mirror of https://git.ffmpeg.org/ffmpeg.git
158 lines
5.3 KiB
C
158 lines
5.3 KiB
C
/*
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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 common functions different backends.
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*/
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#ifndef AVFILTER_DNN_DNN_BACKEND_COMMON_H
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#define AVFILTER_DNN_DNN_BACKEND_COMMON_H
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#include "queue.h"
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#include "../dnn_interface.h"
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#include "libavutil/thread.h"
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#define DNN_BACKEND_COMMON_OPTIONS \
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{ "nireq", "number of request", OFFSET(options.nireq), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INT_MAX, FLAGS }, \
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{ "async", "use DNN async inference", OFFSET(options.async), AV_OPT_TYPE_BOOL, { .i64 = 1 }, 0, 1, FLAGS },
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// one task for one function call from dnn interface
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typedef struct TaskItem {
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void *model; // model for the backend
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AVFrame *in_frame;
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AVFrame *out_frame;
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const char *input_name;
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const char **output_names;
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uint8_t async;
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uint8_t do_ioproc;
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uint32_t nb_output;
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uint32_t inference_todo;
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uint32_t inference_done;
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} TaskItem;
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// one task might have multiple inferences
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typedef struct InferenceItem {
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TaskItem *task;
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uint32_t bbox_index;
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} InferenceItem;
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/**
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* Common Async Execution Mechanism for the DNN Backends.
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*/
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typedef struct DNNAsyncExecModule {
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/**
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* Synchronous inference function for the backend
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* with corresponding request item as the argument.
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*/
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DNNReturnType (*start_inference)(void *request);
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/**
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* Completion Callback for the backend.
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* Expected argument type of callback must match that
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* of the inference function.
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*/
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void (*callback)(void *args);
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/**
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* Argument for the execution functions.
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* i.e. Request item for the backend.
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*/
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void *args;
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#if HAVE_PTHREAD_CANCEL
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pthread_t thread_id;
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pthread_attr_t thread_attr;
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#endif
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} DNNAsyncExecModule;
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int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func_type, DNNExecBaseParams *exec_params);
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/**
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* Fill the Task for Backend Execution. It should be called after
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* checking execution parameters using ff_check_exec_params.
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*
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* @param task pointer to the allocated task
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* @param exec_param pointer to execution parameters
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* @param backend_model void pointer to the backend model
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* @param async flag for async execution. Must be 0 or 1
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* @param do_ioproc flag for IO processing. Must be 0 or 1
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*
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* @retval DNN_SUCCESS if successful
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* @retval DNN_ERROR if flags are invalid or any parameter is NULL
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*/
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DNNReturnType ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc);
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/**
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* Join the Async Execution thread and set module pointers to NULL.
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*
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* @param async_module pointer to DNNAsyncExecModule module
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*
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* @retval DNN_SUCCESS if successful
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* @retval DNN_ERROR if async_module is NULL
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*/
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DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module);
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/**
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* Start asynchronous inference routine for the TensorFlow
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* model on a detached thread. It calls the completion callback
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* after the inference completes. Completion callback and inference
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* function must be set before calling this function.
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*
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* If POSIX threads aren't supported, the execution rolls back
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* to synchronous mode, calling completion callback after inference.
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*
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* @param ctx pointer to the backend context
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* @param async_module pointer to DNNAsyncExecModule module
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*
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* @retval DNN_SUCCESS on the start of async inference.
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* @retval DNN_ERROR in case async inference cannot be started
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*/
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DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module);
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/**
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* Extract input and output frame from the Task Queue after
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* asynchronous inference.
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*
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* @param task_queue pointer to the task queue of the backend
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* @param in double pointer to the input frame
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* @param out double pointer to the output frame
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*
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* @retval DAST_EMPTY_QUEUE if task queue is empty
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* @retval DAST_NOT_READY if inference not completed yet.
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* @retval DAST_SUCCESS if result successfully extracted
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*/
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DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVFrame **out);
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/**
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* Allocate input and output frames and fill the Task
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* with execution parameters.
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*
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* @param task pointer to the allocated task
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* @param exec_params pointer to execution parameters
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* @param backend_model void pointer to the backend model
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* @param input_height height of input frame
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* @param input_width width of input frame
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* @param ctx pointer to the backend context
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*
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* @retval DNN_SUCCESS if successful
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* @retval DNN_ERROR if allocation fails
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*/
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DNNReturnType ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx);
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#endif
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