[FFmpeg-devel,1/4] libavfilter/dnn: move dnn files from libavfilter to libavfilter/dnn

Submitted by Guo, Yejun on July 16, 2019, 5:55 a.m.

Details

Message ID 1563256545-28027-1-git-send-email-yejun.guo@intel.com
State New
Headers show

Commit Message

Guo, Yejun July 16, 2019, 5:55 a.m.
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.guo@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

Comments

Pedro Arthur July 26, 2019, 4:02 p.m.
Hi,
It fails fate source guard header tests,
The headers should be changed from AVFILTER_DNN_BACKEND_xxx to
AVFILTER_DNN_DNN_BACKEND_xxx.
Other than that it LGTM.

Em ter, 16 de jul de 2019 às 02:58, Guo, Yejun <yejun.guo@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.guo@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-request@ffmpeg.org with subject "unsubscribe".
Pedro Arthur July 26, 2019, 4:09 p.m.
Em sex, 26 de jul de 2019 às 13:02, Pedro Arthur <bygrandao@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.guo@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.guo@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-request@ffmpeg.org with subject "unsubscribe".
Guo, Yejun July 28, 2019, 11:14 a.m.
> -----Original Message-----

> From: Pedro Arthur [mailto:bygrandao@gmail.com]

> Sent: Saturday, July 27, 2019 12:10 AM

> To: FFmpeg development discussions and patches <ffmpeg-devel@ffmpeg.org>

> Cc: Guo, Yejun <yejun.guo@intel.com>

> Subject: Re: [FFmpeg-devel] [PATCH 1/4] libavfilter/dnn: move dnn files from

> libavfilter to libavfilter/dnn

> 

> Em sex, 26 de jul de 2019 às 13:02, Pedro Arthur <bygrandao@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.


thanks!

Patch hide | download patch | download mbox

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;
-}