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

Pedro Arthur bygrandao at gmail.com
Fri Jul 26 19:02:03 EEST 2019


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 at 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 at 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
>
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