mirror of https://git.ffmpeg.org/ffmpeg.git
677 lines
21 KiB
C
677 lines
21 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_backend_native.h"
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#include "dnn_backend_native_layer_conv2d.h"
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#include "dnn_backend_native_layer_depth2space.h"
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#include "libavformat/avio.h"
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#include "libavutil/avassert.h"
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#include "dnn_backend_native_layer_pad.h"
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#include "dnn_backend_native_layer_maximum.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;
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TF_Tensor *input_tensor;
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TF_Output *outputs;
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TF_Tensor **output_tensors;
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uint32_t nb_output;
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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 TF_Tensor *allocate_input_tensor(const DNNData *input)
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{
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TF_DataType dt;
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size_t size;
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int64_t input_dims[] = {1, input->height, input->width, input->channels};
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switch (input->dt) {
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case DNN_FLOAT:
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dt = TF_FLOAT;
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size = sizeof(float);
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break;
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case DNN_UINT8:
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dt = TF_UINT8;
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size = 1;
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break;
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default:
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av_assert0(!"should not reach here");
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}
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return TF_AllocateTensor(dt, input_dims, 4,
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input_dims[1] * input_dims[2] * input_dims[3] * size);
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}
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static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input_name)
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{
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TFModel *tf_model = (TFModel *)model;
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TF_Status *status;
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int64_t dims[4];
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TF_Output tf_output;
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tf_output.oper = TF_GraphOperationByName(tf_model->graph, input_name);
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if (!tf_output.oper)
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return DNN_ERROR;
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tf_output.index = 0;
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input->dt = TF_OperationOutputType(tf_output);
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status = TF_NewStatus();
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TF_GraphGetTensorShape(tf_model->graph, tf_output, dims, 4, status);
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if (TF_GetCode(status) != TF_OK){
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TF_DeleteStatus(status);
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return DNN_ERROR;
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}
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TF_DeleteStatus(status);
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// currently only NHWC is supported
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av_assert0(dims[0] == 1);
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input->height = dims[1];
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input->width = dims[2];
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input->channels = dims[3];
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return DNN_SUCCESS;
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}
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static DNNReturnType set_input_output_tf(void *model, DNNData *input, const char *input_name, const char **output_names, uint32_t nb_output)
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{
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TFModel *tf_model = (TFModel *)model;
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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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// Input operation
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tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, input_name);
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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 = allocate_input_tensor(input);
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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
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if (nb_output == 0)
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return DNN_ERROR;
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av_freep(&tf_model->outputs);
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tf_model->outputs = av_malloc_array(nb_output, sizeof(*tf_model->outputs));
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if (!tf_model->outputs)
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return DNN_ERROR;
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for (int i = 0; i < nb_output; ++i) {
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tf_model->outputs[i].oper = TF_GraphOperationByName(tf_model->graph, output_names[i]);
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if (!tf_model->outputs[i].oper){
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av_freep(&tf_model->outputs);
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return DNN_ERROR;
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}
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tf_model->outputs[i].index = 0;
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}
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if (tf_model->output_tensors) {
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for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
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if (tf_model->output_tensors[i]) {
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TF_DeleteTensor(tf_model->output_tensors[i]);
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tf_model->output_tensors[i] = NULL;
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}
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}
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}
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av_freep(&tf_model->output_tensors);
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tf_model->output_tensors = av_mallocz_array(nb_output, sizeof(*tf_model->output_tensors));
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if (!tf_model->output_tensors) {
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av_freep(&tf_model->outputs);
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return DNN_ERROR;
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}
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tf_model->nb_output = nb_output;
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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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return DNN_SUCCESS;
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}
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static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename)
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{
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TF_Buffer *graph_def;
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TF_ImportGraphDefOptions *graph_opts;
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graph_def = read_graph(model_filename);
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if (!graph_def){
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return DNN_ERROR;
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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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return DNN_ERROR;
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}
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return DNN_SUCCESS;
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}
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#define NAME_BUFFER_SIZE 256
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static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Operation **cur_op,
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ConvolutionalParams* params, const int layer)
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{
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TF_Operation *op;
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TF_OperationDescription *op_desc;
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TF_Output input;
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int64_t strides[] = {1, 1, 1, 1};
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TF_Tensor *tensor;
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int64_t dims[4];
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int dims_len;
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char name_buffer[NAME_BUFFER_SIZE];
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int32_t size;
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size = params->input_num * params->output_num * params->kernel_size * params->kernel_size;
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input.index = 0;
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snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
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op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
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TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
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dims[0] = params->output_num;
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dims[1] = params->kernel_size;
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dims[2] = params->kernel_size;
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dims[3] = params->input_num;
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dims_len = 4;
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tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size * sizeof(float));
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memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float));
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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 DNN_ERROR;
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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 DNN_ERROR;
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}
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snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
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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 DNN_ERROR;
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}
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snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
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op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
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input.oper = *cur_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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*cur_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 DNN_ERROR;
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}
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snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
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op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
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TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
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dims[0] = params->output_num;
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dims_len = 1;
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tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num * sizeof(float));
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memcpy(TF_TensorData(tensor), params->biases, params->output_num * sizeof(float));
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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 DNN_ERROR;
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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 DNN_ERROR;
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}
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snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
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op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
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input.oper = *cur_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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*cur_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 DNN_ERROR;
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}
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snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
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switch (params->activation){
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case RELU:
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op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
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break;
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case TANH:
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op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
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break;
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case SIGMOID:
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op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
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break;
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default:
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return DNN_ERROR;
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}
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input.oper = *cur_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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*cur_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 DNN_ERROR;
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}
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return DNN_SUCCESS;
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}
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static DNNReturnType add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
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DepthToSpaceParams *params, const int layer)
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{
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TF_OperationDescription *op_desc;
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TF_Output input;
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char name_buffer[NAME_BUFFER_SIZE];
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snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
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op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", name_buffer);
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input.oper = *cur_op;
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input.index = 0;
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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_SetAttrInt(op_desc, "block_size", params->block_size);
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*cur_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 DNN_ERROR;
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}
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return DNN_SUCCESS;
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}
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static DNNReturnType add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
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LayerPadParams *params, const int layer)
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{
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TF_Operation *op;
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TF_Tensor *tensor;
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TF_OperationDescription *op_desc;
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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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char name_buffer[NAME_BUFFER_SIZE];
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snprintf(name_buffer, NAME_BUFFER_SIZE, "pad%d", layer);
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op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
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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] = params->paddings[0][0];
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pads[1] = params->paddings[0][1];
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pads[2] = params->paddings[1][0];
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pads[3] = params->paddings[1][1];
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pads[4] = params->paddings[2][0];
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pads[5] = params->paddings[2][1];
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pads[6] = params->paddings[3][0];
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pads[7] = params->paddings[3][1];
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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 DNN_ERROR;
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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 DNN_ERROR;
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}
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op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
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input.oper = *cur_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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*cur_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 DNN_ERROR;
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}
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return DNN_SUCCESS;
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}
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static DNNReturnType add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
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DnnLayerMaximumParams *params, const int layer)
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{
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TF_Operation *op;
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TF_Tensor *tensor;
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TF_OperationDescription *op_desc;
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TF_Output input;
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float *y;
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char name_buffer[NAME_BUFFER_SIZE];
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snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum/y%d", layer);
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op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
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TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
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tensor = TF_AllocateTensor(TF_FLOAT, NULL, 0, TF_DataTypeSize(TF_FLOAT));
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y = (float *)TF_TensorData(tensor);
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*y = params->val.y;
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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 DNN_ERROR;
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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 DNN_ERROR;
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}
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snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum%d", layer);
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op_desc = TF_NewOperation(tf_model->graph, "Maximum", name_buffer);
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input.oper = *cur_op;
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input.index = 0;
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TF_AddInput(op_desc, input);
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|
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;
|
|
}
|
|
|
|
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};
|
|
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;
|
|
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);
|
|
}
|
|
|
|
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 DLT_INPUT:
|
|
layer_add_res = DNN_SUCCESS;
|
|
break;
|
|
case DLT_CONV2D:
|
|
layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
|
|
(ConvolutionalParams *)conv_network->layers[layer].params, layer);
|
|
break;
|
|
case DLT_DEPTH_TO_SPACE:
|
|
layer_add_res = add_depth_to_space_layer(tf_model, &op,
|
|
(DepthToSpaceParams *)conv_network->layers[layer].params, layer);
|
|
break;
|
|
case DLT_MIRROR_PAD:
|
|
layer_add_res = add_pad_layer(tf_model, &op,
|
|
(LayerPadParams *)conv_network->layers[layer].params, layer);
|
|
break;
|
|
case DLT_MAXIMUM:
|
|
layer_add_res = add_maximum_layer(tf_model, &op,
|
|
(DnnLayerMaximumParams *)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;
|
|
input.index = 0;
|
|
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;
|
|
model->get_input = &get_input_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]);
|
|
outputs[i].dt = TF_TensorType(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);
|
|
}
|
|
}
|