mirror of https://git.ffmpeg.org/ffmpeg.git
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>
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@ -37,8 +37,8 @@
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#include <tensorflow/c/c_api.h>
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#include <tensorflow/c/c_api.h>
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typedef struct TFModel {
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typedef struct TFModel {
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DNNModel model;
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DnnContext *ctx;
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DnnContext *ctx;
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DNNModel *model;
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TF_Graph *graph;
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TF_Graph *graph;
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TF_Session *session;
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TF_Session *session;
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TF_Status *status;
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TF_Status *status;
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@ -518,7 +518,7 @@ static void dnn_free_model_tf(DNNModel **model)
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TF_DeleteStatus(tf_model->status);
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TF_DeleteStatus(tf_model->status);
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}
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}
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av_freep(&tf_model);
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av_freep(&tf_model);
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av_freep(&model);
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*model = NULL;
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}
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}
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static DNNModel *dnn_load_model_tf(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx)
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static DNNModel *dnn_load_model_tf(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx)
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@ -526,18 +526,11 @@ static DNNModel *dnn_load_model_tf(DnnContext *ctx, DNNFunctionType func_type, A
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DNNModel *model = NULL;
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DNNModel *model = NULL;
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TFModel *tf_model = NULL;
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TFModel *tf_model = NULL;
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model = av_mallocz(sizeof(DNNModel));
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if (!model){
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return NULL;
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}
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tf_model = av_mallocz(sizeof(TFModel));
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tf_model = av_mallocz(sizeof(TFModel));
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if (!tf_model){
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if (!tf_model)
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av_freep(&model);
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return NULL;
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return NULL;
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}
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model = &tf_model->model;
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model->model = tf_model;
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model->model = tf_model;
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tf_model->model = model;
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tf_model->ctx = ctx;
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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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if (load_tf_model(tf_model, ctx->model_filename) != 0){
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@ -650,11 +643,11 @@ static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
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}
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}
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input.data = (float *)TF_TensorData(infer_request->input_tensor);
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input.data = (float *)TF_TensorData(infer_request->input_tensor);
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switch (tf_model->model->func_type) {
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switch (tf_model->model.func_type) {
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case DFT_PROCESS_FRAME:
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case DFT_PROCESS_FRAME:
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if (task->do_ioproc) {
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if (task->do_ioproc) {
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if (tf_model->model->frame_pre_proc != NULL) {
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if (tf_model->model.frame_pre_proc != NULL) {
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tf_model->model->frame_pre_proc(task->in_frame, &input, tf_model->model->filter_ctx);
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tf_model->model.frame_pre_proc(task->in_frame, &input, tf_model->model.filter_ctx);
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} else {
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} else {
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ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
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ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
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}
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}
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@ -664,7 +657,7 @@ static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
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ff_frame_to_dnn_detect(task->in_frame, &input, ctx);
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ff_frame_to_dnn_detect(task->in_frame, &input, ctx);
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break;
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break;
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default:
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default:
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avpriv_report_missing_feature(ctx, "model function type %d", tf_model->model->func_type);
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avpriv_report_missing_feature(ctx, "model function type %d", tf_model->model.func_type);
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break;
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break;
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}
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}
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@ -724,12 +717,12 @@ static void infer_completion_callback(void *args) {
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outputs[i].data = TF_TensorData(infer_request->output_tensors[i]);
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outputs[i].data = TF_TensorData(infer_request->output_tensors[i]);
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outputs[i].dt = (DNNDataType)TF_TensorType(infer_request->output_tensors[i]);
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outputs[i].dt = (DNNDataType)TF_TensorType(infer_request->output_tensors[i]);
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}
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}
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switch (tf_model->model->func_type) {
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switch (tf_model->model.func_type) {
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case DFT_PROCESS_FRAME:
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case DFT_PROCESS_FRAME:
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//it only support 1 output if it's frame in & frame out
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//it only support 1 output if it's frame in & frame out
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if (task->do_ioproc) {
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if (task->do_ioproc) {
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if (tf_model->model->frame_post_proc != NULL) {
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if (tf_model->model.frame_post_proc != NULL) {
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tf_model->model->frame_post_proc(task->out_frame, outputs, tf_model->model->filter_ctx);
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tf_model->model.frame_post_proc(task->out_frame, outputs, tf_model->model.filter_ctx);
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} else {
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} else {
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ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
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ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
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}
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}
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@ -741,11 +734,11 @@ static void infer_completion_callback(void *args) {
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}
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}
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break;
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break;
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case DFT_ANALYTICS_DETECT:
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case DFT_ANALYTICS_DETECT:
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if (!tf_model->model->detect_post_proc) {
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if (!tf_model->model.detect_post_proc) {
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av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n");
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av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n");
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return;
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return;
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}
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}
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tf_model->model->detect_post_proc(task->in_frame, outputs, task->nb_output, tf_model->model->filter_ctx);
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tf_model->model.detect_post_proc(task->in_frame, outputs, task->nb_output, tf_model->model.filter_ctx);
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break;
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break;
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default:
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default:
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av_log(ctx, AV_LOG_ERROR, "Tensorflow backend does not support this kind of dnn filter now\n");
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av_log(ctx, AV_LOG_ERROR, "Tensorflow backend does not support this kind of dnn filter now\n");
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