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https://github.com/FFmpeg/FFmpeg.git
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libavfiter/dnn_backend_openvino: Add multiple output support
Add multiple output support to openvino backend. You can use '&' to split different output when you set output name using command line. Signed-off-by: Wenbin Chen <wenbin.chen@intel.com> Reviewed-by: Guo Yejun <yejun.guo@intel.com>
This commit is contained in:
Wenbin Chen
Guo Yejun
parent
1f56bfc986
commit
22652b576c
@ -43,13 +43,6 @@ int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func
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return AVERROR(EINVAL);
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}
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if (exec_params->nb_output != 1 && backend != DNN_TF) {
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// currently, the filter does not need multiple outputs,
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// so we just pending the support until we really need it.
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avpriv_report_missing_feature(ctx, "multiple outputs");
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return AVERROR(ENOSYS);
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}
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return 0;
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}
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@ -64,7 +64,7 @@ typedef struct OVModel{
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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_port;
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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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@ -77,6 +77,7 @@ typedef struct OVModel{
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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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@ -349,7 +350,7 @@ static void infer_completion_callback(void *args)
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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 output;
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DNNData *outputs;
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OVContext *ctx = &ov_model->ctx;
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#if HAVE_OPENVINO2
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size_t* dims;
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@ -358,45 +359,61 @@ static void infer_completion_callback(void *args)
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ov_shape_t output_shape = {0};
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ov_element_type_e precision;
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memset(&output, 0, sizeof(output));
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status = ov_infer_request_get_output_tensor_by_index(request->infer_request, 0, &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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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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status = ov_tensor_data(output_tensor, &output.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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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_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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return;
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}
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dims = output_shape.dims;
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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_port_get_element_type(ov_model->output_port, &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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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].channels = output_shape.rank > 2 ? dims[output_shape.rank - 3] : 1;
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outputs[i].height = output_shape.rank > 1 ? dims[output_shape.rank - 2] : 1;
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outputs[i].width = 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->options.layout;
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outputs[i].scale = ctx->options.scale;
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outputs[i].mean = ctx->options.mean;
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ov_shape_free(&output_shape);
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return;
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ov_tensor_free(output_tensor);
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output_tensor = NULL;
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}
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output.channels = output_shape.rank > 2 ? dims[output_shape.rank - 3] : 1;
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output.height = output_shape.rank > 1 ? dims[output_shape.rank - 2] : 1;
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output.width = 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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ov_shape_free(&output_shape);
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#else
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IEStatusCode status;
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dimensions_t dims;
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ie_blob_t *output_blob = NULL;
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ie_blob_buffer_t blob_buffer;
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precision_e precision;
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DNNData output;
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status = ie_infer_request_get_blob(request->infer_request, task->output_names[0], &output_blob);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR,
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@ -424,11 +441,12 @@ static void infer_completion_callback(void *args)
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output.height = dims.dims[2];
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output.width = dims.dims[3];
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av_assert0(request->lltask_count <= dims.dims[0]);
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#endif
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output.dt = precision_to_datatype(precision);
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output.layout = ctx->options.layout;
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output.scale = ctx->options.scale;
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output.mean = ctx->options.mean;
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outputs = &output;
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#endif
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av_assert0(request->lltask_count >= 1);
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for (int i = 0; i < request->lltask_count; ++i) {
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@ -438,28 +456,33 @@ static void infer_completion_callback(void *args)
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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_post_proc != NULL) {
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ov_model->model->frame_post_proc(task->out_frame, &output, ov_model->model->filter_ctx);
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ov_model->model->frame_post_proc(task->out_frame, outputs, ov_model->model->filter_ctx);
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} else {
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ff_proc_from_dnn_to_frame(task->out_frame, &output, ctx);
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ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
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}
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} else {
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task->out_frame->width = output.width;
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task->out_frame->height = output.height;
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task->out_frame->width = outputs[0].width;
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task->out_frame->height = outputs[0].height;
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}
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break;
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case DFT_ANALYTICS_DETECT:
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if (!ov_model->model->detect_post_proc) {
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av_log(ctx, AV_LOG_ERROR, "detect filter needs to provide post proc\n");
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return;
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goto end;
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}
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ov_model->model->detect_post_proc(task->in_frame, &output, 1, ov_model->model->filter_ctx);
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ov_model->model->detect_post_proc(task->in_frame, outputs,
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ov_model->nb_outputs,
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ov_model->model->filter_ctx);
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break;
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case DFT_ANALYTICS_CLASSIFY:
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if (!ov_model->model->classify_post_proc) {
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av_log(ctx, AV_LOG_ERROR, "classify filter needs to provide post proc\n");
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return;
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goto end;
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}
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ov_model->model->classify_post_proc(task->in_frame, &output, request->lltasks[i]->bbox_index, ov_model->model->filter_ctx);
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for (int output_i = 0; output_i < ov_model->nb_outputs; output_i++)
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ov_model->model->classify_post_proc(task->in_frame, outputs,
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request->lltasks[i]->bbox_index,
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ov_model->model->filter_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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@ -468,10 +491,17 @@ static void infer_completion_callback(void *args)
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task->inference_done++;
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av_freep(&request->lltasks[i]);
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output.data = (uint8_t *)output.data
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+ output.width * output.height * output.channels * get_datatype_size(output.dt);
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for (int i = 0; i < ov_model->nb_outputs; i++)
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outputs[i].data = (uint8_t *)outputs[i].data +
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outputs[i].width * outputs[i].height * outputs[i].channels * get_datatype_size(outputs[i].dt);
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}
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#if !HAVE_OPENVINO2
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end:
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#if HAVE_OPENVINO2
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av_freep(&outputs);
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ov_shape_free(&output_shape);
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if (output_tensor)
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ov_tensor_free(output_tensor);
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#else
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ie_blob_free(&output_blob);
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#endif
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request->lltask_count = 0;
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@ -525,8 +555,10 @@ static void dnn_free_model_ov(DNNModel **model)
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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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if (ov_model->output_port)
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ov_output_const_port_free(ov_model->output_port);
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for (int i = 0; i < ov_model->nb_outputs; i++)
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if (ov_model->output_ports[i])
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ov_output_const_port_free(ov_model->output_ports[i]);
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av_freep(&ov_model->output_ports);
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if (ov_model->preprocess)
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ov_preprocess_prepostprocessor_free(ov_model->preprocess);
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if (ov_model->compiled_model)
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@ -551,7 +583,7 @@ static void dnn_free_model_ov(DNNModel **model)
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}
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static int init_model_ov(OVModel *ov_model, const char *input_name, const char *output_name)
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static int init_model_ov(OVModel *ov_model, const char *input_name, const char **output_names, int nb_outputs)
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{
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int ret = 0;
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OVContext *ctx = &ov_model->ctx;
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@ -594,17 +626,15 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
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}
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status = ov_preprocess_prepostprocessor_get_input_info_by_name(ov_model->preprocess, input_name, &ov_model->input_info);
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status |= ov_preprocess_prepostprocessor_get_output_info_by_name(ov_model->preprocess, output_name, &ov_model->output_info);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get input/output info from preprocess.\n");
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av_log(ctx, AV_LOG_ERROR, "Failed to get input info from preprocess.\n");
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ret = ov2_map_error(status, NULL);
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goto err;
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}
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status = ov_preprocess_input_info_get_tensor_info(ov_model->input_info, &input_tensor_info);
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status |= ov_preprocess_output_info_get_tensor_info(ov_model->output_info, &output_tensor_info);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get tensor info from input/output.\n");
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av_log(ctx, AV_LOG_ERROR, "Failed to get tensor info from input.\n");
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ret = ov2_map_error(status, NULL);
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goto err;
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}
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@ -642,17 +672,43 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
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}
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status = ov_preprocess_input_tensor_info_set_element_type(input_tensor_info, U8);
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if (ov_model->model->func_type != DFT_PROCESS_FRAME)
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status |= ov_preprocess_output_set_element_type(output_tensor_info, F32);
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else if (fabsf(ctx->options.scale - 1) > 1e-6f || fabsf(ctx->options.mean) > 1e-6f)
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status |= ov_preprocess_output_set_element_type(output_tensor_info, F32);
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else
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status |= ov_preprocess_output_set_element_type(output_tensor_info, U8);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to set input/output element type\n");
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av_log(ctx, AV_LOG_ERROR, "Failed to set input element type\n");
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ret = ov2_map_error(status, NULL);
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goto err;
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}
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ov_model->nb_outputs = nb_outputs;
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for (int i = 0; i < nb_outputs; i++) {
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status = ov_preprocess_prepostprocessor_get_output_info_by_name(
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ov_model->preprocess, output_names[i], &ov_model->output_info);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get output info from preprocess.\n");
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ret = ov2_map_error(status, NULL);
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goto err;
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}
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status |= ov_preprocess_output_info_get_tensor_info(ov_model->output_info, &output_tensor_info);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get tensor info from input/output.\n");
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ret = ov2_map_error(status, NULL);
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goto err;
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}
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if (ov_model->model->func_type != DFT_PROCESS_FRAME)
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status |= ov_preprocess_output_set_element_type(output_tensor_info, F32);
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else if (fabsf(ctx->options.scale - 1) > 1e-6f || fabsf(ctx->options.mean) > 1e-6f)
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status |= ov_preprocess_output_set_element_type(output_tensor_info, F32);
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else
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status |= ov_preprocess_output_set_element_type(output_tensor_info, U8);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to set output element type\n");
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ret = ov2_map_error(status, NULL);
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goto err;
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}
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ov_preprocess_output_tensor_info_free(output_tensor_info);
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output_tensor_info = NULL;
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ov_preprocess_output_info_free(ov_model->output_info);
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ov_model->output_info = NULL;
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}
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// set preprocess steps.
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if (fabsf(ctx->options.scale - 1) > 1e-6f || fabsf(ctx->options.mean) > 1e-6f) {
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ov_preprocess_preprocess_steps_t* input_process_steps = NULL;
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@ -667,11 +723,18 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
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status |= ov_preprocess_preprocess_steps_scale(input_process_steps, ctx->options.scale);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to set preprocess steps\n");
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ov_preprocess_preprocess_steps_free(input_process_steps);
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input_process_steps = NULL;
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ret = ov2_map_error(status, NULL);
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goto err;
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}
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ov_preprocess_preprocess_steps_free(input_process_steps);
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input_process_steps = NULL;
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}
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ov_preprocess_input_tensor_info_free(input_tensor_info);
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input_tensor_info = NULL;
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ov_preprocess_input_info_free(ov_model->input_info);
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ov_model->input_info = NULL;
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//update model
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if(ov_model->ov_model)
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@ -679,20 +742,33 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
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status = ov_preprocess_prepostprocessor_build(ov_model->preprocess, &ov_model->ov_model);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to update OV model\n");
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ov_model_free(tmp_ov_model);
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tmp_ov_model = NULL;
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ret = ov2_map_error(status, NULL);
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goto err;
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}
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ov_model_free(tmp_ov_model);
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//update output_port
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if (ov_model->output_port) {
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ov_output_const_port_free(ov_model->output_port);
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ov_model->output_port = NULL;
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}
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status = ov_model_const_output_by_name(ov_model->ov_model, output_name, &ov_model->output_port);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get output port.\n");
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goto err;
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if (!ov_model->output_ports) {
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ov_model->output_ports = av_calloc(nb_outputs, sizeof(*ov_model->output_ports));
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if (!ov_model->output_ports) {
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ret = AVERROR(ENOMEM);
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goto err;
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}
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} else
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for (int i = 0; i < nb_outputs; i++) {
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ov_output_const_port_free(ov_model->output_ports[i]);
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ov_model->output_ports[i] = NULL;
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}
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for (int i = 0; i < nb_outputs; i++) {
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status = ov_model_const_output_by_name(ov_model->ov_model, output_names[i],
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&ov_model->output_ports[i]);
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if (status != OK) {
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av_log(ctx, AV_LOG_ERROR, "Failed to get output port %s.\n", output_names[i]);
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goto err;
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}
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}
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//compile network
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status = ov_core_compile_model(ov_model->core, ov_model->ov_model, device, 0, &ov_model->compiled_model);
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@ -701,6 +777,7 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
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goto err;
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}
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ov_preprocess_input_model_info_free(input_model_info);
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input_model_info = NULL;
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ov_layout_free(NCHW_layout);
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ov_layout_free(NHWC_layout);
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#else
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@ -745,6 +822,7 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
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ret = DNN_GENERIC_ERROR;
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goto err;
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}
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ov_model->nb_outputs = 1;
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// all models in openvino open model zoo use BGR with range [0.0f, 255.0f] as input,
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// we don't have a AVPixelFormat to describe it, so we'll use AV_PIX_FMT_BGR24 and
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@ -848,6 +926,10 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
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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)
|
||||
@ -1204,11 +1286,6 @@ static int get_output_ov(void *model, const char *input_name, int input_width, i
|
||||
}
|
||||
}
|
||||
|
||||
status = ov_model_const_output_by_name(ov_model->ov_model, output_name, &ov_model->output_port);
|
||||
if (status != OK) {
|
||||
av_log(ctx, AV_LOG_ERROR, "Failed to get output port.\n");
|
||||
return ov2_map_error(status, NULL);
|
||||
}
|
||||
if (!ov_model->compiled_model) {
|
||||
#else
|
||||
if (ctx->options.input_resizable) {
|
||||
@ -1224,7 +1301,7 @@ static int get_output_ov(void *model, const char *input_name, int input_width, i
|
||||
}
|
||||
if (!ov_model->exe_network) {
|
||||
#endif
|
||||
ret = init_model_ov(ov_model, input_name, output_name);
|
||||
ret = init_model_ov(ov_model, input_name, &output_name, 1);
|
||||
if (ret != 0) {
|
||||
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
|
||||
return ret;
|
||||
@ -1397,7 +1474,8 @@ static int dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_p
|
||||
#else
|
||||
if (!ov_model->exe_network) {
|
||||
#endif
|
||||
ret = init_model_ov(ov_model, exec_params->input_name, exec_params->output_names[0]);
|
||||
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;
|
||||
|
||||
@ -360,11 +360,11 @@ static int dnn_detect_post_proc_ssd(AVFrame *frame, DNNData *output, AVFilterCon
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
static int dnn_detect_post_proc_ov(AVFrame *frame, DNNData *output, AVFilterContext *filter_ctx)
|
||||
static int dnn_detect_post_proc_ov(AVFrame *frame, DNNData *output, int nb_outputs,
|
||||
AVFilterContext *filter_ctx)
|
||||
{
|
||||
AVFrameSideData *sd;
|
||||
DnnDetectContext *ctx = filter_ctx->priv;
|
||||
@ -472,7 +472,7 @@ static int dnn_detect_post_proc(AVFrame *frame, DNNData *output, uint32_t nb, AV
|
||||
DnnContext *dnn_ctx = &ctx->dnnctx;
|
||||
switch (dnn_ctx->backend_type) {
|
||||
case DNN_OV:
|
||||
return dnn_detect_post_proc_ov(frame, output, filter_ctx);
|
||||
return dnn_detect_post_proc_ov(frame, output, nb, filter_ctx);
|
||||
case DNN_TF:
|
||||
return dnn_detect_post_proc_tf(frame, output, filter_ctx);
|
||||
default:
|
||||
@ -559,11 +559,6 @@ static int check_output_nb(DnnDetectContext *ctx, DNNBackendType backend_type, i
|
||||
}
|
||||
return 0;
|
||||
case DNN_OV:
|
||||
if (output_nb != 1) {
|
||||
av_log(ctx, AV_LOG_ERROR, "Dnn detect filter with openvino backend needs 1 output only, \
|
||||
but get %d instead\n", output_nb);
|
||||
return AVERROR(EINVAL);
|
||||
}
|
||||
return 0;
|
||||
default:
|
||||
avpriv_report_missing_feature(ctx, "Dnn detect filter does not support current backend\n");
|
||||
|
||||
Loading…
Reference in New Issue
Block a user