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lavfi/dnn_backend_tf: Separate function for filling RequestItem
This commit rearranges the existing code to create separate function for filling request with execution data. Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit is contained in:
Shubhanshu Saxena
Guo Yejun
parent
08d8b3b631
commit
b849228ae0
@ -839,20 +839,16 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_
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return model;
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}
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static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *inference_queue)
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{
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TFModel *tf_model;
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TFContext *ctx;
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TFInferRequest *infer_request;
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static DNNReturnType fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
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DNNData input;
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InferenceItem *inference;
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TaskItem *task;
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DNNData input, *outputs;
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TFInferRequest *infer_request;
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TFContext *ctx = &tf_model->ctx;
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inference = ff_queue_pop_front(inference_queue);
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inference = ff_queue_pop_front(tf_model->inference_queue);
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av_assert0(inference);
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task = inference->task;
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tf_model = task->model;
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ctx = &tf_model->ctx;
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request->inference = inference;
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if (get_input_tf(tf_model, &input, task->input_name) != DNN_SUCCESS)
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@ -916,63 +912,90 @@ static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *inference_q
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infer_request->tf_outputs[i].index = 0;
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}
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TF_SessionRun(tf_model->session, NULL,
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infer_request->tf_input, &infer_request->input_tensor, 1,
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infer_request->tf_outputs, infer_request->output_tensors,
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task->nb_output, NULL, 0, NULL,
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tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK) {
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return DNN_SUCCESS;
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}
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static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *inference_queue)
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{
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TFModel *tf_model;
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TFContext *ctx;
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TFInferRequest *infer_request;
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InferenceItem *inference;
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TaskItem *task;
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DNNData *outputs;
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inference = ff_queue_peek_front(inference_queue);
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task = inference->task;
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tf_model = task->model;
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ctx = &tf_model->ctx;
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if (task->async) {
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avpriv_report_missing_feature(ctx, "Async execution not supported");
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return DNN_ERROR;
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} else {
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if (fill_model_input_tf(tf_model, request) != DNN_SUCCESS) {
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return DNN_ERROR;
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}
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infer_request = request->infer_request;
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TF_SessionRun(tf_model->session, NULL,
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infer_request->tf_input, &infer_request->input_tensor, 1,
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infer_request->tf_outputs, infer_request->output_tensors,
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task->nb_output, NULL, 0, NULL,
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tf_model->status);
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if (TF_GetCode(tf_model->status) != TF_OK) {
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tf_free_request(infer_request);
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av_log(ctx, AV_LOG_ERROR, "Failed to run session when executing model\n");
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return DNN_ERROR;
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}
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outputs = av_malloc_array(task->nb_output, sizeof(*outputs));
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if (!outputs) {
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tf_free_request(infer_request);
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av_log(ctx, AV_LOG_ERROR, "Failed to run session when executing model\n");
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av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *outputs\n");
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return DNN_ERROR;
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}
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}
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outputs = av_malloc_array(task->nb_output, sizeof(*outputs));
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if (!outputs) {
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tf_free_request(infer_request);
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av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *outputs\n");
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return DNN_ERROR;
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}
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for (uint32_t i = 0; i < task->nb_output; ++i) {
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outputs[i].height = TF_Dim(infer_request->output_tensors[i], 1);
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outputs[i].width = TF_Dim(infer_request->output_tensors[i], 2);
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outputs[i].channels = TF_Dim(infer_request->output_tensors[i], 3);
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outputs[i].data = TF_TensorData(infer_request->output_tensors[i]);
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outputs[i].dt = TF_TensorType(infer_request->output_tensors[i]);
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}
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switch (tf_model->model->func_type) {
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case DFT_PROCESS_FRAME:
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//it only support 1 output if it's frame in & frame out
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if (task->do_ioproc) {
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if (tf_model->model->frame_post_proc != NULL) {
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tf_model->model->frame_post_proc(task->out_frame, outputs, tf_model->model->filter_ctx);
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for (uint32_t i = 0; i < task->nb_output; ++i) {
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outputs[i].height = TF_Dim(infer_request->output_tensors[i], 1);
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outputs[i].width = TF_Dim(infer_request->output_tensors[i], 2);
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outputs[i].channels = TF_Dim(infer_request->output_tensors[i], 3);
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outputs[i].data = TF_TensorData(infer_request->output_tensors[i]);
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outputs[i].dt = TF_TensorType(infer_request->output_tensors[i]);
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}
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switch (tf_model->model->func_type) {
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case DFT_PROCESS_FRAME:
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//it only support 1 output if it's frame in & frame out
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if (task->do_ioproc) {
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if (tf_model->model->frame_post_proc != NULL) {
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tf_model->model->frame_post_proc(task->out_frame, outputs, tf_model->model->filter_ctx);
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} else {
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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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ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
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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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} else {
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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 (!tf_model->model->detect_post_proc) {
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av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n");
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break;
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case DFT_ANALYTICS_DETECT:
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if (!tf_model->model->detect_post_proc) {
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av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n");
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return DNN_ERROR;
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}
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tf_model->model->detect_post_proc(task->out_frame, outputs, task->nb_output, tf_model->model->filter_ctx);
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break;
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default:
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tf_free_request(infer_request);
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av_log(ctx, AV_LOG_ERROR, "Tensorflow backend does not support this kind of dnn filter now\n");
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return DNN_ERROR;
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}
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tf_model->model->detect_post_proc(task->out_frame, outputs, task->nb_output, tf_model->model->filter_ctx);
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break;
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default:
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task->inference_done++;
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tf_free_request(infer_request);
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av_log(ctx, AV_LOG_ERROR, "Tensorflow backend does not support this kind of dnn filter now\n");
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return DNN_ERROR;
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av_freep(&outputs);
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ff_safe_queue_push_back(tf_model->request_queue, request);
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return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_ERROR;
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}
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task->inference_done++;
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tf_free_request(infer_request);
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av_freep(&outputs);
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ff_safe_queue_push_back(tf_model->request_queue, request);
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return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_ERROR;
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}
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DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params)
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