mirror of https://git.ffmpeg.org/ffmpeg.git
1265 lines
40 KiB
C
1265 lines
40 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 "libavutil/avstring.h"
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#include "libavutil/cpu.h"
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#include "libavcodec/defs.h"
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#include "../internal.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 "dnn_io_proc.h"
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#include "dnn_backend_common.h"
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#include "safe_queue.h"
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#include <tensorflow/c/c_api.h>
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typedef struct TFOptions{
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char *sess_config;
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uint8_t async;
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uint32_t nireq;
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} TFOptions;
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typedef struct TFContext {
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const AVClass *class;
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TFOptions options;
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} TFContext;
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typedef struct TFModel{
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TFContext ctx;
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DNNModel *model;
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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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SafeQueue *request_queue;
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Queue *lltask_queue;
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Queue *task_queue;
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} TFModel;
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/**
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* Stores execution parameters for single
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* call to the TensorFlow C API
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*/
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typedef struct TFInferRequest {
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TF_Output *tf_outputs;
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TF_Tensor **output_tensors;
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TF_Output *tf_input;
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TF_Tensor *input_tensor;
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} TFInferRequest;
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typedef struct TFRequestItem {
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TFInferRequest *infer_request;
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LastLevelTaskItem *lltask;
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TF_Status *status;
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DNNAsyncExecModule exec_module;
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} TFRequestItem;
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#define OFFSET(x) offsetof(TFContext, x)
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#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
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static const AVOption dnn_tensorflow_options[] = {
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{ "sess_config", "config for SessionOptions", OFFSET(options.sess_config), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
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DNN_BACKEND_COMMON_OPTIONS
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{ NULL }
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};
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AVFILTER_DEFINE_CLASS(dnn_tensorflow);
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static int execute_model_tf(TFRequestItem *request, Queue *lltask_queue);
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static void infer_completion_callback(void *args);
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static inline void destroy_request_item(TFRequestItem **arg);
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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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/**
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* Free the contents of TensorFlow inference request.
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* It does not free the TFInferRequest instance.
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*
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* @param request pointer to TFInferRequest instance.
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* NULL pointer is allowed.
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*/
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static void tf_free_request(TFInferRequest *request)
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{
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if (!request)
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return;
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if (request->input_tensor) {
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TF_DeleteTensor(request->input_tensor);
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request->input_tensor = NULL;
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}
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av_freep(&request->tf_input);
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av_freep(&request->tf_outputs);
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if (request->output_tensors) {
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int nb_output = sizeof(*request->output_tensors)/sizeof(request->output_tensors[0]);
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for (uint32_t i = 0; i < nb_output; ++i) {
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if (request->output_tensors[i]) {
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TF_DeleteTensor(request->output_tensors[i]);
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request->output_tensors[i] = NULL;
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}
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}
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av_freep(&request->output_tensors);
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}
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}
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/**
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* Create a TensorFlow inference request. All properties
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* are initially unallocated and set as NULL.
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*
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* @return pointer to the allocated TFInferRequest instance.
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*/
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static TFInferRequest *tf_create_inference_request(void)
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{
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TFInferRequest *infer_request = av_malloc(sizeof(TFInferRequest));
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if (!infer_request) {
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return NULL;
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}
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infer_request->tf_outputs = NULL;
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infer_request->tf_input = NULL;
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infer_request->input_tensor = NULL;
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infer_request->output_tensors = NULL;
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return infer_request;
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}
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/**
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* Start synchronous inference for the TensorFlow model.
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*
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* @param request pointer to the TFRequestItem for inference
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* @retval 0 if execution is successful
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* @retval AVERROR(EINVAL) if request is NULL
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* @retval DNN_GENERIC_ERROR if execution fails
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*/
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static int tf_start_inference(void *args)
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{
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TFRequestItem *request = args;
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TFInferRequest *infer_request = request->infer_request;
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LastLevelTaskItem *lltask = request->lltask;
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TaskItem *task = lltask->task;
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TFModel *tf_model = task->model;
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if (!request) {
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av_log(&tf_model->ctx, AV_LOG_ERROR, "TFRequestItem is NULL\n");
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return AVERROR(EINVAL);
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}
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TF_SessionRun(tf_model->session, NULL,
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infer_request->tf_input, &infer_request->input_tensor, 1,
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infer_request->tf_outputs, infer_request->output_tensors,
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task->nb_output, NULL, 0, NULL,
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request->status);
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if (TF_GetCode(request->status) != TF_OK) {
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av_log(&tf_model->ctx, AV_LOG_ERROR, "%s", TF_Message(request->status));
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tf_free_request(infer_request);
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if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
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destroy_request_item(&request);
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}
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return DNN_GENERIC_ERROR;
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}
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return 0;
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}
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/**
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* Free the TFRequestItem completely.
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*
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* @param arg Address of the TFInferRequest instance.
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*/
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static inline void destroy_request_item(TFRequestItem **arg) {
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TFRequestItem *request;
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if (!arg) {
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return;
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}
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request = *arg;
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tf_free_request(request->infer_request);
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av_freep(&request->infer_request);
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av_freep(&request->lltask);
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TF_DeleteStatus(request->status);
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ff_dnn_async_module_cleanup(&request->exec_module);
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av_freep(arg);
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}
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static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
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{
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TFModel *tf_model = task->model;
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TFContext *ctx = &tf_model->ctx;
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LastLevelTaskItem *lltask = av_malloc(sizeof(*lltask));
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if (!lltask) {
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av_log(ctx, AV_LOG_ERROR, "Unable to allocate space for LastLevelTaskItem\n");
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return AVERROR(ENOMEM);
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}
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task->inference_todo = 1;
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task->inference_done = 0;
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lltask->task = task;
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if (ff_queue_push_back(lltask_queue, lltask) < 0) {
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av_log(ctx, AV_LOG_ERROR, "Failed to push back lltask_queue.\n");
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av_freep(&lltask);
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return AVERROR(ENOMEM);
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}
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return 0;
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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 = 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 int get_input_tf(void *model, DNNData *input, const char *input_name)
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{
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TFModel *tf_model = model;
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TFContext *ctx = &tf_model->ctx;
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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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av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
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return AVERROR(EINVAL);
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}
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tf_output.index = 0;
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input->dt = TF_OperationOutputType(tf_output);
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input->order = DCO_RGB;
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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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av_log(ctx, AV_LOG_ERROR, "Failed to get input tensor shape: number of dimension incorrect\n");
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return DNN_GENERIC_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 || 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 0;
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}
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static int get_output_tf(void *model, const char *input_name, int input_width, int input_height,
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const char *output_name, int *output_width, int *output_height)
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{
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int ret;
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TFModel *tf_model = model;
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TFContext *ctx = &tf_model->ctx;
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TaskItem task;
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TFRequestItem *request;
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DNNExecBaseParams exec_params = {
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.input_name = input_name,
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.output_names = &output_name,
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.nb_output = 1,
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.in_frame = NULL,
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.out_frame = NULL,
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};
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ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, tf_model, input_height, input_width, ctx);
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if (ret != 0) {
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goto err;
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}
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ret = extract_lltask_from_task(&task, tf_model->lltask_queue);
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if (ret != 0) {
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av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
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goto err;
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}
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request = ff_safe_queue_pop_front(tf_model->request_queue);
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if (!request) {
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av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
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ret = AVERROR(EINVAL);
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goto err;
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}
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ret = execute_model_tf(request, tf_model->lltask_queue);
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*output_width = task.out_frame->width;
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*output_height = task.out_frame->height;
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err:
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av_frame_free(&task.out_frame);
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av_frame_free(&task.in_frame);
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return ret;
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}
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#define SPACE_CHARS " \t\r\n"
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static int hex_to_data(uint8_t *data, const char *p)
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{
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int c, len, v;
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len = 0;
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v = 1;
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for (;;) {
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p += strspn(p, SPACE_CHARS);
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if (*p == '\0')
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break;
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c = av_toupper((unsigned char) *p++);
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if (c >= '0' && c <= '9')
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c = c - '0';
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else if (c >= 'A' && c <= 'F')
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c = c - 'A' + 10;
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else
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break;
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v = (v << 4) | c;
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if (v & 0x100) {
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if (data) {
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data[len] = v;
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}
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len++;
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v = 1;
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}
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}
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return len;
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}
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static int load_tf_model(TFModel *tf_model, const char *model_filename)
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{
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TFContext *ctx = &tf_model->ctx;
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TF_Buffer *graph_def;
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TF_ImportGraphDefOptions *graph_opts;
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TF_SessionOptions *sess_opts;
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const TF_Operation *init_op;
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uint8_t *sess_config = NULL;
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int sess_config_length = 0;
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// prepare the sess config data
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if (tf_model->ctx.options.sess_config != NULL) {
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const char *config;
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/*
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tf_model->ctx.options.sess_config is hex to present the serialized proto
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required by TF_SetConfig below, so we need to first generate the serialized
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proto in a python script, tools/python/tf_sess_config.py is a script example
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to generate the configs of sess_config.
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*/
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if (strncmp(tf_model->ctx.options.sess_config, "0x", 2) != 0) {
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av_log(ctx, AV_LOG_ERROR, "sess_config should start with '0x'\n");
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return AVERROR(EINVAL);
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}
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config = tf_model->ctx.options.sess_config + 2;
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sess_config_length = hex_to_data(NULL, config);
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sess_config = av_mallocz(sess_config_length + AV_INPUT_BUFFER_PADDING_SIZE);
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if (!sess_config) {
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av_log(ctx, AV_LOG_ERROR, "failed to allocate memory\n");
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return AVERROR(ENOMEM);
|
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}
|
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if (hex_to_data(sess_config, config) < 0) {
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av_log(ctx, AV_LOG_ERROR, "failed to convert hex to data\n");
|
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return AVERROR(EINVAL);
|
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}
|
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}
|
|
|
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graph_def = read_graph(model_filename);
|
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if (!graph_def){
|
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av_log(ctx, AV_LOG_ERROR, "Failed to read model \"%s\" graph\n", model_filename);
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av_freep(&sess_config);
|
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return AVERROR(EINVAL);
|
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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);
|
|
if (TF_GetCode(tf_model->status) != TF_OK){
|
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TF_DeleteGraph(tf_model->graph);
|
|
TF_DeleteStatus(tf_model->status);
|
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av_log(ctx, AV_LOG_ERROR, "Failed to import serialized graph to model graph\n");
|
|
av_freep(&sess_config);
|
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return DNN_GENERIC_ERROR;
|
|
}
|
|
|
|
init_op = TF_GraphOperationByName(tf_model->graph, "init");
|
|
sess_opts = TF_NewSessionOptions();
|
|
|
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if (sess_config) {
|
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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",
|
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tf_model->ctx.options.sess_config);
|
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return DNN_GENERIC_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_GENERIC_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_GENERIC_ERROR;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
#define NAME_BUFFER_SIZE 256
|
|
|
|
static int 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 AVERROR(ENOSYS);
|
|
}
|
|
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 0;
|
|
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_GENERIC_ERROR;
|
|
}
|
|
|
|
static int 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_GENERIC_ERROR;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int 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_GENERIC_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_GENERIC_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_GENERIC_ERROR;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int 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_GENERIC_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_GENERIC_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_GENERIC_ERROR;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int 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};
|
|
int 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 AVERROR(EINVAL);
|
|
}
|
|
|
|
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_GENERIC_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 = 0;
|
|
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 != 0){
|
|
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 0;
|
|
}
|
|
|
|
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) != 0){
|
|
if (load_native_model(tf_model, model_filename) != 0){
|
|
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 int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
|
|
DNNData input;
|
|
LastLevelTaskItem *lltask;
|
|
TaskItem *task;
|
|
TFInferRequest *infer_request;
|
|
TFContext *ctx = &tf_model->ctx;
|
|
int ret = 0;
|
|
|
|
lltask = ff_queue_pop_front(tf_model->lltask_queue);
|
|
av_assert0(lltask);
|
|
task = lltask->task;
|
|
request->lltask = lltask;
|
|
|
|
ret = get_input_tf(tf_model, &input, task->input_name);
|
|
if (ret != 0) {
|
|
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");
|
|
ret = AVERROR(ENOMEM);
|
|
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);
|
|
ret = DNN_GENERIC_ERROR;
|
|
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");
|
|
ret = AVERROR(ENOMEM);
|
|
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");
|
|
ret = AVERROR(ENOMEM);
|
|
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");
|
|
ret = AVERROR(ENOMEM);
|
|
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]);
|
|
ret = DNN_GENERIC_ERROR;
|
|
goto err;
|
|
}
|
|
infer_request->tf_outputs[i].index = 0;
|
|
}
|
|
|
|
return 0;
|
|
err:
|
|
tf_free_request(infer_request);
|
|
return ret;
|
|
}
|
|
|
|
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 int execute_model_tf(TFRequestItem *request, Queue *lltask_queue)
|
|
{
|
|
TFModel *tf_model;
|
|
TFContext *ctx;
|
|
LastLevelTaskItem *lltask;
|
|
TaskItem *task;
|
|
int ret = 0;
|
|
|
|
if (ff_queue_size(lltask_queue) == 0) {
|
|
destroy_request_item(&request);
|
|
return 0;
|
|
}
|
|
|
|
lltask = ff_queue_peek_front(lltask_queue);
|
|
task = lltask->task;
|
|
tf_model = task->model;
|
|
ctx = &tf_model->ctx;
|
|
|
|
ret = fill_model_input_tf(tf_model, request);
|
|
if (ret != 0) {
|
|
goto err;
|
|
}
|
|
|
|
if (task->async) {
|
|
if (ff_dnn_start_inference_async(ctx, &request->exec_module) != 0) {
|
|
goto err;
|
|
}
|
|
return 0;
|
|
}
|
|
else {
|
|
ret = tf_start_inference(request);
|
|
if (ret != 0) {
|
|
goto err;
|
|
}
|
|
infer_completion_callback(request);
|
|
return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_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 ret;
|
|
}
|
|
|
|
int 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;
|
|
int ret = 0;
|
|
|
|
ret = ff_check_exec_params(ctx, DNN_TF, model->func_type, exec_params);
|
|
if (ret != 0) {
|
|
return ret;
|
|
}
|
|
|
|
task = av_malloc(sizeof(*task));
|
|
if (!task) {
|
|
av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
|
|
return AVERROR(ENOMEM);
|
|
}
|
|
|
|
ret = ff_dnn_fill_task(task, exec_params, tf_model, ctx->options.async, 1);
|
|
if (ret != 0) {
|
|
av_freep(&task);
|
|
return ret;
|
|
}
|
|
|
|
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 AVERROR(ENOMEM);
|
|
}
|
|
|
|
ret = extract_lltask_from_task(task, tf_model->lltask_queue);
|
|
if (ret != 0) {
|
|
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
|
|
return ret;
|
|
}
|
|
|
|
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 AVERROR(EINVAL);
|
|
}
|
|
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);
|
|
}
|
|
|
|
int ff_dnn_flush_tf(const DNNModel *model)
|
|
{
|
|
TFModel *tf_model = model->model;
|
|
TFContext *ctx = &tf_model->ctx;
|
|
TFRequestItem *request;
|
|
int ret;
|
|
|
|
if (ff_queue_size(tf_model->lltask_queue) == 0) {
|
|
// no pending task need to flush
|
|
return 0;
|
|
}
|
|
|
|
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 AVERROR(EINVAL);
|
|
}
|
|
|
|
ret = fill_model_input_tf(tf_model, request);
|
|
if (ret != 0) {
|
|
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);
|
|
}
|
|
}
|