mirror of https://git.ffmpeg.org/ffmpeg.git
559 lines
17 KiB
C
559 lines
17 KiB
C
/*
|
|
* 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 <tensorflow/c/c_api.h>
|
|
|
|
typedef struct TFModel{
|
|
TF_Graph *graph;
|
|
TF_Session *session;
|
|
TF_Status *status;
|
|
TF_Output input, output;
|
|
TF_Tensor *input_tensor;
|
|
DNNData *output_data;
|
|
} 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 DNNReturnType set_input_output_tf(void *model, DNNData *input, DNNData *output)
|
|
{
|
|
TFModel *tf_model = (TFModel *)model;
|
|
int64_t input_dims[] = {1, input->height, input->width, input->channels};
|
|
TF_SessionOptions *sess_opts;
|
|
const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph, "init");
|
|
TF_Tensor *output_tensor;
|
|
|
|
// Input operation should be named 'x'
|
|
tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, "x");
|
|
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 = TF_AllocateTensor(TF_FLOAT, input_dims, 4,
|
|
input_dims[1] * input_dims[2] * input_dims[3] * sizeof(float));
|
|
if (!tf_model->input_tensor){
|
|
return DNN_ERROR;
|
|
}
|
|
input->data = (float *)TF_TensorData(tf_model->input_tensor);
|
|
|
|
// Output operation should be named 'y'
|
|
tf_model->output.oper = TF_GraphOperationByName(tf_model->graph, "y");
|
|
if (!tf_model->output.oper){
|
|
return DNN_ERROR;
|
|
}
|
|
tf_model->output.index = 0;
|
|
|
|
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;
|
|
}
|
|
}
|
|
|
|
// Execute network to get output height, width and number of channels
|
|
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{
|
|
output->height = TF_Dim(output_tensor, 1);
|
|
output->width = TF_Dim(output_tensor, 2);
|
|
output->channels = TF_Dim(output_tensor, 3);
|
|
output->data = av_malloc(output->height * output->width * output->channels * sizeof(float));
|
|
if (!output->data){
|
|
return DNN_ERROR;
|
|
}
|
|
tf_model->output_data = output;
|
|
TF_DeleteTensor(output_tensor);
|
|
}
|
|
|
|
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:
|
|
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_malloc(sizeof(TFModel));
|
|
if (!tf_model){
|
|
av_freep(&model);
|
|
return NULL;
|
|
}
|
|
tf_model->session = NULL;
|
|
tf_model->input_tensor = NULL;
|
|
tf_model->output_data = 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)
|
|
{
|
|
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);
|
|
}
|
|
if (tf_model->output_data){
|
|
av_freep(&tf_model->output_data->data);
|
|
}
|
|
av_freep(&tf_model);
|
|
av_freep(model);
|
|
}
|
|
}
|