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avfilter/dnn_backend_tf: Simplify memory allocation 

Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
Reviewed-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
This commit is contained in:
Zhao Zhili 2024-05-08 00:08:15 +08:00 committed by Guo Yejun
parent a40df366c4
commit abfefbb33b
1 changed files with 13 additions and 20 deletions
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33
libavfilter/dnn/dnn_backend_tf.c
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@ -37,8 +37,8 @@
#include <tensorflow/c/c_api.h> #include <tensorflow/c/c_api.h>
typedef struct TFModel { typedef struct TFModel {
DNNModel model;
DnnContext *ctx; DnnContext *ctx;
DNNModel *model;
TF_Graph *graph; TF_Graph *graph;
TF_Session *session; TF_Session *session;
TF_Status *status; TF_Status *status;
@ -518,7 +518,7 @@ static void dnn_free_model_tf(DNNModel **model)
TF_DeleteStatus(tf_model->status); TF_DeleteStatus(tf_model->status);
} }
av_freep(&tf_model); av_freep(&tf_model);
av_freep(&model); *model = NULL;
} }
static DNNModel *dnn_load_model_tf(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx) static DNNModel *dnn_load_model_tf(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx)
@ -526,18 +526,11 @@ static DNNModel *dnn_load_model_tf(DnnContext *ctx, DNNFunctionType func_type, A
DNNModel *model = NULL; DNNModel *model = NULL;
TFModel *tf_model = NULL; TFModel *tf_model = NULL;
model = av_mallocz(sizeof(DNNModel));
if (!model){
return NULL;
}
tf_model = av_mallocz(sizeof(TFModel)); tf_model = av_mallocz(sizeof(TFModel));
if (!tf_model){ if (!tf_model)
av_freep(&model);
return NULL; return NULL;
} model = &tf_model->model;
model->model = tf_model; model->model = tf_model;
tf_model->model = model;
tf_model->ctx = ctx; tf_model->ctx = ctx;
if (load_tf_model(tf_model, ctx->model_filename) != 0){ if (load_tf_model(tf_model, ctx->model_filename) != 0){
@ -650,11 +643,11 @@ static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
} }
input.data = (float *)TF_TensorData(infer_request->input_tensor); input.data = (float *)TF_TensorData(infer_request->input_tensor);
switch (tf_model->model->func_type) { switch (tf_model->model.func_type) {
case DFT_PROCESS_FRAME: case DFT_PROCESS_FRAME:
if (task->do_ioproc) { if (task->do_ioproc) {
if (tf_model->model->frame_pre_proc != NULL) { if (tf_model->model.frame_pre_proc != NULL) {
tf_model->model->frame_pre_proc(task->in_frame, &input, tf_model->model->filter_ctx); tf_model->model.frame_pre_proc(task->in_frame, &input, tf_model->model.filter_ctx);
} else { } else {
ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx); ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
} }
@ -664,7 +657,7 @@ static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
ff_frame_to_dnn_detect(task->in_frame, &input, ctx); ff_frame_to_dnn_detect(task->in_frame, &input, ctx);
break; break;
default: default:
avpriv_report_missing_feature(ctx, "model function type %d", tf_model->model->func_type); avpriv_report_missing_feature(ctx, "model function type %d", tf_model->model.func_type);
break; break;
} }
@ -724,12 +717,12 @@ static void infer_completion_callback(void *args) {
outputs[i].data = TF_TensorData(infer_request->output_tensors[i]); outputs[i].data = TF_TensorData(infer_request->output_tensors[i]);
outputs[i].dt = (DNNDataType)TF_TensorType(infer_request->output_tensors[i]); outputs[i].dt = (DNNDataType)TF_TensorType(infer_request->output_tensors[i]);
} }
switch (tf_model->model->func_type) { switch (tf_model->model.func_type) {
case DFT_PROCESS_FRAME: case DFT_PROCESS_FRAME:
//it only support 1 output if it's frame in & frame out //it only support 1 output if it's frame in & frame out
if (task->do_ioproc) { if (task->do_ioproc) {
if (tf_model->model->frame_post_proc != NULL) { if (tf_model->model.frame_post_proc != NULL) {
tf_model->model->frame_post_proc(task->out_frame, outputs, tf_model->model->filter_ctx); tf_model->model.frame_post_proc(task->out_frame, outputs, tf_model->model.filter_ctx);
} else { } else {
ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx); ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
} }
@ -741,11 +734,11 @@ static void infer_completion_callback(void *args) {
} }
break; break;
case DFT_ANALYTICS_DETECT: case DFT_ANALYTICS_DETECT:
if (!tf_model->model->detect_post_proc) { if (!tf_model->model.detect_post_proc) {
av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n"); av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n");
return; return;
} }
tf_model->model->detect_post_proc(task->in_frame, outputs, task->nb_output, tf_model->model->filter_ctx); tf_model->model.detect_post_proc(task->in_frame, outputs, task->nb_output, tf_model->model.filter_ctx);
break; break;
default: default:
av_log(ctx, AV_LOG_ERROR, "Tensorflow backend does not support this kind of dnn filter now\n"); av_log(ctx, AV_LOG_ERROR, "Tensorflow backend does not support this kind of dnn filter now\n");