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lavfi/dnn: refine code for frame pre/proc processing

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This commit is contained in:
Guo, Yejun 2021-03-01 19:23:20 +08:00
parent d2ccbc966b
commit 59021d79a2
6 changed files with 24 additions and 14 deletions

8
libavfilter/dnn/dnn_backend_native.c

@ -310,8 +310,8 @@ static DNNReturnType execute_model_native(const DNNModel *model, const char *inp
input.data = oprd->data; input.data = oprd->data;
input.dt = oprd->data_type; input.dt = oprd->data_type;
if (do_ioproc) { if (do_ioproc) {
if (native_model->model->pre_proc != NULL) { if (native_model->model->frame_pre_proc != NULL) {
native_model->model->pre_proc(in_frame, &input, native_model->model->filter_ctx); native_model->model->frame_pre_proc(in_frame, &input, native_model->model->filter_ctx);
} else { } else {
ff_proc_from_frame_to_dnn(in_frame, &input, native_model->model->func_type, ctx); ff_proc_from_frame_to_dnn(in_frame, &input, native_model->model->func_type, ctx);
} }
@ -358,8 +358,8 @@ static DNNReturnType execute_model_native(const DNNModel *model, const char *inp
output.dt = oprd->data_type; output.dt = oprd->data_type;
if (do_ioproc) { if (do_ioproc) {
if (native_model->model->post_proc != NULL) { if (native_model->model->frame_post_proc != NULL) {
native_model->model->post_proc(out_frame, &output, native_model->model->filter_ctx); native_model->model->frame_post_proc(out_frame, &output, native_model->model->filter_ctx);
} else { } else {
ff_proc_from_dnn_to_frame(out_frame, &output, ctx); ff_proc_from_dnn_to_frame(out_frame, &output, ctx);
} }

8
libavfilter/dnn/dnn_backend_openvino.c

@ -166,8 +166,8 @@ static DNNReturnType fill_model_input_ov(OVModel *ov_model, RequestItem *request
for (int i = 0; i < request->task_count; ++i) { for (int i = 0; i < request->task_count; ++i) {
task = request->tasks[i]; task = request->tasks[i];
if (task->do_ioproc) { if (task->do_ioproc) {
if (ov_model->model->pre_proc != NULL) { if (ov_model->model->frame_pre_proc != NULL) {
ov_model->model->pre_proc(task->in_frame, &input, ov_model->model->filter_ctx); ov_model->model->frame_pre_proc(task->in_frame, &input, ov_model->model->filter_ctx);
} else { } else {
ff_proc_from_frame_to_dnn(task->in_frame, &input, ov_model->model->func_type, ctx); ff_proc_from_frame_to_dnn(task->in_frame, &input, ov_model->model->func_type, ctx);
} }
@ -237,8 +237,8 @@ static void infer_completion_callback(void *args)
for (int i = 0; i < request->task_count; ++i) { for (int i = 0; i < request->task_count; ++i) {
task = request->tasks[i]; task = request->tasks[i];
if (task->do_ioproc) { if (task->do_ioproc) {
if (task->ov_model->model->post_proc != NULL) { if (task->ov_model->model->frame_post_proc != NULL) {
task->ov_model->model->post_proc(task->out_frame, &output, task->ov_model->model->filter_ctx); task->ov_model->model->frame_post_proc(task->out_frame, &output, task->ov_model->model->filter_ctx);
} else { } else {
ff_proc_from_dnn_to_frame(task->out_frame, &output, ctx); ff_proc_from_dnn_to_frame(task->out_frame, &output, ctx);
} }

8
libavfilter/dnn/dnn_backend_tf.c

@ -756,8 +756,8 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n
input.data = (float *)TF_TensorData(input_tensor); input.data = (float *)TF_TensorData(input_tensor);
if (do_ioproc) { if (do_ioproc) {
if (tf_model->model->pre_proc != NULL) { if (tf_model->model->frame_pre_proc != NULL) {
tf_model->model->pre_proc(in_frame, &input, tf_model->model->filter_ctx); tf_model->model->frame_pre_proc(in_frame, &input, tf_model->model->filter_ctx);
} else { } else {
ff_proc_from_frame_to_dnn(in_frame, &input, tf_model->model->func_type, ctx); ff_proc_from_frame_to_dnn(in_frame, &input, tf_model->model->func_type, ctx);
} }
@ -818,8 +818,8 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n
output.dt = TF_TensorType(output_tensors[i]); output.dt = TF_TensorType(output_tensors[i]);
if (do_ioproc) { if (do_ioproc) {
if (tf_model->model->post_proc != NULL) { if (tf_model->model->frame_post_proc != NULL) {
tf_model->model->post_proc(out_frame, &output, tf_model->model->filter_ctx); tf_model->model->frame_post_proc(out_frame, &output, tf_model->model->filter_ctx);
} else { } else {
ff_proc_from_dnn_to_frame(out_frame, &output, ctx); ff_proc_from_dnn_to_frame(out_frame, &output, ctx);
} }

7
libavfilter/dnn_filter_common.c

@ -64,6 +64,13 @@ int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *fil
return 0; return 0;
} }
int ff_dnn_set_frame_proc(DnnContext *ctx, FramePrePostProc pre_proc, FramePrePostProc post_proc)
{
ctx->model->frame_pre_proc = pre_proc;
ctx->model->frame_post_proc = post_proc;
return 0;
}
DNNReturnType ff_dnn_get_input(DnnContext *ctx, DNNData *input) DNNReturnType ff_dnn_get_input(DnnContext *ctx, DNNData *input)
{ {
return ctx->model->get_input(ctx->model->model, input, ctx->model_inputname); return ctx->model->get_input(ctx->model->model, input, ctx->model_inputname);

1
libavfilter/dnn_filter_common.h

@ -48,6 +48,7 @@ typedef struct DnnContext {
int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx); int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx);
int ff_dnn_set_frame_proc(DnnContext *ctx, FramePrePostProc pre_proc, FramePrePostProc post_proc);
DNNReturnType ff_dnn_get_input(DnnContext *ctx, DNNData *input); DNNReturnType ff_dnn_get_input(DnnContext *ctx, DNNData *input);
DNNReturnType ff_dnn_get_output(DnnContext *ctx, int input_width, int input_height, int *output_width, int *output_height); DNNReturnType ff_dnn_get_output(DnnContext *ctx, int input_width, int input_height, int *output_width, int *output_height);
DNNReturnType ff_dnn_execute_model(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame); DNNReturnType ff_dnn_execute_model(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame);

6
libavfilter/dnn_interface.h

@ -63,6 +63,8 @@ typedef struct DNNData{
DNNColorOrder order; DNNColorOrder order;
} DNNData; } DNNData;
typedef int (*FramePrePostProc)(AVFrame *frame, DNNData *model, AVFilterContext *filter_ctx);
typedef struct DNNModel{ typedef struct DNNModel{
// Stores model that can be different for different backends. // Stores model that can be different for different backends.
void *model; void *model;
@ -80,10 +82,10 @@ typedef struct DNNModel{
const char *output_name, int *output_width, int *output_height); const char *output_name, int *output_width, int *output_height);
// set the pre process to transfer data from AVFrame to DNNData // set the pre process to transfer data from AVFrame to DNNData
// the default implementation within DNN is used if it is not provided by the filter // the default implementation within DNN is used if it is not provided by the filter
int (*pre_proc)(AVFrame *frame_in, DNNData *model_input, AVFilterContext *filter_ctx); FramePrePostProc frame_pre_proc;
// set the post process to transfer data from DNNData to AVFrame // set the post process to transfer data from DNNData to AVFrame
// the default implementation within DNN is used if it is not provided by the filter // the default implementation within DNN is used if it is not provided by the filter
int (*post_proc)(AVFrame *frame_out, DNNData *model_output, AVFilterContext *filter_ctx); FramePrePostProc frame_post_proc;
} DNNModel; } DNNModel;
// Stores pointers to functions for loading, executing, freeing DNN models for one of the backends. // Stores pointers to functions for loading, executing, freeing DNN models for one of the backends.