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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_io_proc.h"
#include "libavformat/avio.h"
#include "libavutil/avassert.h"
#include "libavutil/cpu.h"
#include "libavutil/mem.h"
#include "libavutil/opt.h"
#include "libavutil/avstring.h"
#include "libavutil/detection_bbox.h"
#include "../internal.h"
#include "safe_queue.h"
#if HAVE_OPENVINO2
#include <openvino/c/openvino.h>
#else
#include <c_api/ie_c_api.h>
#endif
#include "dnn_backend_common.h"
typedef struct OVOptions{
char *device_type;
int nireq;
uint8_t async;
int batch_size;
int input_resizable;
DNNLayout layout;
float scale;
float mean;
} OVOptions;
typedef struct OVContext {
const AVClass *class;
OVOptions options;
} OVContext;
typedef struct OVModel{
OVContext ctx;
DNNModel *model;
#if HAVE_OPENVINO2
ov_core_t *core;
ov_model_t *ov_model;
ov_compiled_model_t *compiled_model;
ov_output_const_port_t* input_port;
ov_preprocess_input_info_t* input_info;
ov_output_const_port_t** output_ports;
ov_preprocess_output_info_t* output_info;
ov_preprocess_prepostprocessor_t* preprocess;
#else
ie_core_t *core;
ie_network_t *network;
ie_executable_network_t *exe_network;
const char *all_input_names;
const char *all_output_names;
#endif
SafeQueue *request_queue; // holds OVRequestItem
Queue *task_queue; // holds TaskItem
Queue *lltask_queue; // holds LastLevelTaskItem
int nb_outputs;
} OVModel;
// one request for one call to openvino
typedef struct OVRequestItem {
LastLevelTaskItem **lltasks;
uint32_t lltask_count;
#if HAVE_OPENVINO2
ov_infer_request_t *infer_request;
ov_callback_t callback;
#else
ie_complete_call_back_t callback;
ie_infer_request_t *infer_request;
#endif
} 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 },
{ "layout", "input layout of model", OFFSET(options.layout), AV_OPT_TYPE_INT, { .i64 = DL_NONE}, DL_NONE, DL_NHWC, FLAGS, .unit = "layout" },
{ "none", "none", 0, AV_OPT_TYPE_CONST, { .i64 = DL_NONE }, 0, 0, FLAGS, .unit = "layout"},
{ "nchw", "nchw", 0, AV_OPT_TYPE_CONST, { .i64 = DL_NCHW }, 0, 0, FLAGS, .unit = "layout"},
{ "nhwc", "nhwc", 0, AV_OPT_TYPE_CONST, { .i64 = DL_NHWC }, 0, 0, FLAGS, .unit = "layout"},
{ "scale", "Add scale preprocess operation. Divide each element of input by specified value.", OFFSET(options.scale), AV_OPT_TYPE_FLOAT, { .dbl = 0 }, INT_MIN, INT_MAX, FLAGS},
{ "mean", "Add mean preprocess operation. Subtract specified value from each element of input.", OFFSET(options.mean), AV_OPT_TYPE_FLOAT, { .dbl = 0 }, INT_MIN, INT_MAX, FLAGS},
{ NULL }
};
AVFILTER_DEFINE_CLASS(dnn_openvino);
#if HAVE_OPENVINO2
static const struct {
ov_status_e status;
int av_err;
const char *desc;
} ov2_errors[] = {
{ OK, 0, "success" },
{ GENERAL_ERROR, AVERROR_EXTERNAL, "general error" },
{ NOT_IMPLEMENTED, AVERROR(ENOSYS), "not implemented" },
{ NETWORK_NOT_LOADED, AVERROR_EXTERNAL, "network not loaded" },
{ PARAMETER_MISMATCH, AVERROR(EINVAL), "parameter mismatch" },
{ NOT_FOUND, AVERROR_EXTERNAL, "not found" },
{ OUT_OF_BOUNDS, AVERROR(EOVERFLOW), "out of bounds" },
{ UNEXPECTED, AVERROR_EXTERNAL, "unexpected" },
{ REQUEST_BUSY, AVERROR(EBUSY), "request busy" },
{ RESULT_NOT_READY, AVERROR(EBUSY), "result not ready" },
{ NOT_ALLOCATED, AVERROR(ENODATA), "not allocated" },
{ INFER_NOT_STARTED, AVERROR_EXTERNAL, "infer not started" },
{ NETWORK_NOT_READ, AVERROR_EXTERNAL, "network not read" },
{ INFER_CANCELLED, AVERROR(ECANCELED), "infer cancelled" },
{ INVALID_C_PARAM, AVERROR(EINVAL), "invalid C parameter" },
{ UNKNOWN_C_ERROR, AVERROR_UNKNOWN, "unknown C error" },
{ NOT_IMPLEMENT_C_METHOD, AVERROR(ENOSYS), "not implement C method" },
{ UNKNOW_EXCEPTION, AVERROR_UNKNOWN, "unknown exception" },
};
static int ov2_map_error(ov_status_e status, const char **desc)
{
int i;
for (i = 0; i < FF_ARRAY_ELEMS(ov2_errors); i++) {
if (ov2_errors[i].status == status) {
if (desc)
*desc = ov2_errors[i].desc;
return ov2_errors[i].av_err;
}
}
if (desc)
*desc = "unknown error";
return AVERROR_UNKNOWN;
}
#endif
#if HAVE_OPENVINO2
static DNNDataType precision_to_datatype(ov_element_type_e precision)
#else
static DNNDataType precision_to_datatype(precision_e precision)
#endif
{
switch (precision)
{
#if HAVE_OPENVINO2
case F32:
#else
case FP32:
#endif
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 int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
{
DNNData input;
LastLevelTaskItem *lltask;
TaskItem *task;
OVContext *ctx = &ov_model->ctx;
#if HAVE_OPENVINO2
int64_t* dims;
ov_status_e status;
ov_tensor_t* tensor = NULL;
ov_shape_t input_shape = {0};
ov_element_type_e precision;
char *port_name;
#else
dimensions_t dims;
precision_e precision;
ie_blob_buffer_t blob_buffer;
IEStatusCode status;
ie_blob_t *input_blob = NULL;
#endif
memset(&input, 0, sizeof(input));
lltask = ff_queue_peek_front(ov_model->lltask_queue);
av_assert0(lltask);
task = lltask->task;
#if HAVE_OPENVINO2
if (ov_model->input_port) {
ov_output_const_port_free(ov_model->input_port);
ov_model->input_port = NULL;
}
if (task->input_name)
status = ov_model_const_input_by_name(ov_model->ov_model, task->input_name, &ov_model->input_port);
else
status = ov_model_const_input(ov_model->ov_model, &ov_model->input_port);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input port shape.\n");
return ov2_map_error(status, NULL);
}
status = ov_port_get_any_name(ov_model->input_port, &port_name);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input port name.\n");
return ov2_map_error(status, NULL);
}
av_log(ctx, AV_LOG_VERBOSE, "OpenVINO model input: %s\n", port_name);
ov_free(port_name);
port_name = NULL;
status = ov_const_port_get_shape(ov_model->input_port, &input_shape);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input port shape.\n");
return ov2_map_error(status, NULL);
}
dims = input_shape.dims;
status = ov_port_get_element_type(ov_model->input_port, &precision);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input port data type.\n");
ov_shape_free(&input_shape);
return ov2_map_error(status, NULL);
}
for (int i = 0; i < input_shape.rank; i++)
input.dims[i] = dims[i];
input.layout = DL_NHWC;
input.dt = precision_to_datatype(precision);
#else
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_GENERIC_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_GENERIC_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_GENERIC_ERROR;
}
for (int i = 0; i < input_shape.rank; i++)
input.dims[i] = dims[i];
input.layout = DL_NCHW;
input.data = blob_buffer.buffer;
input.dt = precision_to_datatype(precision);
#endif
// all models in openvino open model zoo use BGR as input,
// change to be an option when necessary.
input.order = DCO_BGR;
// We use preprocess_steps to scale input data, so disable scale and mean here.
input.scale = 1;
input.mean = 0;
for (int i = 0; i < ctx->options.batch_size; ++i) {
lltask = ff_queue_pop_front(ov_model->lltask_queue);
if (!lltask) {
break;
}
request->lltasks[i] = lltask;
request->lltask_count = i + 1;
task = lltask->task;
#if HAVE_OPENVINO2
if (tensor)
ov_tensor_free(tensor);
status = ov_tensor_create(precision, input_shape, &tensor);
ov_shape_free(&input_shape);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to create tensor from host prt.\n");
return ov2_map_error(status, NULL);
}
status = ov_tensor_data(tensor, &input.data);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input data.\n");
return ov2_map_error(status, NULL);
}
status = ov_infer_request_set_input_tensor(request->infer_request, tensor);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to Set an input tensor for the model.\n");
return ov2_map_error(status, NULL);
}
#endif
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, lltask->bbox_index, ctx);
break;
default:
av_assert0(!"should not reach here");
break;
}
input.data = (uint8_t *)input.data +
input.dims[1] * input.dims[2] * input.dims[3] * get_datatype_size(input.dt);
}
#if HAVE_OPENVINO2
ov_tensor_free(tensor);
#else
ie_blob_free(&input_blob);
#endif
return 0;
}
static void infer_completion_callback(void *args)
{
OVRequestItem *request = args;
LastLevelTaskItem *lltask = request->lltasks[0];
TaskItem *task = lltask->task;
OVModel *ov_model = task->model;
SafeQueue *requestq = ov_model->request_queue;
DNNData *outputs;
OVContext *ctx = &ov_model->ctx;
#if HAVE_OPENVINO2
size_t* dims;
ov_status_e status;
ov_tensor_t *output_tensor;
ov_shape_t output_shape = {0};
ov_element_type_e precision;
outputs = av_calloc(ov_model->nb_outputs, sizeof(*outputs));
if (!outputs) {
av_log(ctx, AV_LOG_ERROR, "Failed to alloc outputs.");
return;
}
for (int i = 0; i < ov_model->nb_outputs; i++) {
status = ov_infer_request_get_tensor_by_const_port(request->infer_request,
ov_model->output_ports[i],
&output_tensor);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR,
"Failed to get output tensor.");
goto end;
}
status = ov_tensor_data(output_tensor, &outputs[i].data);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR,
"Failed to get output data.");
goto end;
}
status = ov_tensor_get_shape(output_tensor, &output_shape);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get output port shape.\n");
goto end;
}
dims = output_shape.dims;
status = ov_port_get_element_type(ov_model->output_ports[i], &precision);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get output port data type.\n");
goto end;
}
outputs[i].dt = precision_to_datatype(precision);
outputs[i].layout = DL_NCHW;
outputs[i].dims[0] = 1;
outputs[i].dims[1] = output_shape.rank > 2 ? dims[output_shape.rank - 3] : 1;
outputs[i].dims[2] = output_shape.rank > 1 ? dims[output_shape.rank - 2] : 1;
outputs[i].dims[3] = output_shape.rank > 0 ? dims[output_shape.rank - 1] : 1;
av_assert0(request->lltask_count <= dims[0]);
outputs[i].layout = ctx->options.layout;
outputs[i].scale = ctx->options.scale;
outputs[i].mean = ctx->options.mean;
ov_shape_free(&output_shape);
ov_tensor_free(output_tensor);
output_tensor = NULL;
}
#else
IEStatusCode status;
dimensions_t dims;
ie_blob_t *output_blob = NULL;
ie_blob_buffer_t blob_buffer;
precision_e precision;
DNNData output;
status = ie_infer_request_get_blob(request->infer_request, task->output_names[0], &output_blob);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR,
"output \"%s\" may not correct, all output(s) are: \"%s\"\n",
task->output_names[0], ov_model->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.data = blob_buffer.buffer;
output.layout = DL_NCHW;
for (int i = 0; i < 4; i++)
output.dims[i] = dims.dims[i];
av_assert0(request->lltask_count <= dims.dims[0]);
output.dt = precision_to_datatype(precision);
output.layout = ctx->options.layout;
output.scale = ctx->options.scale;
output.mean = ctx->options.mean;
outputs = &output;
#endif
av_assert0(request->lltask_count >= 1);
for (int i = 0; i < request->lltask_count; ++i) {
task = request->lltasks[i]->task;
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, outputs, ov_model->model->filter_ctx);
} else {
ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
}
} else {
task->out_frame->width =
outputs[0].dims[dnn_get_width_idx_by_layout(outputs[0].layout)];
task->out_frame->height =
outputs[0].dims[dnn_get_height_idx_by_layout(outputs[0].layout)];
}
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");
goto end;
}
ov_model->model->detect_post_proc(task->in_frame, outputs,
ov_model->nb_outputs,
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");
goto end;
}
for (int output_i = 0; output_i < ov_model->nb_outputs; output_i++)
ov_model->model->classify_post_proc(task->in_frame, outputs,
request->lltasks[i]->bbox_index,
ov_model->model->filter_ctx);
break;
default:
av_assert0(!"should not reach here");
break;
}
task->inference_done++;
av_freep(&request->lltasks[i]);
for (int i = 0; i < ov_model->nb_outputs; i++)
outputs[i].data = (uint8_t *)outputs[i].data +
outputs[i].dims[1] * outputs[i].dims[2] * outputs[i].dims[3] *
get_datatype_size(outputs[i].dt);
}
end:
#if HAVE_OPENVINO2
av_freep(&outputs);
ov_shape_free(&output_shape);
if (output_tensor)
ov_tensor_free(output_tensor);
#else
ie_blob_free(&output_blob);
#endif
request->lltask_count = 0;
if (ff_safe_queue_push_back(requestq, request) < 0) {
#if HAVE_OPENVINO2
ov_infer_request_free(request->infer_request);
#else
ie_infer_request_free(&request->infer_request);
#endif
av_freep(&request);
av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n");
return;
}
}
static void dnn_free_model_ov(DNNModel **model)
{
OVModel *ov_model;
if (!model || !*model)
return;
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) {
#if HAVE_OPENVINO2
ov_infer_request_free(item->infer_request);
#else
ie_infer_request_free(&item->infer_request);
#endif
}
av_freep(&item->lltasks);
av_freep(&item);
}
ff_safe_queue_destroy(ov_model->request_queue);
while (ff_queue_size(ov_model->lltask_queue) != 0) {
LastLevelTaskItem *item = ff_queue_pop_front(ov_model->lltask_queue);
av_freep(&item);
}
ff_queue_destroy(ov_model->lltask_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 HAVE_OPENVINO2
if (ov_model->input_port)
ov_output_const_port_free(ov_model->input_port);
for (int i = 0; i < ov_model->nb_outputs; i++)
if (ov_model->output_ports[i])
ov_output_const_port_free(ov_model->output_ports[i]);
av_freep(&ov_model->output_ports);
if (ov_model->preprocess)
ov_preprocess_prepostprocessor_free(ov_model->preprocess);
if (ov_model->compiled_model)
ov_compiled_model_free(ov_model->compiled_model);
if (ov_model->ov_model)
ov_model_free(ov_model->ov_model);
if (ov_model->core)
ov_core_free(ov_model->core);
#else
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_free(ov_model->all_output_names);
av_free(ov_model->all_input_names);
#endif
av_opt_free(&ov_model->ctx);
av_freep(&ov_model);
av_freep(model);
}
static int init_model_ov(OVModel *ov_model, const char *input_name, const char **output_names, int nb_outputs)
{
int ret = 0;
OVContext *ctx = &ov_model->ctx;
#if HAVE_OPENVINO2
ov_status_e status;
ov_preprocess_input_tensor_info_t* input_tensor_info = NULL;
ov_preprocess_output_tensor_info_t* output_tensor_info = NULL;
ov_preprocess_input_model_info_t* input_model_info = NULL;
ov_model_t *tmp_ov_model;
ov_layout_t* NHWC_layout = NULL;
ov_layout_t* NCHW_layout = NULL;
const char* NHWC_desc = "NHWC";
const char* NCHW_desc = "NCHW";
const char* device = ctx->options.device_type;
#else
IEStatusCode status;
ie_available_devices_t a_dev;
ie_config_t config = {NULL, NULL, NULL};
char *all_dev_names = NULL;
#endif
// We scale pixel by default when do frame processing.
if (fabsf(ctx->options.scale) < 1e-6f)
ctx->options.scale = ov_model->model->func_type == DFT_PROCESS_FRAME ? 255 : 1;
// batch size
if (ctx->options.batch_size <= 0) {
ctx->options.batch_size = 1;
}
#if HAVE_OPENVINO2
if (ctx->options.batch_size > 1) {
avpriv_report_missing_feature(ctx, "Do not support batch_size > 1 for now,"
"change batch_size to 1.\n");
ctx->options.batch_size = 1;
}
status = ov_preprocess_prepostprocessor_create(ov_model->ov_model, &ov_model->preprocess);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to create preprocess for ov_model.\n");
ret = ov2_map_error(status, NULL);
goto err;
}
if (input_name)
status = ov_preprocess_prepostprocessor_get_input_info_by_name(ov_model->preprocess, input_name, &ov_model->input_info);
else
status = ov_preprocess_prepostprocessor_get_input_info(ov_model->preprocess, &ov_model->input_info);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input info from preprocess.\n");
ret = ov2_map_error(status, NULL);
goto err;
}
status = ov_preprocess_input_info_get_tensor_info(ov_model->input_info, &input_tensor_info);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get tensor info from input.\n");
ret = ov2_map_error(status, NULL);
goto err;
}
//set input layout
status = ov_layout_create(NHWC_desc, &NHWC_layout);
status |= ov_layout_create(NCHW_desc, &NCHW_layout);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to create layout for input.\n");
ret = ov2_map_error(status, NULL);
goto err;
}
status = ov_preprocess_input_tensor_info_set_layout(input_tensor_info, NHWC_layout);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set input tensor layout\n");
ret = ov2_map_error(status, NULL);
goto err;
}
status = ov_preprocess_input_info_get_model_info(ov_model->input_info, &input_model_info);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input model info\n");
ret = ov2_map_error(status, NULL);
goto err;
}
if (ctx->options.layout == DL_NCHW)
status = ov_preprocess_input_model_info_set_layout(input_model_info, NCHW_layout);
else if (ctx->options.layout == DL_NHWC)
status = ov_preprocess_input_model_info_set_layout(input_model_info, NHWC_layout);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get set input model layout\n");
ret = ov2_map_error(status, NULL);
goto err;
}
status = ov_preprocess_input_tensor_info_set_element_type(input_tensor_info, U8);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set input element type\n");
ret = ov2_map_error(status, NULL);
goto err;
}
if (!nb_outputs) {
size_t output_size;
status = ov_model_outputs_size(ov_model->ov_model, &output_size);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get output size.\n");
ret = ov2_map_error(status, NULL);
goto err;
}
nb_outputs = output_size;
}
ov_model->nb_outputs = nb_outputs;
for (int i = 0; i < nb_outputs; i++) {
if (output_names)
status = ov_preprocess_prepostprocessor_get_output_info_by_name(
ov_model->preprocess, output_names[i], &ov_model->output_info);
else
status = ov_preprocess_prepostprocessor_get_output_info_by_index(
ov_model->preprocess, i, &ov_model->output_info);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get output info from preprocess.\n");
ret = ov2_map_error(status, NULL);
goto err;
}
status |= ov_preprocess_output_info_get_tensor_info(ov_model->output_info, &output_tensor_info);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get tensor info from input/output.\n");
ret = ov2_map_error(status, NULL);
goto err;
}
if (ov_model->model->func_type != DFT_PROCESS_FRAME)
status |= ov_preprocess_output_set_element_type(output_tensor_info, F32);
else if (fabsf(ctx->options.scale - 1) > 1e-6f || fabsf(ctx->options.mean) > 1e-6f)
status |= ov_preprocess_output_set_element_type(output_tensor_info, F32);
else
status |= ov_preprocess_output_set_element_type(output_tensor_info, U8);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set output element type\n");
ret = ov2_map_error(status, NULL);
goto err;
}
ov_preprocess_output_tensor_info_free(output_tensor_info);
output_tensor_info = NULL;
ov_preprocess_output_info_free(ov_model->output_info);
ov_model->output_info = NULL;
}
// set preprocess steps.
if (fabsf(ctx->options.scale - 1) > 1e-6f || fabsf(ctx->options.mean) > 1e-6f) {
ov_preprocess_preprocess_steps_t* input_process_steps = NULL;
status = ov_preprocess_input_info_get_preprocess_steps(ov_model->input_info, &input_process_steps);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get preprocess steps\n");
ret = ov2_map_error(status, NULL);
goto err;
}
status = ov_preprocess_preprocess_steps_convert_element_type(input_process_steps, F32);
status |= ov_preprocess_preprocess_steps_mean(input_process_steps, ctx->options.mean);
status |= ov_preprocess_preprocess_steps_scale(input_process_steps, ctx->options.scale);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set preprocess steps\n");
ov_preprocess_preprocess_steps_free(input_process_steps);
input_process_steps = NULL;
ret = ov2_map_error(status, NULL);
goto err;
}
ov_preprocess_preprocess_steps_free(input_process_steps);
input_process_steps = NULL;
}
ov_preprocess_input_tensor_info_free(input_tensor_info);
input_tensor_info = NULL;
ov_preprocess_input_info_free(ov_model->input_info);
ov_model->input_info = NULL;
//update model
if(ov_model->ov_model)
tmp_ov_model = ov_model->ov_model;
status = ov_preprocess_prepostprocessor_build(ov_model->preprocess, &ov_model->ov_model);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to update OV model\n");
ov_model_free(tmp_ov_model);
tmp_ov_model = NULL;
ret = ov2_map_error(status, NULL);
goto err;
}
ov_model_free(tmp_ov_model);
//update output_port
if (!ov_model->output_ports) {
ov_model->output_ports = av_calloc(nb_outputs, sizeof(*ov_model->output_ports));
if (!ov_model->output_ports) {
ret = AVERROR(ENOMEM);
goto err;
}
} else
for (int i = 0; i < nb_outputs; i++) {
ov_output_const_port_free(ov_model->output_ports[i]);
ov_model->output_ports[i] = NULL;
}
for (int i = 0; i < nb_outputs; i++) {
char *port_name;
if (output_names)
status = ov_model_const_output_by_name(ov_model->ov_model, output_names[i],
&ov_model->output_ports[i]);
else
status = ov_model_const_output_by_index(ov_model->ov_model, i,
&ov_model->output_ports[i]);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get output port %s.\n", output_names[i]);
goto err;
}
status = ov_port_get_any_name(ov_model->output_ports[i], &port_name);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get output port name.\n");
goto err;
}
av_log(ctx, AV_LOG_VERBOSE, "OpenVINO model outputs: %s\n", port_name);
ov_free(port_name);
port_name = NULL;
}
//compile network
status = ov_core_compile_model(ov_model->core, ov_model->ov_model, device, 0, &ov_model->compiled_model);
if (status != OK) {
ret = ov2_map_error(status, NULL);
goto err;
}
ov_preprocess_input_model_info_free(input_model_info);
input_model_info = NULL;
ov_layout_free(NCHW_layout);
ov_layout_free(NHWC_layout);
#else
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) {
ret = DNN_GENERIC_ERROR;
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) {
ret = DNN_GENERIC_ERROR;
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) {
if (status == NOT_FOUND) {
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model, failed to set input layout as NHWC, "\
"all input(s) are: \"%s\"\n", input_name, ov_model->all_input_names);
} else{
av_log(ctx, AV_LOG_ERROR, "Failed to set layout as NHWC for input %s\n", input_name);
}
ret = DNN_GENERIC_ERROR;
goto err;
}
status = ie_network_set_output_layout(ov_model->network, output_name, NHWC);
if (status != OK) {
if (status == NOT_FOUND) {
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model, failed to set output layout as NHWC, "\
"all output(s) are: \"%s\"\n", output_name, ov_model->all_output_names);
} else{
av_log(ctx, AV_LOG_ERROR, "Failed to set layout as NHWC for output %s\n", output_name);
}
ret = DNN_GENERIC_ERROR;
goto err;
}
ov_model->nb_outputs = 1;
// 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);
ret = DNN_GENERIC_ERROR;
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");
ret = DNN_GENERIC_ERROR;
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);
ret = AVERROR(ENODEV);
goto err;
}
#endif
// 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) {
ret = AVERROR(ENOMEM);
goto err;
}
for (int i = 0; i < ctx->options.nireq; i++) {
OVRequestItem *item = av_mallocz(sizeof(*item));
if (!item) {
ret = AVERROR(ENOMEM);
goto err;
}
#if HAVE_OPENVINO2
item->callback.callback_func = infer_completion_callback;
#else
item->callback.completeCallBackFunc = infer_completion_callback;
#endif
item->callback.args = item;
if (ff_safe_queue_push_back(ov_model->request_queue, item) < 0) {
av_freep(&item);
ret = AVERROR(ENOMEM);
goto err;
}
#if HAVE_OPENVINO2
status = ov_compiled_model_create_infer_request(ov_model->compiled_model, &item->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to Creates an inference request object.\n");
goto err;
}
#else
status = ie_exec_network_create_infer_request(ov_model->exe_network, &item->infer_request);
if (status != OK) {
ret = DNN_GENERIC_ERROR;
goto err;
}
#endif
item->lltasks = av_malloc_array(ctx->options.batch_size, sizeof(*item->lltasks));
if (!item->lltasks) {
ret = AVERROR(ENOMEM);
goto err;
}
item->lltask_count = 0;
}
ov_model->task_queue = ff_queue_create();
if (!ov_model->task_queue) {
ret = AVERROR(ENOMEM);
goto err;
}
ov_model->lltask_queue = ff_queue_create();
if (!ov_model->lltask_queue) {
ret = AVERROR(ENOMEM);
goto err;
}
return 0;
err:
#if HAVE_OPENVINO2
if (output_tensor_info)
ov_preprocess_output_tensor_info_free(output_tensor_info);
if (ov_model->output_info)
ov_preprocess_output_info_free(ov_model->output_info);
if (NCHW_layout)
ov_layout_free(NCHW_layout);
if (NHWC_layout)
ov_layout_free(NHWC_layout);
if (input_model_info)
ov_preprocess_input_model_info_free(input_model_info);
#endif
dnn_free_model_ov(&ov_model->model);
return ret;
}
static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
{
#if HAVE_OPENVINO2
ov_status_e status;
#else
IEStatusCode status;
#endif
LastLevelTaskItem *lltask;
int ret = 0;
TaskItem *task;
OVContext *ctx;
OVModel *ov_model;
if (ff_queue_size(inferenceq) == 0) {
#if HAVE_OPENVINO2
ov_infer_request_free(request->infer_request);
#else
ie_infer_request_free(&request->infer_request);
#endif
av_freep(&request);
return 0;
}
lltask = ff_queue_peek_front(inferenceq);
task = lltask->task;
ov_model = task->model;
ctx = &ov_model->ctx;
ret = fill_model_input_ov(ov_model, request);
if (ret != 0) {
goto err;
}
#if HAVE_OPENVINO2
if (task->async) {
status = ov_infer_request_set_callback(request->infer_request, &request->callback);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set completion callback for inference\n");
ret = ov2_map_error(status, NULL);
goto err;
}
status = ov_infer_request_start_async(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start async inference\n");
ret = ov2_map_error(status, NULL);
goto err;
}
return 0;
} else {
status = ov_infer_request_infer(request->infer_request);
if (status != OK) {
av_log(NULL, AV_LOG_ERROR, "Failed to start synchronous model inference for OV2\n");
ret = ov2_map_error(status, NULL);
goto err;
}
infer_completion_callback(request);
return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_ERROR;
}
#else
if (task->async) {
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");
ret = DNN_GENERIC_ERROR;
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");
ret = DNN_GENERIC_ERROR;
goto err;
}
return 0;
} else {
status = ie_infer_request_infer(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start synchronous model inference\n");
ret = DNN_GENERIC_ERROR;
goto err;
}
infer_completion_callback(request);
return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_ERROR;
}
#endif
err:
if (ff_safe_queue_push_back(ov_model->request_queue, request) < 0) {
#if HAVE_OPENVINO2
ov_infer_request_free(request->infer_request);
#else
ie_infer_request_free(&request->infer_request);
#endif
av_freep(&request);
}
return ret;
}
static int get_input_ov(void *model, DNNData *input, const char *input_name)
{
OVModel *ov_model = model;
OVContext *ctx = &ov_model->ctx;
int input_resizable = ctx->options.input_resizable;
#if HAVE_OPENVINO2
ov_shape_t input_shape = {0};
ov_element_type_e precision;
ov_status_e status;
if (input_name)
status = ov_model_const_input_by_name(ov_model->ov_model, input_name, &ov_model->input_port);
else
status = ov_model_const_input(ov_model->ov_model, &ov_model->input_port);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input port shape.\n");
return ov2_map_error(status, NULL);
}
status = ov_port_get_element_type(ov_model->input_port, &precision);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input port data type.\n");
return ov2_map_error(status, NULL);
}
status = ov_const_port_get_shape(ov_model->input_port, &input_shape);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input port shape.\n");
return ov2_map_error(status, NULL);
}
for (int i = 0; i < 4; i++)
input->dims[i] = input_shape.dims[i];
if (input_resizable) {
input->dims[dnn_get_width_idx_by_layout(input->layout)] = -1;
input->dims[dnn_get_height_idx_by_layout(input->layout)] = -1;
}
if (input_shape.dims[1] <= 3) // NCHW
input->layout = DL_NCHW;
else // NHWC
input->layout = DL_NHWC;
input->dt = precision_to_datatype(precision);
ov_shape_free(&input_shape);
return 0;
#else
char *model_input_name = NULL;
IEStatusCode status;
size_t model_input_count = 0;
dimensions_t dims;
precision_e precision;
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_GENERIC_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_GENERIC_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_GENERIC_ERROR;
}
for (int i = 0; i < 4; i++)
input->dims[i] = input_shape.dims[i];
if (input_resizable) {
input->dims[dnn_get_width_idx_by_layout(input->layout)] = -1;
input->dims[dnn_get_height_idx_by_layout(input->layout)] = -1;
}
if (input_shape.dims[1] <= 3) // NCHW
input->layout = DL_NCHW;
else // NHWC
input->layout = DL_NHWC;
input->dt = precision_to_datatype(precision);
return 0;
}
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, ov_model->all_input_names);
return AVERROR(EINVAL);
#endif
}
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->height) {
return 0;
}
if (bbox->classify_count == AV_NUM_DETECTION_BBOX_CLASSIFY) {
return 0;
}
}
return 1;
}
static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Queue *lltask_queue, DNNExecBaseParams *exec_params)
{
switch (func_type) {
case DFT_PROCESS_FRAME:
case DFT_ANALYTICS_DETECT:
{
LastLevelTaskItem *lltask = av_malloc(sizeof(*lltask));
if (!lltask) {
return AVERROR(ENOMEM);
}
task->inference_todo = 1;
task->inference_done = 0;
lltask->task = task;
if (ff_queue_push_back(lltask_queue, lltask) < 0) {
av_freep(&lltask);
return AVERROR(ENOMEM);
}
return 0;
}
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 0;
}
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++) {
LastLevelTaskItem *lltask;
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;
}
}
lltask = av_malloc(sizeof(*lltask));
if (!lltask) {
return AVERROR(ENOMEM);
}
task->inference_todo++;
lltask->task = task;
lltask->bbox_index = i;
if (ff_queue_push_back(lltask_queue, lltask) < 0) {
av_freep(&lltask);
return AVERROR(ENOMEM);
}
}
return 0;
}
default:
av_assert0(!"should not reach here");
return AVERROR(EINVAL);
}
}
static int 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)
{
#if HAVE_OPENVINO2
ov_dimension_t dims[4] = {{1, 1}, {1, 1}, {input_height, input_height}, {input_width, input_width}};
ov_status_e status;
ov_shape_t input_shape = {0};
ov_partial_shape_t partial_shape;
#else
IEStatusCode status;
input_shapes_t input_shapes;
#endif
int ret;
OVModel *ov_model = model;
OVContext *ctx = &ov_model->ctx;
TaskItem task;
OVRequestItem *request;
DNNExecBaseParams exec_params = {
.input_name = input_name,
.output_names = output_name ? &output_name : NULL,
.nb_output = 1,
.in_frame = NULL,
.out_frame = NULL,
};
if (ov_model->model->func_type != DFT_PROCESS_FRAME) {
av_log(ctx, AV_LOG_ERROR, "Get output dim only when processing frame.\n");
return AVERROR(EINVAL);
}
#if HAVE_OPENVINO2
if (ctx->options.input_resizable) {
status = ov_partial_shape_create(4, dims, &partial_shape);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to create partial shape.\n");
return ov2_map_error(status, NULL);
}
status = ov_const_port_get_shape(ov_model->input_port, &input_shape);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to create shape for model input resize.\n");
return ov2_map_error(status, NULL);
}
input_shape.dims[2] = input_height;
input_shape.dims[3] = input_width;
status = ov_shape_to_partial_shape(input_shape, &partial_shape);
ov_shape_free(&input_shape);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to create partial shape for model input resize.\n");
return ov2_map_error(status, NULL);
}
status = ov_model_reshape_single_input(ov_model->ov_model, partial_shape);
ov_partial_shape_free(&partial_shape);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to reszie model input.\n");
return ov2_map_error(status, NULL);
}
}
if (!ov_model->compiled_model) {
#else
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_GENERIC_ERROR;
}
}
if (!ov_model->exe_network) {
#endif
ret = init_model_ov(ov_model, input_name, output_name ? &output_name : NULL, 1);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return ret;
}
}
ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, ov_model, input_height, input_width, ctx);
if (ret != 0) {
goto err;
}
ret = extract_lltask_from_task(ov_model->model->func_type, &task, ov_model->lltask_queue, NULL);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
goto err;
}
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
ret = AVERROR(EINVAL);
goto err;
}
ret = execute_model_ov(request, ov_model->lltask_queue);
*output_width = task.out_frame->width;
*output_height = task.out_frame->height;
err:
av_frame_free(&task.out_frame);
av_frame_free(&task.in_frame);
return ret;
}
static DNNModel *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;
#if HAVE_OPENVINO2
ov_core_t* core = NULL;
ov_model_t* ovmodel = NULL;
ov_status_e status;
#else
size_t node_count = 0;
char *node_name = NULL;
IEStatusCode status;
#endif
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;
}
#if HAVE_OPENVINO2
status = ov_core_create(&core);
if (status != OK) {
goto err;
}
ov_model->core = core;
status = ov_core_read_model(core, model_filename, NULL, &ovmodel);
if (status != OK) {
ov_version_t ver;
status = ov_get_openvino_version(&ver);
av_log(NULL, AV_LOG_ERROR, "Failed to read the network from model file %s,\n"
"Please check if the model version matches the runtime OpenVINO Version:\n",
model_filename);
if (status == OK) {
av_log(NULL, AV_LOG_ERROR, "BuildNumber: %s\n", ver.buildNumber);
}
ov_version_free(&ver);
goto err;
}
ov_model->ov_model = ovmodel;
#else
ov_model->all_input_names = NULL;
ov_model->all_output_names = NULL;
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;
}
//get all the input and output names
status = ie_network_get_inputs_number(ov_model->network, &node_count);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input count\n");
goto err;
}
for (size_t i = 0; i < node_count; i++) {
status = ie_network_get_input_name(ov_model->network, i, &node_name);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get No.%d input's name\n", (int)i);
goto err;
}
APPEND_STRING(ov_model->all_input_names, node_name)
ie_network_name_free(&node_name);
}
status = ie_network_get_outputs_number(ov_model->network, &node_count);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get output count\n");
goto err;
}
for (size_t i = 0; i < node_count; i++) {
status = ie_network_get_output_name(ov_model->network, i, &node_name);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get No.%d output's name\n", (int)i);
goto err;
}
APPEND_STRING(ov_model->all_output_names, node_name)
ie_network_name_free(&node_name);
}
#endif
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:
dnn_free_model_ov(&model);
return NULL;
}
static int dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params)
{
OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
OVRequestItem *request;
TaskItem *task;
int ret;
ret = ff_check_exec_params(ctx, DNN_OV, model->func_type, exec_params);
if (ret != 0) {
return ret;
}
#if HAVE_OPENVINO2
if (!ov_model->compiled_model) {
#else
if (!ov_model->exe_network) {
#endif
ret = init_model_ov(ov_model, exec_params->input_name,
exec_params->output_names, exec_params->nb_output);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
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, ov_model, ctx->options.async, 1);
if (ret != 0) {
av_freep(&task);
return ret;
}
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 AVERROR(ENOMEM);
}
ret = extract_lltask_from_task(model->func_type, task, ov_model->lltask_queue, exec_params);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
return ret;
}
if (ctx->options.async) {
while (ff_queue_size(ov_model->lltask_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 AVERROR(EINVAL);
}
ret = execute_model_ov(request, ov_model->lltask_queue);
if (ret != 0) {
return ret;
}
}
return 0;
}
else {
if (model->func_type == DFT_ANALYTICS_CLASSIFY) {
// Classification filter has not been completely
// tested with the sync mode. So, do not support now.
avpriv_report_missing_feature(ctx, "classify for sync execution");
return AVERROR(ENOSYS);
}
if (ctx->options.batch_size > 1) {
avpriv_report_missing_feature(ctx, "batch mode for sync execution");
return AVERROR(ENOSYS);
}
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 AVERROR(EINVAL);
}
return execute_model_ov(request, ov_model->lltask_queue);
}
}
static DNNAsyncStatusType dnn_get_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out)
{
OVModel *ov_model = model->model;
return ff_dnn_get_result_common(ov_model->task_queue, in, out);
}
static int dnn_flush_ov(const DNNModel *model)
{
OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
OVRequestItem *request;
#if HAVE_OPENVINO2
ov_status_e status;
#else
IEStatusCode status;
#endif
int ret;
if (ff_queue_size(ov_model->lltask_queue) == 0) {
// no pending task need to flush
return 0;
}
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 AVERROR(EINVAL);
}
ret = fill_model_input_ov(ov_model, request);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to fill model input.\n");
return ret;
}
#if HAVE_OPENVINO2
status = ov_infer_request_infer(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start sync inference for OV2\n");
return ov2_map_error(status, NULL);
}
#else
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_GENERIC_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_GENERIC_ERROR;
}
#endif
return 0;
}
const DNNModule ff_dnn_backend_openvino = {
.load_model = dnn_load_model_ov,
.execute_model = dnn_execute_model_ov,
.get_result = dnn_get_result_ov,
.flush = dnn_flush_ov,
.free_model = dnn_free_model_ov,
};
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