...@gmail.com> escreveu:
>
> Hi,
> It fails fate source guard header tests,
> The headers should be changed from AVFILTER_DNN_BACKEND_xxx to
> AVFILTER_DNN_DNN_BACKEND_xxx.
Changed locally and pushed.
> Other than that it LGTM.
>
> Em ter, 16 de jul de 2019 às 02:58, Guo, Yejun <yejun....@intel.com> escreveu:
> >
> > it is expected that there will be more files to support native mode,
> > so put all the dnn codes under libavfilter/dnn
> >
> > The main change of this patch is to move the file location, see below:
> > modified: libavfilter/Makefile
> > new file: libavfilter/dnn/Makefile
> > renamed: libavfilter/dnn_backend_native.c ->
> > libavfilter/dnn/dnn_backend_native.c
> > renamed: libavfilter/dnn_backend_native.h ->
> > libavfilter/dnn/dnn_backend_native.h
> > renamed: libavfilter/dnn_backend_tf.c -> libavfilter/dnn/dnn_backend_tf.c
> > renamed: libavfilter/dnn_backend_tf.h -> libavfilter/dnn/dnn_backend_tf.h
> > renamed: libavfilter/dnn_interface.c -> libavfilter/dnn/dnn_interface.c
> >
> > Signed-off-by: Guo, Yejun <yejun....@intel.com>
> > ---
> > libavfilter/Makefile | 3 +-
> > libavfilter/dnn/Makefile | 6 +
> > libavfilter/dnn/dnn_backend_native.c | 389 ++++++++++++++++++++++
> > libavfilter/dnn/dnn_backend_native.h | 74 +++++
> > libavfilter/dnn/dnn_backend_tf.c | 603
> > +++++++++++++++++++++++++++++++++++
> > libavfilter/dnn/dnn_backend_tf.h | 38 +++
> > libavfilter/dnn/dnn_interface.c | 63 ++++
> > libavfilter/dnn_backend_native.c | 389 ----------------------
> > libavfilter/dnn_backend_native.h | 74 -----
> > libavfilter/dnn_backend_tf.c | 603
> > -----------------------------------
> > libavfilter/dnn_backend_tf.h | 38 ---
> > libavfilter/dnn_interface.c | 63 ----
> > 12 files changed, 1174 insertions(+), 1169 deletions(-)
> > create mode 100644 libavfilter/dnn/Makefile
> > create mode 100644 libavfilter/dnn/dnn_backend_native.c
> > create mode 100644 libavfilter/dnn/dnn_backend_native.h
> > create mode 100644 libavfilter/dnn/dnn_backend_tf.c
> > create mode 100644 libavfilter/dnn/dnn_backend_tf.h
> > create mode 100644 libavfilter/dnn/dnn_interface.c
> > delete mode 100644 libavfilter/dnn_backend_native.c
> > delete mode 100644 libavfilter/dnn_backend_native.h
> > delete mode 100644 libavfilter/dnn_backend_tf.c
> > delete mode 100644 libavfilter/dnn_backend_tf.h
> > delete mode 100644 libavfilter/dnn_interface.c
> >
> > diff --git a/libavfilter/Makefile b/libavfilter/Makefile
> > index 455c809..450d781 100644
> > --- a/libavfilter/Makefile
> > +++ b/libavfilter/Makefile
> > @@ -26,9 +26,8 @@ OBJS-$(HAVE_THREADS) += pthread.o
> >
> > # subsystems
> > OBJS-$(CONFIG_QSVVPP) += qsvvpp.o
> > -DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn_backend_tf.o
> > -OBJS-$(CONFIG_DNN) += dnn_interface.o
> > dnn_backend_native.o $(DNN-OBJS-yes)
> > OBJS-$(CONFIG_SCENE_SAD) += scene_sad.o
> > +include $(SRC_PATH)/libavfilter/dnn/Makefile
> >
> > # audio filters
> > OBJS-$(CONFIG_ABENCH_FILTER) += f_bench.o
> > diff --git a/libavfilter/dnn/Makefile b/libavfilter/dnn/Makefile
> > new file mode 100644
> > index 0000000..1d12ade
> > --- /dev/null
> > +++ b/libavfilter/dnn/Makefile
> > @@ -0,0 +1,6 @@
> > +OBJS-$(CONFIG_DNN) += dnn/dnn_interface.o
> > +OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native.o
> > +
> > +DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn/dnn_backend_tf.o
> > +
> > +OBJS-$(CONFIG_DNN) += $(DNN-OBJS-yes)
> > diff --git a/libavfilter/dnn/dnn_backend_native.c
> > b/libavfilter/dnn/dnn_backend_native.c
> > new file mode 100644
> > index 0000000..82e900b
> > --- /dev/null
> > +++ b/libavfilter/dnn/dnn_backend_native.c
> > @@ -0,0 +1,389 @@
> > +/*
> > + * 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);
> > + }
> > +}
> > diff --git a/libavfilter/dnn/dnn_backend_native.h
> > b/libavfilter/dnn/dnn_backend_native.h
> > new file mode 100644
> > index 0000000..532103c
> > --- /dev/null
> > +++ b/libavfilter/dnn/dnn_backend_native.h
> > @@ -0,0 +1,74 @@
> > +/*
> > + * 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 inference functions interface for native backend.
> > + */
> > +
> > +
> > +#ifndef AVFILTER_DNN_BACKEND_NATIVE_H
> > +#define AVFILTER_DNN_BACKEND_NATIVE_H
> > +
> > +#include "../dnn_interface.h"
> > +#include "libavformat/avio.h"
> > +
> > +typedef enum {INPUT, CONV, DEPTH_TO_SPACE} DNNLayerType;
> > +
> > +typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;
> > +
> > +typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNConvPaddingParam;
> > +
> > +typedef struct Layer{
> > + DNNLayerType type;
> > + float *output;
> > + void *params;
> > +} Layer;
> > +
> > +typedef struct ConvolutionalParams{
> > + int32_t input_num, output_num, kernel_size;
> > + DNNActivationFunc activation;
> > + DNNConvPaddingParam padding_method;
> > + int32_t dilation;
> > + float *kernel;
> > + float *biases;
> > +} ConvolutionalParams;
> > +
> > +typedef struct InputParams{
> > + int height, width, channels;
> > +} InputParams;
> > +
> > +typedef struct DepthToSpaceParams{
> > + int block_size;
> > +} DepthToSpaceParams;
> > +
> > +// Represents simple feed-forward convolutional network.
> > +typedef struct ConvolutionalNetwork{
> > + Layer *layers;
> > + int32_t layers_num;
> > +} ConvolutionalNetwork;
> > +
> > +DNNModel *ff_dnn_load_model_native(const char *model_filename);
> > +
> > +DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData
> > *outputs, uint32_t nb_output);
> > +
> > +void ff_dnn_free_model_native(DNNModel **model);
> > +
> > +#endif
> > diff --git a/libavfilter/dnn/dnn_backend_tf.c
> > b/libavfilter/dnn/dnn_backend_tf.c
> > new file mode 100644
> > index 0000000..ba959ae
> > --- /dev/null
> > +++ b/libavfilter/dnn/dnn_backend_tf.c
> > @@ -0,0 +1,603 @@
> > +/*
> > + * 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 tensorflow backend implementation.
> > + */
> > +
> > +#include "dnn_backend_tf.h"
> > +#include "dnn_backend_native.h"
> > +#include "libavformat/avio.h"
> > +#include "libavutil/avassert.h"
> > +
> > +#include <tensorflow/c/c_api.h>
> > +
> > +typedef struct TFModel{
> > + TF_Graph *graph;
> > + TF_Session *session;
> > + TF_Status *status;
> > + TF_Output input;
> > + TF_Tensor *input_tensor;
> > + TF_Output *outputs;
> > + TF_Tensor **output_tensors;
> > + uint32_t nb_output;
> > +} TFModel;
> > +
> > +static void free_buffer(void *data, size_t length)
> > +{
> > + av_freep(&data);
> > +}
> > +
> > +static TF_Buffer *read_graph(const char *model_filename)
> > +{
> > + TF_Buffer *graph_buf;
> > + unsigned char *graph_data = NULL;
> > + AVIOContext *model_file_context;
> > + long size, bytes_read;
> > +
> > + if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) <
> > 0){
> > + return NULL;
> > + }
> > +
> > + size = avio_size(model_file_context);
> > +
> > + graph_data = av_malloc(size);
> > + if (!graph_data){
> > + avio_closep(&model_file_context);
> > + return NULL;
> > + }
> > + bytes_read = avio_read(model_file_context, graph_data, size);
> > + avio_closep(&model_file_context);
> > + if (bytes_read != size){
> > + av_freep(&graph_data);
> > + return NULL;
> > + }
> > +
> > + graph_buf = TF_NewBuffer();
> > + graph_buf->data = (void *)graph_data;
> > + graph_buf->length = size;
> > + graph_buf->data_deallocator = free_buffer;
> > +
> > + return graph_buf;
> > +}
> > +
> > +static TF_Tensor *allocate_input_tensor(const DNNInputData *input)
> > +{
> > + TF_DataType dt;
> > + size_t size;
> > + int64_t input_dims[] = {1, input->height, input->width,
> > input->channels};
> > + switch (input->dt) {
> > + case DNN_FLOAT:
> > + dt = TF_FLOAT;
> > + size = sizeof(float);
> > + break;
> > + case DNN_UINT8:
> > + dt = TF_UINT8;
> > + size = sizeof(char);
> > + break;
> > + default:
> > + av_assert0(!"should not reach here");
> > + }
> > +
> > + return TF_AllocateTensor(dt, input_dims, 4,
> > + input_dims[1] * input_dims[2] * input_dims[3]
> > * size);
> > +}
> > +
> > +static DNNReturnType set_input_output_tf(void *model, DNNInputData *input,
> > const char *input_name, const char **output_names, uint32_t nb_output)
> > +{
> > + TFModel *tf_model = (TFModel *)model;
> > + TF_SessionOptions *sess_opts;
> > + const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph,
> > "init");
> > +
> > + // Input operation
> > + tf_model->input.oper = TF_GraphOperationByName(tf_model->graph,
> > input_name);
> > + if (!tf_model->input.oper){
> > + return DNN_ERROR;
> > + }
> > + tf_model->input.index = 0;
> > + if (tf_model->input_tensor){
> > + TF_DeleteTensor(tf_model->input_tensor);
> > + }
> > + tf_model->input_tensor = allocate_input_tensor(input);
> > + if (!tf_model->input_tensor){
> > + return DNN_ERROR;
> > + }
> > + input->data = (float *)TF_TensorData(tf_model->input_tensor);
> > +
> > + // Output operation
> > + if (nb_output == 0)
> > + return DNN_ERROR;
> > +
> > + av_freep(&tf_model->outputs);
> > + tf_model->outputs = av_malloc_array(nb_output,
> > sizeof(*tf_model->outputs));
> > + if (!tf_model->outputs)
> > + return DNN_ERROR;
> > + for (int i = 0; i < nb_output; ++i) {
> > + tf_model->outputs[i].oper =
> > TF_GraphOperationByName(tf_model->graph, output_names[i]);
> > + if (!tf_model->outputs[i].oper){
> > + av_freep(&tf_model->outputs);
> > + return DNN_ERROR;
> > + }
> > + tf_model->outputs[i].index = 0;
> > + }
> > +
> > + if (tf_model->output_tensors) {
> > + for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> > + if (tf_model->output_tensors[i]) {
> > + TF_DeleteTensor(tf_model->output_tensors[i]);
> > + tf_model->output_tensors[i] = NULL;
> > + }
> > + }
> > + }
> > + av_freep(&tf_model->output_tensors);
> > + tf_model->output_tensors = av_mallocz_array(nb_output,
> > sizeof(*tf_model->output_tensors));
> > + if (!tf_model->output_tensors) {
> > + av_freep(&tf_model->outputs);
> > + return DNN_ERROR;
> > + }
> > +
> > + tf_model->nb_output = nb_output;
> > +
> > + if (tf_model->session){
> > + TF_CloseSession(tf_model->session, tf_model->status);
> > + TF_DeleteSession(tf_model->session, tf_model->status);
> > + }
> > +
> > + sess_opts = TF_NewSessionOptions();
> > + tf_model->session = TF_NewSession(tf_model->graph, sess_opts,
> > tf_model->status);
> > + TF_DeleteSessionOptions(sess_opts);
> > + if (TF_GetCode(tf_model->status) != TF_OK)
> > + {
> > + return DNN_ERROR;
> > + }
> > +
> > + // Run initialization operation with name "init" if it is present in
> > graph
> > + if (init_op){
> > + TF_SessionRun(tf_model->session, NULL,
> > + NULL, NULL, 0,
> > + NULL, NULL, 0,
> > + &init_op, 1, NULL, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK)
> > + {
> > + return DNN_ERROR;
> > + }
> > + }
> > +
> > + return DNN_SUCCESS;
> > +}
> > +
> > +static DNNReturnType load_tf_model(TFModel *tf_model, const char
> > *model_filename)
> > +{
> > + TF_Buffer *graph_def;
> > + TF_ImportGraphDefOptions *graph_opts;
> > +
> > + graph_def = read_graph(model_filename);
> > + if (!graph_def){
> > + return DNN_ERROR;
> > + }
> > + tf_model->graph = TF_NewGraph();
> > + tf_model->status = TF_NewStatus();
> > + graph_opts = TF_NewImportGraphDefOptions();
> > + TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts,
> > tf_model->status);
> > + TF_DeleteImportGraphDefOptions(graph_opts);
> > + TF_DeleteBuffer(graph_def);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + TF_DeleteGraph(tf_model->graph);
> > + TF_DeleteStatus(tf_model->status);
> > + return DNN_ERROR;
> > + }
> > +
> > + return DNN_SUCCESS;
> > +}
> > +
> > +#define NAME_BUFFER_SIZE 256
> > +
> > +static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation
> > *transpose_op, TF_Operation **cur_op,
> > + ConvolutionalParams* params, const int
> > layer)
> > +{
> > + TF_Operation *op;
> > + TF_OperationDescription *op_desc;
> > + TF_Output input;
> > + int64_t strides[] = {1, 1, 1, 1};
> > + TF_Tensor *tensor;
> > + int64_t dims[4];
> > + int dims_len;
> > + char name_buffer[NAME_BUFFER_SIZE];
> > + int32_t size;
> > +
> > + size = params->input_num * params->output_num * params->kernel_size *
> > params->kernel_size;
> > + input.index = 0;
> > +
> > + snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
> > + op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
> > + TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> > + dims[0] = params->output_num;
> > + dims[1] = params->kernel_size;
> > + dims[2] = params->kernel_size;
> > + dims[3] = params->input_num;
> > + dims_len = 4;
> > + tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size *
> > sizeof(float));
> > + memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float));
> > + TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > + op = TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > +
> > + snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
> > + op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
> > + input.oper = op;
> > + TF_AddInput(op_desc, input);
> > + input.oper = transpose_op;
> > + TF_AddInput(op_desc, input);
> > + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > + TF_SetAttrType(op_desc, "Tperm", TF_INT32);
> > + op = TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > +
> > + snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
> > + op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
> > + input.oper = *cur_op;
> > + TF_AddInput(op_desc, input);
> > + input.oper = op;
> > + TF_AddInput(op_desc, input);
> > + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > + TF_SetAttrIntList(op_desc, "strides", strides, 4);
> > + TF_SetAttrString(op_desc, "padding", "VALID", 5);
> > + *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > +
> > + snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
> > + op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
> > + TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> > + dims[0] = params->output_num;
> > + dims_len = 1;
> > + tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len,
> > params->output_num * sizeof(float));
> > + memcpy(TF_TensorData(tensor), params->biases, params->output_num *
> > sizeof(float));
> > + TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > + op = TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > +
> > + snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
> > + op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
> > + input.oper = *cur_op;
> > + TF_AddInput(op_desc, input);
> > + input.oper = op;
> > + TF_AddInput(op_desc, input);
> > + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > + *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > +
> > + snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
> > + switch (params->activation){
> > + case RELU:
> > + op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
> > + break;
> > + case TANH:
> > + op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
> > + break;
> > + case SIGMOID:
> > + op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
> > + break;
> > + default:
> > + return DNN_ERROR;
> > + }
> > + input.oper = *cur_op;
> > + TF_AddInput(op_desc, input);
> > + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > + *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > +
> > + return DNN_SUCCESS;
> > +}
> > +
> > +static DNNReturnType add_depth_to_space_layer(TFModel *tf_model,
> > TF_Operation **cur_op,
> > + DepthToSpaceParams *params,
> > const int layer)
> > +{
> > + TF_OperationDescription *op_desc;
> > + TF_Output input;
> > + char name_buffer[NAME_BUFFER_SIZE];
> > +
> > + snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
> > + op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace",
> > name_buffer);
> > + input.oper = *cur_op;
> > + input.index = 0;
> > + TF_AddInput(op_desc, input);
> > + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > + TF_SetAttrInt(op_desc, "block_size", params->block_size);
> > + *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > +
> > + return DNN_SUCCESS;
> > +}
> > +
> > +static int calculate_pad(const ConvolutionalNetwork *conv_network)
> > +{
> > + ConvolutionalParams *params;
> > + int32_t layer;
> > + int pad = 0;
> > +
> > + for (layer = 0; layer < conv_network->layers_num; ++layer){
> > + if (conv_network->layers[layer].type == CONV){
> > + params = (ConvolutionalParams
> > *)conv_network->layers[layer].params;
> > + pad += params->kernel_size >> 1;
> > + }
> > + }
> > +
> > + return pad;
> > +}
> > +
> > +static DNNReturnType add_pad_op(TFModel *tf_model, TF_Operation **cur_op,
> > const int32_t pad)
> > +{
> > + TF_Operation *op;
> > + TF_Tensor *tensor;
> > + TF_OperationDescription *op_desc;
> > + TF_Output input;
> > + int32_t *pads;
> > + int64_t pads_shape[] = {4, 2};
> > +
> > + input.index = 0;
> > +
> > + op_desc = TF_NewOperation(tf_model->graph, "Const", "pads");
> > + TF_SetAttrType(op_desc, "dtype", TF_INT32);
> > + tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 *
> > sizeof(int32_t));
> > + pads = (int32_t *)TF_TensorData(tensor);
> > + pads[0] = 0; pads[1] = 0;
> > + pads[2] = pad; pads[3] = pad;
> > + pads[4] = pad; pads[5] = pad;
> > + pads[6] = 0; pads[7] = 0;
> > + TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > + op = TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > +
> > + op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
> > + input.oper = *cur_op;
> > + TF_AddInput(op_desc, input);
> > + input.oper = op;
> > + TF_AddInput(op_desc, input);
> > + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > + TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
> > + TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
> > + *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > +
> > + return DNN_SUCCESS;
> > +}
> > +
> > +static DNNReturnType load_native_model(TFModel *tf_model, const char
> > *model_filename)
> > +{
> > + int32_t layer;
> > + TF_OperationDescription *op_desc;
> > + TF_Operation *op;
> > + TF_Operation *transpose_op;
> > + TF_Tensor *tensor;
> > + TF_Output input;
> > + int32_t *transpose_perm;
> > + int64_t transpose_perm_shape[] = {4};
> > + int64_t input_shape[] = {1, -1, -1, -1};
> > + int32_t pad;
> > + DNNReturnType layer_add_res;
> > + DNNModel *native_model = NULL;
> > + ConvolutionalNetwork *conv_network;
> > +
> > + native_model = ff_dnn_load_model_native(model_filename);
> > + if (!native_model){
> > + return DNN_ERROR;
> > + }
> > +
> > + conv_network = (ConvolutionalNetwork *)native_model->model;
> > + pad = calculate_pad(conv_network);
> > + tf_model->graph = TF_NewGraph();
> > + tf_model->status = TF_NewStatus();
> > +
> > +#define CLEANUP_ON_ERROR(tf_model) \
> > + { \
> > + TF_DeleteGraph(tf_model->graph); \
> > + TF_DeleteStatus(tf_model->status); \
> > + return DNN_ERROR; \
> > + }
> > +
> > + op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
> > + TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> > + TF_SetAttrShape(op_desc, "shape", input_shape, 4);
> > + op = TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + CLEANUP_ON_ERROR(tf_model);
> > + }
> > +
> > + if (add_pad_op(tf_model, &op, pad) != DNN_SUCCESS){
> > + CLEANUP_ON_ERROR(tf_model);
> > + }
> > +
> > + op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
> > + TF_SetAttrType(op_desc, "dtype", TF_INT32);
> > + tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 *
> > sizeof(int32_t));
> > + transpose_perm = (int32_t *)TF_TensorData(tensor);
> > + transpose_perm[0] = 1;
> > + transpose_perm[1] = 2;
> > + transpose_perm[2] = 3;
> > + transpose_perm[3] = 0;
> > + TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + CLEANUP_ON_ERROR(tf_model);
> > + }
> > + transpose_op = TF_FinishOperation(op_desc, tf_model->status);
> > +
> > + for (layer = 0; layer < conv_network->layers_num; ++layer){
> > + switch (conv_network->layers[layer].type){
> > + case INPUT:
> > + layer_add_res = DNN_SUCCESS;
> > + break;
> > + case CONV:
> > + layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
> > + (ConvolutionalParams
> > *)conv_network->layers[layer].params, layer);
> > + break;
> > + case DEPTH_TO_SPACE:
> > + layer_add_res = add_depth_to_space_layer(tf_model, &op,
> > + (DepthToSpaceParams
> > *)conv_network->layers[layer].params, layer);
> > + break;
> > + default:
> > + CLEANUP_ON_ERROR(tf_model);
> > + }
> > +
> > + if (layer_add_res != DNN_SUCCESS){
> > + CLEANUP_ON_ERROR(tf_model);
> > + }
> > + }
> > +
> > + op_desc = TF_NewOperation(tf_model->graph, "Identity", "y");
> > + input.oper = op;
> > + TF_AddInput(op_desc, input);
> > + TF_FinishOperation(op_desc, tf_model->status);
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + CLEANUP_ON_ERROR(tf_model);
> > + }
> > +
> > + ff_dnn_free_model_native(&native_model);
> > +
> > + return DNN_SUCCESS;
> > +}
> > +
> > +DNNModel *ff_dnn_load_model_tf(const char *model_filename)
> > +{
> > + DNNModel *model = NULL;
> > + TFModel *tf_model = NULL;
> > +
> > + model = av_malloc(sizeof(DNNModel));
> > + if (!model){
> > + return NULL;
> > + }
> > +
> > + tf_model = av_mallocz(sizeof(TFModel));
> > + if (!tf_model){
> > + av_freep(&model);
> > + return NULL;
> > + }
> > +
> > + if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
> > + if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
> > + av_freep(&tf_model);
> > + av_freep(&model);
> > +
> > + return NULL;
> > + }
> > + }
> > +
> > + model->model = (void *)tf_model;
> > + model->set_input_output = &set_input_output_tf;
> > +
> > + return model;
> > +}
> > +
> > +
> > +
> > +DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData
> > *outputs, uint32_t nb_output)
> > +{
> > + TFModel *tf_model = (TFModel *)model->model;
> > + uint32_t nb = FFMIN(nb_output, tf_model->nb_output);
> > + if (nb == 0)
> > + return DNN_ERROR;
> > +
> > + av_assert0(tf_model->output_tensors);
> > + for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> > + if (tf_model->output_tensors[i]) {
> > + TF_DeleteTensor(tf_model->output_tensors[i]);
> > + tf_model->output_tensors[i] = NULL;
> > + }
> > + }
> > +
> > + TF_SessionRun(tf_model->session, NULL,
> > + &tf_model->input, &tf_model->input_tensor, 1,
> > + tf_model->outputs, tf_model->output_tensors, nb,
> > + NULL, 0, NULL, tf_model->status);
> > +
> > + if (TF_GetCode(tf_model->status) != TF_OK){
> > + return DNN_ERROR;
> > + }
> > +
> > + for (uint32_t i = 0; i < nb; ++i) {
> > + outputs[i].height = TF_Dim(tf_model->output_tensors[i], 1);
> > + outputs[i].width = TF_Dim(tf_model->output_tensors[i], 2);
> > + outputs[i].channels = TF_Dim(tf_model->output_tensors[i], 3);
> > + outputs[i].data = TF_TensorData(tf_model->output_tensors[i]);
> > + }
> > +
> > + return DNN_SUCCESS;
> > +}
> > +
> > +void ff_dnn_free_model_tf(DNNModel **model)
> > +{
> > + TFModel *tf_model;
> > +
> > + if (*model){
> > + tf_model = (TFModel *)(*model)->model;
> > + if (tf_model->graph){
> > + TF_DeleteGraph(tf_model->graph);
> > + }
> > + if (tf_model->session){
> > + TF_CloseSession(tf_model->session, tf_model->status);
> > + TF_DeleteSession(tf_model->session, tf_model->status);
> > + }
> > + if (tf_model->status){
> > + TF_DeleteStatus(tf_model->status);
> > + }
> > + if (tf_model->input_tensor){
> > + TF_DeleteTensor(tf_model->input_tensor);
> > + }
> > + if (tf_model->output_tensors) {
> > + for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> > + if (tf_model->output_tensors[i]) {
> > + TF_DeleteTensor(tf_model->output_tensors[i]);
> > + tf_model->output_tensors[i] = NULL;
> > + }
> > + }
> > + }
> > + av_freep(&tf_model->outputs);
> > + av_freep(&tf_model->output_tensors);
> > + av_freep(&tf_model);
> > + av_freep(model);
> > + }
> > +}
> > diff --git a/libavfilter/dnn/dnn_backend_tf.h
> > b/libavfilter/dnn/dnn_backend_tf.h
> > new file mode 100644
> > index 0000000..bb1c85f
> > --- /dev/null
> > +++ b/libavfilter/dnn/dnn_backend_tf.h
> > @@ -0,0 +1,38 @@
> > +/*
> > + * 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 inference functions interface for TensorFlow backend.
> > + */
> > +
> > +
> > +#ifndef AVFILTER_DNN_BACKEND_TF_H
> > +#define AVFILTER_DNN_BACKEND_TF_H
> > +
> > +#include "../dnn_interface.h"
> > +
> > +DNNModel *ff_dnn_load_model_tf(const char *model_filename);
> > +
> > +DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData
> > *outputs, uint32_t nb_output);
> > +
> > +void ff_dnn_free_model_tf(DNNModel **model);
> > +
> > +#endif
> > diff --git a/libavfilter/dnn/dnn_interface.c
> > b/libavfilter/dnn/dnn_interface.c
> > new file mode 100644
> > index 0000000..62da55f
> > --- /dev/null
> > +++ b/libavfilter/dnn/dnn_interface.c
> > @@ -0,0 +1,63 @@
> > +/*
> > + * 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
> > + * Implements DNN module initialization with specified backend.
> > + */
> > +
> > +#include "../dnn_interface.h"
> > +#include "dnn_backend_native.h"
> > +#include "dnn_backend_tf.h"
> > +#include "libavutil/mem.h"
> > +
> > +DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
> > +{
> > + DNNModule *dnn_module;
> > +
> > + dnn_module = av_malloc(sizeof(DNNModule));
> > + if(!dnn_module){
> > + return NULL;
> > + }
> > +
> > + switch(backend_type){
> > + case DNN_NATIVE:
> > + dnn_module->load_model = &ff_dnn_load_model_native;
> > + dnn_module->execute_model = &ff_dnn_execute_model_native;
> > + dnn_module->free_model = &ff_dnn_free_model_native;
> > + break;
> > + case DNN_TF:
> > + #if (CONFIG_LIBTENSORFLOW == 1)
> > + dnn_module->load_model = &ff_dnn_load_model_tf;
> > + dnn_module->execute_model = &ff_dnn_execute_model_tf;
> > + dnn_module->free_model = &ff_dnn_free_model_tf;
> > + #else
> > + av_freep(&dnn_module);
> > + return NULL;
> > + #endif
> > + break;
> > + default:
> > + av_log(NULL, AV_LOG_ERROR, "Module backend_type is not native or
> > tensorflow\n");
> > + av_freep(&dnn_module);
> > + return NULL;
> > + }
> > +
> > + return dnn_module;
> > +}
> > diff --git a/libavfilter/dnn_backend_native.c
> > b/libavfilter/dnn_backend_native.c
> > deleted file mode 100644
> > index 82e900b..0000000
> > --- a/libavfilter/dnn_backend_native.c
> > +++ /dev/null
> > @@ -1,389 +0,0 @@
> > -/*
> > - * 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);
> > - }
> > -}
> > diff --git a/libavfilter/dnn_backend_native.h
> > b/libavfilter/dnn_backend_native.h
> > deleted file mode 100644
> > index 5917955..0000000
> > --- a/libavfilter/dnn_backend_native.h
> > +++ /dev/null
> > @@ -1,74 +0,0 @@
> > -/*
> > - * 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 inference functions interface for native backend.
> > - */
> > -
> > -
> > -#ifndef AVFILTER_DNN_BACKEND_NATIVE_H
> > -#define AVFILTER_DNN_BACKEND_NATIVE_H
> > -
> > -#include "dnn_interface.h"
> > -#include "libavformat/avio.h"
> > -
> > -typedef enum {INPUT, CONV, DEPTH_TO_SPACE} DNNLayerType;
> > -
> > -typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;
> > -
> > -typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNConvPaddingParam;
> > -
> > -typedef struct Layer{
> > - DNNLayerType type;
> > - float *output;
> > - void *params;
> > -} Layer;
> > -
> > -typedef struct ConvolutionalParams{
> > - int32_t input_num, output_num, kernel_size;
> > - DNNActivationFunc activation;
> > - DNNConvPaddingParam padding_method;
> > - int32_t dilation;
> > - float *kernel;
> > - float *biases;
> > -} ConvolutionalParams;
> > -
> > -typedef struct InputParams{
> > - int height, width, channels;
> > -} InputParams;
> > -
> > -typedef struct DepthToSpaceParams{
> > - int block_size;
> > -} DepthToSpaceParams;
> > -
> > -// Represents simple feed-forward convolutional network.
> > -typedef struct ConvolutionalNetwork{
> > - Layer *layers;
> > - int32_t layers_num;
> > -} ConvolutionalNetwork;
> > -
> > -DNNModel *ff_dnn_load_model_native(const char *model_filename);
> > -
> > -DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData
> > *outputs, uint32_t nb_output);
> > -
> > -void ff_dnn_free_model_native(DNNModel **model);
> > -
> > -#endif
> > diff --git a/libavfilter/dnn_backend_tf.c b/libavfilter/dnn_backend_tf.c
> > deleted file mode 100644
> > index ba959ae..0000000
> > --- a/libavfilter/dnn_backend_tf.c
> > +++ /dev/null
> > @@ -1,603 +0,0 @@
> > -/*
> > - * 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 tensorflow backend implementation.
> > - */
> > -
> > -#include "dnn_backend_tf.h"
> > -#include "dnn_backend_native.h"
> > -#include "libavformat/avio.h"
> > -#include "libavutil/avassert.h"
> > -
> > -#include <tensorflow/c/c_api.h>
> > -
> > -typedef struct TFModel{
> > - TF_Graph *graph;
> > - TF_Session *session;
> > - TF_Status *status;
> > - TF_Output input;
> > - TF_Tensor *input_tensor;
> > - TF_Output *outputs;
> > - TF_Tensor **output_tensors;
> > - uint32_t nb_output;
> > -} TFModel;
> > -
> > -static void free_buffer(void *data, size_t length)
> > -{
> > - av_freep(&data);
> > -}
> > -
> > -static TF_Buffer *read_graph(const char *model_filename)
> > -{
> > - TF_Buffer *graph_buf;
> > - unsigned char *graph_data = NULL;
> > - AVIOContext *model_file_context;
> > - long size, bytes_read;
> > -
> > - if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) <
> > 0){
> > - return NULL;
> > - }
> > -
> > - size = avio_size(model_file_context);
> > -
> > - graph_data = av_malloc(size);
> > - if (!graph_data){
> > - avio_closep(&model_file_context);
> > - return NULL;
> > - }
> > - bytes_read = avio_read(model_file_context, graph_data, size);
> > - avio_closep(&model_file_context);
> > - if (bytes_read != size){
> > - av_freep(&graph_data);
> > - return NULL;
> > - }
> > -
> > - graph_buf = TF_NewBuffer();
> > - graph_buf->data = (void *)graph_data;
> > - graph_buf->length = size;
> > - graph_buf->data_deallocator = free_buffer;
> > -
> > - return graph_buf;
> > -}
> > -
> > -static TF_Tensor *allocate_input_tensor(const DNNInputData *input)
> > -{
> > - TF_DataType dt;
> > - size_t size;
> > - int64_t input_dims[] = {1, input->height, input->width,
> > input->channels};
> > - switch (input->dt) {
> > - case DNN_FLOAT:
> > - dt = TF_FLOAT;
> > - size = sizeof(float);
> > - break;
> > - case DNN_UINT8:
> > - dt = TF_UINT8;
> > - size = sizeof(char);
> > - break;
> > - default:
> > - av_assert0(!"should not reach here");
> > - }
> > -
> > - return TF_AllocateTensor(dt, input_dims, 4,
> > - input_dims[1] * input_dims[2] * input_dims[3]
> > * size);
> > -}
> > -
> > -static DNNReturnType set_input_output_tf(void *model, DNNInputData *input,
> > const char *input_name, const char **output_names, uint32_t nb_output)
> > -{
> > - TFModel *tf_model = (TFModel *)model;
> > - TF_SessionOptions *sess_opts;
> > - const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph,
> > "init");
> > -
> > - // Input operation
> > - tf_model->input.oper = TF_GraphOperationByName(tf_model->graph,
> > input_name);
> > - if (!tf_model->input.oper){
> > - return DNN_ERROR;
> > - }
> > - tf_model->input.index = 0;
> > - if (tf_model->input_tensor){
> > - TF_DeleteTensor(tf_model->input_tensor);
> > - }
> > - tf_model->input_tensor = allocate_input_tensor(input);
> > - if (!tf_model->input_tensor){
> > - return DNN_ERROR;
> > - }
> > - input->data = (float *)TF_TensorData(tf_model->input_tensor);
> > -
> > - // Output operation
> > - if (nb_output == 0)
> > - return DNN_ERROR;
> > -
> > - av_freep(&tf_model->outputs);
> > - tf_model->outputs = av_malloc_array(nb_output,
> > sizeof(*tf_model->outputs));
> > - if (!tf_model->outputs)
> > - return DNN_ERROR;
> > - for (int i = 0; i < nb_output; ++i) {
> > - tf_model->outputs[i].oper =
> > TF_GraphOperationByName(tf_model->graph, output_names[i]);
> > - if (!tf_model->outputs[i].oper){
> > - av_freep(&tf_model->outputs);
> > - return DNN_ERROR;
> > - }
> > - tf_model->outputs[i].index = 0;
> > - }
> > -
> > - if (tf_model->output_tensors) {
> > - for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> > - if (tf_model->output_tensors[i]) {
> > - TF_DeleteTensor(tf_model->output_tensors[i]);
> > - tf_model->output_tensors[i] = NULL;
> > - }
> > - }
> > - }
> > - av_freep(&tf_model->output_tensors);
> > - tf_model->output_tensors = av_mallocz_array(nb_output,
> > sizeof(*tf_model->output_tensors));
> > - if (!tf_model->output_tensors) {
> > - av_freep(&tf_model->outputs);
> > - return DNN_ERROR;
> > - }
> > -
> > - tf_model->nb_output = nb_output;
> > -
> > - if (tf_model->session){
> > - TF_CloseSession(tf_model->session, tf_model->status);
> > - TF_DeleteSession(tf_model->session, tf_model->status);
> > - }
> > -
> > - sess_opts = TF_NewSessionOptions();
> > - tf_model->session = TF_NewSession(tf_model->graph, sess_opts,
> > tf_model->status);
> > - TF_DeleteSessionOptions(sess_opts);
> > - if (TF_GetCode(tf_model->status) != TF_OK)
> > - {
> > - return DNN_ERROR;
> > - }
> > -
> > - // Run initialization operation with name "init" if it is present in
> > graph
> > - if (init_op){
> > - TF_SessionRun(tf_model->session, NULL,
> > - NULL, NULL, 0,
> > - NULL, NULL, 0,
> > - &init_op, 1, NULL, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK)
> > - {
> > - return DNN_ERROR;
> > - }
> > - }
> > -
> > - return DNN_SUCCESS;
> > -}
> > -
> > -static DNNReturnType load_tf_model(TFModel *tf_model, const char
> > *model_filename)
> > -{
> > - TF_Buffer *graph_def;
> > - TF_ImportGraphDefOptions *graph_opts;
> > -
> > - graph_def = read_graph(model_filename);
> > - if (!graph_def){
> > - return DNN_ERROR;
> > - }
> > - tf_model->graph = TF_NewGraph();
> > - tf_model->status = TF_NewStatus();
> > - graph_opts = TF_NewImportGraphDefOptions();
> > - TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts,
> > tf_model->status);
> > - TF_DeleteImportGraphDefOptions(graph_opts);
> > - TF_DeleteBuffer(graph_def);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - TF_DeleteGraph(tf_model->graph);
> > - TF_DeleteStatus(tf_model->status);
> > - return DNN_ERROR;
> > - }
> > -
> > - return DNN_SUCCESS;
> > -}
> > -
> > -#define NAME_BUFFER_SIZE 256
> > -
> > -static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation
> > *transpose_op, TF_Operation **cur_op,
> > - ConvolutionalParams* params, const int
> > layer)
> > -{
> > - TF_Operation *op;
> > - TF_OperationDescription *op_desc;
> > - TF_Output input;
> > - int64_t strides[] = {1, 1, 1, 1};
> > - TF_Tensor *tensor;
> > - int64_t dims[4];
> > - int dims_len;
> > - char name_buffer[NAME_BUFFER_SIZE];
> > - int32_t size;
> > -
> > - size = params->input_num * params->output_num * params->kernel_size *
> > params->kernel_size;
> > - input.index = 0;
> > -
> > - snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
> > - op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
> > - TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> > - dims[0] = params->output_num;
> > - dims[1] = params->kernel_size;
> > - dims[2] = params->kernel_size;
> > - dims[3] = params->input_num;
> > - dims_len = 4;
> > - tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size *
> > sizeof(float));
> > - memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float));
> > - TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > - op = TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > -
> > - snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
> > - op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
> > - input.oper = op;
> > - TF_AddInput(op_desc, input);
> > - input.oper = transpose_op;
> > - TF_AddInput(op_desc, input);
> > - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > - TF_SetAttrType(op_desc, "Tperm", TF_INT32);
> > - op = TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > -
> > - snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
> > - op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
> > - input.oper = *cur_op;
> > - TF_AddInput(op_desc, input);
> > - input.oper = op;
> > - TF_AddInput(op_desc, input);
> > - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > - TF_SetAttrIntList(op_desc, "strides", strides, 4);
> > - TF_SetAttrString(op_desc, "padding", "VALID", 5);
> > - *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > -
> > - snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
> > - op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
> > - TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> > - dims[0] = params->output_num;
> > - dims_len = 1;
> > - tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len,
> > params->output_num * sizeof(float));
> > - memcpy(TF_TensorData(tensor), params->biases, params->output_num *
> > sizeof(float));
> > - TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > - op = TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > -
> > - snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
> > - op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
> > - input.oper = *cur_op;
> > - TF_AddInput(op_desc, input);
> > - input.oper = op;
> > - TF_AddInput(op_desc, input);
> > - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > - *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > -
> > - snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
> > - switch (params->activation){
> > - case RELU:
> > - op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
> > - break;
> > - case TANH:
> > - op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
> > - break;
> > - case SIGMOID:
> > - op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
> > - break;
> > - default:
> > - return DNN_ERROR;
> > - }
> > - input.oper = *cur_op;
> > - TF_AddInput(op_desc, input);
> > - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > - *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > -
> > - return DNN_SUCCESS;
> > -}
> > -
> > -static DNNReturnType add_depth_to_space_layer(TFModel *tf_model,
> > TF_Operation **cur_op,
> > - DepthToSpaceParams *params,
> > const int layer)
> > -{
> > - TF_OperationDescription *op_desc;
> > - TF_Output input;
> > - char name_buffer[NAME_BUFFER_SIZE];
> > -
> > - snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
> > - op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace",
> > name_buffer);
> > - input.oper = *cur_op;
> > - input.index = 0;
> > - TF_AddInput(op_desc, input);
> > - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > - TF_SetAttrInt(op_desc, "block_size", params->block_size);
> > - *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > -
> > - return DNN_SUCCESS;
> > -}
> > -
> > -static int calculate_pad(const ConvolutionalNetwork *conv_network)
> > -{
> > - ConvolutionalParams *params;
> > - int32_t layer;
> > - int pad = 0;
> > -
> > - for (layer = 0; layer < conv_network->layers_num; ++layer){
> > - if (conv_network->layers[layer].type == CONV){
> > - params = (ConvolutionalParams
> > *)conv_network->layers[layer].params;
> > - pad += params->kernel_size >> 1;
> > - }
> > - }
> > -
> > - return pad;
> > -}
> > -
> > -static DNNReturnType add_pad_op(TFModel *tf_model, TF_Operation **cur_op,
> > const int32_t pad)
> > -{
> > - TF_Operation *op;
> > - TF_Tensor *tensor;
> > - TF_OperationDescription *op_desc;
> > - TF_Output input;
> > - int32_t *pads;
> > - int64_t pads_shape[] = {4, 2};
> > -
> > - input.index = 0;
> > -
> > - op_desc = TF_NewOperation(tf_model->graph, "Const", "pads");
> > - TF_SetAttrType(op_desc, "dtype", TF_INT32);
> > - tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 *
> > sizeof(int32_t));
> > - pads = (int32_t *)TF_TensorData(tensor);
> > - pads[0] = 0; pads[1] = 0;
> > - pads[2] = pad; pads[3] = pad;
> > - pads[4] = pad; pads[5] = pad;
> > - pads[6] = 0; pads[7] = 0;
> > - TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > - op = TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > -
> > - op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
> > - input.oper = *cur_op;
> > - TF_AddInput(op_desc, input);
> > - input.oper = op;
> > - TF_AddInput(op_desc, input);
> > - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> > - TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
> > - TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
> > - *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > -
> > - return DNN_SUCCESS;
> > -}
> > -
> > -static DNNReturnType load_native_model(TFModel *tf_model, const char
> > *model_filename)
> > -{
> > - int32_t layer;
> > - TF_OperationDescription *op_desc;
> > - TF_Operation *op;
> > - TF_Operation *transpose_op;
> > - TF_Tensor *tensor;
> > - TF_Output input;
> > - int32_t *transpose_perm;
> > - int64_t transpose_perm_shape[] = {4};
> > - int64_t input_shape[] = {1, -1, -1, -1};
> > - int32_t pad;
> > - DNNReturnType layer_add_res;
> > - DNNModel *native_model = NULL;
> > - ConvolutionalNetwork *conv_network;
> > -
> > - native_model = ff_dnn_load_model_native(model_filename);
> > - if (!native_model){
> > - return DNN_ERROR;
> > - }
> > -
> > - conv_network = (ConvolutionalNetwork *)native_model->model;
> > - pad = calculate_pad(conv_network);
> > - tf_model->graph = TF_NewGraph();
> > - tf_model->status = TF_NewStatus();
> > -
> > -#define CLEANUP_ON_ERROR(tf_model) \
> > - { \
> > - TF_DeleteGraph(tf_model->graph); \
> > - TF_DeleteStatus(tf_model->status); \
> > - return DNN_ERROR; \
> > - }
> > -
> > - op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
> > - TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> > - TF_SetAttrShape(op_desc, "shape", input_shape, 4);
> > - op = TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - CLEANUP_ON_ERROR(tf_model);
> > - }
> > -
> > - if (add_pad_op(tf_model, &op, pad) != DNN_SUCCESS){
> > - CLEANUP_ON_ERROR(tf_model);
> > - }
> > -
> > - op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
> > - TF_SetAttrType(op_desc, "dtype", TF_INT32);
> > - tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 *
> > sizeof(int32_t));
> > - transpose_perm = (int32_t *)TF_TensorData(tensor);
> > - transpose_perm[0] = 1;
> > - transpose_perm[1] = 2;
> > - transpose_perm[2] = 3;
> > - transpose_perm[3] = 0;
> > - TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - CLEANUP_ON_ERROR(tf_model);
> > - }
> > - transpose_op = TF_FinishOperation(op_desc, tf_model->status);
> > -
> > - for (layer = 0; layer < conv_network->layers_num; ++layer){
> > - switch (conv_network->layers[layer].type){
> > - case INPUT:
> > - layer_add_res = DNN_SUCCESS;
> > - break;
> > - case CONV:
> > - layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
> > - (ConvolutionalParams
> > *)conv_network->layers[layer].params, layer);
> > - break;
> > - case DEPTH_TO_SPACE:
> > - layer_add_res = add_depth_to_space_layer(tf_model, &op,
> > - (DepthToSpaceParams
> > *)conv_network->layers[layer].params, layer);
> > - break;
> > - default:
> > - CLEANUP_ON_ERROR(tf_model);
> > - }
> > -
> > - if (layer_add_res != DNN_SUCCESS){
> > - CLEANUP_ON_ERROR(tf_model);
> > - }
> > - }
> > -
> > - op_desc = TF_NewOperation(tf_model->graph, "Identity", "y");
> > - input.oper = op;
> > - TF_AddInput(op_desc, input);
> > - TF_FinishOperation(op_desc, tf_model->status);
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - CLEANUP_ON_ERROR(tf_model);
> > - }
> > -
> > - ff_dnn_free_model_native(&native_model);
> > -
> > - return DNN_SUCCESS;
> > -}
> > -
> > -DNNModel *ff_dnn_load_model_tf(const char *model_filename)
> > -{
> > - DNNModel *model = NULL;
> > - TFModel *tf_model = NULL;
> > -
> > - model = av_malloc(sizeof(DNNModel));
> > - if (!model){
> > - return NULL;
> > - }
> > -
> > - tf_model = av_mallocz(sizeof(TFModel));
> > - if (!tf_model){
> > - av_freep(&model);
> > - return NULL;
> > - }
> > -
> > - if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
> > - if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
> > - av_freep(&tf_model);
> > - av_freep(&model);
> > -
> > - return NULL;
> > - }
> > - }
> > -
> > - model->model = (void *)tf_model;
> > - model->set_input_output = &set_input_output_tf;
> > -
> > - return model;
> > -}
> > -
> > -
> > -
> > -DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData
> > *outputs, uint32_t nb_output)
> > -{
> > - TFModel *tf_model = (TFModel *)model->model;
> > - uint32_t nb = FFMIN(nb_output, tf_model->nb_output);
> > - if (nb == 0)
> > - return DNN_ERROR;
> > -
> > - av_assert0(tf_model->output_tensors);
> > - for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> > - if (tf_model->output_tensors[i]) {
> > - TF_DeleteTensor(tf_model->output_tensors[i]);
> > - tf_model->output_tensors[i] = NULL;
> > - }
> > - }
> > -
> > - TF_SessionRun(tf_model->session, NULL,
> > - &tf_model->input, &tf_model->input_tensor, 1,
> > - tf_model->outputs, tf_model->output_tensors, nb,
> > - NULL, 0, NULL, tf_model->status);
> > -
> > - if (TF_GetCode(tf_model->status) != TF_OK){
> > - return DNN_ERROR;
> > - }
> > -
> > - for (uint32_t i = 0; i < nb; ++i) {
> > - outputs[i].height = TF_Dim(tf_model->output_tensors[i], 1);
> > - outputs[i].width = TF_Dim(tf_model->output_tensors[i], 2);
> > - outputs[i].channels = TF_Dim(tf_model->output_tensors[i], 3);
> > - outputs[i].data = TF_TensorData(tf_model->output_tensors[i]);
> > - }
> > -
> > - return DNN_SUCCESS;
> > -}
> > -
> > -void ff_dnn_free_model_tf(DNNModel **model)
> > -{
> > - TFModel *tf_model;
> > -
> > - if (*model){
> > - tf_model = (TFModel *)(*model)->model;
> > - if (tf_model->graph){
> > - TF_DeleteGraph(tf_model->graph);
> > - }
> > - if (tf_model->session){
> > - TF_CloseSession(tf_model->session, tf_model->status);
> > - TF_DeleteSession(tf_model->session, tf_model->status);
> > - }
> > - if (tf_model->status){
> > - TF_DeleteStatus(tf_model->status);
> > - }
> > - if (tf_model->input_tensor){
> > - TF_DeleteTensor(tf_model->input_tensor);
> > - }
> > - if (tf_model->output_tensors) {
> > - for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> > - if (tf_model->output_tensors[i]) {
> > - TF_DeleteTensor(tf_model->output_tensors[i]);
> > - tf_model->output_tensors[i] = NULL;
> > - }
> > - }
> > - }
> > - av_freep(&tf_model->outputs);
> > - av_freep(&tf_model->output_tensors);
> > - av_freep(&tf_model);
> > - av_freep(model);
> > - }
> > -}
> > diff --git a/libavfilter/dnn_backend_tf.h b/libavfilter/dnn_backend_tf.h
> > deleted file mode 100644
> > index 07877b1..0000000
> > --- a/libavfilter/dnn_backend_tf.h
> > +++ /dev/null
> > @@ -1,38 +0,0 @@
> > -/*
> > - * 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 inference functions interface for TensorFlow backend.
> > - */
> > -
> > -
> > -#ifndef AVFILTER_DNN_BACKEND_TF_H
> > -#define AVFILTER_DNN_BACKEND_TF_H
> > -
> > -#include "dnn_interface.h"
> > -
> > -DNNModel *ff_dnn_load_model_tf(const char *model_filename);
> > -
> > -DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData
> > *outputs, uint32_t nb_output);
> > -
> > -void ff_dnn_free_model_tf(DNNModel **model);
> > -
> > -#endif
> > diff --git a/libavfilter/dnn_interface.c b/libavfilter/dnn_interface.c
> > deleted file mode 100644
> > index 86fc283..0000000
> > --- a/libavfilter/dnn_interface.c
> > +++ /dev/null
> > @@ -1,63 +0,0 @@
> > -/*
> > - * 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
> > - * Implements DNN module initialization with specified backend.
> > - */
> > -
> > -#include "dnn_interface.h"
> > -#include "dnn_backend_native.h"
> > -#include "dnn_backend_tf.h"
> > -#include "libavutil/mem.h"
> > -
> > -DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
> > -{
> > - DNNModule *dnn_module;
> > -
> > - dnn_module = av_malloc(sizeof(DNNModule));
> > - if(!dnn_module){
> > - return NULL;
> > - }
> > -
> > - switch(backend_type){
> > - case DNN_NATIVE:
> > - dnn_module->load_model = &ff_dnn_load_model_native;
> > - dnn_module->execute_model = &ff_dnn_execute_model_native;
> > - dnn_module->free_model = &ff_dnn_free_model_native;
> > - break;
> > - case DNN_TF:
> > - #if (CONFIG_LIBTENSORFLOW == 1)
> > - dnn_module->load_model = &ff_dnn_load_model_tf;
> > - dnn_module->execute_model = &ff_dnn_execute_model_tf;
> > - dnn_module->free_model = &ff_dnn_free_model_tf;
> > - #else
> > - av_freep(&dnn_module);
> > - return NULL;
> > - #endif
> > - break;
> > - default:
> > - av_log(NULL, AV_LOG_ERROR, "Module backend_type is not native or
> > tensorflow\n");
> > - av_freep(&dnn_module);
> > - return NULL;
> > - }
> > -
> > - return dnn_module;
> > -}
> > --
> > 2.7.4
> >
> > _______________________________________________
> > ffmpeg-devel mailing list
> > ffmpeg-devel@ffmpeg.org
> > https://ffmpeg.org/mailman/listinfo/ffmpeg-devel
> >
> > To unsubscribe, visit link above, or email
> > ffmpeg-devel-requ...@ffmpeg.org with subject "unsubscribe".
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