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ffmpeg/libavfilter/dnn/dnn_backend_tf.c

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/*
* 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 "dnn_backend_native_layer_conv2d.h"
#include "dnn_backend_native_layer_depth2space.h"
#include "libavformat/avio.h"
#include "libavutil/avassert.h"
#include "libavutil/avstring.h"
#include "libavutil/cpu.h"
#include "libavcodec/defs.h"
#include "../internal.h"
#include "dnn_backend_native_layer_pad.h"
#include "dnn_backend_native_layer_maximum.h"
#include "dnn_io_proc.h"
#include "dnn_backend_common.h"
#include "safe_queue.h"
#include <tensorflow/c/c_api.h>
typedef struct TFOptions{
char *sess_config;
uint8_t async;
uint32_t nireq;
} TFOptions;
typedef struct TFContext {
const AVClass *class;
TFOptions options;
} TFContext;
typedef struct TFModel{
TFContext ctx;
DNNModel *model;
TF_Graph *graph;
TF_Session *session;
TF_Status *status;
SafeQueue *request_queue;
Queue *lltask_queue;
Queue *task_queue;
} TFModel;
/**
* Stores execution parameters for single
* call to the TensorFlow C API
*/
typedef struct TFInferRequest {
TF_Output *tf_outputs;
TF_Tensor **output_tensors;
TF_Output *tf_input;
TF_Tensor *input_tensor;
} TFInferRequest;
typedef struct TFRequestItem {
TFInferRequest *infer_request;
LastLevelTaskItem *lltask;
TF_Status *status;
DNNAsyncExecModule exec_module;
} TFRequestItem;
#define OFFSET(x) offsetof(TFContext, x)
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
static const AVOption dnn_tensorflow_options[] = {
{ "sess_config", "config for SessionOptions", OFFSET(options.sess_config), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
DNN_BACKEND_COMMON_OPTIONS
{ NULL }
};
AVFILTER_DEFINE_CLASS(dnn_tensorflow);
static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *lltask_queue);
static void infer_completion_callback(void *args);
static inline void destroy_request_item(TFRequestItem **arg);
static void free_buffer(void *data, size_t length)
{
av_freep(&data);
}
/**
* Free the contents of TensorFlow inference request.
* It does not free the TFInferRequest instance.
*
* @param request pointer to TFInferRequest instance.
* NULL pointer is allowed.
*/
static void tf_free_request(TFInferRequest *request)
{
if (!request)
return;
if (request->input_tensor) {
TF_DeleteTensor(request->input_tensor);
request->input_tensor = NULL;
}
av_freep(&request->tf_input);
av_freep(&request->tf_outputs);
if (request->output_tensors) {
int nb_output = sizeof(*request->output_tensors)/sizeof(request->output_tensors[0]);
for (uint32_t i = 0; i < nb_output; ++i) {
if (request->output_tensors[i]) {
TF_DeleteTensor(request->output_tensors[i]);
request->output_tensors[i] = NULL;
}
}
av_freep(&request->output_tensors);
}
}
/**
* Create a TensorFlow inference request. All properties
* are initially unallocated and set as NULL.
*
* @return pointer to the allocated TFInferRequest instance.
*/
static TFInferRequest *tf_create_inference_request(void)
{
TFInferRequest *infer_request = av_malloc(sizeof(TFInferRequest));
if (!infer_request) {
return NULL;
}
infer_request->tf_outputs = NULL;
infer_request->tf_input = NULL;
infer_request->input_tensor = NULL;
infer_request->output_tensors = NULL;
return infer_request;
}
/**
* Start synchronous inference for the TensorFlow model.
*
* @param request pointer to the TFRequestItem for inference
* @retval DNN_SUCCESS if execution is successful
* @retval DNN_ERROR if execution fails
*/
static DNNReturnType tf_start_inference(void *args)
{
TFRequestItem *request = args;
TFInferRequest *infer_request = request->infer_request;
LastLevelTaskItem *lltask = request->lltask;
TaskItem *task = lltask->task;
TFModel *tf_model = task->model;
if (!request) {
av_log(&tf_model->ctx, AV_LOG_ERROR, "TFRequestItem is NULL\n");
return DNN_ERROR;
}
TF_SessionRun(tf_model->session, NULL,
infer_request->tf_input, &infer_request->input_tensor, 1,
infer_request->tf_outputs, infer_request->output_tensors,
task->nb_output, NULL, 0, NULL,
request->status);
if (TF_GetCode(request->status) != TF_OK) {
av_log(&tf_model->ctx, AV_LOG_ERROR, "%s", TF_Message(request->status));
tf_free_request(infer_request);
if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
destroy_request_item(&request);
}
return DNN_ERROR;
}
return DNN_SUCCESS;
}
/**
* Free the TFRequestItem completely.
*
* @param arg Address of the TFInferRequest instance.
*/
static inline void destroy_request_item(TFRequestItem **arg) {
TFRequestItem *request;
if (!arg) {
return;
}
request = *arg;
tf_free_request(request->infer_request);
av_freep(&request->infer_request);
av_freep(&request->lltask);
TF_DeleteStatus(request->status);
ff_dnn_async_module_cleanup(&request->exec_module);
av_freep(arg);
}
static DNNReturnType extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
{
TFModel *tf_model = task->model;
TFContext *ctx = &tf_model->ctx;
LastLevelTaskItem *lltask = av_malloc(sizeof(*lltask));
if (!lltask) {
av_log(ctx, AV_LOG_ERROR, "Unable to allocate space for LastLevelTaskItem\n");
return DNN_ERROR;
}
task->inference_todo = 1;
task->inference_done = 0;
lltask->task = task;
if (ff_queue_push_back(lltask_queue, lltask) < 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to push back lltask_queue.\n");
av_freep(&lltask);
return DNN_ERROR;
}
return DNN_SUCCESS;
}
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 = graph_data;
graph_buf->length = size;
graph_buf->data_deallocator = free_buffer;
return graph_buf;
}
static TF_Tensor *allocate_input_tensor(const DNNData *input)
{
TF_DataType dt;
size_t size;
int64_t input_dims[] = {1, input->height, input->width, input->channels};
switch (input->dt) {
case DNN_FLOAT:
dt = TF_FLOAT;
size = sizeof(float);
break;
case DNN_UINT8:
dt = TF_UINT8;
size = 1;
break;
default:
av_assert0(!"should not reach here");
}
return TF_AllocateTensor(dt, input_dims, 4,
input_dims[1] * input_dims[2] * input_dims[3] * size);
}
static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input_name)
{
TFModel *tf_model = model;
TFContext *ctx = &tf_model->ctx;
TF_Status *status;
int64_t dims[4];
TF_Output tf_output;
tf_output.oper = TF_GraphOperationByName(tf_model->graph, input_name);
if (!tf_output.oper) {
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
return DNN_ERROR;
}
tf_output.index = 0;
input->dt = TF_OperationOutputType(tf_output);
input->order = DCO_RGB;
status = TF_NewStatus();
TF_GraphGetTensorShape(tf_model->graph, tf_output, dims, 4, status);
if (TF_GetCode(status) != TF_OK){
TF_DeleteStatus(status);
av_log(ctx, AV_LOG_ERROR, "Failed to get input tensor shape: number of dimension incorrect\n");
return DNN_ERROR;
}
TF_DeleteStatus(status);
// currently only NHWC is supported
av_assert0(dims[0] == 1 || dims[0] == -1);
input->height = dims[1];
input->width = dims[2];
input->channels = dims[3];
return DNN_SUCCESS;
}
static DNNReturnType get_output_tf(void *model, const char *input_name, int input_width, int input_height,
const char *output_name, int *output_width, int *output_height)
{
DNNReturnType ret;
TFModel *tf_model = model;
TFContext *ctx = &tf_model->ctx;
TaskItem task;
TFRequestItem *request;
DNNExecBaseParams exec_params = {
.input_name = input_name,
.output_names = &output_name,
.nb_output = 1,
.in_frame = NULL,
.out_frame = NULL,
};
if (ff_dnn_fill_gettingoutput_task(&task, &exec_params, tf_model, input_height, input_width, ctx) != DNN_SUCCESS) {
goto err;
}
if (extract_lltask_from_task(&task, tf_model->lltask_queue) != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
ret = DNN_ERROR;
goto err;
}
request = ff_safe_queue_pop_front(tf_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
ret = DNN_ERROR;
goto err;
}
ret = execute_model_tf(request, tf_model->lltask_queue);
*output_width = task.out_frame->width;
*output_height = task.out_frame->height;
err:
av_frame_free(&task.out_frame);
av_frame_free(&task.in_frame);
return ret;
}
#define SPACE_CHARS " \t\r\n"
static int hex_to_data(uint8_t *data, const char *p)
{
int c, len, v;
len = 0;
v = 1;
for (;;) {
p += strspn(p, SPACE_CHARS);
if (*p == '\0')
break;
c = av_toupper((unsigned char) *p++);
if (c >= '0' && c <= '9')
c = c - '0';
else if (c >= 'A' && c <= 'F')
c = c - 'A' + 10;
else
break;
v = (v << 4) | c;
if (v & 0x100) {
if (data) {
data[len] = v;
}
len++;
v = 1;
}
}
return len;
}
static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename)
{
TFContext *ctx = &tf_model->ctx;
TF_Buffer *graph_def;
TF_ImportGraphDefOptions *graph_opts;
TF_SessionOptions *sess_opts;
const TF_Operation *init_op;
uint8_t *sess_config = NULL;
int sess_config_length = 0;
// prepare the sess config data
if (tf_model->ctx.options.sess_config != NULL) {
const char *config;
/*
tf_model->ctx.options.sess_config is hex to present the serialized proto
required by TF_SetConfig below, so we need to first generate the serialized
proto in a python script, tools/python/tf_sess_config.py is a script example
to generate the configs of sess_config.
*/
if (strncmp(tf_model->ctx.options.sess_config, "0x", 2) != 0) {
av_log(ctx, AV_LOG_ERROR, "sess_config should start with '0x'\n");
return DNN_ERROR;
}
config = tf_model->ctx.options.sess_config + 2;
sess_config_length = hex_to_data(NULL, config);
sess_config = av_mallocz(sess_config_length + AV_INPUT_BUFFER_PADDING_SIZE);
if (!sess_config) {
av_log(ctx, AV_LOG_ERROR, "failed to allocate memory\n");
return DNN_ERROR;
}
if (hex_to_data(sess_config, config) < 0) {
av_log(ctx, AV_LOG_ERROR, "failed to convert hex to data\n");
return DNN_ERROR;
}
}
graph_def = read_graph(model_filename);
if (!graph_def){
av_log(ctx, AV_LOG_ERROR, "Failed to read model \"%s\" graph\n", model_filename);
av_freep(&sess_config);
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);
av_log(ctx, AV_LOG_ERROR, "Failed to import serialized graph to model graph\n");
av_freep(&sess_config);
return DNN_ERROR;
}
init_op = TF_GraphOperationByName(tf_model->graph, "init");
sess_opts = TF_NewSessionOptions();
if (sess_config) {
TF_SetConfig(sess_opts, sess_config, sess_config_length,tf_model->status);
av_freep(&sess_config);
if (TF_GetCode(tf_model->status) != TF_OK) {
TF_DeleteGraph(tf_model->graph);
TF_DeleteStatus(tf_model->status);
TF_DeleteSessionOptions(sess_opts);
av_log(ctx, AV_LOG_ERROR, "Failed to set config for sess options with %s\n",
tf_model->ctx.options.sess_config);
return DNN_ERROR;
}
}
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)
{
TF_DeleteGraph(tf_model->graph);
TF_DeleteStatus(tf_model->status);
av_log(ctx, AV_LOG_ERROR, "Failed to create new session with model graph\n");
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)
{
TF_DeleteSession(tf_model->session, tf_model->status);
TF_DeleteGraph(tf_model->graph);
TF_DeleteStatus(tf_model->status);
av_log(ctx, AV_LOG_ERROR, "Failed to run session when initializing\n");
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)
{
TFContext *ctx = &tf_model->ctx;
TF_Operation *op;
TF_OperationDescription *op_desc;
TF_Output input;
int64_t strides[] = {1, 1, 1, 1};
TF_Tensor *kernel_tensor = NULL, *biases_tensor = NULL;
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;
kernel_tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size * sizeof(float));
memcpy(TF_TensorData(kernel_tensor), params->kernel, size * sizeof(float));
TF_SetAttrTensor(op_desc, "value", kernel_tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
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){
goto err;
}
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){
goto err;
}
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;
biases_tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num * sizeof(float));
memcpy(TF_TensorData(biases_tensor), params->biases, params->output_num * sizeof(float));
TF_SetAttrTensor(op_desc, "value", biases_tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
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){
goto err;
}
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:
avpriv_report_missing_feature(ctx, "convolutional activation function %d", params->activation);
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){
goto err;
}
return DNN_SUCCESS;
err:
TF_DeleteTensor(kernel_tensor);
TF_DeleteTensor(biases_tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add conv layer %d\n", layer);
return DNN_ERROR;
}
static DNNReturnType add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
DepthToSpaceParams *params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
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){
av_log(ctx, AV_LOG_ERROR, "Failed to add depth_to_space to layer %d\n", layer);
return DNN_ERROR;
}
return DNN_SUCCESS;
}
static DNNReturnType add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
LayerPadParams *params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
TF_Operation *op;
TF_Tensor *tensor;
TF_OperationDescription *op_desc;
TF_Output input;
int32_t *pads;
int64_t pads_shape[] = {4, 2};
char name_buffer[NAME_BUFFER_SIZE];
snprintf(name_buffer, NAME_BUFFER_SIZE, "pad%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
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] = params->paddings[0][0];
pads[1] = params->paddings[0][1];
pads[2] = params->paddings[1][0];
pads[3] = params->paddings[1][1];
pads[4] = params->paddings[2][0];
pads[5] = params->paddings[2][1];
pads[6] = params->paddings[3][0];
pads[7] = params->paddings[3][1];
TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to set value for pad of layer %d\n", layer);
return DNN_ERROR;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add pad to layer %d\n", layer);
return DNN_ERROR;
}
op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
input.oper = *cur_op;
input.index = 0;
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){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add mirror_pad to layer %d\n", layer);
return DNN_ERROR;
}
return DNN_SUCCESS;
}
static DNNReturnType add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
DnnLayerMaximumParams *params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
TF_Operation *op;
TF_Tensor *tensor;
TF_OperationDescription *op_desc;
TF_Output input;
float *y;
char name_buffer[NAME_BUFFER_SIZE];
snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum/y%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
tensor = TF_AllocateTensor(TF_FLOAT, NULL, 0, TF_DataTypeSize(TF_FLOAT));
y = (float *)TF_TensorData(tensor);
*y = params->val.y;
TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to set value for maximum/y of layer %d", layer);
return DNN_ERROR;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add maximum/y to layer %d\n", layer);
return DNN_ERROR;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Maximum", name_buffer);
input.oper = *cur_op;
input.index = 0;
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){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add maximum to layer %d\n", layer);
return DNN_ERROR;
}
return DNN_SUCCESS;
}
static DNNReturnType load_native_model(TFModel *tf_model, const char *model_filename)
{
TFContext *ctx = &tf_model->ctx;
int32_t layer;
TF_OperationDescription *op_desc;
TF_Operation *op;
TF_Operation *transpose_op;
TF_Tensor *tensor = NULL;
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 *model = NULL;
NativeModel *native_model;
model = ff_dnn_load_model_native(model_filename, DFT_PROCESS_FRAME, NULL, NULL);
if (!model){
av_log(ctx, AV_LOG_ERROR, "Failed to load native model\n");
return DNN_ERROR;
}
native_model = model->model;
tf_model->graph = TF_NewGraph();
tf_model->status = TF_NewStatus();
#define CLEANUP_ON_ERROR(tf_model) \
{ \
TF_DeleteTensor(tensor); \
TF_DeleteGraph(tf_model->graph); \
TF_DeleteStatus(tf_model->status); \
av_log(ctx, AV_LOG_ERROR, "Failed to set value or add operator to layer\n"); \
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);
if (TF_GetCode(tf_model->status) != TF_OK){
CLEANUP_ON_ERROR(tf_model);
}
for (layer = 0; layer < native_model->layers_num; ++layer){
switch (native_model->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 *)native_model->layers[layer].params, layer);
break;
case DLT_DEPTH_TO_SPACE:
layer_add_res = add_depth_to_space_layer(tf_model, &op,
(DepthToSpaceParams *)native_model->layers[layer].params, layer);
break;
case DLT_MIRROR_PAD:
layer_add_res = add_pad_layer(tf_model, &op,
(LayerPadParams *)native_model->layers[layer].params, layer);
break;
case DLT_MAXIMUM:
layer_add_res = add_maximum_layer(tf_model, &op,
(DnnLayerMaximumParams *)native_model->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(&model);
return DNN_SUCCESS;
}
DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx)
{
DNNModel *model = NULL;
TFModel *tf_model = NULL;
TFContext *ctx = NULL;
model = av_mallocz(sizeof(DNNModel));
if (!model){
return NULL;
}
tf_model = av_mallocz(sizeof(TFModel));
if (!tf_model){
av_freep(&model);
return NULL;
}
tf_model->model = model;
ctx = &tf_model->ctx;
ctx->class = &dnn_tensorflow_class;
//parse options
av_opt_set_defaults(ctx);
if (av_opt_set_from_string(ctx, options, NULL, "=", "&") < 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to parse options \"%s\"\n", options);
goto err;
}
if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
goto err;
}
}
if (ctx->options.nireq <= 0) {
ctx->options.nireq = av_cpu_count() / 2 + 1;
}
#if !HAVE_PTHREAD_CANCEL
if (ctx->options.async) {
ctx->options.async = 0;
av_log(filter_ctx, AV_LOG_WARNING, "pthread is not supported, roll back to sync.\n");
}
#endif
tf_model->request_queue = ff_safe_queue_create();
if (!tf_model->request_queue) {
goto err;
}
for (int i = 0; i < ctx->options.nireq; i++) {
TFRequestItem *item = av_mallocz(sizeof(*item));
if (!item) {
goto err;
}
item->lltask = NULL;
item->infer_request = tf_create_inference_request();
if (!item->infer_request) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for TensorFlow inference request\n");
av_freep(&item);
goto err;
}
item->status = TF_NewStatus();
item->exec_module.start_inference = &tf_start_inference;
item->exec_module.callback = &infer_completion_callback;
item->exec_module.args = item;
if (ff_safe_queue_push_back(tf_model->request_queue, item) < 0) {
destroy_request_item(&item);
goto err;
}
}
tf_model->lltask_queue = ff_queue_create();
if (!tf_model->lltask_queue) {
goto err;
}
tf_model->task_queue = ff_queue_create();
if (!tf_model->task_queue) {
goto err;
}
model->model = tf_model;
model->get_input = &get_input_tf;
model->get_output = &get_output_tf;
model->options = options;
model->filter_ctx = filter_ctx;
model->func_type = func_type;
return model;
err:
ff_dnn_free_model_tf(&model);
return NULL;
}
static DNNReturnType fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
DNNData input;
LastLevelTaskItem *lltask;
TaskItem *task;
TFInferRequest *infer_request;
TFContext *ctx = &tf_model->ctx;
lltask = ff_queue_pop_front(tf_model->lltask_queue);
av_assert0(lltask);
task = lltask->task;
request->lltask = lltask;
if (get_input_tf(tf_model, &input, task->input_name) != DNN_SUCCESS) {
goto err;
}
infer_request = request->infer_request;
input.height = task->in_frame->height;
input.width = task->in_frame->width;
infer_request->tf_input = av_malloc(sizeof(TF_Output));
if (!infer_request->tf_input) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input tensor\n");
goto err;
}
infer_request->tf_input->oper = TF_GraphOperationByName(tf_model->graph, task->input_name);
if (!infer_request->tf_input->oper){
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", task->input_name);
goto err;
}
infer_request->tf_input->index = 0;
infer_request->input_tensor = allocate_input_tensor(&input);
if (!infer_request->input_tensor){
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input tensor\n");
goto err;
}
input.data = (float *)TF_TensorData(infer_request->input_tensor);
switch (tf_model->model->func_type) {
case DFT_PROCESS_FRAME:
if (task->do_ioproc) {
if (tf_model->model->frame_pre_proc != NULL) {
tf_model->model->frame_pre_proc(task->in_frame, &input, tf_model->model->filter_ctx);
} else {
ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
}
}
break;
case DFT_ANALYTICS_DETECT:
ff_frame_to_dnn_detect(task->in_frame, &input, ctx);
break;
default:
avpriv_report_missing_feature(ctx, "model function type %d", tf_model->model->func_type);
break;
}
infer_request->tf_outputs = av_malloc_array(task->nb_output, sizeof(TF_Output));
if (infer_request->tf_outputs == NULL) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *tf_outputs\n");
goto err;
}
infer_request->output_tensors = av_calloc(task->nb_output, sizeof(*infer_request->output_tensors));
if (!infer_request->output_tensors) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output tensor\n");
goto err;
}
for (int i = 0; i < task->nb_output; ++i) {
infer_request->output_tensors[i] = NULL;
infer_request->tf_outputs[i].oper = TF_GraphOperationByName(tf_model->graph, task->output_names[i]);
if (!infer_request->tf_outputs[i].oper) {
av_log(ctx, AV_LOG_ERROR, "Could not find output \"%s\" in model\n", task->output_names[i]);
goto err;
}
infer_request->tf_outputs[i].index = 0;
}
return DNN_SUCCESS;
err:
tf_free_request(infer_request);
return DNN_ERROR;
}
static void infer_completion_callback(void *args) {
TFRequestItem *request = args;
LastLevelTaskItem *lltask = request->lltask;
TaskItem *task = lltask->task;
DNNData *outputs;
TFInferRequest *infer_request = request->infer_request;
TFModel *tf_model = task->model;
TFContext *ctx = &tf_model->ctx;
outputs = av_malloc_array(task->nb_output, sizeof(*outputs));
if (!outputs) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *outputs\n");
goto err;
}
for (uint32_t i = 0; i < task->nb_output; ++i) {
outputs[i].height = TF_Dim(infer_request->output_tensors[i], 1);
outputs[i].width = TF_Dim(infer_request->output_tensors[i], 2);
outputs[i].channels = TF_Dim(infer_request->output_tensors[i], 3);
outputs[i].data = TF_TensorData(infer_request->output_tensors[i]);
outputs[i].dt = TF_TensorType(infer_request->output_tensors[i]);
}
switch (tf_model->model->func_type) {
case DFT_PROCESS_FRAME:
//it only support 1 output if it's frame in & frame out
if (task->do_ioproc) {
if (tf_model->model->frame_post_proc != NULL) {
tf_model->model->frame_post_proc(task->out_frame, outputs, tf_model->model->filter_ctx);
} else {
ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
}
} else {
task->out_frame->width = outputs[0].width;
task->out_frame->height = outputs[0].height;
}
break;
case DFT_ANALYTICS_DETECT:
if (!tf_model->model->detect_post_proc) {
av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n");
return;
}
tf_model->model->detect_post_proc(task->in_frame, outputs, task->nb_output, tf_model->model->filter_ctx);
break;
default:
av_log(ctx, AV_LOG_ERROR, "Tensorflow backend does not support this kind of dnn filter now\n");
goto err;
}
task->inference_done++;
err:
tf_free_request(infer_request);
av_freep(&outputs);
if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
destroy_request_item(&request);
av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n");
}
}
static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *lltask_queue)
{
TFModel *tf_model;
TFContext *ctx;
LastLevelTaskItem *lltask;
TaskItem *task;
if (ff_queue_size(lltask_queue) == 0) {
destroy_request_item(&request);
return DNN_SUCCESS;
}
lltask = ff_queue_peek_front(lltask_queue);
task = lltask->task;
tf_model = task->model;
ctx = &tf_model->ctx;
if (fill_model_input_tf(tf_model, request) != DNN_SUCCESS) {
goto err;
}
if (task->async) {
if (ff_dnn_start_inference_async(ctx, &request->exec_module) != DNN_SUCCESS) {
goto err;
}
return DNN_SUCCESS;
} else {
if (tf_start_inference(request) != DNN_SUCCESS) {
goto err;
}
infer_completion_callback(request);
return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_ERROR;
}
err:
tf_free_request(request->infer_request);
if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
destroy_request_item(&request);
}
return DNN_ERROR;
}
DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params)
{
TFModel *tf_model = model->model;
TFContext *ctx = &tf_model->ctx;
TaskItem *task;
TFRequestItem *request;
if (ff_check_exec_params(ctx, DNN_TF, model->func_type, exec_params) != 0) {
return DNN_ERROR;
}
task = av_malloc(sizeof(*task));
if (!task) {
av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
return DNN_ERROR;
}
if (ff_dnn_fill_task(task, exec_params, tf_model, ctx->options.async, 1) != DNN_SUCCESS) {
av_freep(&task);
return DNN_ERROR;
}
if (ff_queue_push_back(tf_model->task_queue, task) < 0) {
av_freep(&task);
av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
return DNN_ERROR;
}
if (extract_lltask_from_task(task, tf_model->lltask_queue) != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
return DNN_ERROR;
}
request = ff_safe_queue_pop_front(tf_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
return DNN_ERROR;
}
return execute_model_tf(request, tf_model->lltask_queue);
}
DNNAsyncStatusType ff_dnn_get_result_tf(const DNNModel *model, AVFrame **in, AVFrame **out)
{
TFModel *tf_model = model->model;
return ff_dnn_get_result_common(tf_model->task_queue, in, out);
}
DNNReturnType ff_dnn_flush_tf(const DNNModel *model)
{
TFModel *tf_model = model->model;
TFContext *ctx = &tf_model->ctx;
TFRequestItem *request;
DNNReturnType ret;
if (ff_queue_size(tf_model->lltask_queue) == 0) {
// no pending task need to flush
return DNN_SUCCESS;
}
request = ff_safe_queue_pop_front(tf_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
return DNN_ERROR;
}
ret = fill_model_input_tf(tf_model, request);
if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "Failed to fill model input.\n");
if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
destroy_request_item(&request);
}
return ret;
}
return ff_dnn_start_inference_async(ctx, &request->exec_module);
}
void ff_dnn_free_model_tf(DNNModel **model)
{
TFModel *tf_model;
if (*model){
tf_model = (*model)->model;
while (ff_safe_queue_size(tf_model->request_queue) != 0) {
TFRequestItem *item = ff_safe_queue_pop_front(tf_model->request_queue);
destroy_request_item(&item);
}
ff_safe_queue_destroy(tf_model->request_queue);
while (ff_queue_size(tf_model->lltask_queue) != 0) {
LastLevelTaskItem *item = ff_queue_pop_front(tf_model->lltask_queue);
av_freep(&item);
}
ff_queue_destroy(tf_model->lltask_queue);
while (ff_queue_size(tf_model->task_queue) != 0) {
TaskItem *item = ff_queue_pop_front(tf_model->task_queue);
av_frame_free(&item->in_frame);
av_frame_free(&item->out_frame);
av_freep(&item);
}
ff_queue_destroy(tf_model->task_queue);
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);
}
av_freep(&tf_model);
av_freep(model);
}
}
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