mirror of
https://git.ffmpeg.org/ffmpeg.git
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6d75d44d90
All that remains in it are things that belong in avfilter_internal.h. Move them there and remove internal.h
1622 lines
56 KiB
C
1622 lines
56 KiB
C
/*
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* Copyright (c) 2020
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*
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* This file is part of FFmpeg.
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*
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* FFmpeg is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* FFmpeg is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with FFmpeg; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*/
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/**
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* @file
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* DNN OpenVINO backend implementation.
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*/
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#include "dnn_io_proc.h"
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#include "libavformat/avio.h"
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#include "libavutil/avassert.h"
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#include "libavutil/cpu.h"
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#include "libavutil/mem.h"
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#include "libavutil/opt.h"
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#include "libavutil/avstring.h"
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#include "libavutil/detection_bbox.h"
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#include "safe_queue.h"
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#if HAVE_OPENVINO2
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#include <openvino/c/openvino.h>
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#else
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#include <c_api/ie_c_api.h>
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#endif
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#include "dnn_backend_common.h"
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typedef struct OVModel{
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DNNModel model;
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DnnContext *ctx;
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#if HAVE_OPENVINO2
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ov_core_t *core;
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ov_model_t *ov_model;
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ov_compiled_model_t *compiled_model;
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ov_output_const_port_t* input_port;
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ov_preprocess_input_info_t* input_info;
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ov_output_const_port_t** output_ports;
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ov_preprocess_output_info_t* output_info;
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ov_preprocess_prepostprocessor_t* preprocess;
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#else
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ie_core_t *core;
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ie_network_t *network;
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ie_executable_network_t *exe_network;
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const char *all_input_names;
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const char *all_output_names;
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#endif
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SafeQueue *request_queue; // holds OVRequestItem
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Queue *task_queue; // holds TaskItem
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Queue *lltask_queue; // holds LastLevelTaskItem
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int nb_outputs;
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} OVModel;
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// one request for one call to openvino
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typedef struct OVRequestItem {
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LastLevelTaskItem **lltasks;
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uint32_t lltask_count;
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#if HAVE_OPENVINO2
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ov_infer_request_t *infer_request;
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ov_callback_t callback;
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#else
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ie_complete_call_back_t callback;
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ie_infer_request_t *infer_request;
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#endif
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} OVRequestItem;
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#define APPEND_STRING(generated_string, iterate_string) \
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generated_string = generated_string ? av_asprintf("%s %s", generated_string, iterate_string) : \
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av_asprintf("%s", iterate_string);
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#define OFFSET(x) offsetof(OVOptions, x)
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#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
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static const AVOption dnn_openvino_options[] = {
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{ "batch_size", "batch size per request", OFFSET(batch_size), AV_OPT_TYPE_INT, { .i64 = 1 }, 1, 1000, FLAGS},
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{ "input_resizable", "can input be resizable or not", OFFSET(input_resizable), AV_OPT_TYPE_BOOL, { .i64 = 0 }, 0, 1, FLAGS },
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{ "layout", "input layout of model", OFFSET(layout), AV_OPT_TYPE_INT, { .i64 = DL_NONE}, DL_NONE, DL_NHWC, FLAGS, .unit = "layout" },
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{ "none", "none", 0, AV_OPT_TYPE_CONST, { .i64 = DL_NONE }, 0, 0, FLAGS, .unit = "layout"},
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{ "nchw", "nchw", 0, AV_OPT_TYPE_CONST, { .i64 = DL_NCHW }, 0, 0, FLAGS, .unit = "layout"},
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{ "nhwc", "nhwc", 0, AV_OPT_TYPE_CONST, { .i64 = DL_NHWC }, 0, 0, FLAGS, .unit = "layout"},
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{ "scale", "Add scale preprocess operation. Divide each element of input by specified value.", OFFSET(scale), AV_OPT_TYPE_FLOAT, { .dbl = 0 }, INT_MIN, INT_MAX, FLAGS},
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{ "mean", "Add mean preprocess operation. Subtract specified value from each element of input.", OFFSET(mean), AV_OPT_TYPE_FLOAT, { .dbl = 0 }, INT_MIN, INT_MAX, FLAGS},
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{ NULL }
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};
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#if HAVE_OPENVINO2
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static const struct {
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ov_status_e status;
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int av_err;
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const char *desc;
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} ov2_errors[] = {
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{ OK, 0, "success" },
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{ GENERAL_ERROR, AVERROR_EXTERNAL, "general error" },
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{ NOT_IMPLEMENTED, AVERROR(ENOSYS), "not implemented" },
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{ NETWORK_NOT_LOADED, AVERROR_EXTERNAL, "network not loaded" },
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{ PARAMETER_MISMATCH, AVERROR(EINVAL), "parameter mismatch" },
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{ NOT_FOUND, AVERROR_EXTERNAL, "not found" },
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{ OUT_OF_BOUNDS, AVERROR(EOVERFLOW), "out of bounds" },
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{ UNEXPECTED, AVERROR_EXTERNAL, "unexpected" },
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{ REQUEST_BUSY, AVERROR(EBUSY), "request busy" },
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{ RESULT_NOT_READY, AVERROR(EBUSY), "result not ready" },
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{ NOT_ALLOCATED, AVERROR(ENODATA), "not allocated" },
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{ INFER_NOT_STARTED, AVERROR_EXTERNAL, "infer not started" },
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{ NETWORK_NOT_READ, AVERROR_EXTERNAL, "network not read" },
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{ INFER_CANCELLED, AVERROR(ECANCELED), "infer cancelled" },
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{ INVALID_C_PARAM, AVERROR(EINVAL), "invalid C parameter" },
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{ UNKNOWN_C_ERROR, AVERROR_UNKNOWN, "unknown C error" },
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{ NOT_IMPLEMENT_C_METHOD, AVERROR(ENOSYS), "not implement C method" },
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{ UNKNOW_EXCEPTION, AVERROR_UNKNOWN, "unknown exception" },
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};
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static int ov2_map_error(ov_status_e status, const char **desc)
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{
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int i;
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for (i = 0; i < FF_ARRAY_ELEMS(ov2_errors); i++) {
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if (ov2_errors[i].status == status) {
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if (desc)
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*desc = ov2_errors[i].desc;
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return ov2_errors[i].av_err;
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}
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}
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if (desc)
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*desc = "unknown error";
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return AVERROR_UNKNOWN;
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}
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#endif
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#if HAVE_OPENVINO2
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static DNNDataType precision_to_datatype(ov_element_type_e precision)
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#else
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static DNNDataType precision_to_datatype(precision_e precision)
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#endif
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{
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switch (precision)
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{
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#if HAVE_OPENVINO2
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case F32:
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#else
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case FP32:
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#endif
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return DNN_FLOAT;
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case U8:
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return DNN_UINT8;
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default:
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av_assert0(!"not supported yet.");
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return DNN_FLOAT;
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}
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}
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static int get_datatype_size(DNNDataType dt)
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{
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switch (dt)
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{
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case DNN_FLOAT:
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return sizeof(float);
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case DNN_UINT8:
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return sizeof(uint8_t);
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default:
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av_assert0(!"not supported yet.");
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return 1;
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}
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}
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static int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
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{
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DNNData input;
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LastLevelTaskItem *lltask;
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TaskItem *task;
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DnnContext *ctx = ov_model->ctx;
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#if HAVE_OPENVINO2
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int64_t* dims;
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ov_status_e status;
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ov_tensor_t* tensor = NULL;
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ov_shape_t input_shape = {0};
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ov_element_type_e precision;
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char *port_name;
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#else
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dimensions_t dims;
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precision_e precision;
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ie_blob_buffer_t blob_buffer;
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IEStatusCode status;
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ie_blob_t *input_blob = NULL;
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#endif
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memset(&input, 0, sizeof(input));
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lltask = ff_queue_peek_front(ov_model->lltask_queue);
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av_assert0(lltask);
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task = lltask->task;
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#if HAVE_OPENVINO2
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if (ov_model->input_port) {
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ov_output_const_port_free(ov_model->input_port);
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ov_model->input_port = NULL;
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}
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if (task->input_name)
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status = ov_model_const_input_by_name(ov_model->ov_model, task->input_name, &ov_model->input_port);
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else
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status = ov_model_const_input(ov_model->ov_model, &ov_model->input_port);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get input port shape.\n");
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return ov2_map_error(status, NULL);
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}
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status = ov_port_get_any_name(ov_model->input_port, &port_name);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get input port name.\n");
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return ov2_map_error(status, NULL);
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}
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av_log(ctx, AV_LOG_VERBOSE, "OpenVINO model input: %s\n", port_name);
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ov_free(port_name);
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port_name = NULL;
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status = ov_const_port_get_shape(ov_model->input_port, &input_shape);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get input port shape.\n");
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return ov2_map_error(status, NULL);
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}
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dims = input_shape.dims;
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status = ov_port_get_element_type(ov_model->input_port, &precision);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get input port data type.\n");
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ov_shape_free(&input_shape);
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return ov2_map_error(status, NULL);
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}
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for (int i = 0; i < input_shape.rank; i++)
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input.dims[i] = dims[i];
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input.layout = DL_NHWC;
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input.dt = precision_to_datatype(precision);
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#else
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status = ie_infer_request_get_blob(request->infer_request, task->input_name, &input_blob);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get input blob with name %s\n", task->input_name);
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return DNN_GENERIC_ERROR;
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}
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status |= ie_blob_get_dims(input_blob, &dims);
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status |= ie_blob_get_precision(input_blob, &precision);
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if (status != OK) {
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ie_blob_free(&input_blob);
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av_log(ctx, AV_LOG_ERROR, "Failed to get input blob dims/precision\n");
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return DNN_GENERIC_ERROR;
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}
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status = ie_blob_get_buffer(input_blob, &blob_buffer);
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if (status != OK) {
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ie_blob_free(&input_blob);
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av_log(ctx, AV_LOG_ERROR, "Failed to get input blob buffer\n");
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return DNN_GENERIC_ERROR;
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}
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for (int i = 0; i < input_shape.rank; i++)
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input.dims[i] = dims[i];
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input.layout = DL_NCHW;
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input.data = blob_buffer.buffer;
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input.dt = precision_to_datatype(precision);
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#endif
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// all models in openvino open model zoo use BGR as input,
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// change to be an option when necessary.
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input.order = DCO_BGR;
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// We use preprocess_steps to scale input data, so disable scale and mean here.
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input.scale = 1;
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input.mean = 0;
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for (int i = 0; i < ctx->ov_option.batch_size; ++i) {
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lltask = ff_queue_pop_front(ov_model->lltask_queue);
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if (!lltask) {
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break;
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}
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request->lltasks[i] = lltask;
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request->lltask_count = i + 1;
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task = lltask->task;
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#if HAVE_OPENVINO2
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if (tensor)
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ov_tensor_free(tensor);
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status = ov_tensor_create(precision, input_shape, &tensor);
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ov_shape_free(&input_shape);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to create tensor from host prt.\n");
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return ov2_map_error(status, NULL);
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}
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status = ov_tensor_data(tensor, &input.data);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get input data.\n");
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return ov2_map_error(status, NULL);
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}
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status = ov_infer_request_set_input_tensor(request->infer_request, tensor);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to Set an input tensor for the model.\n");
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return ov2_map_error(status, NULL);
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}
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#endif
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switch (ov_model->model.func_type) {
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case DFT_PROCESS_FRAME:
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if (task->do_ioproc) {
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if (ov_model->model.frame_pre_proc != NULL) {
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ov_model->model.frame_pre_proc(task->in_frame, &input, ov_model->model.filter_ctx);
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} else {
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ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
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}
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}
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break;
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case DFT_ANALYTICS_DETECT:
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ff_frame_to_dnn_detect(task->in_frame, &input, ctx);
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break;
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case DFT_ANALYTICS_CLASSIFY:
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ff_frame_to_dnn_classify(task->in_frame, &input, lltask->bbox_index, ctx);
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break;
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default:
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av_assert0(!"should not reach here");
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break;
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}
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input.data = (uint8_t *)input.data +
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input.dims[1] * input.dims[2] * input.dims[3] * get_datatype_size(input.dt);
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}
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#if HAVE_OPENVINO2
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ov_tensor_free(tensor);
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#else
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ie_blob_free(&input_blob);
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#endif
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return 0;
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}
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static void infer_completion_callback(void *args)
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{
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OVRequestItem *request = args;
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LastLevelTaskItem *lltask = request->lltasks[0];
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TaskItem *task = lltask->task;
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OVModel *ov_model = task->model;
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SafeQueue *requestq = ov_model->request_queue;
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DNNData *outputs;
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DnnContext *ctx = ov_model->ctx;
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#if HAVE_OPENVINO2
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size_t* dims;
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ov_status_e status;
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ov_tensor_t *output_tensor;
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ov_shape_t output_shape = {0};
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ov_element_type_e precision;
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outputs = av_calloc(ov_model->nb_outputs, sizeof(*outputs));
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if (!outputs) {
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av_log(ctx, AV_LOG_ERROR, "Failed to alloc outputs.");
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return;
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}
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for (int i = 0; i < ov_model->nb_outputs; i++) {
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status = ov_infer_request_get_tensor_by_const_port(request->infer_request,
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ov_model->output_ports[i],
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&output_tensor);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR,
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"Failed to get output tensor.");
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goto end;
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}
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status = ov_tensor_data(output_tensor, &outputs[i].data);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR,
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"Failed to get output data.");
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goto end;
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}
|
|
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status = ov_tensor_get_shape(output_tensor, &output_shape);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get output port shape.\n");
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goto end;
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}
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dims = output_shape.dims;
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status = ov_port_get_element_type(ov_model->output_ports[i], &precision);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get output port data type.\n");
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goto end;
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}
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outputs[i].dt = precision_to_datatype(precision);
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outputs[i].layout = DL_NCHW;
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outputs[i].dims[0] = 1;
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outputs[i].dims[1] = output_shape.rank > 2 ? dims[output_shape.rank - 3] : 1;
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outputs[i].dims[2] = output_shape.rank > 1 ? dims[output_shape.rank - 2] : 1;
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outputs[i].dims[3] = output_shape.rank > 0 ? dims[output_shape.rank - 1] : 1;
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av_assert0(request->lltask_count <= dims[0]);
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outputs[i].layout = ctx->ov_option.layout;
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outputs[i].scale = ctx->ov_option.scale;
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outputs[i].mean = ctx->ov_option.mean;
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ov_shape_free(&output_shape);
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ov_tensor_free(output_tensor);
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output_tensor = NULL;
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}
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|
#else
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|
IEStatusCode status;
|
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dimensions_t dims;
|
|
ie_blob_t *output_blob = NULL;
|
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ie_blob_buffer_t blob_buffer;
|
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precision_e precision;
|
|
DNNData output;
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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);
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|
av_log(ctx, AV_LOG_ERROR, "Failed to access output memory\n");
|
|
return;
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}
|
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|
|
status |= ie_blob_get_dims(output_blob, &dims);
|
|
status |= ie_blob_get_precision(output_blob, &precision);
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if (status != OK) {
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|
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;
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|
for (int i = 0; i < 4; i++)
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output.dims[i] = dims.dims[i];
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|
av_assert0(request->lltask_count <= dims.dims[0]);
|
|
output.dt = precision_to_datatype(precision);
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|
output.layout = ctx->ov_option.layout;
|
|
output.scale = ctx->ov_option.scale;
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|
output.mean = ctx->ov_option.mean;
|
|
outputs = &output;
|
|
#endif
|
|
|
|
av_assert0(request->lltask_count >= 1);
|
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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 = (OVModel *)(*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_freep(&ov_model);
|
|
*model = NULL;
|
|
}
|
|
|
|
|
|
static int init_model_ov(OVModel *ov_model, const char *input_name, const char **output_names, int nb_outputs)
|
|
{
|
|
int ret = 0;
|
|
DnnContext *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->device ? ctx->device : "CPU";
|
|
#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->ov_option.scale) < 1e-6f)
|
|
ctx->ov_option.scale = ov_model->model.func_type == DFT_PROCESS_FRAME ? 255 : 1;
|
|
// batch size
|
|
if (ctx->ov_option.batch_size <= 0) {
|
|
ctx->ov_option.batch_size = 1;
|
|
}
|
|
#if HAVE_OPENVINO2
|
|
if (ctx->ov_option.batch_size > 1) {
|
|
avpriv_report_missing_feature(ctx, "Do not support batch_size > 1 for now,"
|
|
"change batch_size to 1.\n");
|
|
ctx->ov_option.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->ov_option.layout == DL_NCHW)
|
|
status = ov_preprocess_input_model_info_set_layout(input_model_info, NCHW_layout);
|
|
else if (ctx->ov_option.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->ov_option.scale - 1) > 1e-6f || fabsf(ctx->ov_option.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->ov_option.scale - 1) > 1e-6f || fabsf(ctx->ov_option.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->ov_option.mean);
|
|
status |= ov_preprocess_preprocess_steps_scale(input_process_steps, ctx->ov_option.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->ov_option.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->ov_option.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->device, &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->device, all_dev_names);
|
|
ret = AVERROR(ENODEV);
|
|
goto err;
|
|
}
|
|
#endif
|
|
// create infer_requests for async execution
|
|
if (ctx->nireq <= 0) {
|
|
// the default value is a rough estimation
|
|
ctx->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->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->ov_option.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
|
|
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;
|
|
DnnContext *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(DNNModel *model, DNNData *input, const char *input_name)
|
|
{
|
|
OVModel *ov_model = (OVModel *)model;
|
|
DnnContext *ctx = ov_model->ctx;
|
|
int input_resizable = ctx->ov_option.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(DNNModel *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 = (OVModel *)model;
|
|
DnnContext *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->ov_option.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->ov_option.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(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx)
|
|
{
|
|
DNNModel *model = NULL;
|
|
OVModel *ov_model = 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
|
|
|
|
ov_model = av_mallocz(sizeof(OVModel));
|
|
if (!ov_model)
|
|
return NULL;
|
|
ov_model->ctx = ctx;
|
|
model = &ov_model->model;
|
|
|
|
#if HAVE_OPENVINO2
|
|
status = ov_core_create(&core);
|
|
if (status != OK) {
|
|
goto err;
|
|
}
|
|
ov_model->core = core;
|
|
|
|
status = ov_core_read_model(core, ctx->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",
|
|
ctx->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, ctx->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",
|
|
ctx->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->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 = (OVModel *)model;
|
|
DnnContext *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->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->async) {
|
|
while (ff_queue_size(ov_model->lltask_queue) >= ctx->ov_option.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->ov_option.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 = (OVModel *)model;
|
|
return ff_dnn_get_result_common(ov_model->task_queue, in, out);
|
|
}
|
|
|
|
static int dnn_flush_ov(const DNNModel *model)
|
|
{
|
|
OVModel *ov_model = (OVModel *)model;
|
|
DnnContext *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 = {
|
|
.clazz = DNN_DEFINE_CLASS(dnn_openvino),
|
|
.type = DNN_OV,
|
|
.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,
|
|
};
|