From 1df77bab08ac53482f94c4d4be2449cfa50b8e68 Mon Sep 17 00:00:00 2001 From: Shubhanshu Saxena Date: Wed, 2 Mar 2022 23:35:55 +0530 Subject: lavfi/dnn_backend_common: Return specific error codes Switch to returning specific error codes or DNN_GENERIC_ERROR when an error is encountered in the common DNN backend functions. Signed-off-by: Shubhanshu Saxena --- libavfilter/dnn/dnn_backend_common.c | 35 ++++++++++++++++++----------------- libavfilter/dnn/dnn_backend_common.h | 22 +++++++++------------- 2 files changed, 27 insertions(+), 30 deletions(-) (limited to 'libavfilter') diff --git a/libavfilter/dnn/dnn_backend_common.c b/libavfilter/dnn/dnn_backend_common.c index 6a9c4cc87f..64ed441415 100644 --- a/libavfilter/dnn/dnn_backend_common.c +++ b/libavfilter/dnn/dnn_backend_common.c @@ -47,19 +47,19 @@ int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func // currently, the filter does not need multiple outputs, // so we just pending the support until we really need it. avpriv_report_missing_feature(ctx, "multiple outputs"); - return AVERROR(EINVAL); + return AVERROR(ENOSYS); } return 0; } -DNNReturnType ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc) { +int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc) { if (task == NULL || exec_params == NULL || backend_model == NULL) - return DNN_ERROR; + return AVERROR(EINVAL); if (do_ioproc != 0 && do_ioproc != 1) - return DNN_ERROR; + return AVERROR(EINVAL); if (async != 0 && async != 1) - return DNN_ERROR; + return AVERROR(EINVAL); task->do_ioproc = do_ioproc; task->async = async; @@ -89,17 +89,17 @@ static void *async_thread_routine(void *args) return DNN_ASYNC_SUCCESS; } -DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module) +int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module) { void *status = 0; if (!async_module) { - return DNN_ERROR; + return AVERROR(EINVAL); } #if HAVE_PTHREAD_CANCEL pthread_join(async_module->thread_id, &status); if (status == DNN_ASYNC_FAIL) { av_log(NULL, AV_LOG_ERROR, "Last Inference Failed.\n"); - return DNN_ERROR; + return DNN_GENERIC_ERROR; } #endif async_module->start_inference = NULL; @@ -108,30 +108,31 @@ DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module) return DNN_SUCCESS; } -DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module) +int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module) { int ret; void *status = 0; if (!async_module) { av_log(ctx, AV_LOG_ERROR, "async_module is null when starting async inference.\n"); - return DNN_ERROR; + return AVERROR(EINVAL); } #if HAVE_PTHREAD_CANCEL pthread_join(async_module->thread_id, &status); if (status == DNN_ASYNC_FAIL) { av_log(ctx, AV_LOG_ERROR, "Unable to start inference as previous inference failed.\n"); - return DNN_ERROR; + return DNN_GENERIC_ERROR; } ret = pthread_create(&async_module->thread_id, NULL, async_thread_routine, async_module); if (ret != 0) { av_log(ctx, AV_LOG_ERROR, "Unable to start async inference.\n"); - return DNN_ERROR; + return ret; } #else - if (async_module->start_inference(async_module->args) != DNN_SUCCESS) { - return DNN_ERROR; + ret = async_module->start_inference(async_module->args); + if (ret != DNN_SUCCESS) { + return ret; } async_module->callback(async_module->args); #endif @@ -158,7 +159,7 @@ DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVF return DAST_SUCCESS; } -DNNReturnType ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx) +int ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx) { AVFrame *in_frame = NULL; AVFrame *out_frame = NULL; @@ -166,14 +167,14 @@ DNNReturnType ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams * in_frame = av_frame_alloc(); if (!in_frame) { av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input frame\n"); - return DNN_ERROR; + return AVERROR(ENOMEM); } out_frame = av_frame_alloc(); if (!out_frame) { av_frame_free(&in_frame); av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output frame\n"); - return DNN_ERROR; + return AVERROR(ENOMEM); } in_frame->width = input_width; diff --git a/libavfilter/dnn/dnn_backend_common.h b/libavfilter/dnn/dnn_backend_common.h index 6b6a5e21ae..fa79caee1f 100644 --- a/libavfilter/dnn/dnn_backend_common.h +++ b/libavfilter/dnn/dnn_backend_common.h @@ -60,7 +60,7 @@ typedef struct DNNAsyncExecModule { * Synchronous inference function for the backend * with corresponding request item as the argument. */ - DNNReturnType (*start_inference)(void *request); + int (*start_inference)(void *request); /** * Completion Callback for the backend. @@ -92,20 +92,18 @@ int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func * @param async flag for async execution. Must be 0 or 1 * @param do_ioproc flag for IO processing. Must be 0 or 1 * - * @retval DNN_SUCCESS if successful - * @retval DNN_ERROR if flags are invalid or any parameter is NULL + * @returns DNN_SUCCESS if successful or error code otherwise. */ -DNNReturnType ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc); +int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc); /** * Join the Async Execution thread and set module pointers to NULL. * * @param async_module pointer to DNNAsyncExecModule module * - * @retval DNN_SUCCESS if successful - * @retval DNN_ERROR if async_module is NULL + * @returns DNN_SUCCESS if successful or error code otherwise. */ -DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module); +int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module); /** * Start asynchronous inference routine for the TensorFlow @@ -119,10 +117,9 @@ DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module); * @param ctx pointer to the backend context * @param async_module pointer to DNNAsyncExecModule module * - * @retval DNN_SUCCESS on the start of async inference. - * @retval DNN_ERROR in case async inference cannot be started + * @returns DNN_SUCCESS on the start of async inference or error code otherwise. */ -DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module); +int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module); /** * Extract input and output frame from the Task Queue after @@ -149,9 +146,8 @@ DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVF * @param input_width width of input frame * @param ctx pointer to the backend context * - * @retval DNN_SUCCESS if successful - * @retval DNN_ERROR if allocation fails + * @returns DNN_SUCCESS if successful or error code otherwise. */ -DNNReturnType ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx); +int ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx); #endif -- cgit v1.2.3