mirror of https://git.ffmpeg.org/ffmpeg.git
976 lines
32 KiB
C
976 lines
32 KiB
C
/*
|
|
* 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/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 RequestItem
|
|
Queue *task_queue; // holds TaskItem
|
|
Queue *inference_queue; // holds InferenceItem
|
|
} OVModel;
|
|
|
|
// one request for one call to openvino
|
|
typedef struct RequestItem {
|
|
ie_infer_request_t *infer_request;
|
|
InferenceItem **inferences;
|
|
uint32_t inference_count;
|
|
ie_complete_call_back_t callback;
|
|
} RequestItem;
|
|
|
|
#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 },
|
|
{ "nireq", "number of request", OFFSET(options.nireq), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INT_MAX, FLAGS },
|
|
{ "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, RequestItem *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;
|
|
RequestItem *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) {
|
|
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++) {
|
|
RequestItem *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(RequestItem *request, Queue *inferenceq)
|
|
{
|
|
IEStatusCode status;
|
|
DNNReturnType ret;
|
|
InferenceItem *inference;
|
|
TaskItem *task;
|
|
OVContext *ctx;
|
|
OVModel *ov_model;
|
|
|
|
if (ff_queue_size(inferenceq) == 0) {
|
|
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) {
|
|
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;
|
|
} else {
|
|
ret = fill_model_input_ov(ov_model, request);
|
|
if (ret != DNN_SUCCESS) {
|
|
return ret;
|
|
}
|
|
status = ie_infer_request_infer(request->infer_request);
|
|
if (status != OK) {
|
|
av_log(ctx, AV_LOG_ERROR, "Failed to start synchronous model inference\n");
|
|
return DNN_ERROR;
|
|
}
|
|
infer_completion_callback(request);
|
|
return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : 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 (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;
|
|
RequestItem *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;
|
|
RequestItem *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;
|
|
}
|
|
}
|
|
|
|
task.do_ioproc = 1;
|
|
task.async = 0;
|
|
task.input_name = exec_params->input_name;
|
|
task.in_frame = exec_params->in_frame;
|
|
task.output_names = &exec_params->output_names[0];
|
|
task.out_frame = exec_params->out_frame ? exec_params->out_frame : exec_params->in_frame;
|
|
task.nb_output = exec_params->nb_output;
|
|
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");
|
|
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;
|
|
RequestItem *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;
|
|
}
|
|
|
|
task->do_ioproc = 1;
|
|
task->async = 1;
|
|
task->input_name = exec_params->input_name;
|
|
task->in_frame = exec_params->in_frame;
|
|
task->output_names = &exec_params->output_names[0];
|
|
task->out_frame = exec_params->out_frame ? exec_params->out_frame : exec_params->in_frame;
|
|
task->nb_output = exec_params->nb_output;
|
|
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");
|
|
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;
|
|
RequestItem *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) {
|
|
RequestItem *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);
|
|
}
|
|
}
|