@@ -138,7 +138,7 @@ DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_
return DNN_SUCCESS;
}
-DNNAsyncStatusType ff_dnn_get_async_result_common(Queue *task_queue, AVFrame **in, AVFrame **out)
+DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVFrame **out)
{
TaskItem *task = ff_queue_peek_front(task_queue);
@@ -29,7 +29,8 @@
#include "libavutil/thread.h"
#define DNN_BACKEND_COMMON_OPTIONS \
- { "nireq", "number of request", OFFSET(options.nireq), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INT_MAX, FLAGS },
+ { "nireq", "number of request", OFFSET(options.nireq), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INT_MAX, FLAGS }, \
+ { "async", "use DNN async inference", OFFSET(options.async), AV_OPT_TYPE_BOOL, { .i64 = 1 }, 0, 1, FLAGS },
// one task for one function call from dnn interface
typedef struct TaskItem {
@@ -135,7 +136,7 @@ DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_
* @retval DAST_NOT_READY if inference not completed yet.
* @retval DAST_SUCCESS if result successfully extracted
*/
-DNNAsyncStatusType ff_dnn_get_async_result_common(Queue *task_queue, AVFrame **in, AVFrame **out);
+DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVFrame **out);
/**
* Allocate input and output frames and fill the Task
@@ -34,6 +34,7 @@
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
static const AVOption dnn_native_options[] = {
{ "conv2d_threads", "threads num for conv2d layer", OFFSET(options.conv2d_threads), AV_OPT_TYPE_INT, { .i64 = 0 }, INT_MIN, INT_MAX, FLAGS },
+ { "async", "use DNN async inference", OFFSET(options.async), AV_OPT_TYPE_BOOL, { .i64 = 0 }, 0, 1, FLAGS },
{ NULL },
};
@@ -189,6 +190,11 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, DNNFunctionType f
goto fail;
native_model->model = model;
+ if (native_model->ctx.options.async) {
+ av_log(&native_model->ctx, AV_LOG_WARNING, "Async not supported. Rolling back to sync\n");
+ native_model->ctx.options.async = 0;
+ }
+
#if !HAVE_PTHREAD_CANCEL
if (native_model->ctx.options.conv2d_threads > 1){
av_log(&native_model->ctx, AV_LOG_WARNING, "'conv2d_threads' option was set but it is not supported "
@@ -212,6 +218,11 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, DNNFunctionType f
goto fail;
}
+ native_model->task_queue = ff_queue_create();
+ if (!native_model->task_queue) {
+ goto fail;
+ }
+
native_model->inference_queue = ff_queue_create();
if (!native_model->inference_queue) {
goto fail;
@@ -425,17 +436,30 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNExecBasePara
{
NativeModel *native_model = model->model;
NativeContext *ctx = &native_model->ctx;
- TaskItem task;
+ TaskItem *task;
if (ff_check_exec_params(ctx, DNN_NATIVE, model->func_type, exec_params) != 0) {
return DNN_ERROR;
}
- if (ff_dnn_fill_task(&task, exec_params, native_model, 0, 1) != DNN_SUCCESS) {
+ task = av_malloc(sizeof(*task));
+ if (!task) {
+ av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
return DNN_ERROR;
}
- if (extract_inference_from_task(&task, native_model->inference_queue) != DNN_SUCCESS) {
+ if (ff_dnn_fill_task(task, exec_params, native_model, ctx->options.async, 1) != DNN_SUCCESS) {
+ av_freep(&task);
+ return DNN_ERROR;
+ }
+
+ if (ff_queue_push_back(native_model->task_queue, task) < 0) {
+ av_freep(&task);
+ av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
+ return DNN_ERROR;
+ }
+
+ if (extract_inference_from_task(task, native_model->inference_queue) != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
return DNN_ERROR;
}
@@ -443,6 +467,26 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNExecBasePara
return execute_model_native(native_model->inference_queue);
}
+DNNReturnType ff_dnn_flush_native(const DNNModel *model)
+{
+ NativeModel *native_model = model->model;
+
+ if (ff_queue_size(native_model->inference_queue) == 0) {
+ // no pending task need to flush
+ return DNN_SUCCESS;
+ }
+
+ // for now, use sync node with flush operation
+ // Switch to async when it is supported
+ return execute_model_native(native_model->inference_queue);
+}
+
+DNNAsyncStatusType ff_dnn_get_result_native(const DNNModel *model, AVFrame **in, AVFrame **out)
+{
+ NativeModel *native_model = model->model;
+ return ff_dnn_get_result_common(native_model->task_queue, in, out);
+}
+
int32_t ff_calculate_operand_dims_count(const DnnOperand *oprd)
{
int32_t result = 1;
@@ -497,6 +541,15 @@ void ff_dnn_free_model_native(DNNModel **model)
av_freep(&item);
}
ff_queue_destroy(native_model->inference_queue);
+
+ while (ff_queue_size(native_model->task_queue) != 0) {
+ TaskItem *item = ff_queue_pop_front(native_model->task_queue);
+ av_frame_free(&item->in_frame);
+ av_frame_free(&item->out_frame);
+ av_freep(&item);
+ }
+ ff_queue_destroy(native_model->task_queue);
+
av_freep(&native_model);
}
av_freep(model);
@@ -111,6 +111,7 @@ typedef struct InputParams{
} InputParams;
typedef struct NativeOptions{
+ uint8_t async;
uint32_t conv2d_threads;
} NativeOptions;
@@ -127,6 +128,7 @@ typedef struct NativeModel{
int32_t layers_num;
DnnOperand *operands;
int32_t operands_num;
+ Queue *task_queue;
Queue *inference_queue;
} NativeModel;
@@ -134,6 +136,10 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, DNNFunctionType f
DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_params);
+DNNAsyncStatusType ff_dnn_get_result_native(const DNNModel *model, AVFrame **in, AVFrame **out);
+
+DNNReturnType ff_dnn_flush_native(const DNNModel *model);
+
void ff_dnn_free_model_native(DNNModel **model);
// NOTE: User must check for error (return value <= 0) to handle
@@ -39,6 +39,7 @@
typedef struct OVOptions{
char *device_type;
int nireq;
+ uint8_t async;
int batch_size;
int input_resizable;
} OVOptions;
@@ -758,55 +759,6 @@ err:
}
DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params)
-{
- OVModel *ov_model = model->model;
- OVContext *ctx = &ov_model->ctx;
- TaskItem task;
- OVRequestItem *request;
-
- if (ff_check_exec_params(ctx, DNN_OV, model->func_type, exec_params) != 0) {
- return DNN_ERROR;
- }
-
- if (model->func_type == DFT_ANALYTICS_CLASSIFY) {
- // Once we add async support for tensorflow backend and native backend,
- // we'll combine the two sync/async functions in dnn_interface.h to
- // simplify the code in filter, and async will be an option within backends.
- // so, do not support now, and classify filter will not call this function.
- return DNN_ERROR;
- }
-
- if (ctx->options.batch_size > 1) {
- avpriv_report_missing_feature(ctx, "batch mode for sync execution");
- return DNN_ERROR;
- }
-
- if (!ov_model->exe_network) {
- if (init_model_ov(ov_model, exec_params->input_name, exec_params->output_names[0]) != DNN_SUCCESS) {
- av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
- return DNN_ERROR;
- }
- }
-
- if (ff_dnn_fill_task(&task, exec_params, ov_model, 0, 1) != DNN_SUCCESS) {
- return DNN_ERROR;
- }
-
- if (extract_inference_from_task(ov_model->model->func_type, &task, ov_model->inference_queue, exec_params) != DNN_SUCCESS) {
- av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
- return DNN_ERROR;
- }
-
- request = ff_safe_queue_pop_front(ov_model->request_queue);
- if (!request) {
- av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- return DNN_ERROR;
- }
-
- return execute_model_ov(request, ov_model->inference_queue);
-}
-
-DNNReturnType ff_dnn_execute_model_async_ov(const DNNModel *model, DNNExecBaseParams *exec_params)
{
OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
@@ -831,7 +783,8 @@ DNNReturnType ff_dnn_execute_model_async_ov(const DNNModel *model, DNNExecBasePa
return DNN_ERROR;
}
- if (ff_dnn_fill_task(task, exec_params, ov_model, 1, 1) != DNN_SUCCESS) {
+ if (ff_dnn_fill_task(task, exec_params, ov_model, ctx->options.async, 1) != DNN_SUCCESS) {
+ av_freep(&task);
return DNN_ERROR;
}
@@ -846,26 +799,47 @@ DNNReturnType ff_dnn_execute_model_async_ov(const DNNModel *model, DNNExecBasePa
return DNN_ERROR;
}
- while (ff_queue_size(ov_model->inference_queue) >= ctx->options.batch_size) {
+ if (ctx->options.async) {
+ while (ff_queue_size(ov_model->inference_queue) >= ctx->options.batch_size) {
+ request = ff_safe_queue_pop_front(ov_model->request_queue);
+ if (!request) {
+ av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
+ return DNN_ERROR;
+ }
+
+ ret = execute_model_ov(request, ov_model->inference_queue);
+ if (ret != DNN_SUCCESS) {
+ return ret;
+ }
+ }
+
+ return DNN_SUCCESS;
+ }
+ else {
+ if (model->func_type == DFT_ANALYTICS_CLASSIFY) {
+ // Classification filter has not been completely
+ // tested with the sync mode. So, do not support now.
+ return DNN_ERROR;
+ }
+
+ if (ctx->options.batch_size > 1) {
+ avpriv_report_missing_feature(ctx, "batch mode for sync execution");
+ return DNN_ERROR;
+ }
+
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
return DNN_ERROR;
}
-
- ret = execute_model_ov(request, ov_model->inference_queue);
- if (ret != DNN_SUCCESS) {
- return ret;
- }
+ return execute_model_ov(request, ov_model->inference_queue);
}
-
- return DNN_SUCCESS;
}
-DNNAsyncStatusType ff_dnn_get_async_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out)
+DNNAsyncStatusType ff_dnn_get_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out)
{
OVModel *ov_model = model->model;
- return ff_dnn_get_async_result_common(ov_model->task_queue, in, out);
+ return ff_dnn_get_result_common(ov_model->task_queue, in, out);
}
DNNReturnType ff_dnn_flush_ov(const DNNModel *model)
@@ -32,8 +32,7 @@
DNNModel *ff_dnn_load_model_ov(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx);
DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params);
-DNNReturnType ff_dnn_execute_model_async_ov(const DNNModel *model, DNNExecBaseParams *exec_params);
-DNNAsyncStatusType ff_dnn_get_async_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out);
+DNNAsyncStatusType ff_dnn_get_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out);
DNNReturnType ff_dnn_flush_ov(const DNNModel *model);
void ff_dnn_free_model_ov(DNNModel **model);
@@ -42,6 +42,7 @@
typedef struct TFOptions{
char *sess_config;
+ uint8_t async;
uint32_t nireq;
} TFOptions;
@@ -1121,34 +1122,6 @@ err:
DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params)
{
- TFModel *tf_model = model->model;
- TFContext *ctx = &tf_model->ctx;
- TaskItem task;
- TFRequestItem *request;
-
- if (ff_check_exec_params(ctx, DNN_TF, model->func_type, exec_params) != 0) {
- return DNN_ERROR;
- }
-
- if (ff_dnn_fill_task(&task, exec_params, tf_model, 0, 1) != DNN_SUCCESS) {
- return DNN_ERROR;
- }
-
- if (extract_inference_from_task(&task, tf_model->inference_queue) != DNN_SUCCESS) {
- av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
- return DNN_ERROR;
- }
-
- request = ff_safe_queue_pop_front(tf_model->request_queue);
- if (!request) {
- av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- return DNN_ERROR;
- }
-
- return execute_model_tf(request, tf_model->inference_queue);
-}
-
-DNNReturnType ff_dnn_execute_model_async_tf(const DNNModel *model, DNNExecBaseParams *exec_params) {
TFModel *tf_model = model->model;
TFContext *ctx = &tf_model->ctx;
TaskItem *task;
@@ -1164,7 +1137,7 @@ DNNReturnType ff_dnn_execute_model_async_tf(const DNNModel *model, DNNExecBasePa
return DNN_ERROR;
}
- if (ff_dnn_fill_task(task, exec_params, tf_model, 1, 1) != DNN_SUCCESS) {
+ if (ff_dnn_fill_task(task, exec_params, tf_model, ctx->options.async, 1) != DNN_SUCCESS) {
av_freep(&task);
return DNN_ERROR;
}
@@ -1188,10 +1161,10 @@ DNNReturnType ff_dnn_execute_model_async_tf(const DNNModel *model, DNNExecBasePa
return execute_model_tf(request, tf_model->inference_queue);
}
-DNNAsyncStatusType ff_dnn_get_async_result_tf(const DNNModel *model, AVFrame **in, AVFrame **out)
+DNNAsyncStatusType ff_dnn_get_result_tf(const DNNModel *model, AVFrame **in, AVFrame **out)
{
TFModel *tf_model = model->model;
- return ff_dnn_get_async_result_common(tf_model->task_queue, in, out);
+ return ff_dnn_get_result_common(tf_model->task_queue, in, out);
}
DNNReturnType ff_dnn_flush_tf(const DNNModel *model)
@@ -32,8 +32,7 @@
DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx);
DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params);
-DNNReturnType ff_dnn_execute_model_async_tf(const DNNModel *model, DNNExecBaseParams *exec_params);
-DNNAsyncStatusType ff_dnn_get_async_result_tf(const DNNModel *model, AVFrame **in, AVFrame **out);
+DNNAsyncStatusType ff_dnn_get_result_tf(const DNNModel *model, AVFrame **in, AVFrame **out);
DNNReturnType ff_dnn_flush_tf(const DNNModel *model);
void ff_dnn_free_model_tf(DNNModel **model);
@@ -42,14 +42,15 @@ DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
case DNN_NATIVE:
dnn_module->load_model = &ff_dnn_load_model_native;
dnn_module->execute_model = &ff_dnn_execute_model_native;
+ dnn_module->get_result = &ff_dnn_get_result_native;
+ dnn_module->flush = &ff_dnn_flush_native;
dnn_module->free_model = &ff_dnn_free_model_native;
break;
case DNN_TF:
#if (CONFIG_LIBTENSORFLOW == 1)
dnn_module->load_model = &ff_dnn_load_model_tf;
dnn_module->execute_model = &ff_dnn_execute_model_tf;
- dnn_module->execute_model_async = &ff_dnn_execute_model_async_tf;
- dnn_module->get_async_result = &ff_dnn_get_async_result_tf;
+ dnn_module->get_result = &ff_dnn_get_result_tf;
dnn_module->flush = &ff_dnn_flush_tf;
dnn_module->free_model = &ff_dnn_free_model_tf;
#else
@@ -61,8 +62,7 @@ DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
#if (CONFIG_LIBOPENVINO == 1)
dnn_module->load_model = &ff_dnn_load_model_ov;
dnn_module->execute_model = &ff_dnn_execute_model_ov;
- dnn_module->execute_model_async = &ff_dnn_execute_model_async_ov;
- dnn_module->get_async_result = &ff_dnn_get_async_result_ov;
+ dnn_module->get_result = &ff_dnn_get_result_ov;
dnn_module->flush = &ff_dnn_flush_ov;
dnn_module->free_model = &ff_dnn_free_model_ov;
#else
@@ -84,11 +84,6 @@ int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *fil
return AVERROR(EINVAL);
}
- if (!ctx->dnn_module->execute_model_async && ctx->async) {
- ctx->async = 0;
- av_log(filter_ctx, AV_LOG_WARNING, "this backend does not support async execution, roll back to sync.\n");
- }
-
#if !HAVE_PTHREAD_CANCEL
if (ctx->async) {
ctx->async = 0;
@@ -141,18 +136,6 @@ DNNReturnType ff_dnn_execute_model(DnnContext *ctx, AVFrame *in_frame, AVFrame *
return (ctx->dnn_module->execute_model)(ctx->model, &exec_params);
}
-DNNReturnType ff_dnn_execute_model_async(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame)
-{
- DNNExecBaseParams exec_params = {
- .input_name = ctx->model_inputname,
- .output_names = (const char **)ctx->model_outputnames,
- .nb_output = ctx->nb_outputs,
- .in_frame = in_frame,
- .out_frame = out_frame,
- };
- return (ctx->dnn_module->execute_model_async)(ctx->model, &exec_params);
-}
-
DNNReturnType ff_dnn_execute_model_classification(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame, const char *target)
{
DNNExecClassificationParams class_params = {
@@ -165,12 +148,12 @@ DNNReturnType ff_dnn_execute_model_classification(DnnContext *ctx, AVFrame *in_f
},
.target = target,
};
- return (ctx->dnn_module->execute_model_async)(ctx->model, &class_params.base);
+ return (ctx->dnn_module->execute_model)(ctx->model, &class_params.base);
}
-DNNAsyncStatusType ff_dnn_get_async_result(DnnContext *ctx, AVFrame **in_frame, AVFrame **out_frame)
+DNNAsyncStatusType ff_dnn_get_result(DnnContext *ctx, AVFrame **in_frame, AVFrame **out_frame)
{
- return (ctx->dnn_module->get_async_result)(ctx->model, in_frame, out_frame);
+ return (ctx->dnn_module->get_result)(ctx->model, in_frame, out_frame);
}
DNNReturnType ff_dnn_flush(DnnContext *ctx)
@@ -56,9 +56,8 @@ int ff_dnn_set_classify_post_proc(DnnContext *ctx, ClassifyPostProc post_proc);
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_execute_model(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame);
-DNNReturnType ff_dnn_execute_model_async(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame);
DNNReturnType ff_dnn_execute_model_classification(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame, const char *target);
-DNNAsyncStatusType ff_dnn_get_async_result(DnnContext *ctx, AVFrame **in_frame, AVFrame **out_frame);
+DNNAsyncStatusType ff_dnn_get_result(DnnContext *ctx, AVFrame **in_frame, AVFrame **out_frame);
DNNReturnType ff_dnn_flush(DnnContext *ctx);
void ff_dnn_uninit(DnnContext *ctx);
@@ -114,10 +114,8 @@ typedef struct DNNModule{
DNNModel *(*load_model)(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx);
// Executes model with specified input and output. Returns DNN_ERROR otherwise.
DNNReturnType (*execute_model)(const DNNModel *model, DNNExecBaseParams *exec_params);
- // Executes model with specified input and output asynchronously. Returns DNN_ERROR otherwise.
- DNNReturnType (*execute_model_async)(const DNNModel *model, DNNExecBaseParams *exec_params);
// Retrieve inference result.
- DNNAsyncStatusType (*get_async_result)(const DNNModel *model, AVFrame **in, AVFrame **out);
+ DNNAsyncStatusType (*get_result)(const DNNModel *model, AVFrame **in, AVFrame **out);
// Flush all the pending tasks.
DNNReturnType (*flush)(const DNNModel *model);
// Frees memory allocated for model.
@@ -68,6 +68,7 @@ static int query_formats(AVFilterContext *ctx)
static int filter_frame(AVFilterLink *inlink, AVFrame *in)
{
+ DNNAsyncStatusType async_state = 0;
AVFilterContext *ctx = inlink->dst;
AVFilterLink *outlink = ctx->outputs[0];
DRContext *dr_context = ctx->priv;
@@ -88,6 +89,12 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in)
av_frame_free(&in);
return AVERROR(EIO);
}
+ do {
+ async_state = ff_dnn_get_result(&dr_context->dnnctx, &in, &out);
+ } while (async_state == DAST_NOT_READY);
+
+ if (async_state != DAST_SUCCESS)
+ return AVERROR(EINVAL);
av_frame_free(&in);
@@ -224,7 +224,7 @@ static int dnn_classify_flush_frame(AVFilterLink *outlink, int64_t pts, int64_t
do {
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
- async_state = ff_dnn_get_async_result(&ctx->dnnctx, &in_frame, &out_frame);
+ async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
if (out_frame) {
av_assert0(in_frame == out_frame);
ret = ff_filter_frame(outlink, out_frame);
@@ -268,7 +268,7 @@ static int dnn_classify_activate(AVFilterContext *filter_ctx)
do {
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
- async_state = ff_dnn_get_async_result(&ctx->dnnctx, &in_frame, &out_frame);
+ async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
if (out_frame) {
av_assert0(in_frame == out_frame);
ret = ff_filter_frame(outlink, out_frame);
@@ -424,7 +424,7 @@ static int dnn_detect_flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *o
do {
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
- async_state = ff_dnn_get_async_result(&ctx->dnnctx, &in_frame, &out_frame);
+ async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
if (out_frame) {
av_assert0(in_frame == out_frame);
ret = ff_filter_frame(outlink, out_frame);
@@ -458,7 +458,7 @@ static int dnn_detect_activate_async(AVFilterContext *filter_ctx)
if (ret < 0)
return ret;
if (ret > 0) {
- if (ff_dnn_execute_model_async(&ctx->dnnctx, in, in) != DNN_SUCCESS) {
+ if (ff_dnn_execute_model(&ctx->dnnctx, in, in) != DNN_SUCCESS) {
return AVERROR(EIO);
}
}
@@ -468,7 +468,7 @@ static int dnn_detect_activate_async(AVFilterContext *filter_ctx)
do {
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
- async_state = ff_dnn_get_async_result(&ctx->dnnctx, &in_frame, &out_frame);
+ async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
if (out_frame) {
av_assert0(in_frame == out_frame);
ret = ff_filter_frame(outlink, out_frame);
@@ -537,5 +537,5 @@ const AVFilter ff_vf_dnn_detect = {
FILTER_INPUTS(dnn_detect_inputs),
FILTER_OUTPUTS(dnn_detect_outputs),
.priv_class = &dnn_detect_class,
- .activate = dnn_detect_activate,
+ .activate = dnn_detect_activate_async,
};
@@ -328,7 +328,7 @@ static int flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *out_pts)
do {
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
- async_state = ff_dnn_get_async_result(&ctx->dnnctx, &in_frame, &out_frame);
+ async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
if (out_frame) {
if (isPlanarYUV(in_frame->format))
copy_uv_planes(ctx, out_frame, in_frame);
@@ -370,7 +370,7 @@ static int activate_async(AVFilterContext *filter_ctx)
return AVERROR(ENOMEM);
}
av_frame_copy_props(out, in);
- if (ff_dnn_execute_model_async(&ctx->dnnctx, in, out) != DNN_SUCCESS) {
+ if (ff_dnn_execute_model(&ctx->dnnctx, in, out) != DNN_SUCCESS) {
return AVERROR(EIO);
}
}
@@ -380,7 +380,7 @@ static int activate_async(AVFilterContext *filter_ctx)
do {
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
- async_state = ff_dnn_get_async_result(&ctx->dnnctx, &in_frame, &out_frame);
+ async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
if (out_frame) {
if (isPlanarYUV(in_frame->format))
copy_uv_planes(ctx, out_frame, in_frame);
@@ -454,5 +454,5 @@ const AVFilter ff_vf_dnn_processing = {
FILTER_INPUTS(dnn_processing_inputs),
FILTER_OUTPUTS(dnn_processing_outputs),
.priv_class = &dnn_processing_class,
- .activate = activate,
+ .activate = activate_async,
};
@@ -119,6 +119,7 @@ static int config_output(AVFilterLink *outlink)
static int filter_frame(AVFilterLink *inlink, AVFrame *in)
{
+ DNNAsyncStatusType async_state = 0;
AVFilterContext *context = inlink->dst;
SRContext *ctx = context->priv;
AVFilterLink *outlink = context->outputs[0];
@@ -148,6 +149,13 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in)
return AVERROR(EIO);
}
+ do {
+ async_state = ff_dnn_get_result(&ctx->dnnctx, &in, &out);
+ } while (async_state == DAST_NOT_READY);
+
+ if (async_state != DAST_SUCCESS)
+ return AVERROR(EINVAL);
+
if (ctx->sws_uv_scale) {
sws_scale(ctx->sws_uv_scale, (const uint8_t **)(in->data + 1), in->linesize + 1,
0, ctx->sws_uv_height, out->data + 1, out->linesize + 1);
This commit unifies the async and sync mode from the DNN filters' perspective. As of this commit, the Native backend only supports synchronous execution mode. Now the user can switch between async and sync mode by using the 'async' option in the backend_configs. The values can be 1 for async and 0 for sync mode of execution. This commit affects the following filters: 1. vf_dnn_classify 2. vf_dnn_detect 3. vf_dnn_processing 4. vf_sr 5. vf_derain Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com> --- libavfilter/dnn/dnn_backend_common.c | 2 +- libavfilter/dnn/dnn_backend_common.h | 5 +- libavfilter/dnn/dnn_backend_native.c | 59 +++++++++++++++- libavfilter/dnn/dnn_backend_native.h | 6 ++ libavfilter/dnn/dnn_backend_openvino.c | 94 ++++++++++---------------- libavfilter/dnn/dnn_backend_openvino.h | 3 +- libavfilter/dnn/dnn_backend_tf.c | 35 ++-------- libavfilter/dnn/dnn_backend_tf.h | 3 +- libavfilter/dnn/dnn_interface.c | 8 +-- libavfilter/dnn_filter_common.c | 23 +------ libavfilter/dnn_filter_common.h | 3 +- libavfilter/dnn_interface.h | 4 +- libavfilter/vf_derain.c | 7 ++ libavfilter/vf_dnn_classify.c | 4 +- libavfilter/vf_dnn_detect.c | 8 +-- libavfilter/vf_dnn_processing.c | 8 +-- libavfilter/vf_sr.c | 8 +++ 17 files changed, 140 insertions(+), 140 deletions(-)