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lavfi/dnn: DNNAsyncExecModule Execution Failure Handling
This commit adds the case handling if the asynchronous execution of a request fails by checking the exit status of the thread when joining before starting another execution. On failure, it does the cleanup as well. Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
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@ -23,6 +23,9 @@
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#include "dnn_backend_common.h"
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#define DNN_ASYNC_SUCCESS (void *)0
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#define DNN_ASYNC_FAIL (void *)-1
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int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func_type, DNNExecBaseParams *exec_params)
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{
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if (!exec_params) {
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@ -79,18 +82,25 @@ static void *async_thread_routine(void *args)
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DNNAsyncExecModule *async_module = args;
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void *request = async_module->args;
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async_module->start_inference(request);
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if (async_module->start_inference(request) != DNN_SUCCESS) {
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return DNN_ASYNC_FAIL;
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}
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async_module->callback(request);
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return NULL;
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return DNN_ASYNC_SUCCESS;
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}
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DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module)
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{
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void *status = 0;
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if (!async_module) {
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return DNN_ERROR;
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}
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#if HAVE_PTHREAD_CANCEL
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pthread_join(async_module->thread_id, NULL);
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pthread_join(async_module->thread_id, &status);
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if (status == DNN_ASYNC_FAIL) {
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av_log(NULL, AV_LOG_ERROR, "Last Inference Failed.\n");
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return DNN_ERROR;
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}
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#endif
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async_module->start_inference = NULL;
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async_module->callback = NULL;
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@ -101,6 +111,7 @@ DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module)
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DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module)
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{
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int ret;
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void *status = 0;
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if (!async_module) {
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av_log(ctx, AV_LOG_ERROR, "async_module is null when starting async inference.\n");
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@ -108,7 +119,11 @@ DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_
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}
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#if HAVE_PTHREAD_CANCEL
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pthread_join(async_module->thread_id, NULL);
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pthread_join(async_module->thread_id, &status);
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if (status == DNN_ASYNC_FAIL) {
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av_log(ctx, AV_LOG_ERROR, "Unable to start inference as previous inference failed.\n");
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return DNN_ERROR;
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}
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ret = pthread_create(&async_module->thread_id, NULL, async_thread_routine, async_module);
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if (ret != 0) {
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av_log(ctx, AV_LOG_ERROR, "Unable to start async inference.\n");
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@ -91,6 +91,7 @@ AVFILTER_DEFINE_CLASS(dnn_tensorflow);
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static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *inference_queue);
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static void infer_completion_callback(void *args);
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static inline void destroy_request_item(TFRequestItem **arg);
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static void free_buffer(void *data, size_t length)
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{
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@ -172,6 +173,10 @@ static DNNReturnType tf_start_inference(void *args)
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request->status);
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if (TF_GetCode(request->status) != TF_OK) {
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av_log(&tf_model->ctx, AV_LOG_ERROR, "%s", TF_Message(request->status));
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tf_free_request(infer_request);
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if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
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destroy_request_item(&request);
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}
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return DNN_ERROR;
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}
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return DNN_SUCCESS;
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@ -1095,7 +1100,10 @@ static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *inference_q
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}
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if (task->async) {
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return ff_dnn_start_inference_async(ctx, &request->exec_module);
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if (ff_dnn_start_inference_async(ctx, &request->exec_module) != DNN_SUCCESS) {
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goto err;
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}
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return DNN_SUCCESS;
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} else {
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if (tf_start_inference(request) != DNN_SUCCESS) {
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goto err;
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