This commit rearranges the existing code to create two separate functions
for filling request with execution data and the completion callback.
Signed-off-by: Shubhanshu Saxena <shubhanshu....@gmail.com>
---
libavfilter/dnn/dnn_backend_tf.c | 81 ++++++++++++++++++++++----------
1 file changed, 57 insertions(+), 24 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_tf.c b/libavfilter/dnn/dnn_backend_tf.c
index 793b108e55..5d34da5db1 100644
--- a/libavfilter/dnn/dnn_backend_tf.c
+++ b/libavfilter/dnn/dnn_backend_tf.c
@@ -826,20 +826,16 @@ DNNModel *ff_dnn_load_model_tf(const char
*model_filename, DNNFunctionType func_
return model;
}
-static DNNReturnType execute_model_tf(RequestItem *request, Queue
*inference_queue)
-{
- TFModel *tf_model;
- TFContext *ctx;
- tf_infer_request *infer_request;
+static DNNReturnType fill_model_input_tf(TFModel *tf_model, RequestItem
*request) {
+ DNNData input;
InferenceItem *inference;
TaskItem *task;
- DNNData input, *outputs;
+ tf_infer_request *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)
@@ -852,7 +848,7 @@ static DNNReturnType execute_model_tf(RequestItem *request,
Queue *inference_que
infer_request->tf_input = av_malloc(sizeof(TF_Output));
infer_request->tf_input->oper = TF_GraphOperationByName(tf_model->graph,
task->input_name);
if (!infer_request->tf_input->oper){
- av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n",
input_name);
+ av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n",
task->input_name);
return DNN_ERROR;
}
infer_request->tf_input->index = 0;
@@ -902,22 +898,23 @@ static DNNReturnType execute_model_tf(RequestItem
*request, Queue *inference_que
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) {
- tf_free_request(infer_request);
- av_log(ctx, AV_LOG_ERROR, "Failed to run session when executing
model\n");
- return DNN_ERROR;
- }
+ return DNN_SUCCESS;
+}
+
+static void infer_completion_callback(void *args) {
+ RequestItem *request = args;
+ InferenceItem *inference = request->inference;
+ TaskItem *task = inference->task;
+ DNNData *outputs;
+ tf_infer_request *infer_request = request->infer_request;
+ TFModel *tf_model = task->model;
+ TFContext *ctx = &tf_model->ctx;
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;
+ return;
}
for (uint32_t i = 0; i < task->nb_output; ++i) {
@@ -944,7 +941,7 @@ static DNNReturnType execute_model_tf(RequestItem *request,
Queue *inference_que
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;
+ return;
}
tf_model->model->detect_post_proc(task->out_frame, outputs,
task->nb_output, tf_model->model->filter_ctx);
break;
@@ -955,7 +952,7 @@ static DNNReturnType execute_model_tf(RequestItem *request,
Queue *inference_que
}
}
av_log(ctx, AV_LOG_ERROR, "Tensorflow backend does not support this
kind of dnn filter now\n");
- return DNN_ERROR;
+ return;
}
for (uint32_t i = 0; i < task->nb_output; ++i) {
if (infer_request->output_tensors[i]) {
@@ -966,7 +963,43 @@ static DNNReturnType execute_model_tf(RequestItem
*request, Queue *inference_que
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;
+}
+
+static DNNReturnType execute_model_tf(RequestItem *request, Queue
*inference_queue)
+{
+ TFModel *tf_model;
+ TFContext *ctx;
+ tf_infer_request *infer_request;
+ InferenceItem *inference;
+ TaskItem *task;
+
+ 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;
+ }
+ infer_completion_callback(request);
+ return (task->inference_done == task->inference_todo) ? DNN_SUCCESS :
DNN_ERROR;
+ }
}
DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams
*exec_params)
--
2.25.1
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