| /* |
| * Copyright (c) 2020 |
| * |
| * This file is part of FFmpeg. |
| * |
| * FFmpeg is free software; you can redistribute it and/or |
| * modify it under the terms of the GNU Lesser General Public |
| * License as published by the Free Software Foundation; either |
| * version 2.1 of the License, or (at your option) any later version. |
| * |
| * FFmpeg is distributed in the hope that it will be useful, |
| * but WITHOUT ANY WARRANTY; without even the implied warranty of |
| * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
| * Lesser General Public License for more details. |
| * |
| * You should have received a copy of the GNU Lesser General Public |
| * License along with FFmpeg; if not, write to the Free Software |
| * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA |
| */ |
| |
| /** |
| * @file |
| * DNN OpenVINO backend implementation. |
| */ |
| |
| #include "dnn_backend_openvino.h" |
| #include "dnn_io_proc.h" |
| #include "libavformat/avio.h" |
| #include "libavutil/avassert.h" |
| #include "libavutil/opt.h" |
| #include "libavutil/avstring.h" |
| #include "../internal.h" |
| #include "queue.h" |
| #include "safe_queue.h" |
| #include <c_api/ie_c_api.h> |
| |
| typedef struct OVOptions{ |
| char *device_type; |
| int nireq; |
| int batch_size; |
| int input_resizable; |
| } OVOptions; |
| |
| typedef struct OVContext { |
| const AVClass *class; |
| OVOptions options; |
| } OVContext; |
| |
| typedef struct OVModel{ |
| OVContext ctx; |
| DNNModel *model; |
| ie_core_t *core; |
| ie_network_t *network; |
| ie_executable_network_t *exe_network; |
| ie_infer_request_t *infer_request; |
| |
| /* for async execution */ |
| SafeQueue *request_queue; // holds RequestItem |
| Queue *task_queue; // holds TaskItem |
| } OVModel; |
| |
| typedef struct TaskItem { |
| OVModel *ov_model; |
| const char *input_name; |
| AVFrame *in_frame; |
| const char *output_name; |
| AVFrame *out_frame; |
| int do_ioproc; |
| int async; |
| int done; |
| } TaskItem; |
| |
| typedef struct RequestItem { |
| ie_infer_request_t *infer_request; |
| TaskItem **tasks; |
| int task_count; |
| ie_complete_call_back_t callback; |
| } RequestItem; |
| |
| #define APPEND_STRING(generated_string, iterate_string) \ |
| generated_string = generated_string ? av_asprintf("%s %s", generated_string, iterate_string) : \ |
| av_asprintf("%s", iterate_string); |
| |
| #define OFFSET(x) offsetof(OVContext, x) |
| #define FLAGS AV_OPT_FLAG_FILTERING_PARAM |
| static const AVOption dnn_openvino_options[] = { |
| { "device", "device to run model", OFFSET(options.device_type), AV_OPT_TYPE_STRING, { .str = "CPU" }, 0, 0, FLAGS }, |
| { "nireq", "number of request", OFFSET(options.nireq), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INT_MAX, FLAGS }, |
| { "batch_size", "batch size per request", OFFSET(options.batch_size), AV_OPT_TYPE_INT, { .i64 = 1 }, 1, 1000, FLAGS}, |
| { "input_resizable", "can input be resizable or not", OFFSET(options.input_resizable), AV_OPT_TYPE_BOOL, { .i64 = 0 }, 0, 1, FLAGS }, |
| { NULL } |
| }; |
| |
| AVFILTER_DEFINE_CLASS(dnn_openvino); |
| |
| static DNNDataType precision_to_datatype(precision_e precision) |
| { |
| switch (precision) |
| { |
| case FP32: |
| return DNN_FLOAT; |
| case U8: |
| return DNN_UINT8; |
| default: |
| av_assert0(!"not supported yet."); |
| return DNN_FLOAT; |
| } |
| } |
| |
| static int get_datatype_size(DNNDataType dt) |
| { |
| switch (dt) |
| { |
| case DNN_FLOAT: |
| return sizeof(float); |
| case DNN_UINT8: |
| return sizeof(uint8_t); |
| default: |
| av_assert0(!"not supported yet."); |
| return 1; |
| } |
| } |
| |
| static DNNReturnType fill_model_input_ov(OVModel *ov_model, RequestItem *request) |
| { |
| dimensions_t dims; |
| precision_e precision; |
| ie_blob_buffer_t blob_buffer; |
| OVContext *ctx = &ov_model->ctx; |
| IEStatusCode status; |
| DNNData input; |
| ie_blob_t *input_blob = NULL; |
| TaskItem *task = request->tasks[0]; |
| |
| status = ie_infer_request_get_blob(request->infer_request, task->input_name, &input_blob); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to get input blob with name %s\n", task->input_name); |
| return DNN_ERROR; |
| } |
| |
| status |= ie_blob_get_dims(input_blob, &dims); |
| status |= ie_blob_get_precision(input_blob, &precision); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to get input blob dims/precision\n"); |
| return DNN_ERROR; |
| } |
| |
| status = ie_blob_get_buffer(input_blob, &blob_buffer); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to get input blob buffer\n"); |
| return DNN_ERROR; |
| } |
| |
| input.height = dims.dims[2]; |
| input.width = dims.dims[3]; |
| input.channels = dims.dims[1]; |
| input.data = blob_buffer.buffer; |
| input.dt = precision_to_datatype(precision); |
| // all models in openvino open model zoo use BGR as input, |
| // change to be an option when necessary. |
| input.order = DCO_BGR; |
| |
| av_assert0(request->task_count <= dims.dims[0]); |
| for (int i = 0; i < request->task_count; ++i) { |
| task = request->tasks[i]; |
| if (task->do_ioproc) { |
| if (ov_model->model->pre_proc != NULL) { |
| ov_model->model->pre_proc(task->in_frame, &input, ov_model->model->filter_ctx); |
| } else { |
| ff_proc_from_frame_to_dnn(task->in_frame, &input, ov_model->model->func_type, ctx); |
| } |
| } |
| input.data = (uint8_t *)input.data |
| + input.width * input.height * input.channels * get_datatype_size(input.dt); |
| } |
| ie_blob_free(&input_blob); |
| |
| return DNN_SUCCESS; |
| } |
| |
| static void infer_completion_callback(void *args) |
| { |
| dimensions_t dims; |
| precision_e precision; |
| IEStatusCode status; |
| RequestItem *request = args; |
| TaskItem *task = request->tasks[0]; |
| SafeQueue *requestq = task->ov_model->request_queue; |
| ie_blob_t *output_blob = NULL; |
| ie_blob_buffer_t blob_buffer; |
| DNNData output; |
| OVContext *ctx = &task->ov_model->ctx; |
| |
| status = ie_infer_request_get_blob(request->infer_request, task->output_name, &output_blob); |
| if (status != OK) { |
| //incorrect output name |
| char *model_output_name = NULL; |
| char *all_output_names = NULL; |
| size_t model_output_count = 0; |
| av_log(ctx, AV_LOG_ERROR, "Failed to get model output data\n"); |
| status = ie_network_get_outputs_number(task->ov_model->network, &model_output_count); |
| for (size_t i = 0; i < model_output_count; i++) { |
| status = ie_network_get_output_name(task->ov_model->network, i, &model_output_name); |
| APPEND_STRING(all_output_names, model_output_name) |
| } |
| av_log(ctx, AV_LOG_ERROR, |
| "output \"%s\" may not correct, all output(s) are: \"%s\"\n", |
| task->output_name, all_output_names); |
| return; |
| } |
| |
| status = ie_blob_get_buffer(output_blob, &blob_buffer); |
| if (status != OK) { |
| 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) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to get dims or precision of output\n"); |
| return; |
| } |
| |
| output.channels = dims.dims[1]; |
| output.height = dims.dims[2]; |
| output.width = dims.dims[3]; |
| output.dt = precision_to_datatype(precision); |
| output.data = blob_buffer.buffer; |
| |
| av_assert0(request->task_count <= dims.dims[0]); |
| av_assert0(request->task_count >= 1); |
| for (int i = 0; i < request->task_count; ++i) { |
| task = request->tasks[i]; |
| if (task->do_ioproc) { |
| if (task->ov_model->model->post_proc != NULL) { |
| task->ov_model->model->post_proc(task->out_frame, &output, task->ov_model->model->filter_ctx); |
| } else { |
| ff_proc_from_dnn_to_frame(task->out_frame, &output, ctx); |
| } |
| } else { |
| task->out_frame->width = output.width; |
| task->out_frame->height = output.height; |
| } |
| task->done = 1; |
| output.data = (uint8_t *)output.data |
| + output.width * output.height * output.channels * get_datatype_size(output.dt); |
| } |
| ie_blob_free(&output_blob); |
| |
| request->task_count = 0; |
| |
| if (task->async) { |
| if (ff_safe_queue_push_back(requestq, request) < 0) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n"); |
| return; |
| } |
| } |
| } |
| |
| static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, const char *output_name) |
| { |
| OVContext *ctx = &ov_model->ctx; |
| IEStatusCode status; |
| ie_available_devices_t a_dev; |
| ie_config_t config = {NULL, NULL, NULL}; |
| char *all_dev_names = NULL; |
| |
| // batch size |
| if (ctx->options.batch_size <= 0) { |
| ctx->options.batch_size = 1; |
| } |
| |
| if (ctx->options.batch_size > 1) { |
| input_shapes_t input_shapes; |
| status = ie_network_get_input_shapes(ov_model->network, &input_shapes); |
| if (status != OK) |
| goto err; |
| for (int i = 0; i < input_shapes.shape_num; i++) |
| input_shapes.shapes[i].shape.dims[0] = ctx->options.batch_size; |
| status = ie_network_reshape(ov_model->network, input_shapes); |
| ie_network_input_shapes_free(&input_shapes); |
| if (status != OK) |
| goto err; |
| } |
| |
| // The order of dims in the openvino is fixed and it is always NCHW for 4-D data. |
| // while we pass NHWC data from FFmpeg to openvino |
| status = ie_network_set_input_layout(ov_model->network, input_name, NHWC); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to set layout as NHWC for input %s\n", input_name); |
| goto err; |
| } |
| status = ie_network_set_output_layout(ov_model->network, output_name, NHWC); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to set layout as NHWC for output %s\n", output_name); |
| goto err; |
| } |
| |
| // all models in openvino open model zoo use BGR with range [0.0f, 255.0f] as input, |
| // we don't have a AVPixelFormat to descibe 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); |
| return DNN_ERROR; |
| } |
| } |
| |
| status = ie_core_load_network(ov_model->core, ov_model->network, ctx->options.device_type, &config, &ov_model->exe_network); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to load OpenVINO model network\n"); |
| status = ie_core_get_available_devices(ov_model->core, &a_dev); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to get available devices\n"); |
| goto err; |
| } |
| for (int i = 0; i < a_dev.num_devices; i++) { |
| APPEND_STRING(all_dev_names, a_dev.devices[i]) |
| } |
| av_log(ctx, AV_LOG_ERROR,"device %s may not be supported, all available devices are: \"%s\"\n", |
| ctx->options.device_type, all_dev_names); |
| goto err; |
| } |
| |
| // create infer_request for sync execution |
| status = ie_exec_network_create_infer_request(ov_model->exe_network, &ov_model->infer_request); |
| if (status != OK) |
| goto err; |
| |
| // create infer_requests for async execution |
| if (ctx->options.nireq <= 0) { |
| // the default value is a rough estimation |
| ctx->options.nireq = av_cpu_count() / 2 + 1; |
| } |
| |
| ov_model->request_queue = ff_safe_queue_create(); |
| if (!ov_model->request_queue) { |
| goto err; |
| } |
| |
| for (int i = 0; i < ctx->options.nireq; i++) { |
| RequestItem *item = av_mallocz(sizeof(*item)); |
| if (!item) { |
| goto err; |
| } |
| |
| status = ie_exec_network_create_infer_request(ov_model->exe_network, &item->infer_request); |
| if (status != OK) { |
| av_freep(&item); |
| goto err; |
| } |
| |
| item->tasks = av_malloc_array(ctx->options.batch_size, sizeof(*item->tasks)); |
| if (!item->tasks) { |
| av_freep(&item); |
| goto err; |
| } |
| item->task_count = 0; |
| |
| item->callback.completeCallBackFunc = infer_completion_callback; |
| item->callback.args = item; |
| if (ff_safe_queue_push_back(ov_model->request_queue, item) < 0) { |
| av_freep(&item); |
| goto err; |
| } |
| } |
| |
| ov_model->task_queue = ff_queue_create(); |
| if (!ov_model->task_queue) { |
| goto err; |
| } |
| |
| return DNN_SUCCESS; |
| |
| err: |
| ff_dnn_free_model_ov(&ov_model->model); |
| return DNN_ERROR; |
| } |
| |
| static DNNReturnType execute_model_ov(RequestItem *request) |
| { |
| IEStatusCode status; |
| DNNReturnType ret; |
| TaskItem *task = request->tasks[0]; |
| OVContext *ctx = &task->ov_model->ctx; |
| |
| if (task->async) { |
| if (request->task_count < ctx->options.batch_size) { |
| if (ff_safe_queue_push_front(task->ov_model->request_queue, request) < 0) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n"); |
| return DNN_ERROR; |
| } |
| return DNN_SUCCESS; |
| } |
| ret = fill_model_input_ov(task->ov_model, request); |
| if (ret != DNN_SUCCESS) { |
| return ret; |
| } |
| status = ie_infer_set_completion_callback(request->infer_request, &request->callback); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to set completion callback for inference\n"); |
| return DNN_ERROR; |
| } |
| status = ie_infer_request_infer_async(request->infer_request); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to start async inference\n"); |
| return DNN_ERROR; |
| } |
| return DNN_SUCCESS; |
| } else { |
| ret = fill_model_input_ov(task->ov_model, request); |
| if (ret != DNN_SUCCESS) { |
| return ret; |
| } |
| status = ie_infer_request_infer(request->infer_request); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to start synchronous model inference\n"); |
| return DNN_ERROR; |
| } |
| infer_completion_callback(request); |
| return task->done ? DNN_SUCCESS : DNN_ERROR; |
| } |
| } |
| |
| static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input_name) |
| { |
| OVModel *ov_model = model; |
| OVContext *ctx = &ov_model->ctx; |
| char *model_input_name = NULL; |
| char *all_input_names = NULL; |
| IEStatusCode status; |
| size_t model_input_count = 0; |
| dimensions_t dims; |
| precision_e precision; |
| int input_resizable = ctx->options.input_resizable; |
| |
| status = ie_network_get_inputs_number(ov_model->network, &model_input_count); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to get input count\n"); |
| return DNN_ERROR; |
| } |
| |
| for (size_t i = 0; i < model_input_count; i++) { |
| status = ie_network_get_input_name(ov_model->network, i, &model_input_name); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to get No.%d input's name\n", (int)i); |
| return DNN_ERROR; |
| } |
| if (strcmp(model_input_name, input_name) == 0) { |
| ie_network_name_free(&model_input_name); |
| status |= ie_network_get_input_dims(ov_model->network, input_name, &dims); |
| status |= ie_network_get_input_precision(ov_model->network, input_name, &precision); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to get No.%d input's dims or precision\n", (int)i); |
| return DNN_ERROR; |
| } |
| |
| input->channels = dims.dims[1]; |
| input->height = input_resizable ? -1 : dims.dims[2]; |
| input->width = input_resizable ? -1 : dims.dims[3]; |
| input->dt = precision_to_datatype(precision); |
| return DNN_SUCCESS; |
| } else { |
| //incorrect input name |
| APPEND_STRING(all_input_names, model_input_name) |
| } |
| |
| ie_network_name_free(&model_input_name); |
| } |
| |
| av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model, all input(s) are: \"%s\"\n", input_name, all_input_names); |
| return DNN_ERROR; |
| } |
| |
| static DNNReturnType get_output_ov(void *model, const char *input_name, int input_width, int input_height, |
| const char *output_name, int *output_width, int *output_height) |
| { |
| DNNReturnType ret; |
| OVModel *ov_model = model; |
| OVContext *ctx = &ov_model->ctx; |
| TaskItem task; |
| RequestItem request; |
| AVFrame *in_frame = av_frame_alloc(); |
| AVFrame *out_frame = NULL; |
| TaskItem *ptask = &task; |
| IEStatusCode status; |
| input_shapes_t input_shapes; |
| |
| if (!in_frame) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input frame\n"); |
| return DNN_ERROR; |
| } |
| out_frame = av_frame_alloc(); |
| if (!out_frame) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output frame\n"); |
| av_frame_free(&in_frame); |
| return DNN_ERROR; |
| } |
| in_frame->width = input_width; |
| in_frame->height = input_height; |
| |
| if (ctx->options.input_resizable) { |
| status = ie_network_get_input_shapes(ov_model->network, &input_shapes); |
| input_shapes.shapes->shape.dims[2] = input_height; |
| input_shapes.shapes->shape.dims[3] = input_width; |
| status |= ie_network_reshape(ov_model->network, input_shapes); |
| ie_network_input_shapes_free(&input_shapes); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to reshape input size for %s\n", input_name); |
| return DNN_ERROR; |
| } |
| } |
| |
| if (!ov_model->exe_network) { |
| if (init_model_ov(ov_model, input_name, output_name) != DNN_SUCCESS) { |
| av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n"); |
| return DNN_ERROR; |
| } |
| } |
| |
| task.done = 0; |
| task.do_ioproc = 0; |
| task.async = 0; |
| task.input_name = input_name; |
| task.in_frame = in_frame; |
| task.output_name = output_name; |
| task.out_frame = out_frame; |
| task.ov_model = ov_model; |
| |
| request.infer_request = ov_model->infer_request; |
| request.task_count = 1; |
| request.tasks = &ptask; |
| |
| ret = execute_model_ov(&request); |
| *output_width = out_frame->width; |
| *output_height = out_frame->height; |
| |
| av_frame_free(&out_frame); |
| av_frame_free(&in_frame); |
| return ret; |
| } |
| |
| DNNModel *ff_dnn_load_model_ov(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx) |
| { |
| DNNModel *model = NULL; |
| OVModel *ov_model = NULL; |
| OVContext *ctx = NULL; |
| IEStatusCode status; |
| |
| model = av_mallocz(sizeof(DNNModel)); |
| if (!model){ |
| return NULL; |
| } |
| |
| ov_model = av_mallocz(sizeof(OVModel)); |
| if (!ov_model) { |
| av_freep(&model); |
| return NULL; |
| } |
| model->model = ov_model; |
| ov_model->model = model; |
| ov_model->ctx.class = &dnn_openvino_class; |
| ctx = &ov_model->ctx; |
| |
| //parse options |
| av_opt_set_defaults(ctx); |
| if (av_opt_set_from_string(ctx, options, NULL, "=", "&") < 0) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to parse options \"%s\"\n", options); |
| goto err; |
| } |
| |
| status = ie_core_create("", &ov_model->core); |
| if (status != OK) |
| goto err; |
| |
| status = ie_core_read_network(ov_model->core, model_filename, NULL, &ov_model->network); |
| if (status != OK) { |
| ie_version_t ver; |
| ver = ie_c_api_version(); |
| av_log(ctx, AV_LOG_ERROR, "Failed to read the network from model file %s,\n" |
| "Please check if the model version matches the runtime OpenVINO %s\n", |
| model_filename, ver.api_version); |
| ie_version_free(&ver); |
| goto err; |
| } |
| |
| model->get_input = &get_input_ov; |
| model->get_output = &get_output_ov; |
| model->options = options; |
| model->filter_ctx = filter_ctx; |
| model->func_type = func_type; |
| |
| return model; |
| |
| err: |
| ff_dnn_free_model_ov(&model); |
| return NULL; |
| } |
| |
| DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, const char *input_name, AVFrame *in_frame, |
| const char **output_names, uint32_t nb_output, AVFrame *out_frame) |
| { |
| OVModel *ov_model = model->model; |
| OVContext *ctx = &ov_model->ctx; |
| TaskItem task; |
| RequestItem request; |
| TaskItem *ptask = &task; |
| |
| if (!in_frame) { |
| av_log(ctx, AV_LOG_ERROR, "in frame is NULL when execute model.\n"); |
| return DNN_ERROR; |
| } |
| |
| if (!out_frame && model->func_type == DFT_PROCESS_FRAME) { |
| av_log(ctx, AV_LOG_ERROR, "out frame is NULL when execute model.\n"); |
| return DNN_ERROR; |
| } |
| |
| if (nb_output != 1) { |
| // currently, the filter does not need multiple outputs, |
| // so we just pending the support until we really need it. |
| avpriv_report_missing_feature(ctx, "multiple outputs"); |
| return DNN_ERROR; |
| } |
| |
| if (ctx->options.batch_size > 1) { |
| avpriv_report_missing_feature(ctx, "batch mode for sync execution"); |
| return DNN_ERROR; |
| } |
| |
| if (!ov_model->exe_network) { |
| if (init_model_ov(ov_model, input_name, output_names[0]) != DNN_SUCCESS) { |
| av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n"); |
| return DNN_ERROR; |
| } |
| } |
| |
| task.done = 0; |
| task.do_ioproc = 1; |
| task.async = 0; |
| task.input_name = input_name; |
| task.in_frame = in_frame; |
| task.output_name = output_names[0]; |
| task.out_frame = out_frame; |
| task.ov_model = ov_model; |
| |
| request.infer_request = ov_model->infer_request; |
| request.task_count = 1; |
| request.tasks = &ptask; |
| |
| return execute_model_ov(&request); |
| } |
| |
| DNNReturnType ff_dnn_execute_model_async_ov(const DNNModel *model, const char *input_name, AVFrame *in_frame, |
| const char **output_names, uint32_t nb_output, AVFrame *out_frame) |
| { |
| OVModel *ov_model = model->model; |
| OVContext *ctx = &ov_model->ctx; |
| RequestItem *request; |
| TaskItem *task; |
| |
| if (!in_frame) { |
| av_log(ctx, AV_LOG_ERROR, "in frame is NULL when async execute model.\n"); |
| return DNN_ERROR; |
| } |
| |
| if (!out_frame && model->func_type == DFT_PROCESS_FRAME) { |
| av_log(ctx, AV_LOG_ERROR, "out frame is NULL when async execute model.\n"); |
| return DNN_ERROR; |
| } |
| |
| task = av_malloc(sizeof(*task)); |
| if (!task) { |
| av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n"); |
| return DNN_ERROR; |
| } |
| |
| if (!ov_model->exe_network) { |
| if (init_model_ov(ov_model, input_name, output_names[0]) != DNN_SUCCESS) { |
| av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n"); |
| return DNN_ERROR; |
| } |
| } |
| |
| task->done = 0; |
| task->do_ioproc = 1; |
| task->async = 1; |
| task->input_name = input_name; |
| task->in_frame = in_frame; |
| task->output_name = output_names[0]; |
| task->out_frame = out_frame; |
| task->ov_model = ov_model; |
| if (ff_queue_push_back(ov_model->task_queue, task) < 0) { |
| av_freep(&task); |
| av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n"); |
| return DNN_ERROR; |
| } |
| |
| request = ff_safe_queue_pop_front(ov_model->request_queue); |
| if (!request) { |
| av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n"); |
| return DNN_ERROR; |
| } |
| |
| request->tasks[request->task_count++] = task; |
| return execute_model_ov(request); |
| } |
| |
| DNNAsyncStatusType ff_dnn_get_async_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out) |
| { |
| OVModel *ov_model = model->model; |
| TaskItem *task = ff_queue_peek_front(ov_model->task_queue); |
| |
| if (!task) { |
| return DAST_EMPTY_QUEUE; |
| } |
| |
| if (!task->done) { |
| return DAST_NOT_READY; |
| } |
| |
| *in = task->in_frame; |
| *out = task->out_frame; |
| ff_queue_pop_front(ov_model->task_queue); |
| av_freep(&task); |
| |
| return DAST_SUCCESS; |
| } |
| |
| DNNReturnType ff_dnn_flush_ov(const DNNModel *model) |
| { |
| OVModel *ov_model = model->model; |
| OVContext *ctx = &ov_model->ctx; |
| RequestItem *request; |
| IEStatusCode status; |
| DNNReturnType ret; |
| |
| request = ff_safe_queue_pop_front(ov_model->request_queue); |
| if (!request) { |
| av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n"); |
| return DNN_ERROR; |
| } |
| |
| if (request->task_count == 0) { |
| // no pending task need to flush |
| if (ff_safe_queue_push_back(ov_model->request_queue, request) < 0) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n"); |
| return DNN_ERROR; |
| } |
| return DNN_SUCCESS; |
| } |
| |
| ret = fill_model_input_ov(ov_model, request); |
| if (ret != DNN_SUCCESS) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to fill model input.\n"); |
| return ret; |
| } |
| status = ie_infer_set_completion_callback(request->infer_request, &request->callback); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to set completion callback for inference\n"); |
| return DNN_ERROR; |
| } |
| status = ie_infer_request_infer_async(request->infer_request); |
| if (status != OK) { |
| av_log(ctx, AV_LOG_ERROR, "Failed to start async inference\n"); |
| return DNN_ERROR; |
| } |
| |
| return DNN_SUCCESS; |
| } |
| |
| void ff_dnn_free_model_ov(DNNModel **model) |
| { |
| if (*model){ |
| OVModel *ov_model = (*model)->model; |
| while (ff_safe_queue_size(ov_model->request_queue) != 0) { |
| RequestItem *item = ff_safe_queue_pop_front(ov_model->request_queue); |
| if (item && item->infer_request) { |
| ie_infer_request_free(&item->infer_request); |
| } |
| av_freep(&item->tasks); |
| av_freep(&item); |
| } |
| ff_safe_queue_destroy(ov_model->request_queue); |
| |
| while (ff_queue_size(ov_model->task_queue) != 0) { |
| TaskItem *item = ff_queue_pop_front(ov_model->task_queue); |
| av_frame_free(&item->in_frame); |
| av_frame_free(&item->out_frame); |
| av_freep(&item); |
| } |
| ff_queue_destroy(ov_model->task_queue); |
| |
| if (ov_model->infer_request) |
| ie_infer_request_free(&ov_model->infer_request); |
| if (ov_model->exe_network) |
| ie_exec_network_free(&ov_model->exe_network); |
| if (ov_model->network) |
| ie_network_free(&ov_model->network); |
| if (ov_model->core) |
| ie_core_free(&ov_model->core); |
| av_freep(&ov_model); |
| av_freep(model); |
| } |
| } |
