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....@intel.com> escreveu:
>
> it is expected that there will be more files to support native mode,
> so put all the dnn codes under libavfilter/dnn
>
> The main change of this patch is to move the file location, see below:
> modified: libavfilter/Makefile
> new file: libavfilter/dnn/Makefile
> renamed: libavfilter/dnn_backend_native.c ->
> libavfilter/dnn/dnn_backend_native.c
> renamed: libavfilter/dnn_backend_native.h ->
> libavfilter/dnn/dnn_backend_native.h
> renamed: libavfilter/dnn_backend_tf.c -> libavfilter/dnn/dnn_backend_tf.c
> renamed: libavfilter/dnn_backend_tf.h -> libavfilter/dnn/dnn_backend_tf.h
> renamed: libavfilter/dnn_interface.c -> libavfilter/dnn/dnn_interface.c
>
> Signed-off-by: Guo, Yejun <yejun....@intel.com>
> ---
> libavfilter/Makefile | 3 +-
> libavfilter/dnn/Makefile | 6 +
> libavfilter/dnn/dnn_backend_native.c | 389 ++++++++++++++++++++++
> libavfilter/dnn/dnn_backend_native.h | 74 +++++
> libavfilter/dnn/dnn_backend_tf.c | 603
> +++++++++++++++++++++++++++++++++++
> libavfilter/dnn/dnn_backend_tf.h | 38 +++
> libavfilter/dnn/dnn_interface.c | 63 ++++
> libavfilter/dnn_backend_native.c | 389 ----------------------
> libavfilter/dnn_backend_native.h | 74 -----
> libavfilter/dnn_backend_tf.c | 603
> -----------------------------------
> libavfilter/dnn_backend_tf.h | 38 ---
> libavfilter/dnn_interface.c | 63 ----
> 12 files changed, 1174 insertions(+), 1169 deletions(-)
> create mode 100644 libavfilter/dnn/Makefile
> create mode 100644 libavfilter/dnn/dnn_backend_native.c
> create mode 100644 libavfilter/dnn/dnn_backend_native.h
> create mode 100644 libavfilter/dnn/dnn_backend_tf.c
> create mode 100644 libavfilter/dnn/dnn_backend_tf.h
> create mode 100644 libavfilter/dnn/dnn_interface.c
> delete mode 100644 libavfilter/dnn_backend_native.c
> delete mode 100644 libavfilter/dnn_backend_native.h
> delete mode 100644 libavfilter/dnn_backend_tf.c
> delete mode 100644 libavfilter/dnn_backend_tf.h
> delete mode 100644 libavfilter/dnn_interface.c
>
> diff --git a/libavfilter/Makefile b/libavfilter/Makefile
> index 455c809..450d781 100644
> --- a/libavfilter/Makefile
> +++ b/libavfilter/Makefile
> @@ -26,9 +26,8 @@ OBJS-$(HAVE_THREADS) += pthread.o
>
> # subsystems
> OBJS-$(CONFIG_QSVVPP) += qsvvpp.o
> -DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn_backend_tf.o
> -OBJS-$(CONFIG_DNN) += dnn_interface.o
> dnn_backend_native.o $(DNN-OBJS-yes)
> OBJS-$(CONFIG_SCENE_SAD) += scene_sad.o
> +include $(SRC_PATH)/libavfilter/dnn/Makefile
>
> # audio filters
> OBJS-$(CONFIG_ABENCH_FILTER) += f_bench.o
> diff --git a/libavfilter/dnn/Makefile b/libavfilter/dnn/Makefile
> new file mode 100644
> index 0000000..1d12ade
> --- /dev/null
> +++ b/libavfilter/dnn/Makefile
> @@ -0,0 +1,6 @@
> +OBJS-$(CONFIG_DNN) += dnn/dnn_interface.o
> +OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native.o
> +
> +DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn/dnn_backend_tf.o
> +
> +OBJS-$(CONFIG_DNN) += $(DNN-OBJS-yes)
> diff --git a/libavfilter/dnn/dnn_backend_native.c
> b/libavfilter/dnn/dnn_backend_native.c
> new file mode 100644
> index 0000000..82e900b
> --- /dev/null
> +++ b/libavfilter/dnn/dnn_backend_native.c
> @@ -0,0 +1,389 @@
> +/*
> + * Copyright (c) 2018 Sergey Lavrushkin
> + *
> + * This file is part of FFmpeg.
> + *
> + * FFmpeg is free software; you can redistribute it and/or
> + * modify it under the terms of the GNU Lesser General Public
> + * License as published by the Free Software Foundation; either
> + * version 2.1 of the License, or (at your option) any later version.
> + *
> + * FFmpeg is distributed in the hope that it will be useful,
> + * but WITHOUT ANY WARRANTY; without even the implied warranty of
> + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
> + * Lesser General Public License for more details.
> + *
> + * You should have received a copy of the GNU Lesser General Public
> + * License along with FFmpeg; if not, write to the Free Software
> + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
> USA
> + */
> +
> +/**
> + * @file
> + * DNN native backend implementation.
> + */
> +
> +#include "dnn_backend_native.h"
> +#include "libavutil/avassert.h"
> +
> +static DNNReturnType set_input_output_native(void *model, DNNInputData
> *input, const char *input_name, const char **output_names, uint32_t nb_output)
> +{
> + ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
> + InputParams *input_params;
> + ConvolutionalParams *conv_params;
> + DepthToSpaceParams *depth_to_space_params;
> + int cur_width, cur_height, cur_channels;
> + int32_t layer;
> +
> + if (network->layers_num <= 0 || network->layers[0].type != INPUT){
> + return DNN_ERROR;
> + }
> + else{
> + input_params = (InputParams *)network->layers[0].params;
> + input_params->width = cur_width = input->width;
> + input_params->height = cur_height = input->height;
> + input_params->channels = cur_channels = input->channels;
> + if (input->data){
> + av_freep(&input->data);
> + }
> + av_assert0(input->dt == DNN_FLOAT);
> + network->layers[0].output = input->data = av_malloc(cur_height *
> cur_width * cur_channels * sizeof(float));
> + if (!network->layers[0].output){
> + return DNN_ERROR;
> + }
> + }
> +
> + for (layer = 1; layer < network->layers_num; ++layer){
> + switch (network->layers[layer].type){
> + case CONV:
> + conv_params = (ConvolutionalParams
> *)network->layers[layer].params;
> + if (conv_params->input_num != cur_channels){
> + return DNN_ERROR;
> + }
> + cur_channels = conv_params->output_num;
> +
> + if (conv_params->padding_method == VALID) {
> + int pad_size = (conv_params->kernel_size - 1) *
> conv_params->dilation;
> + cur_height -= pad_size;
> + cur_width -= pad_size;
> + }
> + break;
> + case DEPTH_TO_SPACE:
> + depth_to_space_params = (DepthToSpaceParams
> *)network->layers[layer].params;
> + if (cur_channels % (depth_to_space_params->block_size *
> depth_to_space_params->block_size) != 0){
> + return DNN_ERROR;
> + }
> + cur_channels = cur_channels / (depth_to_space_params->block_size
> * depth_to_space_params->block_size);
> + cur_height *= depth_to_space_params->block_size;
> + cur_width *= depth_to_space_params->block_size;
> + break;
> + default:
> + return DNN_ERROR;
> + }
> + if (network->layers[layer].output){
> + av_freep(&network->layers[layer].output);
> + }
> +
> + if (cur_height <= 0 || cur_width <= 0)
> + return DNN_ERROR;
> +
> + network->layers[layer].output = av_malloc(cur_height * cur_width *
> cur_channels * sizeof(float));
> + if (!network->layers[layer].output){
> + return DNN_ERROR;
> + }
> + }
> +
> + return DNN_SUCCESS;
> +}
> +
> +// Loads model and its parameters that are stored in a binary file with
> following structure:
> +// layers_num,layer_type,layer_parameterss,layer_type,layer_parameters...
> +// For CONV layer: activation_function, input_num, output_num, kernel_size,
> kernel, biases
> +// For DEPTH_TO_SPACE layer: block_size
> +DNNModel *ff_dnn_load_model_native(const char *model_filename)
> +{
> + DNNModel *model = NULL;
> + ConvolutionalNetwork *network = NULL;
> + AVIOContext *model_file_context;
> + int file_size, dnn_size, kernel_size, i;
> + int32_t layer;
> + DNNLayerType layer_type;
> + ConvolutionalParams *conv_params;
> + DepthToSpaceParams *depth_to_space_params;
> +
> + model = av_malloc(sizeof(DNNModel));
> + if (!model){
> + return NULL;
> + }
> +
> + if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
> + av_freep(&model);
> + return NULL;
> + }
> + file_size = avio_size(model_file_context);
> +
> + network = av_malloc(sizeof(ConvolutionalNetwork));
> + if (!network){
> + avio_closep(&model_file_context);
> + av_freep(&model);
> + return NULL;
> + }
> + model->model = (void *)network;
> +
> + network->layers_num = 1 + (int32_t)avio_rl32(model_file_context);
> + dnn_size = 4;
> +
> + network->layers = av_malloc(network->layers_num * sizeof(Layer));
> + if (!network->layers){
> + av_freep(&network);
> + avio_closep(&model_file_context);
> + av_freep(&model);
> + return NULL;
> + }
> +
> + for (layer = 0; layer < network->layers_num; ++layer){
> + network->layers[layer].output = NULL;
> + network->layers[layer].params = NULL;
> + }
> + network->layers[0].type = INPUT;
> + network->layers[0].params = av_malloc(sizeof(InputParams));
> + if (!network->layers[0].params){
> + avio_closep(&model_file_context);
> + ff_dnn_free_model_native(&model);
> + return NULL;
> + }
> +
> + for (layer = 1; layer < network->layers_num; ++layer){
> + layer_type = (int32_t)avio_rl32(model_file_context);
> + dnn_size += 4;
> + switch (layer_type){
> + case CONV:
> + conv_params = av_malloc(sizeof(ConvolutionalParams));
> + if (!conv_params){
> + avio_closep(&model_file_context);
> + ff_dnn_free_model_native(&model);
> + return NULL;
> + }
> + conv_params->dilation = (int32_t)avio_rl32(model_file_context);
> + conv_params->padding_method =
> (int32_t)avio_rl32(model_file_context);
> + conv_params->activation = (int32_t)avio_rl32(model_file_context);
> + conv_params->input_num = (int32_t)avio_rl32(model_file_context);
> + conv_params->output_num = (int32_t)avio_rl32(model_file_context);
> + conv_params->kernel_size =
> (int32_t)avio_rl32(model_file_context);
> + kernel_size = conv_params->input_num * conv_params->output_num *
> + conv_params->kernel_size *
> conv_params->kernel_size;
> + dnn_size += 24 + (kernel_size + conv_params->output_num << 2);
> + if (dnn_size > file_size || conv_params->input_num <= 0 ||
> + conv_params->output_num <= 0 || conv_params->kernel_size <=
> 0){
> + avio_closep(&model_file_context);
> + ff_dnn_free_model_native(&model);
> + return NULL;
> + }
> + conv_params->kernel = av_malloc(kernel_size * sizeof(float));
> + conv_params->biases = av_malloc(conv_params->output_num *
> sizeof(float));
> + if (!conv_params->kernel || !conv_params->biases){
> + avio_closep(&model_file_context);
> + ff_dnn_free_model_native(&model);
> + return NULL;
> + }
> + for (i = 0; i < kernel_size; ++i){
> + conv_params->kernel[i] =
> av_int2float(avio_rl32(model_file_context));
> + }
> + for (i = 0; i < conv_params->output_num; ++i){
> + conv_params->biases[i] =
> av_int2float(avio_rl32(model_file_context));
> + }
> + network->layers[layer].type = CONV;
> + network->layers[layer].params = conv_params;
> + break;
> + case DEPTH_TO_SPACE:
> + depth_to_space_params = av_malloc(sizeof(DepthToSpaceParams));
> + if (!depth_to_space_params){
> + avio_closep(&model_file_context);
> + ff_dnn_free_model_native(&model);
> + return NULL;
> + }
> + depth_to_space_params->block_size =
> (int32_t)avio_rl32(model_file_context);
> + dnn_size += 4;
> + network->layers[layer].type = DEPTH_TO_SPACE;
> + network->layers[layer].params = depth_to_space_params;
> + break;
> + default:
> + avio_closep(&model_file_context);
> + ff_dnn_free_model_native(&model);
> + return NULL;
> + }
> + }
> +
> + avio_closep(&model_file_context);
> +
> + if (dnn_size != file_size){
> + ff_dnn_free_model_native(&model);
> + return NULL;
> + }
> +
> + model->set_input_output = &set_input_output_native;
> +
> + return model;
> +}
> +
> +#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
> +
> +static void convolve(const float *input, float *output, const
> ConvolutionalParams *conv_params, int width, int height)
> +{
> + int radius = conv_params->kernel_size >> 1;
> + int src_linesize = width * conv_params->input_num;
> + int filter_linesize = conv_params->kernel_size * conv_params->input_num;
> + int filter_size = conv_params->kernel_size * filter_linesize;
> + int pad_size = (conv_params->padding_method == VALID) ?
> (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;
> +
> + for (int y = pad_size; y < height - pad_size; ++y) {
> + for (int x = pad_size; x < width - pad_size; ++x) {
> + for (int n_filter = 0; n_filter < conv_params->output_num;
> ++n_filter) {
> + output[n_filter] = conv_params->biases[n_filter];
> +
> + for (int ch = 0; ch < conv_params->input_num; ++ch) {
> + for (int kernel_y = 0; kernel_y <
> conv_params->kernel_size; ++kernel_y) {
> + for (int kernel_x = 0; kernel_x <
> conv_params->kernel_size; ++kernel_x) {
> + float input_pel;
> + if (conv_params->padding_method ==
> SAME_CLAMP_TO_EDGE) {
> + int y_pos = CLAMP_TO_EDGE(y + (kernel_y -
> radius) * conv_params->dilation, height);
> + int x_pos = CLAMP_TO_EDGE(x + (kernel_x -
> radius) * conv_params->dilation, width);
> + input_pel = input[y_pos * src_linesize +
> x_pos * conv_params->input_num + ch];
> + } else {
> + int y_pos = y + (kernel_y - radius) *
> conv_params->dilation;
> + int x_pos = x + (kernel_x - radius) *
> conv_params->dilation;
> + input_pel = (x_pos < 0 || x_pos >= width ||
> y_pos < 0 || y_pos >= height) ? 0.0 :
> + input[y_pos *
> src_linesize + x_pos * conv_params->input_num + ch];
> + }
> +
> +
> + output[n_filter] += input_pel *
> conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
> +
> kernel_x * conv_params->input_num + ch];
> + }
> + }
> + }
> + switch (conv_params->activation){
> + case RELU:
> + output[n_filter] = FFMAX(output[n_filter], 0.0);
> + break;
> + case TANH:
> + output[n_filter] = 2.0f / (1.0f + exp(-2.0f *
> output[n_filter])) - 1.0f;
> + break;
> + case SIGMOID:
> + output[n_filter] = 1.0f / (1.0f +
> exp(-output[n_filter]));
> + break;
> + case NONE:
> + break;
> + case LEAKY_RELU:
> + output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 *
> FFMIN(output[n_filter], 0.0);
> + }
> + }
> + output += conv_params->output_num;
> + }
> + }
> +}
> +
> +static void depth_to_space(const float *input, float *output, int
> block_size, int width, int height, int channels)
> +{
> + int y, x, by, bx, ch;
> + int new_channels = channels / (block_size * block_size);
> + int output_linesize = width * channels;
> + int by_linesize = output_linesize / block_size;
> + int x_linesize = new_channels * block_size;
> +
> + for (y = 0; y < height; ++y){
> + for (x = 0; x < width; ++x){
> + for (by = 0; by < block_size; ++by){
> + for (bx = 0; bx < block_size; ++bx){
> + for (ch = 0; ch < new_channels; ++ch){
> + output[by * by_linesize + x * x_linesize + bx *
> new_channels + ch] = input[ch];
> + }
> + input += new_channels;
> + }
> + }
> + }
> + output += output_linesize;
> + }
> +}
> +
> +DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData
> *outputs, uint32_t nb_output)
> +{
> + ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
> + int cur_width, cur_height, cur_channels;
> + int32_t layer;
> + InputParams *input_params;
> + ConvolutionalParams *conv_params;
> + DepthToSpaceParams *depth_to_space_params;
> +
> + if (network->layers_num <= 0 || network->layers[0].type != INPUT ||
> !network->layers[0].output){
> + return DNN_ERROR;
> + }
> + else{
> + input_params = (InputParams *)network->layers[0].params;
> + cur_width = input_params->width;
> + cur_height = input_params->height;
> + cur_channels = input_params->channels;
> + }
> +
> + for (layer = 1; layer < network->layers_num; ++layer){
> + if (!network->layers[layer].output){
> + return DNN_ERROR;
> + }
> + switch (network->layers[layer].type){
> + case CONV:
> + conv_params = (ConvolutionalParams
> *)network->layers[layer].params;
> + convolve(network->layers[layer - 1].output,
> network->layers[layer].output, conv_params, cur_width, cur_height);
> + cur_channels = conv_params->output_num;
> + if (conv_params->padding_method == VALID) {
> + int pad_size = (conv_params->kernel_size - 1) *
> conv_params->dilation;
> + cur_height -= pad_size;
> + cur_width -= pad_size;
> + }
> + break;
> + case DEPTH_TO_SPACE:
> + depth_to_space_params = (DepthToSpaceParams
> *)network->layers[layer].params;
> + depth_to_space(network->layers[layer - 1].output,
> network->layers[layer].output,
> + depth_to_space_params->block_size, cur_width,
> cur_height, cur_channels);
> + cur_height *= depth_to_space_params->block_size;
> + cur_width *= depth_to_space_params->block_size;
> + cur_channels /= depth_to_space_params->block_size *
> depth_to_space_params->block_size;
> + break;
> + case INPUT:
> + return DNN_ERROR;
> + }
> + }
> +
> + // native mode does not support multiple outputs yet
> + if (nb_output > 1)
> + return DNN_ERROR;
> + outputs[0].data = network->layers[network->layers_num - 1].output;
> + outputs[0].height = cur_height;
> + outputs[0].width = cur_width;
> + outputs[0].channels = cur_channels;
> +
> + return DNN_SUCCESS;
> +}
> +
> +void ff_dnn_free_model_native(DNNModel **model)
> +{
> + ConvolutionalNetwork *network;
> + ConvolutionalParams *conv_params;
> + int32_t layer;
> +
> + if (*model)
> + {
> + network = (ConvolutionalNetwork *)(*model)->model;
> + for (layer = 0; layer < network->layers_num; ++layer){
> + av_freep(&network->layers[layer].output);
> + if (network->layers[layer].type == CONV){
> + conv_params = (ConvolutionalParams
> *)network->layers[layer].params;
> + av_freep(&conv_params->kernel);
> + av_freep(&conv_params->biases);
> + }
> + av_freep(&network->layers[layer].params);
> + }
> + av_freep(&network->layers);
> + av_freep(&network);
> + av_freep(model);
> + }
> +}
> diff --git a/libavfilter/dnn/dnn_backend_native.h
> b/libavfilter/dnn/dnn_backend_native.h
> new file mode 100644
> index 0000000..532103c
> --- /dev/null
> +++ b/libavfilter/dnn/dnn_backend_native.h
> @@ -0,0 +1,74 @@
> +/*
> + * Copyright (c) 2018 Sergey Lavrushkin
> + *
> + * This file is part of FFmpeg.
> + *
> + * FFmpeg is free software; you can redistribute it and/or
> + * modify it under the terms of the GNU Lesser General Public
> + * License as published by the Free Software Foundation; either
> + * version 2.1 of the License, or (at your option) any later version.
> + *
> + * FFmpeg is distributed in the hope that it will be useful,
> + * but WITHOUT ANY WARRANTY; without even the implied warranty of
> + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
> + * Lesser General Public License for more details.
> + *
> + * You should have received a copy of the GNU Lesser General Public
> + * License along with FFmpeg; if not, write to the Free Software
> + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
> USA
> + */
> +
> +/**
> + * @file
> + * DNN inference functions interface for native backend.
> + */
> +
> +
> +#ifndef AVFILTER_DNN_BACKEND_NATIVE_H
> +#define AVFILTER_DNN_BACKEND_NATIVE_H
> +
> +#include "../dnn_interface.h"
> +#include "libavformat/avio.h"
> +
> +typedef enum {INPUT, CONV, DEPTH_TO_SPACE} DNNLayerType;
> +
> +typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;
> +
> +typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNConvPaddingParam;
> +
> +typedef struct Layer{
> + DNNLayerType type;
> + float *output;
> + void *params;
> +} Layer;
> +
> +typedef struct ConvolutionalParams{
> + int32_t input_num, output_num, kernel_size;
> + DNNActivationFunc activation;
> + DNNConvPaddingParam padding_method;
> + int32_t dilation;
> + float *kernel;
> + float *biases;
> +} ConvolutionalParams;
> +
> +typedef struct InputParams{
> + int height, width, channels;
> +} InputParams;
> +
> +typedef struct DepthToSpaceParams{
> + int block_size;
> +} DepthToSpaceParams;
> +
> +// Represents simple feed-forward convolutional network.
> +typedef struct ConvolutionalNetwork{
> + Layer *layers;
> + int32_t layers_num;
> +} ConvolutionalNetwork;
> +
> +DNNModel *ff_dnn_load_model_native(const char *model_filename);
> +
> +DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData
> *outputs, uint32_t nb_output);
> +
> +void ff_dnn_free_model_native(DNNModel **model);
> +
> +#endif
> diff --git a/libavfilter/dnn/dnn_backend_tf.c
> b/libavfilter/dnn/dnn_backend_tf.c
> new file mode 100644
> index 0000000..ba959ae
> --- /dev/null
> +++ b/libavfilter/dnn/dnn_backend_tf.c
> @@ -0,0 +1,603 @@
> +/*
> + * Copyright (c) 2018 Sergey Lavrushkin
> + *
> + * This file is part of FFmpeg.
> + *
> + * FFmpeg is free software; you can redistribute it and/or
> + * modify it under the terms of the GNU Lesser General Public
> + * License as published by the Free Software Foundation; either
> + * version 2.1 of the License, or (at your option) any later version.
> + *
> + * FFmpeg is distributed in the hope that it will be useful,
> + * but WITHOUT ANY WARRANTY; without even the implied warranty of
> + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
> + * Lesser General Public License for more details.
> + *
> + * You should have received a copy of the GNU Lesser General Public
> + * License along with FFmpeg; if not, write to the Free Software
> + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
> USA
> + */
> +
> +/**
> + * @file
> + * DNN tensorflow backend implementation.
> + */
> +
> +#include "dnn_backend_tf.h"
> +#include "dnn_backend_native.h"
> +#include "libavformat/avio.h"
> +#include "libavutil/avassert.h"
> +
> +#include <tensorflow/c/c_api.h>
> +
> +typedef struct TFModel{
> + TF_Graph *graph;
> + TF_Session *session;
> + TF_Status *status;
> + TF_Output input;
> + TF_Tensor *input_tensor;
> + TF_Output *outputs;
> + TF_Tensor **output_tensors;
> + uint32_t nb_output;
> +} TFModel;
> +
> +static void free_buffer(void *data, size_t length)
> +{
> + av_freep(&data);
> +}
> +
> +static TF_Buffer *read_graph(const char *model_filename)
> +{
> + TF_Buffer *graph_buf;
> + unsigned char *graph_data = NULL;
> + AVIOContext *model_file_context;
> + long size, bytes_read;
> +
> + if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
> + return NULL;
> + }
> +
> + size = avio_size(model_file_context);
> +
> + graph_data = av_malloc(size);
> + if (!graph_data){
> + avio_closep(&model_file_context);
> + return NULL;
> + }
> + bytes_read = avio_read(model_file_context, graph_data, size);
> + avio_closep(&model_file_context);
> + if (bytes_read != size){
> + av_freep(&graph_data);
> + return NULL;
> + }
> +
> + graph_buf = TF_NewBuffer();
> + graph_buf->data = (void *)graph_data;
> + graph_buf->length = size;
> + graph_buf->data_deallocator = free_buffer;
> +
> + return graph_buf;
> +}
> +
> +static TF_Tensor *allocate_input_tensor(const DNNInputData *input)
> +{
> + TF_DataType dt;
> + size_t size;
> + int64_t input_dims[] = {1, input->height, input->width, input->channels};
> + switch (input->dt) {
> + case DNN_FLOAT:
> + dt = TF_FLOAT;
> + size = sizeof(float);
> + break;
> + case DNN_UINT8:
> + dt = TF_UINT8;
> + size = sizeof(char);
> + break;
> + default:
> + av_assert0(!"should not reach here");
> + }
> +
> + return TF_AllocateTensor(dt, input_dims, 4,
> + input_dims[1] * input_dims[2] * input_dims[3] *
> size);
> +}
> +
> +static DNNReturnType set_input_output_tf(void *model, DNNInputData *input,
> const char *input_name, const char **output_names, uint32_t nb_output)
> +{
> + TFModel *tf_model = (TFModel *)model;
> + TF_SessionOptions *sess_opts;
> + const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph,
> "init");
> +
> + // Input operation
> + tf_model->input.oper = TF_GraphOperationByName(tf_model->graph,
> input_name);
> + if (!tf_model->input.oper){
> + return DNN_ERROR;
> + }
> + tf_model->input.index = 0;
> + if (tf_model->input_tensor){
> + TF_DeleteTensor(tf_model->input_tensor);
> + }
> + tf_model->input_tensor = allocate_input_tensor(input);
> + if (!tf_model->input_tensor){
> + return DNN_ERROR;
> + }
> + input->data = (float *)TF_TensorData(tf_model->input_tensor);
> +
> + // Output operation
> + if (nb_output == 0)
> + return DNN_ERROR;
> +
> + av_freep(&tf_model->outputs);
> + tf_model->outputs = av_malloc_array(nb_output,
> sizeof(*tf_model->outputs));
> + if (!tf_model->outputs)
> + return DNN_ERROR;
> + for (int i = 0; i < nb_output; ++i) {
> + tf_model->outputs[i].oper = TF_GraphOperationByName(tf_model->graph,
> output_names[i]);
> + if (!tf_model->outputs[i].oper){
> + av_freep(&tf_model->outputs);
> + return DNN_ERROR;
> + }
> + tf_model->outputs[i].index = 0;
> + }
> +
> + if (tf_model->output_tensors) {
> + for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> + if (tf_model->output_tensors[i]) {
> + TF_DeleteTensor(tf_model->output_tensors[i]);
> + tf_model->output_tensors[i] = NULL;
> + }
> + }
> + }
> + av_freep(&tf_model->output_tensors);
> + tf_model->output_tensors = av_mallocz_array(nb_output,
> sizeof(*tf_model->output_tensors));
> + if (!tf_model->output_tensors) {
> + av_freep(&tf_model->outputs);
> + return DNN_ERROR;
> + }
> +
> + tf_model->nb_output = nb_output;
> +
> + if (tf_model->session){
> + TF_CloseSession(tf_model->session, tf_model->status);
> + TF_DeleteSession(tf_model->session, tf_model->status);
> + }
> +
> + sess_opts = TF_NewSessionOptions();
> + tf_model->session = TF_NewSession(tf_model->graph, sess_opts,
> tf_model->status);
> + TF_DeleteSessionOptions(sess_opts);
> + if (TF_GetCode(tf_model->status) != TF_OK)
> + {
> + return DNN_ERROR;
> + }
> +
> + // Run initialization operation with name "init" if it is present in
> graph
> + if (init_op){
> + TF_SessionRun(tf_model->session, NULL,
> + NULL, NULL, 0,
> + NULL, NULL, 0,
> + &init_op, 1, NULL, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK)
> + {
> + return DNN_ERROR;
> + }
> + }
> +
> + return DNN_SUCCESS;
> +}
> +
> +static DNNReturnType load_tf_model(TFModel *tf_model, const char
> *model_filename)
> +{
> + TF_Buffer *graph_def;
> + TF_ImportGraphDefOptions *graph_opts;
> +
> + graph_def = read_graph(model_filename);
> + if (!graph_def){
> + return DNN_ERROR;
> + }
> + tf_model->graph = TF_NewGraph();
> + tf_model->status = TF_NewStatus();
> + graph_opts = TF_NewImportGraphDefOptions();
> + TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts,
> tf_model->status);
> + TF_DeleteImportGraphDefOptions(graph_opts);
> + TF_DeleteBuffer(graph_def);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + TF_DeleteGraph(tf_model->graph);
> + TF_DeleteStatus(tf_model->status);
> + return DNN_ERROR;
> + }
> +
> + return DNN_SUCCESS;
> +}
> +
> +#define NAME_BUFFER_SIZE 256
> +
> +static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation
> *transpose_op, TF_Operation **cur_op,
> + ConvolutionalParams* params, const int
> layer)
> +{
> + TF_Operation *op;
> + TF_OperationDescription *op_desc;
> + TF_Output input;
> + int64_t strides[] = {1, 1, 1, 1};
> + TF_Tensor *tensor;
> + int64_t dims[4];
> + int dims_len;
> + char name_buffer[NAME_BUFFER_SIZE];
> + int32_t size;
> +
> + size = params->input_num * params->output_num * params->kernel_size *
> params->kernel_size;
> + input.index = 0;
> +
> + snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
> + op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
> + TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> + dims[0] = params->output_num;
> + dims[1] = params->kernel_size;
> + dims[2] = params->kernel_size;
> + dims[3] = params->input_num;
> + dims_len = 4;
> + tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size *
> sizeof(float));
> + memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float));
> + TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> + op = TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> +
> + snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
> + op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
> + input.oper = op;
> + TF_AddInput(op_desc, input);
> + input.oper = transpose_op;
> + TF_AddInput(op_desc, input);
> + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> + TF_SetAttrType(op_desc, "Tperm", TF_INT32);
> + op = TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> +
> + snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
> + op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
> + input.oper = *cur_op;
> + TF_AddInput(op_desc, input);
> + input.oper = op;
> + TF_AddInput(op_desc, input);
> + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> + TF_SetAttrIntList(op_desc, "strides", strides, 4);
> + TF_SetAttrString(op_desc, "padding", "VALID", 5);
> + *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> +
> + snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
> + op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
> + TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> + dims[0] = params->output_num;
> + dims_len = 1;
> + tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num
> * sizeof(float));
> + memcpy(TF_TensorData(tensor), params->biases, params->output_num *
> sizeof(float));
> + TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> + op = TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> +
> + snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
> + op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
> + input.oper = *cur_op;
> + TF_AddInput(op_desc, input);
> + input.oper = op;
> + TF_AddInput(op_desc, input);
> + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> + *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> +
> + snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
> + switch (params->activation){
> + case RELU:
> + op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
> + break;
> + case TANH:
> + op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
> + break;
> + case SIGMOID:
> + op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
> + break;
> + default:
> + return DNN_ERROR;
> + }
> + input.oper = *cur_op;
> + TF_AddInput(op_desc, input);
> + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> + *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> +
> + return DNN_SUCCESS;
> +}
> +
> +static DNNReturnType add_depth_to_space_layer(TFModel *tf_model,
> TF_Operation **cur_op,
> + DepthToSpaceParams *params,
> const int layer)
> +{
> + TF_OperationDescription *op_desc;
> + TF_Output input;
> + char name_buffer[NAME_BUFFER_SIZE];
> +
> + snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
> + op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", name_buffer);
> + input.oper = *cur_op;
> + input.index = 0;
> + TF_AddInput(op_desc, input);
> + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> + TF_SetAttrInt(op_desc, "block_size", params->block_size);
> + *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> +
> + return DNN_SUCCESS;
> +}
> +
> +static int calculate_pad(const ConvolutionalNetwork *conv_network)
> +{
> + ConvolutionalParams *params;
> + int32_t layer;
> + int pad = 0;
> +
> + for (layer = 0; layer < conv_network->layers_num; ++layer){
> + if (conv_network->layers[layer].type == CONV){
> + params = (ConvolutionalParams
> *)conv_network->layers[layer].params;
> + pad += params->kernel_size >> 1;
> + }
> + }
> +
> + return pad;
> +}
> +
> +static DNNReturnType add_pad_op(TFModel *tf_model, TF_Operation **cur_op,
> const int32_t pad)
> +{
> + TF_Operation *op;
> + TF_Tensor *tensor;
> + TF_OperationDescription *op_desc;
> + TF_Output input;
> + int32_t *pads;
> + int64_t pads_shape[] = {4, 2};
> +
> + input.index = 0;
> +
> + op_desc = TF_NewOperation(tf_model->graph, "Const", "pads");
> + TF_SetAttrType(op_desc, "dtype", TF_INT32);
> + tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 *
> sizeof(int32_t));
> + pads = (int32_t *)TF_TensorData(tensor);
> + pads[0] = 0; pads[1] = 0;
> + pads[2] = pad; pads[3] = pad;
> + pads[4] = pad; pads[5] = pad;
> + pads[6] = 0; pads[7] = 0;
> + TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> + op = TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> +
> + op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
> + input.oper = *cur_op;
> + TF_AddInput(op_desc, input);
> + input.oper = op;
> + TF_AddInput(op_desc, input);
> + TF_SetAttrType(op_desc, "T", TF_FLOAT);
> + TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
> + TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
> + *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> +
> + return DNN_SUCCESS;
> +}
> +
> +static DNNReturnType load_native_model(TFModel *tf_model, const char
> *model_filename)
> +{
> + int32_t layer;
> + TF_OperationDescription *op_desc;
> + TF_Operation *op;
> + TF_Operation *transpose_op;
> + TF_Tensor *tensor;
> + TF_Output input;
> + int32_t *transpose_perm;
> + int64_t transpose_perm_shape[] = {4};
> + int64_t input_shape[] = {1, -1, -1, -1};
> + int32_t pad;
> + DNNReturnType layer_add_res;
> + DNNModel *native_model = NULL;
> + ConvolutionalNetwork *conv_network;
> +
> + native_model = ff_dnn_load_model_native(model_filename);
> + if (!native_model){
> + return DNN_ERROR;
> + }
> +
> + conv_network = (ConvolutionalNetwork *)native_model->model;
> + pad = calculate_pad(conv_network);
> + tf_model->graph = TF_NewGraph();
> + tf_model->status = TF_NewStatus();
> +
> +#define CLEANUP_ON_ERROR(tf_model) \
> + { \
> + TF_DeleteGraph(tf_model->graph); \
> + TF_DeleteStatus(tf_model->status); \
> + return DNN_ERROR; \
> + }
> +
> + op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
> + TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> + TF_SetAttrShape(op_desc, "shape", input_shape, 4);
> + op = TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + CLEANUP_ON_ERROR(tf_model);
> + }
> +
> + if (add_pad_op(tf_model, &op, pad) != DNN_SUCCESS){
> + CLEANUP_ON_ERROR(tf_model);
> + }
> +
> + op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
> + TF_SetAttrType(op_desc, "dtype", TF_INT32);
> + tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 *
> sizeof(int32_t));
> + transpose_perm = (int32_t *)TF_TensorData(tensor);
> + transpose_perm[0] = 1;
> + transpose_perm[1] = 2;
> + transpose_perm[2] = 3;
> + transpose_perm[3] = 0;
> + TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + CLEANUP_ON_ERROR(tf_model);
> + }
> + transpose_op = TF_FinishOperation(op_desc, tf_model->status);
> +
> + for (layer = 0; layer < conv_network->layers_num; ++layer){
> + switch (conv_network->layers[layer].type){
> + case INPUT:
> + layer_add_res = DNN_SUCCESS;
> + break;
> + case CONV:
> + layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
> + (ConvolutionalParams
> *)conv_network->layers[layer].params, layer);
> + break;
> + case DEPTH_TO_SPACE:
> + layer_add_res = add_depth_to_space_layer(tf_model, &op,
> + (DepthToSpaceParams
> *)conv_network->layers[layer].params, layer);
> + break;
> + default:
> + CLEANUP_ON_ERROR(tf_model);
> + }
> +
> + if (layer_add_res != DNN_SUCCESS){
> + CLEANUP_ON_ERROR(tf_model);
> + }
> + }
> +
> + op_desc = TF_NewOperation(tf_model->graph, "Identity", "y");
> + input.oper = op;
> + TF_AddInput(op_desc, input);
> + TF_FinishOperation(op_desc, tf_model->status);
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + CLEANUP_ON_ERROR(tf_model);
> + }
> +
> + ff_dnn_free_model_native(&native_model);
> +
> + return DNN_SUCCESS;
> +}
> +
> +DNNModel *ff_dnn_load_model_tf(const char *model_filename)
> +{
> + DNNModel *model = NULL;
> + TFModel *tf_model = NULL;
> +
> + model = av_malloc(sizeof(DNNModel));
> + if (!model){
> + return NULL;
> + }
> +
> + tf_model = av_mallocz(sizeof(TFModel));
> + if (!tf_model){
> + av_freep(&model);
> + return NULL;
> + }
> +
> + if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
> + if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
> + av_freep(&tf_model);
> + av_freep(&model);
> +
> + return NULL;
> + }
> + }
> +
> + model->model = (void *)tf_model;
> + model->set_input_output = &set_input_output_tf;
> +
> + return model;
> +}
> +
> +
> +
> +DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData
> *outputs, uint32_t nb_output)
> +{
> + TFModel *tf_model = (TFModel *)model->model;
> + uint32_t nb = FFMIN(nb_output, tf_model->nb_output);
> + if (nb == 0)
> + return DNN_ERROR;
> +
> + av_assert0(tf_model->output_tensors);
> + for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> + if (tf_model->output_tensors[i]) {
> + TF_DeleteTensor(tf_model->output_tensors[i]);
> + tf_model->output_tensors[i] = NULL;
> + }
> + }
> +
> + TF_SessionRun(tf_model->session, NULL,
> + &tf_model->input, &tf_model->input_tensor, 1,
> + tf_model->outputs, tf_model->output_tensors, nb,
> + NULL, 0, NULL, tf_model->status);
> +
> + if (TF_GetCode(tf_model->status) != TF_OK){
> + return DNN_ERROR;
> + }
> +
> + for (uint32_t i = 0; i < nb; ++i) {
> + outputs[i].height = TF_Dim(tf_model->output_tensors[i], 1);
> + outputs[i].width = TF_Dim(tf_model->output_tensors[i], 2);
> + outputs[i].channels = TF_Dim(tf_model->output_tensors[i], 3);
> + outputs[i].data = TF_TensorData(tf_model->output_tensors[i]);
> + }
> +
> + return DNN_SUCCESS;
> +}
> +
> +void ff_dnn_free_model_tf(DNNModel **model)
> +{
> + TFModel *tf_model;
> +
> + if (*model){
> + tf_model = (TFModel *)(*model)->model;
> + if (tf_model->graph){
> + TF_DeleteGraph(tf_model->graph);
> + }
> + if (tf_model->session){
> + TF_CloseSession(tf_model->session, tf_model->status);
> + TF_DeleteSession(tf_model->session, tf_model->status);
> + }
> + if (tf_model->status){
> + TF_DeleteStatus(tf_model->status);
> + }
> + if (tf_model->input_tensor){
> + TF_DeleteTensor(tf_model->input_tensor);
> + }
> + if (tf_model->output_tensors) {
> + for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> + if (tf_model->output_tensors[i]) {
> + TF_DeleteTensor(tf_model->output_tensors[i]);
> + tf_model->output_tensors[i] = NULL;
> + }
> + }
> + }
> + av_freep(&tf_model->outputs);
> + av_freep(&tf_model->output_tensors);
> + av_freep(&tf_model);
> + av_freep(model);
> + }
> +}
> diff --git a/libavfilter/dnn/dnn_backend_tf.h
> b/libavfilter/dnn/dnn_backend_tf.h
> new file mode 100644
> index 0000000..bb1c85f
> --- /dev/null
> +++ b/libavfilter/dnn/dnn_backend_tf.h
> @@ -0,0 +1,38 @@
> +/*
> + * Copyright (c) 2018 Sergey Lavrushkin
> + *
> + * This file is part of FFmpeg.
> + *
> + * FFmpeg is free software; you can redistribute it and/or
> + * modify it under the terms of the GNU Lesser General Public
> + * License as published by the Free Software Foundation; either
> + * version 2.1 of the License, or (at your option) any later version.
> + *
> + * FFmpeg is distributed in the hope that it will be useful,
> + * but WITHOUT ANY WARRANTY; without even the implied warranty of
> + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
> + * Lesser General Public License for more details.
> + *
> + * You should have received a copy of the GNU Lesser General Public
> + * License along with FFmpeg; if not, write to the Free Software
> + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
> USA
> + */
> +
> +/**
> + * @file
> + * DNN inference functions interface for TensorFlow backend.
> + */
> +
> +
> +#ifndef AVFILTER_DNN_BACKEND_TF_H
> +#define AVFILTER_DNN_BACKEND_TF_H
> +
> +#include "../dnn_interface.h"
> +
> +DNNModel *ff_dnn_load_model_tf(const char *model_filename);
> +
> +DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData
> *outputs, uint32_t nb_output);
> +
> +void ff_dnn_free_model_tf(DNNModel **model);
> +
> +#endif
> diff --git a/libavfilter/dnn/dnn_interface.c b/libavfilter/dnn/dnn_interface.c
> new file mode 100644
> index 0000000..62da55f
> --- /dev/null
> +++ b/libavfilter/dnn/dnn_interface.c
> @@ -0,0 +1,63 @@
> +/*
> + * Copyright (c) 2018 Sergey Lavrushkin
> + *
> + * This file is part of FFmpeg.
> + *
> + * FFmpeg is free software; you can redistribute it and/or
> + * modify it under the terms of the GNU Lesser General Public
> + * License as published by the Free Software Foundation; either
> + * version 2.1 of the License, or (at your option) any later version.
> + *
> + * FFmpeg is distributed in the hope that it will be useful,
> + * but WITHOUT ANY WARRANTY; without even the implied warranty of
> + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
> + * Lesser General Public License for more details.
> + *
> + * You should have received a copy of the GNU Lesser General Public
> + * License along with FFmpeg; if not, write to the Free Software
> + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
> USA
> + */
> +
> +/**
> + * @file
> + * Implements DNN module initialization with specified backend.
> + */
> +
> +#include "../dnn_interface.h"
> +#include "dnn_backend_native.h"
> +#include "dnn_backend_tf.h"
> +#include "libavutil/mem.h"
> +
> +DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
> +{
> + DNNModule *dnn_module;
> +
> + dnn_module = av_malloc(sizeof(DNNModule));
> + if(!dnn_module){
> + return NULL;
> + }
> +
> + switch(backend_type){
> + case DNN_NATIVE:
> + dnn_module->load_model = &ff_dnn_load_model_native;
> + dnn_module->execute_model = &ff_dnn_execute_model_native;
> + dnn_module->free_model = &ff_dnn_free_model_native;
> + break;
> + case DNN_TF:
> + #if (CONFIG_LIBTENSORFLOW == 1)
> + dnn_module->load_model = &ff_dnn_load_model_tf;
> + dnn_module->execute_model = &ff_dnn_execute_model_tf;
> + dnn_module->free_model = &ff_dnn_free_model_tf;
> + #else
> + av_freep(&dnn_module);
> + return NULL;
> + #endif
> + break;
> + default:
> + av_log(NULL, AV_LOG_ERROR, "Module backend_type is not native or
> tensorflow\n");
> + av_freep(&dnn_module);
> + return NULL;
> + }
> +
> + return dnn_module;
> +}
> diff --git a/libavfilter/dnn_backend_native.c
> b/libavfilter/dnn_backend_native.c
> deleted file mode 100644
> index 82e900b..0000000
> --- a/libavfilter/dnn_backend_native.c
> +++ /dev/null
> @@ -1,389 +0,0 @@
> -/*
> - * Copyright (c) 2018 Sergey Lavrushkin
> - *
> - * This file is part of FFmpeg.
> - *
> - * FFmpeg is free software; you can redistribute it and/or
> - * modify it under the terms of the GNU Lesser General Public
> - * License as published by the Free Software Foundation; either
> - * version 2.1 of the License, or (at your option) any later version.
> - *
> - * FFmpeg is distributed in the hope that it will be useful,
> - * but WITHOUT ANY WARRANTY; without even the implied warranty of
> - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
> - * Lesser General Public License for more details.
> - *
> - * You should have received a copy of the GNU Lesser General Public
> - * License along with FFmpeg; if not, write to the Free Software
> - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
> USA
> - */
> -
> -/**
> - * @file
> - * DNN native backend implementation.
> - */
> -
> -#include "dnn_backend_native.h"
> -#include "libavutil/avassert.h"
> -
> -static DNNReturnType set_input_output_native(void *model, DNNInputData
> *input, const char *input_name, const char **output_names, uint32_t nb_output)
> -{
> - ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
> - InputParams *input_params;
> - ConvolutionalParams *conv_params;
> - DepthToSpaceParams *depth_to_space_params;
> - int cur_width, cur_height, cur_channels;
> - int32_t layer;
> -
> - if (network->layers_num <= 0 || network->layers[0].type != INPUT){
> - return DNN_ERROR;
> - }
> - else{
> - input_params = (InputParams *)network->layers[0].params;
> - input_params->width = cur_width = input->width;
> - input_params->height = cur_height = input->height;
> - input_params->channels = cur_channels = input->channels;
> - if (input->data){
> - av_freep(&input->data);
> - }
> - av_assert0(input->dt == DNN_FLOAT);
> - network->layers[0].output = input->data = av_malloc(cur_height *
> cur_width * cur_channels * sizeof(float));
> - if (!network->layers[0].output){
> - return DNN_ERROR;
> - }
> - }
> -
> - for (layer = 1; layer < network->layers_num; ++layer){
> - switch (network->layers[layer].type){
> - case CONV:
> - conv_params = (ConvolutionalParams
> *)network->layers[layer].params;
> - if (conv_params->input_num != cur_channels){
> - return DNN_ERROR;
> - }
> - cur_channels = conv_params->output_num;
> -
> - if (conv_params->padding_method == VALID) {
> - int pad_size = (conv_params->kernel_size - 1) *
> conv_params->dilation;
> - cur_height -= pad_size;
> - cur_width -= pad_size;
> - }
> - break;
> - case DEPTH_TO_SPACE:
> - depth_to_space_params = (DepthToSpaceParams
> *)network->layers[layer].params;
> - if (cur_channels % (depth_to_space_params->block_size *
> depth_to_space_params->block_size) != 0){
> - return DNN_ERROR;
> - }
> - cur_channels = cur_channels / (depth_to_space_params->block_size
> * depth_to_space_params->block_size);
> - cur_height *= depth_to_space_params->block_size;
> - cur_width *= depth_to_space_params->block_size;
> - break;
> - default:
> - return DNN_ERROR;
> - }
> - if (network->layers[layer].output){
> - av_freep(&network->layers[layer].output);
> - }
> -
> - if (cur_height <= 0 || cur_width <= 0)
> - return DNN_ERROR;
> -
> - network->layers[layer].output = av_malloc(cur_height * cur_width *
> cur_channels * sizeof(float));
> - if (!network->layers[layer].output){
> - return DNN_ERROR;
> - }
> - }
> -
> - return DNN_SUCCESS;
> -}
> -
> -// Loads model and its parameters that are stored in a binary file with
> following structure:
> -// layers_num,layer_type,layer_parameterss,layer_type,layer_parameters...
> -// For CONV layer: activation_function, input_num, output_num, kernel_size,
> kernel, biases
> -// For DEPTH_TO_SPACE layer: block_size
> -DNNModel *ff_dnn_load_model_native(const char *model_filename)
> -{
> - DNNModel *model = NULL;
> - ConvolutionalNetwork *network = NULL;
> - AVIOContext *model_file_context;
> - int file_size, dnn_size, kernel_size, i;
> - int32_t layer;
> - DNNLayerType layer_type;
> - ConvolutionalParams *conv_params;
> - DepthToSpaceParams *depth_to_space_params;
> -
> - model = av_malloc(sizeof(DNNModel));
> - if (!model){
> - return NULL;
> - }
> -
> - if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
> - av_freep(&model);
> - return NULL;
> - }
> - file_size = avio_size(model_file_context);
> -
> - network = av_malloc(sizeof(ConvolutionalNetwork));
> - if (!network){
> - avio_closep(&model_file_context);
> - av_freep(&model);
> - return NULL;
> - }
> - model->model = (void *)network;
> -
> - network->layers_num = 1 + (int32_t)avio_rl32(model_file_context);
> - dnn_size = 4;
> -
> - network->layers = av_malloc(network->layers_num * sizeof(Layer));
> - if (!network->layers){
> - av_freep(&network);
> - avio_closep(&model_file_context);
> - av_freep(&model);
> - return NULL;
> - }
> -
> - for (layer = 0; layer < network->layers_num; ++layer){
> - network->layers[layer].output = NULL;
> - network->layers[layer].params = NULL;
> - }
> - network->layers[0].type = INPUT;
> - network->layers[0].params = av_malloc(sizeof(InputParams));
> - if (!network->layers[0].params){
> - avio_closep(&model_file_context);
> - ff_dnn_free_model_native(&model);
> - return NULL;
> - }
> -
> - for (layer = 1; layer < network->layers_num; ++layer){
> - layer_type = (int32_t)avio_rl32(model_file_context);
> - dnn_size += 4;
> - switch (layer_type){
> - case CONV:
> - conv_params = av_malloc(sizeof(ConvolutionalParams));
> - if (!conv_params){
> - avio_closep(&model_file_context);
> - ff_dnn_free_model_native(&model);
> - return NULL;
> - }
> - conv_params->dilation = (int32_t)avio_rl32(model_file_context);
> - conv_params->padding_method =
> (int32_t)avio_rl32(model_file_context);
> - conv_params->activation = (int32_t)avio_rl32(model_file_context);
> - conv_params->input_num = (int32_t)avio_rl32(model_file_context);
> - conv_params->output_num = (int32_t)avio_rl32(model_file_context);
> - conv_params->kernel_size =
> (int32_t)avio_rl32(model_file_context);
> - kernel_size = conv_params->input_num * conv_params->output_num *
> - conv_params->kernel_size *
> conv_params->kernel_size;
> - dnn_size += 24 + (kernel_size + conv_params->output_num << 2);
> - if (dnn_size > file_size || conv_params->input_num <= 0 ||
> - conv_params->output_num <= 0 || conv_params->kernel_size <=
> 0){
> - avio_closep(&model_file_context);
> - ff_dnn_free_model_native(&model);
> - return NULL;
> - }
> - conv_params->kernel = av_malloc(kernel_size * sizeof(float));
> - conv_params->biases = av_malloc(conv_params->output_num *
> sizeof(float));
> - if (!conv_params->kernel || !conv_params->biases){
> - avio_closep(&model_file_context);
> - ff_dnn_free_model_native(&model);
> - return NULL;
> - }
> - for (i = 0; i < kernel_size; ++i){
> - conv_params->kernel[i] =
> av_int2float(avio_rl32(model_file_context));
> - }
> - for (i = 0; i < conv_params->output_num; ++i){
> - conv_params->biases[i] =
> av_int2float(avio_rl32(model_file_context));
> - }
> - network->layers[layer].type = CONV;
> - network->layers[layer].params = conv_params;
> - break;
> - case DEPTH_TO_SPACE:
> - depth_to_space_params = av_malloc(sizeof(DepthToSpaceParams));
> - if (!depth_to_space_params){
> - avio_closep(&model_file_context);
> - ff_dnn_free_model_native(&model);
> - return NULL;
> - }
> - depth_to_space_params->block_size =
> (int32_t)avio_rl32(model_file_context);
> - dnn_size += 4;
> - network->layers[layer].type = DEPTH_TO_SPACE;
> - network->layers[layer].params = depth_to_space_params;
> - break;
> - default:
> - avio_closep(&model_file_context);
> - ff_dnn_free_model_native(&model);
> - return NULL;
> - }
> - }
> -
> - avio_closep(&model_file_context);
> -
> - if (dnn_size != file_size){
> - ff_dnn_free_model_native(&model);
> - return NULL;
> - }
> -
> - model->set_input_output = &set_input_output_native;
> -
> - return model;
> -}
> -
> -#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
> -
> -static void convolve(const float *input, float *output, const
> ConvolutionalParams *conv_params, int width, int height)
> -{
> - int radius = conv_params->kernel_size >> 1;
> - int src_linesize = width * conv_params->input_num;
> - int filter_linesize = conv_params->kernel_size * conv_params->input_num;
> - int filter_size = conv_params->kernel_size * filter_linesize;
> - int pad_size = (conv_params->padding_method == VALID) ?
> (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;
> -
> - for (int y = pad_size; y < height - pad_size; ++y) {
> - for (int x = pad_size; x < width - pad_size; ++x) {
> - for (int n_filter = 0; n_filter < conv_params->output_num;
> ++n_filter) {
> - output[n_filter] = conv_params->biases[n_filter];
> -
> - for (int ch = 0; ch < conv_params->input_num; ++ch) {
> - for (int kernel_y = 0; kernel_y <
> conv_params->kernel_size; ++kernel_y) {
> - for (int kernel_x = 0; kernel_x <
> conv_params->kernel_size; ++kernel_x) {
> - float input_pel;
> - if (conv_params->padding_method ==
> SAME_CLAMP_TO_EDGE) {
> - int y_pos = CLAMP_TO_EDGE(y + (kernel_y -
> radius) * conv_params->dilation, height);
> - int x_pos = CLAMP_TO_EDGE(x + (kernel_x -
> radius) * conv_params->dilation, width);
> - input_pel = input[y_pos * src_linesize +
> x_pos * conv_params->input_num + ch];
> - } else {
> - int y_pos = y + (kernel_y - radius) *
> conv_params->dilation;
> - int x_pos = x + (kernel_x - radius) *
> conv_params->dilation;
> - input_pel = (x_pos < 0 || x_pos >= width ||
> y_pos < 0 || y_pos >= height) ? 0.0 :
> - input[y_pos *
> src_linesize + x_pos * conv_params->input_num + ch];
> - }
> -
> -
> - output[n_filter] += input_pel *
> conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
> -
> kernel_x * conv_params->input_num + ch];
> - }
> - }
> - }
> - switch (conv_params->activation){
> - case RELU:
> - output[n_filter] = FFMAX(output[n_filter], 0.0);
> - break;
> - case TANH:
> - output[n_filter] = 2.0f / (1.0f + exp(-2.0f *
> output[n_filter])) - 1.0f;
> - break;
> - case SIGMOID:
> - output[n_filter] = 1.0f / (1.0f +
> exp(-output[n_filter]));
> - break;
> - case NONE:
> - break;
> - case LEAKY_RELU:
> - output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 *
> FFMIN(output[n_filter], 0.0);
> - }
> - }
> - output += conv_params->output_num;
> - }
> - }
> -}
> -
> -static void depth_to_space(const float *input, float *output, int
> block_size, int width, int height, int channels)
> -{
> - int y, x, by, bx, ch;
> - int new_channels = channels / (block_size * block_size);
> - int output_linesize = width * channels;
> - int by_linesize = output_linesize / block_size;
> - int x_linesize = new_channels * block_size;
> -
> - for (y = 0; y < height; ++y){
> - for (x = 0; x < width; ++x){
> - for (by = 0; by < block_size; ++by){
> - for (bx = 0; bx < block_size; ++bx){
> - for (ch = 0; ch < new_channels; ++ch){
> - output[by * by_linesize + x * x_linesize + bx *
> new_channels + ch] = input[ch];
> - }
> - input += new_channels;
> - }
> - }
> - }
> - output += output_linesize;
> - }
> -}
> -
> -DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData
> *outputs, uint32_t nb_output)
> -{
> - ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
> - int cur_width, cur_height, cur_channels;
> - int32_t layer;
> - InputParams *input_params;
> - ConvolutionalParams *conv_params;
> - DepthToSpaceParams *depth_to_space_params;
> -
> - if (network->layers_num <= 0 || network->layers[0].type != INPUT ||
> !network->layers[0].output){
> - return DNN_ERROR;
> - }
> - else{
> - input_params = (InputParams *)network->layers[0].params;
> - cur_width = input_params->width;
> - cur_height = input_params->height;
> - cur_channels = input_params->channels;
> - }
> -
> - for (layer = 1; layer < network->layers_num; ++layer){
> - if (!network->layers[layer].output){
> - return DNN_ERROR;
> - }
> - switch (network->layers[layer].type){
> - case CONV:
> - conv_params = (ConvolutionalParams
> *)network->layers[layer].params;
> - convolve(network->layers[layer - 1].output,
> network->layers[layer].output, conv_params, cur_width, cur_height);
> - cur_channels = conv_params->output_num;
> - if (conv_params->padding_method == VALID) {
> - int pad_size = (conv_params->kernel_size - 1) *
> conv_params->dilation;
> - cur_height -= pad_size;
> - cur_width -= pad_size;
> - }
> - break;
> - case DEPTH_TO_SPACE:
> - depth_to_space_params = (DepthToSpaceParams
> *)network->layers[layer].params;
> - depth_to_space(network->layers[layer - 1].output,
> network->layers[layer].output,
> - depth_to_space_params->block_size, cur_width,
> cur_height, cur_channels);
> - cur_height *= depth_to_space_params->block_size;
> - cur_width *= depth_to_space_params->block_size;
> - cur_channels /= depth_to_space_params->block_size *
> depth_to_space_params->block_size;
> - break;
> - case INPUT:
> - return DNN_ERROR;
> - }
> - }
> -
> - // native mode does not support multiple outputs yet
> - if (nb_output > 1)
> - return DNN_ERROR;
> - outputs[0].data = network->layers[network->layers_num - 1].output;
> - outputs[0].height = cur_height;
> - outputs[0].width = cur_width;
> - outputs[0].channels = cur_channels;
> -
> - return DNN_SUCCESS;
> -}
> -
> -void ff_dnn_free_model_native(DNNModel **model)
> -{
> - ConvolutionalNetwork *network;
> - ConvolutionalParams *conv_params;
> - int32_t layer;
> -
> - if (*model)
> - {
> - network = (ConvolutionalNetwork *)(*model)->model;
> - for (layer = 0; layer < network->layers_num; ++layer){
> - av_freep(&network->layers[layer].output);
> - if (network->layers[layer].type == CONV){
> - conv_params = (ConvolutionalParams
> *)network->layers[layer].params;
> - av_freep(&conv_params->kernel);
> - av_freep(&conv_params->biases);
> - }
> - av_freep(&network->layers[layer].params);
> - }
> - av_freep(&network->layers);
> - av_freep(&network);
> - av_freep(model);
> - }
> -}
> diff --git a/libavfilter/dnn_backend_native.h
> b/libavfilter/dnn_backend_native.h
> deleted file mode 100644
> index 5917955..0000000
> --- a/libavfilter/dnn_backend_native.h
> +++ /dev/null
> @@ -1,74 +0,0 @@
> -/*
> - * Copyright (c) 2018 Sergey Lavrushkin
> - *
> - * This file is part of FFmpeg.
> - *
> - * FFmpeg is free software; you can redistribute it and/or
> - * modify it under the terms of the GNU Lesser General Public
> - * License as published by the Free Software Foundation; either
> - * version 2.1 of the License, or (at your option) any later version.
> - *
> - * FFmpeg is distributed in the hope that it will be useful,
> - * but WITHOUT ANY WARRANTY; without even the implied warranty of
> - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
> - * Lesser General Public License for more details.
> - *
> - * You should have received a copy of the GNU Lesser General Public
> - * License along with FFmpeg; if not, write to the Free Software
> - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
> USA
> - */
> -
> -/**
> - * @file
> - * DNN inference functions interface for native backend.
> - */
> -
> -
> -#ifndef AVFILTER_DNN_BACKEND_NATIVE_H
> -#define AVFILTER_DNN_BACKEND_NATIVE_H
> -
> -#include "dnn_interface.h"
> -#include "libavformat/avio.h"
> -
> -typedef enum {INPUT, CONV, DEPTH_TO_SPACE} DNNLayerType;
> -
> -typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;
> -
> -typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNConvPaddingParam;
> -
> -typedef struct Layer{
> - DNNLayerType type;
> - float *output;
> - void *params;
> -} Layer;
> -
> -typedef struct ConvolutionalParams{
> - int32_t input_num, output_num, kernel_size;
> - DNNActivationFunc activation;
> - DNNConvPaddingParam padding_method;
> - int32_t dilation;
> - float *kernel;
> - float *biases;
> -} ConvolutionalParams;
> -
> -typedef struct InputParams{
> - int height, width, channels;
> -} InputParams;
> -
> -typedef struct DepthToSpaceParams{
> - int block_size;
> -} DepthToSpaceParams;
> -
> -// Represents simple feed-forward convolutional network.
> -typedef struct ConvolutionalNetwork{
> - Layer *layers;
> - int32_t layers_num;
> -} ConvolutionalNetwork;
> -
> -DNNModel *ff_dnn_load_model_native(const char *model_filename);
> -
> -DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData
> *outputs, uint32_t nb_output);
> -
> -void ff_dnn_free_model_native(DNNModel **model);
> -
> -#endif
> diff --git a/libavfilter/dnn_backend_tf.c b/libavfilter/dnn_backend_tf.c
> deleted file mode 100644
> index ba959ae..0000000
> --- a/libavfilter/dnn_backend_tf.c
> +++ /dev/null
> @@ -1,603 +0,0 @@
> -/*
> - * Copyright (c) 2018 Sergey Lavrushkin
> - *
> - * This file is part of FFmpeg.
> - *
> - * FFmpeg is free software; you can redistribute it and/or
> - * modify it under the terms of the GNU Lesser General Public
> - * License as published by the Free Software Foundation; either
> - * version 2.1 of the License, or (at your option) any later version.
> - *
> - * FFmpeg is distributed in the hope that it will be useful,
> - * but WITHOUT ANY WARRANTY; without even the implied warranty of
> - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
> - * Lesser General Public License for more details.
> - *
> - * You should have received a copy of the GNU Lesser General Public
> - * License along with FFmpeg; if not, write to the Free Software
> - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
> USA
> - */
> -
> -/**
> - * @file
> - * DNN tensorflow backend implementation.
> - */
> -
> -#include "dnn_backend_tf.h"
> -#include "dnn_backend_native.h"
> -#include "libavformat/avio.h"
> -#include "libavutil/avassert.h"
> -
> -#include <tensorflow/c/c_api.h>
> -
> -typedef struct TFModel{
> - TF_Graph *graph;
> - TF_Session *session;
> - TF_Status *status;
> - TF_Output input;
> - TF_Tensor *input_tensor;
> - TF_Output *outputs;
> - TF_Tensor **output_tensors;
> - uint32_t nb_output;
> -} TFModel;
> -
> -static void free_buffer(void *data, size_t length)
> -{
> - av_freep(&data);
> -}
> -
> -static TF_Buffer *read_graph(const char *model_filename)
> -{
> - TF_Buffer *graph_buf;
> - unsigned char *graph_data = NULL;
> - AVIOContext *model_file_context;
> - long size, bytes_read;
> -
> - if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
> - return NULL;
> - }
> -
> - size = avio_size(model_file_context);
> -
> - graph_data = av_malloc(size);
> - if (!graph_data){
> - avio_closep(&model_file_context);
> - return NULL;
> - }
> - bytes_read = avio_read(model_file_context, graph_data, size);
> - avio_closep(&model_file_context);
> - if (bytes_read != size){
> - av_freep(&graph_data);
> - return NULL;
> - }
> -
> - graph_buf = TF_NewBuffer();
> - graph_buf->data = (void *)graph_data;
> - graph_buf->length = size;
> - graph_buf->data_deallocator = free_buffer;
> -
> - return graph_buf;
> -}
> -
> -static TF_Tensor *allocate_input_tensor(const DNNInputData *input)
> -{
> - TF_DataType dt;
> - size_t size;
> - int64_t input_dims[] = {1, input->height, input->width, input->channels};
> - switch (input->dt) {
> - case DNN_FLOAT:
> - dt = TF_FLOAT;
> - size = sizeof(float);
> - break;
> - case DNN_UINT8:
> - dt = TF_UINT8;
> - size = sizeof(char);
> - break;
> - default:
> - av_assert0(!"should not reach here");
> - }
> -
> - return TF_AllocateTensor(dt, input_dims, 4,
> - input_dims[1] * input_dims[2] * input_dims[3] *
> size);
> -}
> -
> -static DNNReturnType set_input_output_tf(void *model, DNNInputData *input,
> const char *input_name, const char **output_names, uint32_t nb_output)
> -{
> - TFModel *tf_model = (TFModel *)model;
> - TF_SessionOptions *sess_opts;
> - const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph,
> "init");
> -
> - // Input operation
> - tf_model->input.oper = TF_GraphOperationByName(tf_model->graph,
> input_name);
> - if (!tf_model->input.oper){
> - return DNN_ERROR;
> - }
> - tf_model->input.index = 0;
> - if (tf_model->input_tensor){
> - TF_DeleteTensor(tf_model->input_tensor);
> - }
> - tf_model->input_tensor = allocate_input_tensor(input);
> - if (!tf_model->input_tensor){
> - return DNN_ERROR;
> - }
> - input->data = (float *)TF_TensorData(tf_model->input_tensor);
> -
> - // Output operation
> - if (nb_output == 0)
> - return DNN_ERROR;
> -
> - av_freep(&tf_model->outputs);
> - tf_model->outputs = av_malloc_array(nb_output,
> sizeof(*tf_model->outputs));
> - if (!tf_model->outputs)
> - return DNN_ERROR;
> - for (int i = 0; i < nb_output; ++i) {
> - tf_model->outputs[i].oper = TF_GraphOperationByName(tf_model->graph,
> output_names[i]);
> - if (!tf_model->outputs[i].oper){
> - av_freep(&tf_model->outputs);
> - return DNN_ERROR;
> - }
> - tf_model->outputs[i].index = 0;
> - }
> -
> - if (tf_model->output_tensors) {
> - for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> - if (tf_model->output_tensors[i]) {
> - TF_DeleteTensor(tf_model->output_tensors[i]);
> - tf_model->output_tensors[i] = NULL;
> - }
> - }
> - }
> - av_freep(&tf_model->output_tensors);
> - tf_model->output_tensors = av_mallocz_array(nb_output,
> sizeof(*tf_model->output_tensors));
> - if (!tf_model->output_tensors) {
> - av_freep(&tf_model->outputs);
> - return DNN_ERROR;
> - }
> -
> - tf_model->nb_output = nb_output;
> -
> - if (tf_model->session){
> - TF_CloseSession(tf_model->session, tf_model->status);
> - TF_DeleteSession(tf_model->session, tf_model->status);
> - }
> -
> - sess_opts = TF_NewSessionOptions();
> - tf_model->session = TF_NewSession(tf_model->graph, sess_opts,
> tf_model->status);
> - TF_DeleteSessionOptions(sess_opts);
> - if (TF_GetCode(tf_model->status) != TF_OK)
> - {
> - return DNN_ERROR;
> - }
> -
> - // Run initialization operation with name "init" if it is present in
> graph
> - if (init_op){
> - TF_SessionRun(tf_model->session, NULL,
> - NULL, NULL, 0,
> - NULL, NULL, 0,
> - &init_op, 1, NULL, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK)
> - {
> - return DNN_ERROR;
> - }
> - }
> -
> - return DNN_SUCCESS;
> -}
> -
> -static DNNReturnType load_tf_model(TFModel *tf_model, const char
> *model_filename)
> -{
> - TF_Buffer *graph_def;
> - TF_ImportGraphDefOptions *graph_opts;
> -
> - graph_def = read_graph(model_filename);
> - if (!graph_def){
> - return DNN_ERROR;
> - }
> - tf_model->graph = TF_NewGraph();
> - tf_model->status = TF_NewStatus();
> - graph_opts = TF_NewImportGraphDefOptions();
> - TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts,
> tf_model->status);
> - TF_DeleteImportGraphDefOptions(graph_opts);
> - TF_DeleteBuffer(graph_def);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - TF_DeleteGraph(tf_model->graph);
> - TF_DeleteStatus(tf_model->status);
> - return DNN_ERROR;
> - }
> -
> - return DNN_SUCCESS;
> -}
> -
> -#define NAME_BUFFER_SIZE 256
> -
> -static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation
> *transpose_op, TF_Operation **cur_op,
> - ConvolutionalParams* params, const int
> layer)
> -{
> - TF_Operation *op;
> - TF_OperationDescription *op_desc;
> - TF_Output input;
> - int64_t strides[] = {1, 1, 1, 1};
> - TF_Tensor *tensor;
> - int64_t dims[4];
> - int dims_len;
> - char name_buffer[NAME_BUFFER_SIZE];
> - int32_t size;
> -
> - size = params->input_num * params->output_num * params->kernel_size *
> params->kernel_size;
> - input.index = 0;
> -
> - snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
> - op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
> - TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> - dims[0] = params->output_num;
> - dims[1] = params->kernel_size;
> - dims[2] = params->kernel_size;
> - dims[3] = params->input_num;
> - dims_len = 4;
> - tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size *
> sizeof(float));
> - memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float));
> - TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> - op = TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> -
> - snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
> - op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
> - input.oper = op;
> - TF_AddInput(op_desc, input);
> - input.oper = transpose_op;
> - TF_AddInput(op_desc, input);
> - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> - TF_SetAttrType(op_desc, "Tperm", TF_INT32);
> - op = TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> -
> - snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
> - op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
> - input.oper = *cur_op;
> - TF_AddInput(op_desc, input);
> - input.oper = op;
> - TF_AddInput(op_desc, input);
> - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> - TF_SetAttrIntList(op_desc, "strides", strides, 4);
> - TF_SetAttrString(op_desc, "padding", "VALID", 5);
> - *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> -
> - snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
> - op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
> - TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> - dims[0] = params->output_num;
> - dims_len = 1;
> - tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num
> * sizeof(float));
> - memcpy(TF_TensorData(tensor), params->biases, params->output_num *
> sizeof(float));
> - TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> - op = TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> -
> - snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
> - op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
> - input.oper = *cur_op;
> - TF_AddInput(op_desc, input);
> - input.oper = op;
> - TF_AddInput(op_desc, input);
> - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> - *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> -
> - snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
> - switch (params->activation){
> - case RELU:
> - op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
> - break;
> - case TANH:
> - op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
> - break;
> - case SIGMOID:
> - op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
> - break;
> - default:
> - return DNN_ERROR;
> - }
> - input.oper = *cur_op;
> - TF_AddInput(op_desc, input);
> - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> - *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> -
> - return DNN_SUCCESS;
> -}
> -
> -static DNNReturnType add_depth_to_space_layer(TFModel *tf_model,
> TF_Operation **cur_op,
> - DepthToSpaceParams *params,
> const int layer)
> -{
> - TF_OperationDescription *op_desc;
> - TF_Output input;
> - char name_buffer[NAME_BUFFER_SIZE];
> -
> - snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
> - op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", name_buffer);
> - input.oper = *cur_op;
> - input.index = 0;
> - TF_AddInput(op_desc, input);
> - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> - TF_SetAttrInt(op_desc, "block_size", params->block_size);
> - *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> -
> - return DNN_SUCCESS;
> -}
> -
> -static int calculate_pad(const ConvolutionalNetwork *conv_network)
> -{
> - ConvolutionalParams *params;
> - int32_t layer;
> - int pad = 0;
> -
> - for (layer = 0; layer < conv_network->layers_num; ++layer){
> - if (conv_network->layers[layer].type == CONV){
> - params = (ConvolutionalParams
> *)conv_network->layers[layer].params;
> - pad += params->kernel_size >> 1;
> - }
> - }
> -
> - return pad;
> -}
> -
> -static DNNReturnType add_pad_op(TFModel *tf_model, TF_Operation **cur_op,
> const int32_t pad)
> -{
> - TF_Operation *op;
> - TF_Tensor *tensor;
> - TF_OperationDescription *op_desc;
> - TF_Output input;
> - int32_t *pads;
> - int64_t pads_shape[] = {4, 2};
> -
> - input.index = 0;
> -
> - op_desc = TF_NewOperation(tf_model->graph, "Const", "pads");
> - TF_SetAttrType(op_desc, "dtype", TF_INT32);
> - tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 *
> sizeof(int32_t));
> - pads = (int32_t *)TF_TensorData(tensor);
> - pads[0] = 0; pads[1] = 0;
> - pads[2] = pad; pads[3] = pad;
> - pads[4] = pad; pads[5] = pad;
> - pads[6] = 0; pads[7] = 0;
> - TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> - op = TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> -
> - op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
> - input.oper = *cur_op;
> - TF_AddInput(op_desc, input);
> - input.oper = op;
> - TF_AddInput(op_desc, input);
> - TF_SetAttrType(op_desc, "T", TF_FLOAT);
> - TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
> - TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
> - *cur_op = TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> -
> - return DNN_SUCCESS;
> -}
> -
> -static DNNReturnType load_native_model(TFModel *tf_model, const char
> *model_filename)
> -{
> - int32_t layer;
> - TF_OperationDescription *op_desc;
> - TF_Operation *op;
> - TF_Operation *transpose_op;
> - TF_Tensor *tensor;
> - TF_Output input;
> - int32_t *transpose_perm;
> - int64_t transpose_perm_shape[] = {4};
> - int64_t input_shape[] = {1, -1, -1, -1};
> - int32_t pad;
> - DNNReturnType layer_add_res;
> - DNNModel *native_model = NULL;
> - ConvolutionalNetwork *conv_network;
> -
> - native_model = ff_dnn_load_model_native(model_filename);
> - if (!native_model){
> - return DNN_ERROR;
> - }
> -
> - conv_network = (ConvolutionalNetwork *)native_model->model;
> - pad = calculate_pad(conv_network);
> - tf_model->graph = TF_NewGraph();
> - tf_model->status = TF_NewStatus();
> -
> -#define CLEANUP_ON_ERROR(tf_model) \
> - { \
> - TF_DeleteGraph(tf_model->graph); \
> - TF_DeleteStatus(tf_model->status); \
> - return DNN_ERROR; \
> - }
> -
> - op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
> - TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
> - TF_SetAttrShape(op_desc, "shape", input_shape, 4);
> - op = TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - CLEANUP_ON_ERROR(tf_model);
> - }
> -
> - if (add_pad_op(tf_model, &op, pad) != DNN_SUCCESS){
> - CLEANUP_ON_ERROR(tf_model);
> - }
> -
> - op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
> - TF_SetAttrType(op_desc, "dtype", TF_INT32);
> - tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 *
> sizeof(int32_t));
> - transpose_perm = (int32_t *)TF_TensorData(tensor);
> - transpose_perm[0] = 1;
> - transpose_perm[1] = 2;
> - transpose_perm[2] = 3;
> - transpose_perm[3] = 0;
> - TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - CLEANUP_ON_ERROR(tf_model);
> - }
> - transpose_op = TF_FinishOperation(op_desc, tf_model->status);
> -
> - for (layer = 0; layer < conv_network->layers_num; ++layer){
> - switch (conv_network->layers[layer].type){
> - case INPUT:
> - layer_add_res = DNN_SUCCESS;
> - break;
> - case CONV:
> - layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
> - (ConvolutionalParams
> *)conv_network->layers[layer].params, layer);
> - break;
> - case DEPTH_TO_SPACE:
> - layer_add_res = add_depth_to_space_layer(tf_model, &op,
> - (DepthToSpaceParams
> *)conv_network->layers[layer].params, layer);
> - break;
> - default:
> - CLEANUP_ON_ERROR(tf_model);
> - }
> -
> - if (layer_add_res != DNN_SUCCESS){
> - CLEANUP_ON_ERROR(tf_model);
> - }
> - }
> -
> - op_desc = TF_NewOperation(tf_model->graph, "Identity", "y");
> - input.oper = op;
> - TF_AddInput(op_desc, input);
> - TF_FinishOperation(op_desc, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - CLEANUP_ON_ERROR(tf_model);
> - }
> -
> - ff_dnn_free_model_native(&native_model);
> -
> - return DNN_SUCCESS;
> -}
> -
> -DNNModel *ff_dnn_load_model_tf(const char *model_filename)
> -{
> - DNNModel *model = NULL;
> - TFModel *tf_model = NULL;
> -
> - model = av_malloc(sizeof(DNNModel));
> - if (!model){
> - return NULL;
> - }
> -
> - tf_model = av_mallocz(sizeof(TFModel));
> - if (!tf_model){
> - av_freep(&model);
> - return NULL;
> - }
> -
> - if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
> - if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
> - av_freep(&tf_model);
> - av_freep(&model);
> -
> - return NULL;
> - }
> - }
> -
> - model->model = (void *)tf_model;
> - model->set_input_output = &set_input_output_tf;
> -
> - return model;
> -}
> -
> -
> -
> -DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData
> *outputs, uint32_t nb_output)
> -{
> - TFModel *tf_model = (TFModel *)model->model;
> - uint32_t nb = FFMIN(nb_output, tf_model->nb_output);
> - if (nb == 0)
> - return DNN_ERROR;
> -
> - av_assert0(tf_model->output_tensors);
> - for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> - if (tf_model->output_tensors[i]) {
> - TF_DeleteTensor(tf_model->output_tensors[i]);
> - tf_model->output_tensors[i] = NULL;
> - }
> - }
> -
> - TF_SessionRun(tf_model->session, NULL,
> - &tf_model->input, &tf_model->input_tensor, 1,
> - tf_model->outputs, tf_model->output_tensors, nb,
> - NULL, 0, NULL, tf_model->status);
> -
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> -
> - for (uint32_t i = 0; i < nb; ++i) {
> - outputs[i].height = TF_Dim(tf_model->output_tensors[i], 1);
> - outputs[i].width = TF_Dim(tf_model->output_tensors[i], 2);
> - outputs[i].channels = TF_Dim(tf_model->output_tensors[i], 3);
> - outputs[i].data = TF_TensorData(tf_model->output_tensors[i]);
> - }
> -
> - return DNN_SUCCESS;
> -}
> -
> -void ff_dnn_free_model_tf(DNNModel **model)
> -{
> - TFModel *tf_model;
> -
> - if (*model){
> - tf_model = (TFModel *)(*model)->model;
> - if (tf_model->graph){
> - TF_DeleteGraph(tf_model->graph);
> - }
> - if (tf_model->session){
> - TF_CloseSession(tf_model->session, tf_model->status);
> - TF_DeleteSession(tf_model->session, tf_model->status);
> - }
> - if (tf_model->status){
> - TF_DeleteStatus(tf_model->status);
> - }
> - if (tf_model->input_tensor){
> - TF_DeleteTensor(tf_model->input_tensor);
> - }
> - if (tf_model->output_tensors) {
> - for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
> - if (tf_model->output_tensors[i]) {
> - TF_DeleteTensor(tf_model->output_tensors[i]);
> - tf_model->output_tensors[i] = NULL;
> - }
> - }
> - }
> - av_freep(&tf_model->outputs);
> - av_freep(&tf_model->output_tensors);
> - av_freep(&tf_model);
> - av_freep(model);
> - }
> -}
> diff --git a/libavfilter/dnn_backend_tf.h b/libavfilter/dnn_backend_tf.h
> deleted file mode 100644
> index 07877b1..0000000
> --- a/libavfilter/dnn_backend_tf.h
> +++ /dev/null
> @@ -1,38 +0,0 @@
> -/*
> - * Copyright (c) 2018 Sergey Lavrushkin
> - *
> - * This file is part of FFmpeg.
> - *
> - * FFmpeg is free software; you can redistribute it and/or
> - * modify it under the terms of the GNU Lesser General Public
> - * License as published by the Free Software Foundation; either
> - * version 2.1 of the License, or (at your option) any later version.
> - *
> - * FFmpeg is distributed in the hope that it will be useful,
> - * but WITHOUT ANY WARRANTY; without even the implied warranty of
> - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
> - * Lesser General Public License for more details.
> - *
> - * You should have received a copy of the GNU Lesser General Public
> - * License along with FFmpeg; if not, write to the Free Software
> - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
> USA
> - */
> -
> -/**
> - * @file
> - * DNN inference functions interface for TensorFlow backend.
> - */
> -
> -
> -#ifndef AVFILTER_DNN_BACKEND_TF_H
> -#define AVFILTER_DNN_BACKEND_TF_H
> -
> -#include "dnn_interface.h"
> -
> -DNNModel *ff_dnn_load_model_tf(const char *model_filename);
> -
> -DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData
> *outputs, uint32_t nb_output);
> -
> -void ff_dnn_free_model_tf(DNNModel **model);
> -
> -#endif
> diff --git a/libavfilter/dnn_interface.c b/libavfilter/dnn_interface.c
> deleted file mode 100644
> index 86fc283..0000000
> --- a/libavfilter/dnn_interface.c
> +++ /dev/null
> @@ -1,63 +0,0 @@
> -/*
> - * Copyright (c) 2018 Sergey Lavrushkin
> - *
> - * This file is part of FFmpeg.
> - *
> - * FFmpeg is free software; you can redistribute it and/or
> - * modify it under the terms of the GNU Lesser General Public
> - * License as published by the Free Software Foundation; either
> - * version 2.1 of the License, or (at your option) any later version.
> - *
> - * FFmpeg is distributed in the hope that it will be useful,
> - * but WITHOUT ANY WARRANTY; without even the implied warranty of
> - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
> - * Lesser General Public License for more details.
> - *
> - * You should have received a copy of the GNU Lesser General Public
> - * License along with FFmpeg; if not, write to the Free Software
> - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
> USA
> - */
> -
> -/**
> - * @file
> - * Implements DNN module initialization with specified backend.
> - */
> -
> -#include "dnn_interface.h"
> -#include "dnn_backend_native.h"
> -#include "dnn_backend_tf.h"
> -#include "libavutil/mem.h"
> -
> -DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
> -{
> - DNNModule *dnn_module;
> -
> - dnn_module = av_malloc(sizeof(DNNModule));
> - if(!dnn_module){
> - return NULL;
> - }
> -
> - switch(backend_type){
> - case DNN_NATIVE:
> - dnn_module->load_model = &ff_dnn_load_model_native;
> - dnn_module->execute_model = &ff_dnn_execute_model_native;
> - dnn_module->free_model = &ff_dnn_free_model_native;
> - break;
> - case DNN_TF:
> - #if (CONFIG_LIBTENSORFLOW == 1)
> - dnn_module->load_model = &ff_dnn_load_model_tf;
> - dnn_module->execute_model = &ff_dnn_execute_model_tf;
> - dnn_module->free_model = &ff_dnn_free_model_tf;
> - #else
> - av_freep(&dnn_module);
> - return NULL;
> - #endif
> - break;
> - default:
> - av_log(NULL, AV_LOG_ERROR, "Module backend_type is not native or
> tensorflow\n");
> - av_freep(&dnn_module);
> - return NULL;
> - }
> -
> - return dnn_module;
> -}
> --
> 2.7.4
>
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