mirror of https://git.ffmpeg.org/ffmpeg.git
579 lines
18 KiB
C
579 lines
18 KiB
C
/*
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* Copyright (c) 2018 Sergey Lavrushkin
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*
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* This file is part of FFmpeg.
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*
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* FFmpeg is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* FFmpeg is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with FFmpeg; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*/
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/**
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* @file
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* DNN tensorflow backend implementation.
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*/
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#include "dnn_backend_tf.h"
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#include "dnn_srcnn.h"
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#include "dnn_espcn.h"
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#include "libavformat/avio.h"
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#include <tensorflow/c/c_api.h>
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typedef struct TFModel{
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TF_Graph *graph;
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TF_Session *session;
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TF_Status *status;
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TF_Output input, output;
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TF_Tensor *input_tensor;
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DNNData *output_data;
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} TFModel;
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static void free_buffer(void *data, size_t length)
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{
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av_freep(&data);
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}
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static TF_Buffer *read_graph(const char *model_filename)
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{
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TF_Buffer *graph_buf;
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unsigned char *graph_data = NULL;
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AVIOContext *model_file_context;
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long size, bytes_read;
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if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
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return NULL;
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}
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size = avio_size(model_file_context);
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graph_data = av_malloc(size);
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if (!graph_data){
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avio_closep(&model_file_context);
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return NULL;
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}
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bytes_read = avio_read(model_file_context, graph_data, size);
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avio_closep(&model_file_context);
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if (bytes_read != size){
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av_freep(&graph_data);
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return NULL;
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}
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graph_buf = TF_NewBuffer();
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graph_buf->data = (void *)graph_data;
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graph_buf->length = size;
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graph_buf->data_deallocator = free_buffer;
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return graph_buf;
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}
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static DNNReturnType set_input_output_tf(void *model, DNNData *input, DNNData *output)
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{
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TFModel *tf_model = (TFModel *)model;
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int64_t input_dims[] = {1, input->height, input->width, input->channels};
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TF_SessionOptions *sess_opts;
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const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph, "init");
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TF_Tensor *output_tensor;
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// Input operation should be named 'x'
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tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, "x");
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if (!tf_model->input.oper){
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return DNN_ERROR;
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}
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tf_model->input.index = 0;
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if (tf_model->input_tensor){
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TF_DeleteTensor(tf_model->input_tensor);
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}
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tf_model->input_tensor = TF_AllocateTensor(TF_FLOAT, input_dims, 4,
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input_dims[1] * input_dims[2] * input_dims[3] * sizeof(float));
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if (!tf_model->input_tensor){
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return DNN_ERROR;
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}
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input->data = (float *)TF_TensorData(tf_model->input_tensor);
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// Output operation should be named 'y'
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tf_model->output.oper = TF_GraphOperationByName(tf_model->graph, "y");
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if (!tf_model->output.oper){
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return DNN_ERROR;
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}
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tf_model->output.index = 0;
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if (tf_model->session){
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TF_CloseSession(tf_model->session, tf_model->status);
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TF_DeleteSession(tf_model->session, tf_model->status);
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}
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sess_opts = TF_NewSessionOptions();
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tf_model->session = TF_NewSession(tf_model->graph, sess_opts, tf_model->status);
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TF_DeleteSessionOptions(sess_opts);
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if (TF_GetCode(tf_model->status) != TF_OK)
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{
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return DNN_ERROR;
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}
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// Run initialization operation with name "init" if it is present in graph
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if (init_op){
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TF_SessionRun(tf_model->session, NULL,
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NULL, NULL, 0,
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NULL, NULL, 0,
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&init_op, 1, NULL, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK)
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{
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return DNN_ERROR;
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}
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}
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// Execute network to get output height, width and number of channels
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TF_SessionRun(tf_model->session, NULL,
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&tf_model->input, &tf_model->input_tensor, 1,
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&tf_model->output, &output_tensor, 1,
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NULL, 0, NULL, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return DNN_ERROR;
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}
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else{
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output->height = TF_Dim(output_tensor, 1);
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output->width = TF_Dim(output_tensor, 2);
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output->channels = TF_Dim(output_tensor, 3);
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output->data = av_malloc(output->height * output->width * output->channels * sizeof(float));
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if (!output->data){
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return DNN_ERROR;
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}
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tf_model->output_data = output;
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TF_DeleteTensor(output_tensor);
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}
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return DNN_SUCCESS;
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}
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DNNModel *ff_dnn_load_model_tf(const char *model_filename)
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{
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DNNModel *model = NULL;
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TFModel *tf_model = NULL;
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TF_Buffer *graph_def;
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TF_ImportGraphDefOptions *graph_opts;
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model = av_malloc(sizeof(DNNModel));
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if (!model){
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return NULL;
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}
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tf_model = av_malloc(sizeof(TFModel));
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if (!tf_model){
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av_freep(&model);
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return NULL;
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}
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tf_model->session = NULL;
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tf_model->input_tensor = NULL;
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tf_model->output_data = NULL;
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graph_def = read_graph(model_filename);
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if (!graph_def){
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av_freep(&tf_model);
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av_freep(&model);
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return NULL;
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}
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tf_model->graph = TF_NewGraph();
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tf_model->status = TF_NewStatus();
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graph_opts = TF_NewImportGraphDefOptions();
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TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts, tf_model->status);
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TF_DeleteImportGraphDefOptions(graph_opts);
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TF_DeleteBuffer(graph_def);
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if (TF_GetCode(tf_model->status) != TF_OK){
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TF_DeleteGraph(tf_model->graph);
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TF_DeleteStatus(tf_model->status);
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av_freep(&tf_model);
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av_freep(&model);
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return NULL;
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}
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model->model = (void *)tf_model;
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model->set_input_output = &set_input_output_tf;
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return model;
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}
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static TF_Operation *add_pad_op(TFModel *tf_model, TF_Operation *input_op, int32_t pad)
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{
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TF_OperationDescription *op_desc;
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TF_Operation *op;
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TF_Tensor *tensor;
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TF_Output input;
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int32_t *pads;
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int64_t pads_shape[] = {4, 2};
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op_desc = TF_NewOperation(tf_model->graph, "Const", "pads");
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TF_SetAttrType(op_desc, "dtype", TF_INT32);
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tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 * sizeof(int32_t));
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pads = (int32_t *)TF_TensorData(tensor);
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pads[0] = 0; pads[1] = 0;
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pads[2] = pad; pads[3] = pad;
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pads[4] = pad; pads[5] = pad;
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pads[6] = 0; pads[7] = 0;
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TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return NULL;
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}
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op = TF_FinishOperation(op_desc, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return NULL;
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}
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op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
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input.oper = input_op;
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input.index = 0;
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TF_AddInput(op_desc, input);
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input.oper = op;
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TF_AddInput(op_desc, input);
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TF_SetAttrType(op_desc, "T", TF_FLOAT);
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TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
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TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
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op = TF_FinishOperation(op_desc, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return NULL;
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}
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return op;
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}
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static TF_Operation *add_const_op(TFModel *tf_model, const float *values, const int64_t *dims, int dims_len, const char *name)
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{
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int dim;
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TF_OperationDescription *op_desc;
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TF_Tensor *tensor;
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size_t len;
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op_desc = TF_NewOperation(tf_model->graph, "Const", name);
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TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
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len = sizeof(float);
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for (dim = 0; dim < dims_len; ++dim){
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len *= dims[dim];
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}
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tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, len);
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memcpy(TF_TensorData(tensor), values, len);
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TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return NULL;
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}
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return TF_FinishOperation(op_desc, tf_model->status);
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}
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static TF_Operation* add_conv_layers(TFModel *tf_model, const float **consts, const int64_t **consts_dims,
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const int *consts_dims_len, const char **activations,
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TF_Operation *input_op, int layers_num)
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{
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int i;
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TF_OperationDescription *op_desc;
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TF_Operation *op;
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TF_Operation *transpose_op;
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TF_Output input;
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int64_t strides[] = {1, 1, 1, 1};
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int32_t *transpose_perm;
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TF_Tensor *tensor;
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int64_t transpose_perm_shape[] = {4};
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#define NAME_BUFF_SIZE 256
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char name_buffer[NAME_BUFF_SIZE];
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op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
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TF_SetAttrType(op_desc, "dtype", TF_INT32);
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tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 * sizeof(int32_t));
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transpose_perm = (int32_t *)TF_TensorData(tensor);
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transpose_perm[0] = 1;
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transpose_perm[1] = 2;
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transpose_perm[2] = 3;
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transpose_perm[3] = 0;
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TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return NULL;
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}
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transpose_op = TF_FinishOperation(op_desc, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return NULL;
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}
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input.index = 0;
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for (i = 0; i < layers_num; ++i){
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snprintf(name_buffer, NAME_BUFF_SIZE, "conv_kernel%d", i);
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op = add_const_op(tf_model, consts[i << 1], consts_dims[i << 1], consts_dims_len[i << 1], name_buffer);
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if (TF_GetCode(tf_model->status) != TF_OK || op == NULL){
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return NULL;
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}
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snprintf(name_buffer, NAME_BUFF_SIZE, "transpose%d", i);
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op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
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input.oper = op;
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TF_AddInput(op_desc, input);
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input.oper = transpose_op;
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TF_AddInput(op_desc, input);
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TF_SetAttrType(op_desc, "T", TF_FLOAT);
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TF_SetAttrType(op_desc, "Tperm", TF_INT32);
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op = TF_FinishOperation(op_desc, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return NULL;
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}
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snprintf(name_buffer, NAME_BUFF_SIZE, "conv2d%d", i);
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op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
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input.oper = input_op;
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TF_AddInput(op_desc, input);
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input.oper = op;
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TF_AddInput(op_desc, input);
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TF_SetAttrType(op_desc, "T", TF_FLOAT);
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TF_SetAttrIntList(op_desc, "strides", strides, 4);
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TF_SetAttrString(op_desc, "padding", "VALID", 5);
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input_op = TF_FinishOperation(op_desc, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return NULL;
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}
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snprintf(name_buffer, NAME_BUFF_SIZE, "conv_biases%d", i);
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op = add_const_op(tf_model, consts[(i << 1) + 1], consts_dims[(i << 1) + 1], consts_dims_len[(i << 1) + 1], name_buffer);
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if (TF_GetCode(tf_model->status) != TF_OK || op == NULL){
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return NULL;
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}
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snprintf(name_buffer, NAME_BUFF_SIZE, "bias_add%d", i);
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op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
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input.oper = input_op;
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TF_AddInput(op_desc, input);
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input.oper = op;
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TF_AddInput(op_desc, input);
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TF_SetAttrType(op_desc, "T", TF_FLOAT);
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input_op = TF_FinishOperation(op_desc, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return NULL;
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}
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snprintf(name_buffer, NAME_BUFF_SIZE, "activation%d", i);
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op_desc = TF_NewOperation(tf_model->graph, activations[i], name_buffer);
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input.oper = input_op;
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TF_AddInput(op_desc, input);
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TF_SetAttrType(op_desc, "T", TF_FLOAT);
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input_op = TF_FinishOperation(op_desc, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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return NULL;
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}
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}
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return input_op;
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}
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DNNModel *ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
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{
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DNNModel *model = NULL;
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TFModel *tf_model = NULL;
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TF_OperationDescription *op_desc;
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TF_Operation *op;
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TF_Output input;
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static const int64_t input_shape[] = {1, -1, -1, 1};
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static const char tanh[] = "Tanh";
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static const char sigmoid[] = "Sigmoid";
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static const char relu[] = "Relu";
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static const float *srcnn_consts[] = {
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srcnn_conv1_kernel,
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srcnn_conv1_bias,
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srcnn_conv2_kernel,
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srcnn_conv2_bias,
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srcnn_conv3_kernel,
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srcnn_conv3_bias
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};
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static const long int *srcnn_consts_dims[] = {
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srcnn_conv1_kernel_dims,
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srcnn_conv1_bias_dims,
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srcnn_conv2_kernel_dims,
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srcnn_conv2_bias_dims,
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srcnn_conv3_kernel_dims,
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srcnn_conv3_bias_dims
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};
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static const int srcnn_consts_dims_len[] = {
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4,
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1,
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4,
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1,
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4,
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1
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};
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static const char *srcnn_activations[] = {
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relu,
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relu,
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relu
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};
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static const float *espcn_consts[] = {
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espcn_conv1_kernel,
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espcn_conv1_bias,
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espcn_conv2_kernel,
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espcn_conv2_bias,
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espcn_conv3_kernel,
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espcn_conv3_bias
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};
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static const long int *espcn_consts_dims[] = {
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espcn_conv1_kernel_dims,
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espcn_conv1_bias_dims,
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espcn_conv2_kernel_dims,
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espcn_conv2_bias_dims,
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espcn_conv3_kernel_dims,
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espcn_conv3_bias_dims
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};
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static const int espcn_consts_dims_len[] = {
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4,
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1,
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4,
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1,
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4,
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1
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};
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static const char *espcn_activations[] = {
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tanh,
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tanh,
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sigmoid
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};
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input.index = 0;
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model = av_malloc(sizeof(DNNModel));
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if (!model){
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return NULL;
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}
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tf_model = av_malloc(sizeof(TFModel));
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if (!tf_model){
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av_freep(&model);
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return NULL;
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}
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tf_model->session = NULL;
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tf_model->input_tensor = NULL;
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tf_model->output_data = NULL;
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tf_model->graph = TF_NewGraph();
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tf_model->status = TF_NewStatus();
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|
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#define CLEANUP_ON_ERROR(tf_model, model) { \
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TF_DeleteGraph(tf_model->graph); \
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TF_DeleteStatus(tf_model->status); \
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av_freep(&tf_model); \
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av_freep(&model); \
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return NULL; \
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}
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op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
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TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
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TF_SetAttrShape(op_desc, "shape", input_shape, 4);
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op = TF_FinishOperation(op_desc, tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK){
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CLEANUP_ON_ERROR(tf_model, model);
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}
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|
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switch (model_type){
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case DNN_SRCNN:
|
|
op = add_pad_op(tf_model, op, 6);
|
|
if (!op){
|
|
CLEANUP_ON_ERROR(tf_model, model);
|
|
}
|
|
op = add_conv_layers(tf_model, srcnn_consts,
|
|
srcnn_consts_dims, srcnn_consts_dims_len,
|
|
srcnn_activations, op, 3);
|
|
if (!op){
|
|
CLEANUP_ON_ERROR(tf_model, model);
|
|
}
|
|
break;
|
|
case DNN_ESPCN:
|
|
op = add_pad_op(tf_model, op, 4);
|
|
if (!op){
|
|
CLEANUP_ON_ERROR(tf_model, model);
|
|
}
|
|
op = add_conv_layers(tf_model, espcn_consts,
|
|
espcn_consts_dims, espcn_consts_dims_len,
|
|
espcn_activations, op, 3);
|
|
if (!op){
|
|
CLEANUP_ON_ERROR(tf_model, model);
|
|
}
|
|
|
|
op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", "depth_to_space");
|
|
input.oper = op;
|
|
TF_AddInput(op_desc, input);
|
|
TF_SetAttrType(op_desc, "T", TF_FLOAT);
|
|
TF_SetAttrInt(op_desc, "block_size", 2);
|
|
op = TF_FinishOperation(op_desc, tf_model->status);
|
|
if (TF_GetCode(tf_model->status) != TF_OK){
|
|
CLEANUP_ON_ERROR(tf_model, model);
|
|
}
|
|
break;
|
|
default:
|
|
CLEANUP_ON_ERROR(tf_model, 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, model);
|
|
}
|
|
|
|
model->model = (void *)tf_model;
|
|
model->set_input_output = &set_input_output_tf;
|
|
|
|
return model;
|
|
}
|
|
|
|
DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model)
|
|
{
|
|
TFModel *tf_model = (TFModel *)model->model;
|
|
TF_Tensor *output_tensor;
|
|
|
|
TF_SessionRun(tf_model->session, NULL,
|
|
&tf_model->input, &tf_model->input_tensor, 1,
|
|
&tf_model->output, &output_tensor, 1,
|
|
NULL, 0, NULL, tf_model->status);
|
|
|
|
if (TF_GetCode(tf_model->status) != TF_OK){
|
|
return DNN_ERROR;
|
|
}
|
|
else{
|
|
memcpy(tf_model->output_data->data, TF_TensorData(output_tensor),
|
|
tf_model->output_data->height * tf_model->output_data->width *
|
|
tf_model->output_data->channels * sizeof(float));
|
|
TF_DeleteTensor(output_tensor);
|
|
|
|
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);
|
|
}
|
|
av_freep(&tf_model->output_data->data);
|
|
av_freep(&tf_model);
|
|
av_freep(model);
|
|
}
|
|
}
|