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libavfilter: Remove DNNReturnType from DNN Module 

This patch removes all occurences of DNNReturnType from the DNN module.
This commit replaces DNN_SUCCESS by 0 (essentially the same), so the
functions with DNNReturnType now return 0 in case of success, the negative
values otherwise.

Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit is contained in:
Shubhanshu Saxena 2022-03-02 23:35:56 +05:30 committed by Guo Yejun
parent 1df77bab08
commit d0a999a0ab
16 changed files with 92 additions and 94 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
libavfilter/dnn/dnn_backend_common.c
View File

@ -70,7 +70,7 @@ int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backe
task->nb_output = exec_params->nb_output;
task->output_names = exec_params->output_names;
return DNN_SUCCESS;
return 0;
}
/**
@ -82,7 +82,7 @@ static void *async_thread_routine(void *args)
DNNAsyncExecModule *async_module = args;
void *request = async_module->args;
if (async_module->start_inference(request) != DNN_SUCCESS) {
if (async_module->start_inference(request) != 0) {
return DNN_ASYNC_FAIL;
}
async_module->callback(request);
@ -105,7 +105,7 @@ int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module)
async_module->start_inference = NULL;
async_module->callback = NULL;
async_module->args = NULL;
return DNN_SUCCESS;
return 0;
}
int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module)
@ -131,12 +131,12 @@ int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module)
}
#else
ret = async_module->start_inference(async_module->args);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
return ret;
}
async_module->callback(async_module->args);
#endif
return DNN_SUCCESS;
return 0;
}
DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVFrame **out)

8
libavfilter/dnn/dnn_backend_common.h
View File

@ -92,7 +92,7 @@ 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
*
* @returns DNN_SUCCESS if successful or error code otherwise.
* @returns 0 if successful or error code otherwise.
*/
int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc);
@ -101,7 +101,7 @@ int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backe
*
* @param async_module pointer to DNNAsyncExecModule module
*
* @returns DNN_SUCCESS if successful or error code otherwise.
* @returns 0 if successful or error code otherwise.
*/
int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module);
@ -117,7 +117,7 @@ int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module);
* @param ctx pointer to the backend context
* @param async_module pointer to DNNAsyncExecModule module
*
* @returns DNN_SUCCESS on the start of async inference or error code otherwise.
* @returns 0 on the start of async inference or error code otherwise.
*/
int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module);
@ -146,7 +146,7 @@ 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
*
* @returns DNN_SUCCESS if successful or error code otherwise.
* @returns 0 if successful or error code otherwise.
*/
int ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx);

16
libavfilter/dnn/dnn_backend_native.c
View File

@ -67,7 +67,7 @@ static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
av_freep(&lltask);
return AVERROR(ENOMEM);
}
return DNN_SUCCESS;
return 0;
}
static int get_input_native(void *model, DNNData *input, const char *input_name)
@ -87,7 +87,7 @@ static int get_input_native(void *model, DNNData *input, const char *input_name)
input->height = oprd->dims[1];
input->width = oprd->dims[2];
input->channels = oprd->dims[3];
return DNN_SUCCESS;
return 0;
}
}
@ -112,12 +112,12 @@ static int get_output_native(void *model, const char *input_name, int input_widt
};
ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, native_model, input_height, input_width, ctx);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
goto err;
}
ret = extract_lltask_from_task(&task, native_model->lltask_queue);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
goto err;
}
@ -387,7 +387,7 @@ static int execute_model_native(Queue *lltask_queue)
native_model->layers[layer].output_operand_index,
native_model->layers[layer].params,
&native_model->ctx);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to execute model\n");
goto err;
}
@ -451,7 +451,7 @@ int ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_p
}
ret = ff_dnn_fill_task(task, exec_params, native_model, ctx->options.async, 1);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_freep(&task);
return ret;
}
@ -463,7 +463,7 @@ int ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_p
}
ret = extract_lltask_from_task(task, native_model->lltask_queue);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
return ret;
}
@ -477,7 +477,7 @@ int ff_dnn_flush_native(const DNNModel *model)
if (ff_queue_size(native_model->lltask_queue) == 0) {
// no pending task need to flush
return DNN_SUCCESS;
return 0;
}
// for now, use sync node with flush operation

2
libavfilter/dnn/dnn_backend_native_layer_avgpool.c
View File

@ -143,5 +143,5 @@ int ff_dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_ope
}
}
return DNN_SUCCESS;
return 0;
}

4
libavfilter/dnn/dnn_backend_native_layer_conv2d.c
View File

@ -190,7 +190,7 @@ int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_opera
#if HAVE_PTHREAD_CANCEL
int thread_num = (ctx->options.conv2d_threads <= 0 || ctx->options.conv2d_threads > av_cpu_count())
? (av_cpu_count() + 1) : (ctx->options.conv2d_threads);
int ret = DNN_SUCCESS, thread_stride;
int ret = 0, thread_stride;
ThreadParam *thread_param;
#else
ThreadParam thread_param = { 0 };
@ -260,6 +260,6 @@ int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_opera
thread_param.thread_end = height - pad_size;
dnn_execute_layer_conv2d_thread(&thread_param);
return DNN_SUCCESS;
return 0;
#endif
}

2
libavfilter/dnn/dnn_backend_native_layer_dense.c
View File

@ -147,5 +147,5 @@ int ff_dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operan
output += dense_params->output_num;
}
}
return DNN_SUCCESS;
return 0;
}

2
libavfilter/dnn/dnn_backend_native_layer_depth2space.c
View File

@ -98,5 +98,5 @@ int ff_dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_
}
output += output_linesize;
}
return DNN_SUCCESS;
return 0;
}

48
libavfilter/dnn/dnn_backend_openvino.c
View File

@ -191,7 +191,7 @@ static int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
}
ie_blob_free(&input_blob);
return DNN_SUCCESS;
return 0;
}
static void infer_completion_callback(void *args)
@ -303,7 +303,7 @@ static void infer_completion_callback(void *args)
static int init_model_ov(OVModel *ov_model, const char *input_name, const char *output_name)
{
int ret = DNN_SUCCESS;
int ret = 0;
OVContext *ctx = &ov_model->ctx;
IEStatusCode status;
ie_available_devices_t a_dev;
@ -433,7 +433,7 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
goto err;
}
return DNN_SUCCESS;
return 0;
err:
ff_dnn_free_model_ov(&ov_model->model);
@ -444,7 +444,7 @@ static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
{
IEStatusCode status;
LastLevelTaskItem *lltask;
int ret = DNN_SUCCESS;
int ret = 0;
TaskItem *task;
OVContext *ctx;
OVModel *ov_model;
@ -452,7 +452,7 @@ static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
if (ff_queue_size(inferenceq) == 0) {
ie_infer_request_free(&request->infer_request);
av_freep(&request);
return DNN_SUCCESS;
return 0;
}
lltask = ff_queue_peek_front(inferenceq);
@ -462,7 +462,7 @@ static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
if (task->async) {
ret = fill_model_input_ov(ov_model, request);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
goto err;
}
status = ie_infer_set_completion_callback(request->infer_request, &request->callback);
@ -477,10 +477,10 @@ static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
ret = DNN_GENERIC_ERROR;
goto err;
}
return DNN_SUCCESS;
return 0;
} else {
ret = fill_model_input_ov(ov_model, request);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
goto err;
}
status = ie_infer_request_infer(request->infer_request);
@ -490,7 +490,7 @@ static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
goto err;
}
infer_completion_callback(request);
return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_GENERIC_ERROR;
return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_ERROR;
}
err:
if (ff_safe_queue_push_back(ov_model->request_queue, request) < 0) {
@ -537,7 +537,7 @@ static int get_input_ov(void *model, DNNData *input, const char *input_name)
input->height = input_resizable ? -1 : dims.dims[2];
input->width = input_resizable ? -1 : dims.dims[3];
input->dt = precision_to_datatype(precision);
return DNN_SUCCESS;
return 0;
} else {
//incorrect input name
APPEND_STRING(all_input_names, model_input_name)
@ -604,7 +604,7 @@ static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Q
av_freep(&lltask);
return AVERROR(ENOMEM);
}
return DNN_SUCCESS;
return 0;
}
case DFT_ANALYTICS_CLASSIFY:
{
@ -617,7 +617,7 @@ static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Q
task->inference_done = 0;
if (!contain_valid_detection_bbox(frame)) {
return DNN_SUCCESS;
return 0;
}
sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
@ -645,7 +645,7 @@ static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Q
return AVERROR(ENOMEM);
}
}
return DNN_SUCCESS;
return 0;
}
default:
av_assert0(!"should not reach here");
@ -690,19 +690,19 @@ static int get_output_ov(void *model, const char *input_name, int input_width, i
if (!ov_model->exe_network) {
ret = init_model_ov(ov_model, input_name, output_name);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return ret;
}
}
ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, ov_model, input_height, input_width, ctx);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
goto err;
}
ret = extract_lltask_from_task(ov_model->model->func_type, &task, ov_model->lltask_queue, NULL);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
goto err;
}
@ -795,7 +795,7 @@ int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_param
if (!ov_model->exe_network) {
ret = init_model_ov(ov_model, exec_params->input_name, exec_params->output_names[0]);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return ret;
}
@ -808,7 +808,7 @@ int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_param
}
ret = ff_dnn_fill_task(task, exec_params, ov_model, ctx->options.async, 1);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_freep(&task);
return ret;
}
@ -820,7 +820,7 @@ int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_param
}
ret = extract_lltask_from_task(model->func_type, task, ov_model->lltask_queue, exec_params);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
return ret;
}
@ -834,12 +834,12 @@ int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_param
}
ret = execute_model_ov(request, ov_model->lltask_queue);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
return ret;
}
}
return DNN_SUCCESS;
return 0;
}
else {
if (model->func_type == DFT_ANALYTICS_CLASSIFY) {
@ -879,7 +879,7 @@ int ff_dnn_flush_ov(const DNNModel *model)
if (ff_queue_size(ov_model->lltask_queue) == 0) {
// no pending task need to flush
return DNN_SUCCESS;
return 0;
}
request = ff_safe_queue_pop_front(ov_model->request_queue);
@ -889,7 +889,7 @@ int ff_dnn_flush_ov(const DNNModel *model)
}
ret = fill_model_input_ov(ov_model, request);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to fill model input.\n");
return ret;
}
@ -904,7 +904,7 @@ int ff_dnn_flush_ov(const DNNModel *model)
return DNN_GENERIC_ERROR;
}
return DNN_SUCCESS;
return 0;
}
void ff_dnn_free_model_ov(DNNModel **model)

56
libavfilter/dnn/dnn_backend_tf.c
View File

@ -151,7 +151,7 @@ static TFInferRequest *tf_create_inference_request(void)
* Start synchronous inference for the TensorFlow model.
*
* @param request pointer to the TFRequestItem for inference
* @retval DNN_SUCCESS if execution is successful
* @retval 0 if execution is successful
* @retval AVERROR(EINVAL) if request is NULL
* @retval DNN_GENERIC_ERROR if execution fails
*/
@ -181,7 +181,7 @@ static int tf_start_inference(void *args)
}
return DNN_GENERIC_ERROR;
}
return DNN_SUCCESS;
return 0;
}
/**
@ -220,7 +220,7 @@ static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
av_freep(&lltask);
return AVERROR(ENOMEM);
}
return DNN_SUCCESS;
return 0;
}
static TF_Buffer *read_graph(const char *model_filename)
@ -311,7 +311,7 @@ static int get_input_tf(void *model, DNNData *input, const char *input_name)
input->width = dims[2];
input->channels = dims[3];
return DNN_SUCCESS;
return 0;
}
static int get_output_tf(void *model, const char *input_name, int input_width, int input_height,
@ -331,12 +331,12 @@ static int get_output_tf(void *model, const char *input_name, int input_width, i
};
ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, tf_model, input_height, input_width, ctx);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
goto err;
}
ret = extract_lltask_from_task(&task, tf_model->lltask_queue);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
goto err;
}
@ -487,7 +487,7 @@ static int load_tf_model(TFModel *tf_model, const char *model_filename)
}
}
return DNN_SUCCESS;
return 0;
}
#define NAME_BUFFER_SIZE 256
@ -606,7 +606,7 @@ static int add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Oper
goto err;
}
return DNN_SUCCESS;
return 0;
err:
TF_DeleteTensor(kernel_tensor);
TF_DeleteTensor(biases_tensor);
@ -635,7 +635,7 @@ static int add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
return DNN_GENERIC_ERROR;
}
return DNN_SUCCESS;
return 0;
}
static int add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
@ -693,7 +693,7 @@ static int add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
return DNN_GENERIC_ERROR;
}
return DNN_SUCCESS;
return 0;
}
static int add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
@ -742,7 +742,7 @@ static int add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
return DNN_GENERIC_ERROR;
}
return DNN_SUCCESS;
return 0;
}
static int load_native_model(TFModel *tf_model, const char *model_filename)
@ -808,7 +808,7 @@ static int load_native_model(TFModel *tf_model, const char *model_filename)
for (layer = 0; layer < native_model->layers_num; ++layer){
switch (native_model->layers[layer].type){
case DLT_INPUT:
layer_add_res = DNN_SUCCESS;
layer_add_res = 0;
break;
case DLT_CONV2D:
layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
@ -830,7 +830,7 @@ static int load_native_model(TFModel *tf_model, const char *model_filename)
CLEANUP_ON_ERROR(tf_model);
}
if (layer_add_res != DNN_SUCCESS){
if (layer_add_res != 0){
CLEANUP_ON_ERROR(tf_model);
}
}
@ -846,7 +846,7 @@ static int load_native_model(TFModel *tf_model, const char *model_filename)
ff_dnn_free_model_native(&model);
return DNN_SUCCESS;
return 0;
}
DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx)
@ -876,8 +876,8 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_
goto err;
}
if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
if (load_tf_model(tf_model, model_filename) != 0){
if (load_native_model(tf_model, model_filename) != 0){
goto err;
}
}
@ -958,7 +958,7 @@ static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
request->lltask = lltask;
ret = get_input_tf(tf_model, &input, task->input_name);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
goto err;
}
@ -1032,7 +1032,7 @@ static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
infer_request->tf_outputs[i].index = 0;
}
return DNN_SUCCESS;
return 0;
err:
tf_free_request(infer_request);
return ret;
@ -1106,7 +1106,7 @@ static int execute_model_tf(TFRequestItem *request, Queue *lltask_queue)
if (ff_queue_size(lltask_queue) == 0) {
destroy_request_item(&request);
return DNN_SUCCESS;
return 0;
}
lltask = ff_queue_peek_front(lltask_queue);
@ -1115,23 +1115,23 @@ static int execute_model_tf(TFRequestItem *request, Queue *lltask_queue)
ctx = &tf_model->ctx;
ret = fill_model_input_tf(tf_model, request);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
goto err;
}
if (task->async) {
if (ff_dnn_start_inference_async(ctx, &request->exec_module) != DNN_SUCCESS) {
if (ff_dnn_start_inference_async(ctx, &request->exec_module) != 0) {
goto err;
}
return DNN_SUCCESS;
return 0;
}
else {
ret = tf_start_inference(request);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
goto err;
}
infer_completion_callback(request);
return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_GENERIC_ERROR;
return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_ERROR;
}
err:
tf_free_request(request->infer_request);
@ -1161,7 +1161,7 @@ int ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_param
}
ret = ff_dnn_fill_task(task, exec_params, tf_model, ctx->options.async, 1);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_freep(&task);
return ret;
}
@ -1173,7 +1173,7 @@ int ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_param
}
ret = extract_lltask_from_task(task, tf_model->lltask_queue);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
return ret;
}
@ -1201,7 +1201,7 @@ int ff_dnn_flush_tf(const DNNModel *model)
if (ff_queue_size(tf_model->lltask_queue) == 0) {
// no pending task need to flush
return DNN_SUCCESS;
return 0;
}
request = ff_safe_queue_pop_front(tf_model->request_queue);
@ -1211,7 +1211,7 @@ int ff_dnn_flush_tf(const DNNModel *model)
}
ret = fill_model_input_tf(tf_model, request);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to fill model input.\n");
if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
destroy_request_item(&request);

14
libavfilter/dnn/dnn_io_proc.c
View File

@ -57,12 +57,12 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
(const int[4]){frame->width * 3 * sizeof(float), 0, 0, 0}, 0, frame->height,
(uint8_t * const*)frame->data, frame->linesize);
sws_freeContext(sws_ctx);
return DNN_SUCCESS;
return 0;
case AV_PIX_FMT_GRAYF32:
av_image_copy_plane(frame->data[0], frame->linesize[0],
output->data, bytewidth,
bytewidth, frame->height);
return DNN_SUCCESS;
return 0;
case AV_PIX_FMT_YUV420P:
case AV_PIX_FMT_YUV422P:
case AV_PIX_FMT_YUV444P:
@ -88,13 +88,13 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
(const int[4]){frame->width * sizeof(float), 0, 0, 0}, 0, frame->height,
(uint8_t * const*)frame->data, frame->linesize);
sws_freeContext(sws_ctx);
return DNN_SUCCESS;
return 0;
default:
avpriv_report_missing_feature(log_ctx, "%s", av_get_pix_fmt_name(frame->format));
return AVERROR(ENOSYS);
}
return DNN_SUCCESS;
return 0;
}
int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
@ -169,7 +169,7 @@ int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
return AVERROR(ENOSYS);
}
return DNN_SUCCESS;
return 0;
}
static enum AVPixelFormat get_pixel_format(DNNData *data)
@ -197,7 +197,7 @@ int ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t bbox_index
uint8_t *bbox_data[4];
struct SwsContext *sws_ctx;
int linesizes[4];
int ret = DNN_SUCCESS;
int ret = 0;
enum AVPixelFormat fmt;
int left, top, width, height;
const AVDetectionBBoxHeader *header;
@ -255,7 +255,7 @@ int ff_frame_to_dnn_detect(AVFrame *frame, DNNData *input, void *log_ctx)
{
struct SwsContext *sws_ctx;
int linesizes[4];
int ret = DNN_SUCCESS;
int ret = 0;
enum AVPixelFormat fmt = get_pixel_format(input);
sws_ctx = sws_getContext(frame->width, frame->height, frame->format,
input->width, input->height, fmt,

2
libavfilter/dnn_interface.h
View File

@ -32,8 +32,6 @@
#define DNN_GENERIC_ERROR FFERRTAG('D','N','N','!')
typedef enum {DNN_SUCCESS, DNN_ERROR} DNNReturnType;
typedef enum {DNN_NATIVE, DNN_TF, DNN_OV} DNNBackendType;
typedef enum {DNN_FLOAT = 1, DNN_UINT8 = 4} DNNDataType;

2
libavfilter/vf_derain.c
View File

@ -74,7 +74,7 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in)
av_frame_copy_props(out, in);
dnn_result = ff_dnn_execute_model(&dr_context->dnnctx, in, out);
if (dnn_result != DNN_SUCCESS){
if (dnn_result != 0){
av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
av_frame_free(&in);
return dnn_result;

4
libavfilter/vf_dnn_classify.c
View File

@ -213,7 +213,7 @@ static int dnn_classify_flush_frame(AVFilterLink *outlink, int64_t pts, int64_t
DNNAsyncStatusType async_state;
ret = ff_dnn_flush(&ctx->dnnctx);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
return -1;
}
@ -253,7 +253,7 @@ static int dnn_classify_activate(AVFilterContext *filter_ctx)
if (ret < 0)
return ret;
if (ret > 0) {
if (ff_dnn_execute_model_classification(&ctx->dnnctx, in, NULL, ctx->target) != DNN_SUCCESS) {
if (ff_dnn_execute_model_classification(&ctx->dnnctx, in, NULL, ctx->target) != 0) {
return AVERROR(EIO);
}
}

4
libavfilter/vf_dnn_detect.c
View File

@ -356,7 +356,7 @@ static int dnn_detect_flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *o
DNNAsyncStatusType async_state;
ret = ff_dnn_flush(&ctx->dnnctx);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
return -1;
}
@ -396,7 +396,7 @@ static int dnn_detect_activate(AVFilterContext *filter_ctx)
if (ret < 0)
return ret;
if (ret > 0) {
if (ff_dnn_execute_model(&ctx->dnnctx, in, NULL) != DNN_SUCCESS) {
if (ff_dnn_execute_model(&ctx->dnnctx, in, NULL) != 0) {
return AVERROR(EIO);
}
}

8
libavfilter/vf_dnn_processing.c
View File

@ -139,7 +139,7 @@ static int config_input(AVFilterLink *inlink)
int check;
result = ff_dnn_get_input(&ctx->dnnctx, &model_input);
if (result != DNN_SUCCESS) {
if (result != 0) {
av_log(ctx, AV_LOG_ERROR, "could not get input from the model\n");
return result;
}
@ -199,7 +199,7 @@ static int config_output(AVFilterLink *outlink)
// have a try run in case that the dnn model resize the frame
result = ff_dnn_get_output(&ctx->dnnctx, inlink->w, inlink->h, &outlink->w, &outlink->h);
if (result != DNN_SUCCESS) {
if (result != 0) {
av_log(ctx, AV_LOG_ERROR, "could not get output from the model\n");
return result;
}
@ -247,7 +247,7 @@ static int flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *out_pts)
DNNAsyncStatusType async_state;
ret = ff_dnn_flush(&ctx->dnnctx);
if (ret != DNN_SUCCESS) {
if (ret != 0) {
return -1;
}
@ -296,7 +296,7 @@ static int activate(AVFilterContext *filter_ctx)
return AVERROR(ENOMEM);
}
av_frame_copy_props(out, in);
if (ff_dnn_execute_model(&ctx->dnnctx, in, out) != DNN_SUCCESS) {
if (ff_dnn_execute_model(&ctx->dnnctx, in, out) != 0) {
return AVERROR(EIO);
}
}

4
libavfilter/vf_sr.c
View File

@ -82,7 +82,7 @@ static int config_output(AVFilterLink *outlink)
// have a try run in case that the dnn model resize the frame
result = ff_dnn_get_output(&ctx->dnnctx, inlink->w, inlink->h, &out_width, &out_height);
if (result != DNN_SUCCESS) {
if (result != 0) {
av_log(ctx, AV_LOG_ERROR, "could not get output from the model\n");
return result;
}
@ -139,7 +139,7 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in)
dnn_result = ff_dnn_execute_model(&ctx->dnnctx, in, out);
}
if (dnn_result != DNN_SUCCESS){
if (dnn_result != 0){
av_log(ctx, AV_LOG_ERROR, "failed to execute loaded model\n");
av_frame_free(&in);
av_frame_free(&out);