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lavfi/dnn: Extract TaskItem and InferenceItem from OpenVino Backend

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Extract TaskItem and InferenceItem from OpenVino backend and convert
ov_model to void in TaskItem.

Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit is contained in:
Shubhanshu Saxena 2021-06-05 23:38:02 +05:30 committed by Guo Yejun
parent 580e168a94
commit f5ab8905fd
2 changed files with 40 additions and 37 deletions
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19
libavfilter/dnn/dnn_backend_common.h
View File

@ -26,6 +26,25 @@
#include "../dnn_interface.h"
// one task for one function call from dnn interface
typedef struct TaskItem {
void *model; // model for the backend
AVFrame *in_frame;
AVFrame *out_frame;
const char *input_name;
const char *output_name;
int async;
int do_ioproc;
uint32_t inference_todo;
uint32_t inference_done;
} TaskItem;
// one task might have multiple inferences
typedef struct InferenceItem {
TaskItem *task;
uint32_t bbox_index;
} InferenceItem;
int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func_type, DNNExecBaseParams *exec_params);
#endif

58
libavfilter/dnn/dnn_backend_openvino.c
View File

@ -59,25 +59,6 @@ typedef struct OVModel{
Queue *inference_queue; // holds InferenceItem
} OVModel;
// one task for one function call from dnn interface
typedef struct TaskItem {
OVModel *ov_model;
const char *input_name;
AVFrame *in_frame;
const char *output_name;
AVFrame *out_frame;
int do_ioproc;
int async;
uint32_t inference_todo;
uint32_t inference_done;
} TaskItem;
// one task might have multiple inferences
typedef struct InferenceItem {
TaskItem *task;
uint32_t bbox_index;
} InferenceItem;
// one request for one call to openvino
typedef struct RequestItem {
ie_infer_request_t *infer_request;
@ -184,7 +165,7 @@ static DNNReturnType fill_model_input_ov(OVModel *ov_model, RequestItem *request
request->inferences[i] = inference;
request->inference_count = i + 1;
task = inference->task;
switch (task->ov_model->model->func_type) {
switch (ov_model->model->func_type) {
case DFT_PROCESS_FRAME:
if (task->do_ioproc) {
if (ov_model->model->frame_pre_proc != NULL) {
@ -220,11 +201,12 @@ static void infer_completion_callback(void *args)
RequestItem *request = args;
InferenceItem *inference = request->inferences[0];
TaskItem *task = inference->task;
SafeQueue *requestq = task->ov_model->request_queue;
OVModel *ov_model = task->model;
SafeQueue *requestq = ov_model->request_queue;
ie_blob_t *output_blob = NULL;
ie_blob_buffer_t blob_buffer;
DNNData output;
OVContext *ctx = &task->ov_model->ctx;
OVContext *ctx = &ov_model->ctx;
status = ie_infer_request_get_blob(request->infer_request, task->output_name, &output_blob);
if (status != OK) {
@ -233,9 +215,9 @@ static void infer_completion_callback(void *args)
char *all_output_names = NULL;
size_t model_output_count = 0;
av_log(ctx, AV_LOG_ERROR, "Failed to get model output data\n");
status = ie_network_get_outputs_number(task->ov_model->network, &model_output_count);
status = ie_network_get_outputs_number(ov_model->network, &model_output_count);
for (size_t i = 0; i < model_output_count; i++) {
status = ie_network_get_output_name(task->ov_model->network, i, &model_output_name);
status = ie_network_get_output_name(ov_model->network, i, &model_output_name);
APPEND_STRING(all_output_names, model_output_name)
}
av_log(ctx, AV_LOG_ERROR,
@ -271,11 +253,11 @@ static void infer_completion_callback(void *args)
task = request->inferences[i]->task;
task->inference_done++;
switch (task->ov_model->model->func_type) {
switch (ov_model->model->func_type) {
case DFT_PROCESS_FRAME:
if (task->do_ioproc) {
if (task->ov_model->model->frame_post_proc != NULL) {
task->ov_model->model->frame_post_proc(task->out_frame, &output, task->ov_model->model->filter_ctx);
if (ov_model->model->frame_post_proc != NULL) {
ov_model->model->frame_post_proc(task->out_frame, &output, ov_model->model->filter_ctx);
} else {
ff_proc_from_dnn_to_frame(task->out_frame, &output, ctx);
}
@ -285,18 +267,18 @@ static void infer_completion_callback(void *args)
}
break;
case DFT_ANALYTICS_DETECT:
if (!task->ov_model->model->detect_post_proc) {
if (!ov_model->model->detect_post_proc) {
av_log(ctx, AV_LOG_ERROR, "detect filter needs to provide post proc\n");
return;
}
task->ov_model->model->detect_post_proc(task->out_frame, &output, 1, task->ov_model->model->filter_ctx);
ov_model->model->detect_post_proc(task->out_frame, &output, 1, ov_model->model->filter_ctx);
break;
case DFT_ANALYTICS_CLASSIFY:
if (!task->ov_model->model->classify_post_proc) {
if (!ov_model->model->classify_post_proc) {
av_log(ctx, AV_LOG_ERROR, "classify filter needs to provide post proc\n");
return;
}
task->ov_model->model->classify_post_proc(task->out_frame, &output, request->inferences[i]->bbox_index, task->ov_model->model->filter_ctx);
ov_model->model->classify_post_proc(task->out_frame, &output, request->inferences[i]->bbox_index, ov_model->model->filter_ctx);
break;
default:
av_assert0(!"should not reach here");
@ -445,6 +427,7 @@ static DNNReturnType execute_model_ov(RequestItem *request, Queue *inferenceq)
InferenceItem *inference;
TaskItem *task;
OVContext *ctx;
OVModel *ov_model;
if (ff_queue_size(inferenceq) == 0) {
return DNN_SUCCESS;
@ -452,10 +435,11 @@ static DNNReturnType execute_model_ov(RequestItem *request, Queue *inferenceq)
inference = ff_queue_peek_front(inferenceq);
task = inference->task;
ctx = &task->ov_model->ctx;
ov_model = task->model;
ctx = &ov_model->ctx;
if (task->async) {
ret = fill_model_input_ov(task->ov_model, request);
ret = fill_model_input_ov(ov_model, request);
if (ret != DNN_SUCCESS) {
return ret;
}
@ -471,7 +455,7 @@ static DNNReturnType execute_model_ov(RequestItem *request, Queue *inferenceq)
}
return DNN_SUCCESS;
} else {
ret = fill_model_input_ov(task->ov_model, request);
ret = fill_model_input_ov(ov_model, request);
if (ret != DNN_SUCCESS) {
return ret;
}
@ -694,7 +678,7 @@ static DNNReturnType get_output_ov(void *model, const char *input_name, int inpu
task.in_frame = in_frame;
task.output_name = output_name;
task.out_frame = out_frame;
task.ov_model = ov_model;
task.model = ov_model;
if (extract_inference_from_task(ov_model->model->func_type, &task, ov_model->inference_queue, NULL) != DNN_SUCCESS) {
av_frame_free(&out_frame);
@ -814,7 +798,7 @@ DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *
task.in_frame = exec_params->in_frame;
task.output_name = exec_params->output_names[0];
task.out_frame = exec_params->out_frame ? exec_params->out_frame : exec_params->in_frame;
task.ov_model = ov_model;
task.model = ov_model;
if (extract_inference_from_task(ov_model->model->func_type, &task, ov_model->inference_queue, exec_params) != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
@ -861,7 +845,7 @@ DNNReturnType ff_dnn_execute_model_async_ov(const DNNModel *model, DNNExecBasePa
task->in_frame = exec_params->in_frame;
task->output_name = exec_params->output_names[0];
task->out_frame = exec_params->out_frame ? exec_params->out_frame : exec_params->in_frame;
task->ov_model = ov_model;
task->model = ov_model;
if (ff_queue_push_back(ov_model->task_queue, task) < 0) {
av_freep(&task);
av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");