libavfilter/dnn: add batch mode for async execution

the default number of batch_size is 1

Signed-off-by: Xie, Lin <lin.xie@intel.com>
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>

libavfilter/dnn/dnn_backend_openvino.c

@@ -37,6 +37,7 @@
 typedef struct OVOptions{
     char *device_type;
     int nireq;
+    int batch_size;
 } OVOptions;
 
 typedef struct OVContext {
@@ -70,7 +71,8 @@ typedef struct TaskItem {
 
 typedef struct RequestItem {
     ie_infer_request_t *infer_request;
-    TaskItem *task;
+    TaskItem **tasks;
+    int task_count;
     ie_complete_call_back_t callback;
 } RequestItem;
 
@@ -83,6 +85,7 @@ typedef struct RequestItem {
 static const AVOption dnn_openvino_options[] = {
     { "device", "device to run model", OFFSET(options.device_type), AV_OPT_TYPE_STRING, { .str = "CPU" }, 0, 0, FLAGS },
     { "nireq",  "number of request",   OFFSET(options.nireq),       AV_OPT_TYPE_INT,    { .i64 = 0 },     0, INT_MAX, FLAGS },
+    { "batch_size",  "batch size per request", OFFSET(options.batch_size),  AV_OPT_TYPE_INT,    { .i64 = 1 },     1, 1000, FLAGS},
     { NULL }
 };
 
@@ -100,7 +103,19 @@ static DNNDataType precision_to_datatype(precision_e precision)
     }
 }
 
-static DNNReturnType fill_model_input_ov(OVModel *ov_model, TaskItem *task, RequestItem *request)
+static int get_datatype_size(DNNDataType dt)
+{
+    switch (dt)
+    {
+    case DNN_FLOAT:
+        return sizeof(float);
+    default:
+        av_assert0(!"not supported yet.");
+        return 1;
+    }
+}
+
+static DNNReturnType fill_model_input_ov(OVModel *ov_model, RequestItem *request)
 {
     dimensions_t dims;
     precision_e precision;
@@ -109,6 +124,7 @@ static DNNReturnType fill_model_input_ov(OVModel *ov_model, TaskItem *task, Requ
     IEStatusCode status;
     DNNData input;
     ie_blob_t *input_blob = NULL;
+    TaskItem *task = request->tasks[0];
 
     status = ie_infer_request_get_blob(request->infer_request, task->input_name, &input_blob);
     if (status != OK) {
@@ -134,12 +150,19 @@ static DNNReturnType fill_model_input_ov(OVModel *ov_model, TaskItem *task, Requ
     input.channels = dims.dims[1];
     input.data = blob_buffer.buffer;
     input.dt = precision_to_datatype(precision);
-    if (task->do_ioproc) {
-        if (ov_model->model->pre_proc != NULL) {
-            ov_model->model->pre_proc(task->in_frame, &input, ov_model->model->filter_ctx);
-        } else {
-            proc_from_frame_to_dnn(task->in_frame, &input, ctx);
+
+    av_assert0(request->task_count <= dims.dims[0]);
+    for (int i = 0; i < request->task_count; ++i) {
+        task = request->tasks[i];
+        if (task->do_ioproc) {
+            if (ov_model->model->pre_proc != NULL) {
+                ov_model->model->pre_proc(task->in_frame, &input, ov_model->model->filter_ctx);
+            } else {
+                proc_from_frame_to_dnn(task->in_frame, &input, ctx);
+            }
         }
+        input.data = (uint8_t *)input.data
+                     + input.width * input.height * input.channels * get_datatype_size(input.dt);
     }
     ie_blob_free(&input_blob);
 
@@ -152,7 +175,7 @@ static void infer_completion_callback(void *args)
     precision_e precision;
     IEStatusCode status;
     RequestItem *request = args;
-    TaskItem *task = request->task;
+    TaskItem *task = request->tasks[0];
     ie_blob_t *output_blob = NULL;
     ie_blob_buffer_t blob_buffer;
     DNNData output;
@@ -194,41 +217,56 @@ static void infer_completion_callback(void *args)
     output.width    = dims.dims[3];
     output.dt       = precision_to_datatype(precision);
     output.data     = blob_buffer.buffer;
-    if (task->do_ioproc) {
-        if (task->ov_model->model->post_proc != NULL) {
-            task->ov_model->model->post_proc(task->out_frame, &output, task->ov_model->model->filter_ctx);
+
+    av_assert0(request->task_count <= dims.dims[0]);
+    av_assert0(request->task_count >= 1);
+    for (int i = 0; i < request->task_count; ++i) {
+        task = request->tasks[i];
+        if (task->do_ioproc) {
+            if (task->ov_model->model->post_proc != NULL) {
+                task->ov_model->model->post_proc(task->out_frame, &output, task->ov_model->model->filter_ctx);
+            } else {
+                proc_from_dnn_to_frame(task->out_frame, &output, ctx);
+            }
         } else {
-            proc_from_dnn_to_frame(task->out_frame, &output, ctx);
+            task->out_frame->width = output.width;
+            task->out_frame->height = output.height;
         }
-    } else {
-        task->out_frame->width = output.width;
-        task->out_frame->height = output.height;
+        task->done = 1;
+        output.data = (uint8_t *)output.data
+                      + output.width * output.height * output.channels * get_datatype_size(output.dt);
     }
     ie_blob_free(&output_blob);
 
+    request->task_count = 0;
+
     if (task->async) {
-        request->task = NULL;
         if (ff_safe_queue_push_back(task->ov_model->request_queue, request) < 0) {
             av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n");
             return;
         }
     }
-
-    task->done = 1;
 }
 
-static DNNReturnType execute_model_ov(TaskItem *task, RequestItem *request)
+static DNNReturnType execute_model_ov(RequestItem *request)
 {
     IEStatusCode status;
+    DNNReturnType ret;
+    TaskItem *task = request->tasks[0];
     OVContext *ctx = &task->ov_model->ctx;
 
-    DNNReturnType ret = fill_model_input_ov(task->ov_model, task, request);
-    if (ret != DNN_SUCCESS) {
-        return ret;
-    }
-
     if (task->async) {
-        request->task = task;
+        if (request->task_count < ctx->options.batch_size) {
+            if (ff_safe_queue_push_front(task->ov_model->request_queue, request) < 0) {
+                av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n");
+                return DNN_ERROR;
+            }
+            return DNN_SUCCESS;
+        }
+        ret = fill_model_input_ov(task->ov_model, request);
+        if (ret != DNN_SUCCESS) {
+            return ret;
+        }
         status = ie_infer_set_completion_callback(request->infer_request, &request->callback);
         if (status != OK) {
             av_log(ctx, AV_LOG_ERROR, "Failed to set completion callback for inference\n");
@@ -241,12 +279,15 @@ static DNNReturnType execute_model_ov(TaskItem *task, RequestItem *request)
         }
         return DNN_SUCCESS;
     } else {
+        ret = fill_model_input_ov(task->ov_model, request);
+        if (ret != DNN_SUCCESS) {
+            return ret;
+        }
         status = ie_infer_request_infer(request->infer_request);
         if (status != OK) {
             av_log(ctx, AV_LOG_ERROR, "Failed to start synchronous model inference\n");
             return DNN_ERROR;
         }
-        request->task = task;
         infer_completion_callback(request);
         return task->done ? DNN_SUCCESS : DNN_ERROR;
     }
@@ -319,6 +360,7 @@ static DNNReturnType get_output_ov(void *model, const char *input_name, int inpu
     RequestItem request;
     AVFrame *in_frame = av_frame_alloc();
     AVFrame *out_frame = NULL;
+    TaskItem *ptask = &task;
 
     if (!in_frame) {
         av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input frame\n");
@@ -343,8 +385,10 @@ static DNNReturnType get_output_ov(void *model, const char *input_name, int inpu
     task.ov_model = ov_model;
 
     request.infer_request = ov_model->infer_request;
+    request.task_count = 1;
+    request.tasks = &ptask;
 
-    ret = execute_model_ov(&task, &request);
+    ret = execute_model_ov(&request);
     *output_width = out_frame->width;
     *output_height = out_frame->height;
 
@@ -393,6 +437,24 @@ DNNModel *ff_dnn_load_model_ov(const char *model_filename, const char *options,
     if (status != OK)
         goto err;
 
+    // batch size
+    if (ctx->options.batch_size <= 0) {
+        ctx->options.batch_size = 1;
+    }
+
+    if (ctx->options.batch_size > 1) {
+        input_shapes_t input_shapes;
+        status = ie_network_get_input_shapes(ov_model->network, &input_shapes);
+        if (status != OK)
+            goto err;
+        for (int i = 0; i < input_shapes.shape_num; i++)
+            input_shapes.shapes[i].shape.dims[0] = ctx->options.batch_size;
+        status = ie_network_reshape(ov_model->network, input_shapes);
+        ie_network_input_shapes_free(&input_shapes);
+        if (status != OK)
+            goto err;
+    }
+
     status = ie_core_load_network(ov_model->core, ov_model->network, ctx->options.device_type, &config, &ov_model->exe_network);
     if (status != OK) {
         av_log(ctx, AV_LOG_ERROR, "Failed to init OpenVINO model\n");
@@ -426,17 +488,24 @@ DNNModel *ff_dnn_load_model_ov(const char *model_filename, const char *options,
     }
 
     for (int i = 0; i < ctx->options.nireq; i++) {
-        ie_infer_request_t *request;
         RequestItem *item = av_mallocz(sizeof(*item));
         if (!item) {
             goto err;
         }
-        status = ie_exec_network_create_infer_request(ov_model->exe_network, &request);
+
+        status = ie_exec_network_create_infer_request(ov_model->exe_network, &item->infer_request);
         if (status != OK) {
             av_freep(&item);
             goto err;
         }
-        item->infer_request = request;
+
+        item->tasks = av_malloc_array(ctx->options.batch_size, sizeof(*item->tasks));
+        if (!item->tasks) {
+            av_freep(&item);
+            goto err;
+        }
+        item->task_count = 0;
+
         item->callback.completeCallBackFunc = infer_completion_callback;
         item->callback.args = item;
         if (ff_safe_queue_push_back(ov_model->request_queue, item) < 0) {
@@ -469,6 +538,7 @@ DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, const char *input_n
     OVContext *ctx = &ov_model->ctx;
     TaskItem task;
     RequestItem request;
+    TaskItem *ptask = &task;
 
     if (!in_frame) {
         av_log(ctx, AV_LOG_ERROR, "in frame is NULL when execute model.\n");
@@ -487,6 +557,11 @@ DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, const char *input_n
         return DNN_ERROR;
     }
 
+    if (ctx->options.batch_size > 1) {
+        av_log(ctx, AV_LOG_ERROR, "do not support batch mode for sync execution.\n");
+        return DNN_ERROR;
+    }
+
     task.done = 0;
     task.do_ioproc = 1;
     task.async = 0;
@@ -497,8 +572,10 @@ DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, const char *input_n
     task.ov_model = ov_model;
 
     request.infer_request = ov_model->infer_request;
+    request.task_count = 1;
+    request.tasks = &ptask;
 
-    return execute_model_ov(&task, &request);
+    return execute_model_ov(&request);
 }
 
 DNNReturnType ff_dnn_execute_model_async_ov(const DNNModel *model, const char *input_name, AVFrame *in_frame,
@@ -545,7 +622,8 @@ DNNReturnType ff_dnn_execute_model_async_ov(const DNNModel *model, const char *i
         return DNN_ERROR;
     }
 
-    return execute_model_ov(task, request);
+    request->tasks[request->task_count++] = task;
+    return execute_model_ov(request);
 }
 
 DNNAsyncStatusType ff_dnn_get_async_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out)
@@ -569,6 +647,48 @@ DNNAsyncStatusType ff_dnn_get_async_result_ov(const DNNModel *model, AVFrame **i
     return DAST_SUCCESS;
 }
 
+DNNReturnType ff_dnn_flush_ov(const DNNModel *model)
+{
+    OVModel *ov_model = (OVModel *)model->model;
+    OVContext *ctx = &ov_model->ctx;
+    RequestItem *request;
+    IEStatusCode status;
+    DNNReturnType ret;
+
+    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;
+    }
+
+    if (request->task_count == 0) {
+        // no pending task need to flush
+        if (ff_safe_queue_push_back(ov_model->request_queue, request) < 0) {
+            av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n");
+            return DNN_ERROR;
+        }
+        return DNN_SUCCESS;
+    }
+
+    ret = fill_model_input_ov(ov_model, request);
+    if (ret != DNN_SUCCESS) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to fill model input.\n");
+        return ret;
+    }
+    status = ie_infer_set_completion_callback(request->infer_request, &request->callback);
+    if (status != OK) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to set completion callback for inference\n");
+        return DNN_ERROR;
+    }
+    status = ie_infer_request_infer_async(request->infer_request);
+    if (status != OK) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to start async inference\n");
+        return DNN_ERROR;
+    }
+
+    return DNN_SUCCESS;
+}
+
 void ff_dnn_free_model_ov(DNNModel **model)
 {
     if (*model){
@@ -578,12 +698,15 @@ void ff_dnn_free_model_ov(DNNModel **model)
             if (item && item->infer_request) {
                 ie_infer_request_free(&item->infer_request);
             }
+            av_freep(&item->tasks);
             av_freep(&item);
         }
         ff_safe_queue_destroy(ov_model->request_queue);
 
         while (ff_queue_size(ov_model->task_queue) != 0) {
             TaskItem *item = ff_queue_pop_front(ov_model->task_queue);
+            av_frame_free(&item->in_frame);
+            av_frame_free(&item->out_frame);
             av_freep(&item);
         }
         ff_queue_destroy(ov_model->task_queue);

libavfilter/dnn/dnn_backend_openvino.h

@@ -36,6 +36,7 @@ DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, const char *input_n
 DNNReturnType ff_dnn_execute_model_async_ov(const DNNModel *model, const char *input_name, AVFrame *in_frame,
                                             const char **output_names, uint32_t nb_output, AVFrame *out_frame);
 DNNAsyncStatusType ff_dnn_get_async_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);
 

libavfilter/dnn/dnn_interface.c

@@ -60,6 +60,7 @@ DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
         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->flush = &ff_dnn_flush_ov;
         dnn_module->free_model = &ff_dnn_free_model_ov;
     #else
         av_freep(&dnn_module);

libavfilter/dnn_interface.h

@@ -82,6 +82,8 @@ typedef struct DNNModule{
                                          const char **output_names, uint32_t nb_output, AVFrame *out_frame);
     // Retrieve inference result.
     DNNAsyncStatusType (*get_async_result)(const DNNModel *model, AVFrame **in, AVFrame **out);
+    // Flush all the pending tasks.
+    DNNReturnType (*flush)(const DNNModel *model);
     // Frees memory allocated for model.
     void (*free_model)(DNNModel **model);
 } DNNModule;

libavfilter/vf_dnn_processing.c

@@ -33,6 +33,7 @@
 #include "formats.h"
 #include "internal.h"
 #include "libswscale/swscale.h"
+#include "libavutil/time.h"
 
 typedef struct DnnProcessingContext {
     const AVClass *class;
@@ -369,6 +370,37 @@ static int activate_sync(AVFilterContext *filter_ctx)
     return FFERROR_NOT_READY;
 }
 
+static int flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *out_pts)
+{
+    DnnProcessingContext *ctx = outlink->src->priv;
+    int ret;
+    DNNAsyncStatusType async_state;
+
+    ret = (ctx->dnn_module->flush)(ctx->model);
+    if (ret != DNN_SUCCESS) {
+        return -1;
+    }
+
+    do {
+        AVFrame *in_frame = NULL;
+        AVFrame *out_frame = NULL;
+        async_state = (ctx->dnn_module->get_async_result)(ctx->model, &in_frame, &out_frame);
+        if (out_frame) {
+            if (isPlanarYUV(in_frame->format))
+                copy_uv_planes(ctx, out_frame, in_frame);
+            av_frame_free(&in_frame);
+            ret = ff_filter_frame(outlink, out_frame);
+            if (ret < 0)
+                return ret;
+            if (out_pts)
+                *out_pts = out_frame->pts + pts;
+        }
+        av_usleep(5000);
+    } while (async_state >= DAST_NOT_READY);
+
+    return 0;
+}
+
 static int activate_async(AVFilterContext *filter_ctx)
 {
     AVFilterLink *inlink = filter_ctx->inputs[0];
@@ -396,7 +428,7 @@ static int activate_async(AVFilterContext *filter_ctx)
             av_frame_copy_props(out, in);
             if ((ctx->dnn_module->execute_model_async)(ctx->model, ctx->model_inputname, in,
                                                        (const char **)&ctx->model_outputname, 1, out) != DNN_SUCCESS) {
-                return FFERROR_NOT_READY;
+                return AVERROR(EIO);
             }
         }
     } while (ret > 0);
@@ -423,14 +455,16 @@ static int activate_async(AVFilterContext *filter_ctx)
 
     if (ff_inlink_acknowledge_status(inlink, &status, &pts)) {
         if (status == AVERROR_EOF) {
-            ff_outlink_set_status(outlink, status, pts);
+            int64_t out_pts = pts;
+            ret = flush_frame(outlink, pts, &out_pts);
+            ff_outlink_set_status(outlink, status, out_pts);
             return ret;
         }
     }
 
     FF_FILTER_FORWARD_WANTED(outlink, inlink);
 
-    return FFERROR_NOT_READY;
+    return 0;
 }
 
 static int activate(AVFilterContext *filter_ctx)