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ffmpeg/libavfilter/dnn/dnn_backend_openvino.c

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/*
* Copyright (c) 2020
*
* 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 OpenVINO backend implementation.
*/
#include "dnn_backend_openvino.h"
#include "dnn_io_proc.h"
#include "libavformat/avio.h"
#include "libavutil/avassert.h"
#include "libavutil/cpu.h"
#include "libavutil/opt.h"
#include "libavutil/avstring.h"
#include "libavutil/detection_bbox.h"
#include "../internal.h"
#include "queue.h"
#include "safe_queue.h"
#include <c_api/ie_c_api.h>
#include "dnn_backend_common.h"
typedef struct OVOptions{
char *device_type;
int nireq;
int batch_size;
int input_resizable;
} OVOptions;
typedef struct OVContext {
const AVClass *class;
OVOptions options;
} OVContext;
typedef struct OVModel{
OVContext ctx;
DNNModel *model;
ie_core_t *core;
ie_network_t *network;
ie_executable_network_t *exe_network;
SafeQueue *request_queue; // holds OVRequestItem
Queue *task_queue; // holds TaskItem
Queue *inference_queue; // holds InferenceItem
} OVModel;
// one request for one call to openvino
typedef struct OVRequestItem {
ie_infer_request_t *infer_request;
InferenceItem **inferences;
uint32_t inference_count;
ie_complete_call_back_t callback;
} OVRequestItem;
#define APPEND_STRING(generated_string, iterate_string) \
generated_string = generated_string ? av_asprintf("%s %s", generated_string, iterate_string) : \
av_asprintf("%s", iterate_string);
#define OFFSET(x) offsetof(OVContext, x)
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
static const AVOption dnn_openvino_options[] = {
{ "device", "device to run model", OFFSET(options.device_type), AV_OPT_TYPE_STRING, { .str = "CPU" }, 0, 0, FLAGS },
DNN_BACKEND_COMMON_OPTIONS
{ "batch_size", "batch size per request", OFFSET(options.batch_size), AV_OPT_TYPE_INT, { .i64 = 1 }, 1, 1000, FLAGS},
{ "input_resizable", "can input be resizable or not", OFFSET(options.input_resizable), AV_OPT_TYPE_BOOL, { .i64 = 0 }, 0, 1, FLAGS },
{ NULL }
};
AVFILTER_DEFINE_CLASS(dnn_openvino);
static DNNDataType precision_to_datatype(precision_e precision)
{
switch (precision)
{
case FP32:
return DNN_FLOAT;
case U8:
return DNN_UINT8;
default:
av_assert0(!"not supported yet.");
return DNN_FLOAT;
}
}
static int get_datatype_size(DNNDataType dt)
{
switch (dt)
{
case DNN_FLOAT:
return sizeof(float);
case DNN_UINT8:
return sizeof(uint8_t);
default:
av_assert0(!"not supported yet.");
return 1;
}
}
static DNNReturnType fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
{
dimensions_t dims;
precision_e precision;
ie_blob_buffer_t blob_buffer;
OVContext *ctx = &ov_model->ctx;
IEStatusCode status;
DNNData input;
ie_blob_t *input_blob = NULL;
InferenceItem *inference;
TaskItem *task;
inference = ff_queue_peek_front(ov_model->inference_queue);
av_assert0(inference);
task = inference->task;
status = ie_infer_request_get_blob(request->infer_request, task->input_name, &input_blob);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input blob with name %s\n", task->input_name);
return DNN_ERROR;
}
status |= ie_blob_get_dims(input_blob, &dims);
status |= ie_blob_get_precision(input_blob, &precision);
if (status != OK) {
ie_blob_free(&input_blob);
av_log(ctx, AV_LOG_ERROR, "Failed to get input blob dims/precision\n");
return DNN_ERROR;
}
status = ie_blob_get_buffer(input_blob, &blob_buffer);
if (status != OK) {
ie_blob_free(&input_blob);
av_log(ctx, AV_LOG_ERROR, "Failed to get input blob buffer\n");
return DNN_ERROR;
}
input.height = dims.dims[2];
input.width = dims.dims[3];
input.channels = dims.dims[1];
input.data = blob_buffer.buffer;
input.dt = precision_to_datatype(precision);
// all models in openvino open model zoo use BGR as input,
// change to be an option when necessary.
input.order = DCO_BGR;
for (int i = 0; i < ctx->options.batch_size; ++i) {
inference = ff_queue_pop_front(ov_model->inference_queue);
if (!inference) {
break;
}
request->inferences[i] = inference;
request->inference_count = i + 1;
task = inference->task;
switch (ov_model->model->func_type) {
case DFT_PROCESS_FRAME:
if (task->do_ioproc) {
if (ov_model->model->frame_pre_proc != NULL) {
ov_model->model->frame_pre_proc(task->in_frame, &input, ov_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;
case DFT_ANALYTICS_CLASSIFY:
ff_frame_to_dnn_classify(task->in_frame, &input, inference->bbox_index, ctx);
break;
default:
av_assert0(!"should not reach here");
break;
}
input.data = (uint8_t *)input.data
+ input.width * input.height * input.channels * get_datatype_size(input.dt);
}
ie_blob_free(&input_blob);
return DNN_SUCCESS;
}
static void infer_completion_callback(void *args)
{
dimensions_t dims;
precision_e precision;
IEStatusCode status;
OVRequestItem *request = args;
InferenceItem *inference = request->inferences[0];
TaskItem *task = inference->task;
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 = &ov_model->ctx;
status = ie_infer_request_get_blob(request->infer_request, task->output_names[0], &output_blob);
if (status != OK) {
//incorrect output name
char *model_output_name = NULL;
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(ov_model->network, &model_output_count);
for (size_t i = 0; i < model_output_count; i++) {
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,
"output \"%s\" may not correct, all output(s) are: \"%s\"\n",
task->output_names[0], all_output_names);
return;
}
status = ie_blob_get_buffer(output_blob, &blob_buffer);
if (status != OK) {
ie_blob_free(&output_blob);
av_log(ctx, AV_LOG_ERROR, "Failed to access output memory\n");
return;
}
status |= ie_blob_get_dims(output_blob, &dims);
status |= ie_blob_get_precision(output_blob, &precision);
if (status != OK) {
ie_blob_free(&output_blob);
av_log(ctx, AV_LOG_ERROR, "Failed to get dims or precision of output\n");
return;
}
output.channels = dims.dims[1];
output.height = dims.dims[2];
output.width = dims.dims[3];
output.dt = precision_to_datatype(precision);
output.data = blob_buffer.buffer;
av_assert0(request->inference_count <= dims.dims[0]);
av_assert0(request->inference_count >= 1);
for (int i = 0; i < request->inference_count; ++i) {
task = request->inferences[i]->task;
task->inference_done++;
switch (ov_model->model->func_type) {
case DFT_PROCESS_FRAME:
if (task->do_ioproc) {
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);
}
} else {
task->out_frame->width = output.width;
task->out_frame->height = output.height;
}
break;
case DFT_ANALYTICS_DETECT:
if (!ov_model->model->detect_post_proc) {
av_log(ctx, AV_LOG_ERROR, "detect filter needs to provide post proc\n");
return;
}
ov_model->model->detect_post_proc(task->out_frame, &output, 1, ov_model->model->filter_ctx);
break;
case DFT_ANALYTICS_CLASSIFY:
if (!ov_model->model->classify_post_proc) {
av_log(ctx, AV_LOG_ERROR, "classify filter needs to provide post proc\n");
return;
}
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");
break;
}
av_freep(&request->inferences[i]);
output.data = (uint8_t *)output.data
+ output.width * output.height * output.channels * get_datatype_size(output.dt);
}
ie_blob_free(&output_blob);
request->inference_count = 0;
if (ff_safe_queue_push_back(requestq, request) < 0) {
ie_infer_request_free(&request->infer_request);
av_freep(&request);
av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n");
return;
}
}
static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, const char *output_name)
{
OVContext *ctx = &ov_model->ctx;
IEStatusCode status;
ie_available_devices_t a_dev;
ie_config_t config = {NULL, NULL, NULL};
char *all_dev_names = NULL;
// batch size
if (ctx->options.batch_size <= 0) {
ctx->options.batch_size = 1;
}
if (ctx->options.batch_size > 1) {
input_shapes_t input_shapes;
status = ie_network_get_input_shapes(ov_model->network, &input_shapes);
if (status != OK)
goto err;
for (int i = 0; i < input_shapes.shape_num; i++)
input_shapes.shapes[i].shape.dims[0] = ctx->options.batch_size;
status = ie_network_reshape(ov_model->network, input_shapes);
ie_network_input_shapes_free(&input_shapes);
if (status != OK)
goto err;
}
// The order of dims in the openvino is fixed and it is always NCHW for 4-D data.
// while we pass NHWC data from FFmpeg to openvino
status = ie_network_set_input_layout(ov_model->network, input_name, NHWC);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set layout as NHWC for input %s\n", input_name);
goto err;
}
status = ie_network_set_output_layout(ov_model->network, output_name, NHWC);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set layout as NHWC for output %s\n", output_name);
goto err;
}
// all models in openvino open model zoo use BGR with range [0.0f, 255.0f] as input,
// we don't have a AVPixelFormat to describe it, so we'll use AV_PIX_FMT_BGR24 and
// ask openvino to do the conversion internally.
// the current supported SR model (frame processing) is generated from tensorflow model,
// and its input is Y channel as float with range [0.0f, 1.0f], so do not set for this case.
// TODO: we need to get a final clear&general solution with all backends/formats considered.
if (ov_model->model->func_type != DFT_PROCESS_FRAME) {
status = ie_network_set_input_precision(ov_model->network, input_name, U8);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set input precision as U8 for %s\n", input_name);
goto err;
}
}
status = ie_core_load_network(ov_model->core, ov_model->network, ctx->options.device_type, &config, &ov_model->exe_network);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to load OpenVINO model network\n");
status = ie_core_get_available_devices(ov_model->core, &a_dev);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get available devices\n");
goto err;
}
for (int i = 0; i < a_dev.num_devices; i++) {
APPEND_STRING(all_dev_names, a_dev.devices[i])
}
av_log(ctx, AV_LOG_ERROR,"device %s may not be supported, all available devices are: \"%s\"\n",
ctx->options.device_type, all_dev_names);
goto err;
}
// create infer_requests for async execution
if (ctx->options.nireq <= 0) {
// the default value is a rough estimation
ctx->options.nireq = av_cpu_count() / 2 + 1;
}
ov_model->request_queue = ff_safe_queue_create();
if (!ov_model->request_queue) {
goto err;
}
for (int i = 0; i < ctx->options.nireq; i++) {
OVRequestItem *item = av_mallocz(sizeof(*item));
if (!item) {
goto err;
}
item->callback.completeCallBackFunc = infer_completion_callback;
item->callback.args = item;
if (ff_safe_queue_push_back(ov_model->request_queue, item) < 0) {
av_freep(&item);
goto err;
}
status = ie_exec_network_create_infer_request(ov_model->exe_network, &item->infer_request);
if (status != OK) {
goto err;
}
item->inferences = av_malloc_array(ctx->options.batch_size, sizeof(*item->inferences));
if (!item->inferences) {
goto err;
}
item->inference_count = 0;
}
ov_model->task_queue = ff_queue_create();
if (!ov_model->task_queue) {
goto err;
}
ov_model->inference_queue = ff_queue_create();
if (!ov_model->inference_queue) {
goto err;
}
return DNN_SUCCESS;
err:
ff_dnn_free_model_ov(&ov_model->model);
return DNN_ERROR;
}
static DNNReturnType execute_model_ov(OVRequestItem *request, Queue *inferenceq)
{
IEStatusCode status;
DNNReturnType ret;
InferenceItem *inference;
TaskItem *task;
OVContext *ctx;
OVModel *ov_model;
if (ff_queue_size(inferenceq) == 0) {
ie_infer_request_free(&request->infer_request);
av_freep(&request);
return DNN_SUCCESS;
}
inference = ff_queue_peek_front(inferenceq);
task = inference->task;
ov_model = task->model;
ctx = &ov_model->ctx;
if (task->async) {
ret = fill_model_input_ov(ov_model, request);
if (ret != DNN_SUCCESS) {
goto err;
}
status = ie_infer_set_completion_callback(request->infer_request, &request->callback);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set completion callback for inference\n");
goto err;
}
status = ie_infer_request_infer_async(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start async inference\n");
goto err;
}
return DNN_SUCCESS;
} else {
ret = fill_model_input_ov(ov_model, request);
if (ret != DNN_SUCCESS) {
goto err;
}
status = ie_infer_request_infer(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start synchronous model inference\n");
goto err;
}
infer_completion_callback(request);
return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_ERROR;
}
err:
if (ff_safe_queue_push_back(ov_model->request_queue, request) < 0) {
ie_infer_request_free(&request->infer_request);
av_freep(&request);
}
return DNN_ERROR;
}
static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input_name)
{
OVModel *ov_model = model;
OVContext *ctx = &ov_model->ctx;
char *model_input_name = NULL;
char *all_input_names = NULL;
IEStatusCode status;
size_t model_input_count = 0;
dimensions_t dims;
precision_e precision;
int input_resizable = ctx->options.input_resizable;
status = ie_network_get_inputs_number(ov_model->network, &model_input_count);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input count\n");
return DNN_ERROR;
}
for (size_t i = 0; i < model_input_count; i++) {
status = ie_network_get_input_name(ov_model->network, i, &model_input_name);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get No.%d input's name\n", (int)i);
return DNN_ERROR;
}
if (strcmp(model_input_name, input_name) == 0) {
ie_network_name_free(&model_input_name);
status |= ie_network_get_input_dims(ov_model->network, input_name, &dims);
status |= ie_network_get_input_precision(ov_model->network, input_name, &precision);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get No.%d input's dims or precision\n", (int)i);
return DNN_ERROR;
}
input->channels = dims.dims[1];
input->height = input_resizable ? -1 : dims.dims[2];
input->width = input_resizable ? -1 : dims.dims[3];
input->dt = precision_to_datatype(precision);
return DNN_SUCCESS;
} else {
//incorrect input name
APPEND_STRING(all_input_names, model_input_name)
}
ie_network_name_free(&model_input_name);
}
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model, all input(s) are: \"%s\"\n", input_name, all_input_names);
return DNN_ERROR;
}
static int contain_valid_detection_bbox(AVFrame *frame)
{
AVFrameSideData *sd;
const AVDetectionBBoxHeader *header;
const AVDetectionBBox *bbox;
sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
if (!sd) { // this frame has nothing detected
return 0;
}
if (!sd->size) {
return 0;
}
header = (const AVDetectionBBoxHeader *)sd->data;
if (!header->nb_bboxes) {
return 0;
}
for (uint32_t i = 0; i < header->nb_bboxes; i++) {
bbox = av_get_detection_bbox(header, i);
if (bbox->x < 0 || bbox->w < 0 || bbox->x + bbox->w >= frame->width) {
return 0;
}
if (bbox->y < 0 || bbox->h < 0 || bbox->y + bbox->h >= frame->width) {
return 0;
}
if (bbox->classify_count == AV_NUM_DETECTION_BBOX_CLASSIFY) {
return 0;
}
}
return 1;
}
static DNNReturnType extract_inference_from_task(DNNFunctionType func_type, TaskItem *task, Queue *inference_queue, DNNExecBaseParams *exec_params)
{
switch (func_type) {
case DFT_PROCESS_FRAME:
case DFT_ANALYTICS_DETECT:
{
InferenceItem *inference = av_malloc(sizeof(*inference));
if (!inference) {
return DNN_ERROR;
}
task->inference_todo = 1;
task->inference_done = 0;
inference->task = task;
if (ff_queue_push_back(inference_queue, inference) < 0) {
av_freep(&inference);
return DNN_ERROR;
}
return DNN_SUCCESS;
}
case DFT_ANALYTICS_CLASSIFY:
{
const AVDetectionBBoxHeader *header;
AVFrame *frame = task->in_frame;
AVFrameSideData *sd;
DNNExecClassificationParams *params = (DNNExecClassificationParams *)exec_params;
task->inference_todo = 0;
task->inference_done = 0;
if (!contain_valid_detection_bbox(frame)) {
return DNN_SUCCESS;
}
sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
header = (const AVDetectionBBoxHeader *)sd->data;
for (uint32_t i = 0; i < header->nb_bboxes; i++) {
InferenceItem *inference;
const AVDetectionBBox *bbox = av_get_detection_bbox(header, i);
if (params->target) {
if (av_strncasecmp(bbox->detect_label, params->target, sizeof(bbox->detect_label)) != 0) {
continue;
}
}
inference = av_malloc(sizeof(*inference));
if (!inference) {
return DNN_ERROR;
}
task->inference_todo++;
inference->task = task;
inference->bbox_index = i;
if (ff_queue_push_back(inference_queue, inference) < 0) {
av_freep(&inference);
return DNN_ERROR;
}
}
return DNN_SUCCESS;
}
default:
av_assert0(!"should not reach here");
return DNN_ERROR;
}
}
static DNNReturnType get_output_ov(void *model, const char *input_name, int input_width, int input_height,
const char *output_name, int *output_width, int *output_height)
{
DNNReturnType ret;
OVModel *ov_model = model;
OVContext *ctx = &ov_model->ctx;
TaskItem task;
OVRequestItem *request;
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
IEStatusCode status;
input_shapes_t input_shapes;
if (ov_model->model->func_type != DFT_PROCESS_FRAME) {
av_log(ctx, AV_LOG_ERROR, "Get output dim only when processing frame.\n");
return DNN_ERROR;
}
if (ctx->options.input_resizable) {
status = ie_network_get_input_shapes(ov_model->network, &input_shapes);
input_shapes.shapes->shape.dims[2] = input_height;
input_shapes.shapes->shape.dims[3] = input_width;
status |= ie_network_reshape(ov_model->network, input_shapes);
ie_network_input_shapes_free(&input_shapes);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to reshape input size for %s\n", input_name);
return DNN_ERROR;
}
}
if (!ov_model->exe_network) {
if (init_model_ov(ov_model, input_name, output_name) != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return DNN_ERROR;
}
}
in_frame = av_frame_alloc();
if (!in_frame) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input frame\n");
return DNN_ERROR;
}
in_frame->width = input_width;
in_frame->height = input_height;
out_frame = av_frame_alloc();
if (!out_frame) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output frame\n");
av_frame_free(&in_frame);
return DNN_ERROR;
}
task.do_ioproc = 0;
task.async = 0;
task.input_name = input_name;
task.in_frame = in_frame;
task.output_names = &output_name;
task.out_frame = out_frame;
task.nb_output = 1;
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);
av_frame_free(&in_frame);
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
return DNN_ERROR;
}
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_frame_free(&out_frame);
av_frame_free(&in_frame);
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
return DNN_ERROR;
}
ret = execute_model_ov(request, ov_model->inference_queue);
*output_width = out_frame->width;
*output_height = out_frame->height;
av_frame_free(&out_frame);
av_frame_free(&in_frame);
return ret;
}
DNNModel *ff_dnn_load_model_ov(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx)
{
DNNModel *model = NULL;
OVModel *ov_model = NULL;
OVContext *ctx = NULL;
IEStatusCode status;
model = av_mallocz(sizeof(DNNModel));
if (!model){
return NULL;
}
ov_model = av_mallocz(sizeof(OVModel));
if (!ov_model) {
av_freep(&model);
return NULL;
}
model->model = ov_model;
ov_model->model = model;
ov_model->ctx.class = &dnn_openvino_class;
ctx = &ov_model->ctx;
//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;
}
status = ie_core_create("", &ov_model->core);
if (status != OK)
goto err;
status = ie_core_read_network(ov_model->core, model_filename, NULL, &ov_model->network);
if (status != OK) {
ie_version_t ver;
ver = ie_c_api_version();
av_log(ctx, AV_LOG_ERROR, "Failed to read the network from model file %s,\n"
"Please check if the model version matches the runtime OpenVINO %s\n",
model_filename, ver.api_version);
ie_version_free(&ver);
goto err;
}
model->get_input = &get_input_ov;
model->get_output = &get_output_ov;
model->options = options;
model->filter_ctx = filter_ctx;
model->func_type = func_type;
return model;
err:
ff_dnn_free_model_ov(&model);
return NULL;
}
DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params)
{
OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
TaskItem task;
OVRequestItem *request;
if (ff_check_exec_params(ctx, DNN_OV, model->func_type, exec_params) != 0) {
return DNN_ERROR;
}
if (model->func_type == DFT_ANALYTICS_CLASSIFY) {
// Once we add async support for tensorflow backend and native backend,
// we'll combine the two sync/async functions in dnn_interface.h to
// simplify the code in filter, and async will be an option within backends.
// so, do not support now, and classify filter will not call this function.
return DNN_ERROR;
}
if (ctx->options.batch_size > 1) {
avpriv_report_missing_feature(ctx, "batch mode for sync execution");
return DNN_ERROR;
}
if (!ov_model->exe_network) {
if (init_model_ov(ov_model, exec_params->input_name, exec_params->output_names[0]) != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return DNN_ERROR;
}
}
if (ff_dnn_fill_task(&task, exec_params, ov_model, 0, 1) != DNN_SUCCESS) {
return DNN_ERROR;
}
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");
return DNN_ERROR;
}
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
return DNN_ERROR;
}
return execute_model_ov(request, ov_model->inference_queue);
}
DNNReturnType ff_dnn_execute_model_async_ov(const DNNModel *model, DNNExecBaseParams *exec_params)
{
OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
OVRequestItem *request;
TaskItem *task;
DNNReturnType ret;
if (ff_check_exec_params(ctx, DNN_OV, model->func_type, exec_params) != 0) {
return DNN_ERROR;
}
if (!ov_model->exe_network) {
if (init_model_ov(ov_model, exec_params->input_name, exec_params->output_names[0]) != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
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, ov_model, 1, 1) != DNN_SUCCESS) {
return DNN_ERROR;
}
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");
return DNN_ERROR;
}
if (extract_inference_from_task(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");
return DNN_ERROR;
}
while (ff_queue_size(ov_model->inference_queue) >= ctx->options.batch_size) {
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
return DNN_ERROR;
}
ret = execute_model_ov(request, ov_model->inference_queue);
if (ret != DNN_SUCCESS) {
return ret;
}
}
return DNN_SUCCESS;
}
DNNAsyncStatusType ff_dnn_get_async_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out)
{
OVModel *ov_model = model->model;
TaskItem *task = ff_queue_peek_front(ov_model->task_queue);
if (!task) {
return DAST_EMPTY_QUEUE;
}
if (task->inference_done != task->inference_todo) {
return DAST_NOT_READY;
}
*in = task->in_frame;
*out = task->out_frame;
ff_queue_pop_front(ov_model->task_queue);
av_freep(&task);
return DAST_SUCCESS;
}
DNNReturnType ff_dnn_flush_ov(const DNNModel *model)
{
OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
OVRequestItem *request;
IEStatusCode status;
DNNReturnType ret;
if (ff_queue_size(ov_model->inference_queue) == 0) {
// no pending task need to flush
return DNN_SUCCESS;
}
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
return DNN_ERROR;
}
ret = fill_model_input_ov(ov_model, request);
if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "Failed to fill model input.\n");
return ret;
}
status = ie_infer_set_completion_callback(request->infer_request, &request->callback);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set completion callback for inference\n");
return DNN_ERROR;
}
status = ie_infer_request_infer_async(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start async inference\n");
return DNN_ERROR;
}
return DNN_SUCCESS;
}
void ff_dnn_free_model_ov(DNNModel **model)
{
if (*model){
OVModel *ov_model = (*model)->model;
while (ff_safe_queue_size(ov_model->request_queue) != 0) {
OVRequestItem *item = ff_safe_queue_pop_front(ov_model->request_queue);
if (item && item->infer_request) {
ie_infer_request_free(&item->infer_request);
}
av_freep(&item->inferences);
av_freep(&item);
}
ff_safe_queue_destroy(ov_model->request_queue);
while (ff_queue_size(ov_model->inference_queue) != 0) {
InferenceItem *item = ff_queue_pop_front(ov_model->inference_queue);
av_freep(&item);
}
ff_queue_destroy(ov_model->inference_queue);
while (ff_queue_size(ov_model->task_queue) != 0) {
TaskItem *item = ff_queue_pop_front(ov_model->task_queue);
av_frame_free(&item->in_frame);
av_frame_free(&item->out_frame);
av_freep(&item);
}
ff_queue_destroy(ov_model->task_queue);
if (ov_model->exe_network)
ie_exec_network_free(&ov_model->exe_network);
if (ov_model->network)
ie_network_free(&ov_model->network);
if (ov_model->core)
ie_core_free(&ov_model->core);
av_freep(&ov_model);
av_freep(model);
}
}
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