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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 2021-07-05 16:00:56 +05:30 committed by Guo Yejun
parent 08d8b3b631
commit b849228ae0
1 changed files with 80 additions and 57 deletions
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137
libavfilter/dnn/dnn_backend_tf.c
View File

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