avfilter/dnn: Remove a level of dereference
For code such as 'model->model = ov_model' is confusing. We can just drop the member variable and use cast to get the subclass. Signed-off-by: Zhao Zhili <zhilizhao@tencent.com> Reviewed-by: Wenbin Chen <wenbin.chen@intel.com> Reviewed-by: Guo Yejun <yejun.guo@intel.com>
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@ -517,7 +517,7 @@ static void dnn_free_model_ov(DNNModel **model)
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if (!model || !*model)
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return;
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ov_model = (*model)->model;
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ov_model = (OVModel *)(*model);
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while (ff_safe_queue_size(ov_model->request_queue) != 0) {
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OVRequestItem *item = ff_safe_queue_pop_front(ov_model->request_queue);
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if (item && item->infer_request) {
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@ -1059,9 +1059,9 @@ err:
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return ret;
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}
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static int get_input_ov(void *model, DNNData *input, const char *input_name)
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static int get_input_ov(DNNModel *model, DNNData *input, const char *input_name)
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{
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OVModel *ov_model = model;
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OVModel *ov_model = (OVModel *)model;
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DnnContext *ctx = ov_model->ctx;
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int input_resizable = ctx->ov_option.input_resizable;
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@ -1255,7 +1255,7 @@ static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Q
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}
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}
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static int get_output_ov(void *model, const char *input_name, int input_width, int input_height,
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static int get_output_ov(DNNModel *model, const char *input_name, int input_width, int input_height,
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const char *output_name, int *output_width, int *output_height)
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{
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#if HAVE_OPENVINO2
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@ -1268,7 +1268,7 @@ static int get_output_ov(void *model, const char *input_name, int input_width, i
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input_shapes_t input_shapes;
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#endif
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int ret;
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OVModel *ov_model = model;
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OVModel *ov_model = (OVModel *)model;
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DnnContext *ctx = ov_model->ctx;
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TaskItem task;
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OVRequestItem *request;
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@ -1383,7 +1383,6 @@ static DNNModel *dnn_load_model_ov(DnnContext *ctx, DNNFunctionType func_type, A
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return NULL;
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ov_model->ctx = ctx;
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model = &ov_model->model;
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model->model = ov_model;
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#if HAVE_OPENVINO2
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status = ov_core_create(&core);
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@ -1470,7 +1469,7 @@ err:
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static int dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params)
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{
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OVModel *ov_model = model->model;
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OVModel *ov_model = (OVModel *)model;
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DnnContext *ctx = ov_model->ctx;
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OVRequestItem *request;
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TaskItem *task;
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@ -1558,13 +1557,13 @@ static int dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_p
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static DNNAsyncStatusType dnn_get_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out)
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{
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OVModel *ov_model = model->model;
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OVModel *ov_model = (OVModel *)model;
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return ff_dnn_get_result_common(ov_model->task_queue, in, out);
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}
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static int dnn_flush_ov(const DNNModel *model)
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{
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OVModel *ov_model = model->model;
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OVModel *ov_model = (OVModel *)model;
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DnnContext *ctx = ov_model->ctx;
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OVRequestItem *request;
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#if HAVE_OPENVINO2
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@ -262,9 +262,9 @@ static TF_Tensor *allocate_input_tensor(const DNNData *input)
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input_dims[1] * input_dims[2] * input_dims[3] * size);
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}
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static int get_input_tf(void *model, DNNData *input, const char *input_name)
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static int get_input_tf(DNNModel *model, DNNData *input, const char *input_name)
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{
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TFModel *tf_model = model;
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TFModel *tf_model = (TFModel *)model;
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DnnContext *ctx = tf_model->ctx;
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TF_Status *status;
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TF_DataType dt;
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@ -310,11 +310,11 @@ static int get_input_tf(void *model, DNNData *input, const char *input_name)
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return 0;
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}
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static int get_output_tf(void *model, const char *input_name, int input_width, int input_height,
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static int get_output_tf(DNNModel *model, const char *input_name, int input_width, int input_height,
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const char *output_name, int *output_width, int *output_height)
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{
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int ret;
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TFModel *tf_model = model;
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TFModel *tf_model = (TFModel *)model;
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DnnContext *ctx = tf_model->ctx;
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TaskItem task;
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TFRequestItem *request;
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@ -486,7 +486,7 @@ static void dnn_free_model_tf(DNNModel **model)
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if (!model || !*model)
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return;
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tf_model = (*model)->model;
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tf_model = (TFModel *)(*model);
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while (ff_safe_queue_size(tf_model->request_queue) != 0) {
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TFRequestItem *item = ff_safe_queue_pop_front(tf_model->request_queue);
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destroy_request_item(&item);
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@ -530,7 +530,6 @@ static DNNModel *dnn_load_model_tf(DnnContext *ctx, DNNFunctionType func_type, A
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if (!tf_model)
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return NULL;
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model = &tf_model->model;
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model->model = tf_model;
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tf_model->ctx = ctx;
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if (load_tf_model(tf_model, ctx->model_filename) != 0){
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@ -611,7 +610,7 @@ static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
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task = lltask->task;
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request->lltask = lltask;
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ret = get_input_tf(tf_model, &input, task->input_name);
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ret = get_input_tf(&tf_model->model, &input, task->input_name);
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if (ret != 0) {
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goto err;
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}
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@ -803,7 +802,7 @@ err:
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static int dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params)
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{
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TFModel *tf_model = model->model;
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TFModel *tf_model = (TFModel *)model;
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DnnContext *ctx = tf_model->ctx;
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TaskItem *task;
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TFRequestItem *request;
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@ -851,13 +850,13 @@ static int dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_p
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static DNNAsyncStatusType dnn_get_result_tf(const DNNModel *model, AVFrame **in, AVFrame **out)
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{
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TFModel *tf_model = model->model;
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TFModel *tf_model = (TFModel *)model;
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return ff_dnn_get_result_common(tf_model->task_queue, in, out);
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}
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static int dnn_flush_tf(const DNNModel *model)
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{
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TFModel *tf_model = model->model;
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TFModel *tf_model = (TFModel *)model;
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DnnContext *ctx = tf_model->ctx;
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TFRequestItem *request;
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int ret;
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@ -119,7 +119,7 @@ static void dnn_free_model_th(DNNModel **model)
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if (!model || !*model)
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return;
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th_model = (THModel *) (*model)->model;
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th_model = (THModel *) (*model);
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while (ff_safe_queue_size(th_model->request_queue) != 0) {
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THRequestItem *item = (THRequestItem *)ff_safe_queue_pop_front(th_model->request_queue);
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destroy_request_item(&item);
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@ -144,7 +144,7 @@ static void dnn_free_model_th(DNNModel **model)
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*model = NULL;
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}
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static int get_input_th(void *model, DNNData *input, const char *input_name)
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static int get_input_th(DNNModel *model, DNNData *input, const char *input_name)
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{
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input->dt = DNN_FLOAT;
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input->order = DCO_RGB;
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@ -179,7 +179,7 @@ static int fill_model_input_th(THModel *th_model, THRequestItem *request)
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task = lltask->task;
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infer_request = request->infer_request;
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ret = get_input_th(th_model, &input, NULL);
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ret = get_input_th(&th_model->model, &input, NULL);
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if ( ret != 0) {
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goto err;
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}
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@ -356,7 +356,7 @@ err:
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return ret;
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}
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static int get_output_th(void *model, const char *input_name, int input_width, int input_height,
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static int get_output_th(DNNModel *model, const char *input_name, int input_width, int input_height,
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const char *output_name, int *output_width, int *output_height)
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{
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int ret = 0;
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@ -421,7 +421,6 @@ static DNNModel *dnn_load_model_th(DnnContext *ctx, DNNFunctionType func_type, A
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if (!th_model)
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return NULL;
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model = &th_model->model;
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model->model = th_model;
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th_model->ctx = ctx;
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c10::Device device = c10::Device(device_name);
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@ -489,7 +488,7 @@ fail:
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static int dnn_execute_model_th(const DNNModel *model, DNNExecBaseParams *exec_params)
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{
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THModel *th_model = (THModel *)model->model;
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THModel *th_model = (THModel *)model;
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DnnContext *ctx = th_model->ctx;
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TaskItem *task;
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THRequestItem *request;
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@ -538,13 +537,13 @@ static int dnn_execute_model_th(const DNNModel *model, DNNExecBaseParams *exec_p
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static DNNAsyncStatusType dnn_get_result_th(const DNNModel *model, AVFrame **in, AVFrame **out)
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{
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THModel *th_model = (THModel *)model->model;
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THModel *th_model = (THModel *)model;
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return ff_dnn_get_result_common(th_model->task_queue, in, out);
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}
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static int dnn_flush_th(const DNNModel *model)
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{
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THModel *th_model = (THModel *)model->model;
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THModel *th_model = (THModel *)model;
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THRequestItem *request;
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if (ff_queue_size(th_model->lltask_queue) == 0)
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@ -157,15 +157,15 @@ int ff_dnn_set_classify_post_proc(DnnContext *ctx, ClassifyPostProc post_proc)
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int ff_dnn_get_input(DnnContext *ctx, DNNData *input)
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{
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return ctx->model->get_input(ctx->model->model, input, ctx->model_inputname);
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return ctx->model->get_input(ctx->model, input, ctx->model_inputname);
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}
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int ff_dnn_get_output(DnnContext *ctx, int input_width, int input_height, int *output_width, int *output_height)
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{
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char * output_name = ctx->model_outputnames && ctx->backend_type != DNN_TH ?
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ctx->model_outputnames[0] : NULL;
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return ctx->model->get_output(ctx->model->model, ctx->model_inputname, input_width, input_height,
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(const char *)output_name, output_width, output_height);
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return ctx->model->get_output(ctx->model, ctx->model_inputname, input_width, input_height,
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(const char *)output_name, output_width, output_height);
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}
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int ff_dnn_execute_model(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame)
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@ -95,17 +95,15 @@ typedef int (*DetectPostProc)(AVFrame *frame, DNNData *output, uint32_t nb, AVFi
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typedef int (*ClassifyPostProc)(AVFrame *frame, DNNData *output, uint32_t bbox_index, AVFilterContext *filter_ctx);
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typedef struct DNNModel{
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// Stores model that can be different for different backends.
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void *model;
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// Stores FilterContext used for the interaction between AVFrame and DNNData
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AVFilterContext *filter_ctx;
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// Stores function type of the model
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DNNFunctionType func_type;
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// Gets model input information
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// Just reuse struct DNNData here, actually the DNNData.data field is not needed.
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int (*get_input)(void *model, DNNData *input, const char *input_name);
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int (*get_input)(struct DNNModel *model, DNNData *input, const char *input_name);
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// Gets model output width/height with given input w/h
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int (*get_output)(void *model, const char *input_name, int input_width, int input_height,
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int (*get_output)(struct DNNModel *model, const char *input_name, int input_width, int input_height,
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const char *output_name, int *output_width, int *output_height);
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// set the pre process to transfer data from AVFrame to DNNData
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// the default implementation within DNN is used if it is not provided by the filter
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