*/
#include "dnn_backend_openvino.h"
+#include "dnn_io_proc.h"
#include "libavformat/avio.h"
#include "libavutil/avassert.h"
+#include "libavutil/opt.h"
+#include "libavutil/avstring.h"
+#include "../internal.h"
+#include "queue.h"
+#include "safe_queue.h"
#include <c_api/ie_c_api.h>
+typedef struct OVOptions{
+ char *device_type;
+ int nireq;
+ int batch_size;
+ int input_resizable;
+} OVOptions;
+
typedef struct OVContext {
const AVClass *class;
+ OVOptions options;
} OVContext;
typedef struct OVModel{
OVContext ctx;
+ DNNModel *model;
ie_core_t *core;
ie_network_t *network;
ie_executable_network_t *exe_network;
ie_infer_request_t *infer_request;
- ie_blob_t *input_blob;
+
+ /* for async execution */
+ SafeQueue *request_queue; // holds RequestItem
+ Queue *task_queue; // holds TaskItem
} OVModel;
-static const AVClass dnn_openvino_class = {
- .class_name = "dnn_openvino",
- .item_name = av_default_item_name,
- .option = NULL,
- .version = LIBAVUTIL_VERSION_INT,
- .category = AV_CLASS_CATEGORY_FILTER,
+typedef struct TaskItem {
+ OVModel *ov_model;
+ const char *input_name;
+ AVFrame *in_frame;
+ const char *output_name;
+ AVFrame *out_frame;
+ int do_ioproc;
+ int async;
+ int done;
+} TaskItem;
+
+typedef struct RequestItem {
+ ie_infer_request_t *infer_request;
+ TaskItem **tasks;
+ int task_count;
+ ie_complete_call_back_t callback;
+} RequestItem;
+
+#define APPEND_STRING(generated_string, iterate_string) \
+ generated_string = generated_string ? av_asprintf("%s %s", generated_string, iterate_string) : \
+ av_asprintf("%s", iterate_string);
+
+#define OFFSET(x) offsetof(OVContext, x)
+#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
+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},
+ { "input_resizable", "can input be resizable or not", OFFSET(options.input_resizable), AV_OPT_TYPE_BOOL, { .i64 = 0 }, 0, 1, FLAGS },
+ { NULL }
};
+AVFILTER_DEFINE_CLASS(dnn_openvino);
+
static DNNDataType precision_to_datatype(precision_e precision)
{
switch (precision)
{
case FP32:
return DNN_FLOAT;
+ case U8:
+ return DNN_UINT8;
default:
av_assert0(!"not supported yet.");
return DNN_FLOAT;
}
}
+static int get_datatype_size(DNNDataType dt)
+{
+ switch (dt)
+ {
+ case DNN_FLOAT:
+ return sizeof(float);
+ case DNN_UINT8:
+ return sizeof(uint8_t);
+ 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;
+ ie_blob_buffer_t blob_buffer;
+ OVContext *ctx = &ov_model->ctx;
+ 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) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to get input blob with name %s\n", task->input_name);
+ return DNN_ERROR;
+ }
+
+ status |= ie_blob_get_dims(input_blob, &dims);
+ status |= ie_blob_get_precision(input_blob, &precision);
+ if (status != OK) {
+ ie_blob_free(&input_blob);
+ av_log(ctx, AV_LOG_ERROR, "Failed to get input blob dims/precision\n");
+ return DNN_ERROR;
+ }
+
+ status = ie_blob_get_buffer(input_blob, &blob_buffer);
+ if (status != OK) {
+ ie_blob_free(&input_blob);
+ av_log(ctx, AV_LOG_ERROR, "Failed to get input blob buffer\n");
+ return DNN_ERROR;
+ }
+
+ input.height = dims.dims[2];
+ input.width = dims.dims[3];
+ input.channels = dims.dims[1];
+ input.data = blob_buffer.buffer;
+ input.dt = precision_to_datatype(precision);
+ // all models in openvino open model zoo use BGR as input,
+ // change to be an option when necessary.
+ input.order = DCO_BGR;
+
+ 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->frame_pre_proc != NULL) {
+ ov_model->model->frame_pre_proc(task->in_frame, &input, ov_model->model->filter_ctx);
+ } else {
+ ff_proc_from_frame_to_dnn(task->in_frame, &input, ov_model->model->func_type, ctx);
+ }
+ }
+ input.data = (uint8_t *)input.data
+ + input.width * input.height * input.channels * get_datatype_size(input.dt);
+ }
+ ie_blob_free(&input_blob);
+
+ return DNN_SUCCESS;
+}
+
+static void infer_completion_callback(void *args)
+{
+ dimensions_t dims;
+ precision_e precision;
+ IEStatusCode status;
+ RequestItem *request = args;
+ TaskItem *task = request->tasks[0];
+ SafeQueue *requestq = task->ov_model->request_queue;
+ ie_blob_t *output_blob = NULL;
+ ie_blob_buffer_t blob_buffer;
+ DNNData output;
+ OVContext *ctx = &task->ov_model->ctx;
+
+ status = ie_infer_request_get_blob(request->infer_request, task->output_name, &output_blob);
+ if (status != OK) {
+ //incorrect output name
+ char *model_output_name = NULL;
+ char *all_output_names = NULL;
+ size_t model_output_count = 0;
+ av_log(ctx, AV_LOG_ERROR, "Failed to get model output data\n");
+ status = ie_network_get_outputs_number(task->ov_model->network, &model_output_count);
+ for (size_t i = 0; i < model_output_count; i++) {
+ status = ie_network_get_output_name(task->ov_model->network, i, &model_output_name);
+ APPEND_STRING(all_output_names, model_output_name)
+ }
+ av_log(ctx, AV_LOG_ERROR,
+ "output \"%s\" may not correct, all output(s) are: \"%s\"\n",
+ task->output_name, all_output_names);
+ return;
+ }
+
+ status = ie_blob_get_buffer(output_blob, &blob_buffer);
+ if (status != OK) {
+ ie_blob_free(&output_blob);
+ av_log(ctx, AV_LOG_ERROR, "Failed to access output memory\n");
+ return;
+ }
+
+ status |= ie_blob_get_dims(output_blob, &dims);
+ status |= ie_blob_get_precision(output_blob, &precision);
+ if (status != OK) {
+ ie_blob_free(&output_blob);
+ av_log(ctx, AV_LOG_ERROR, "Failed to get dims or precision of output\n");
+ return;
+ }
+
+ output.channels = dims.dims[1];
+ output.height = dims.dims[2];
+ output.width = dims.dims[3];
+ output.dt = precision_to_datatype(precision);
+ output.data = blob_buffer.buffer;
+
+ 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];
+
+ switch (task->ov_model->model->func_type) {
+ case DFT_PROCESS_FRAME:
+ if (task->do_ioproc) {
+ if (task->ov_model->model->frame_post_proc != NULL) {
+ task->ov_model->model->frame_post_proc(task->out_frame, &output, task->ov_model->model->filter_ctx);
+ } else {
+ ff_proc_from_dnn_to_frame(task->out_frame, &output, ctx);
+ }
+ } else {
+ task->out_frame->width = output.width;
+ task->out_frame->height = output.height;
+ }
+ break;
+ case DFT_ANALYTICS_DETECT:
+ if (!task->ov_model->model->detect_post_proc) {
+ av_log(ctx, AV_LOG_ERROR, "detect filter needs to provide post proc\n");
+ return;
+ }
+ task->ov_model->model->detect_post_proc(task->out_frame, &output, 1, task->ov_model->model->filter_ctx);
+ break;
+ default:
+ av_assert0(!"should not reach here");
+ break;
+ }
+
+ 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) {
+ if (ff_safe_queue_push_back(requestq, request) < 0) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n");
+ return;
+ }
+ }
+}
+
+static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, const char *output_name)
+{
+ OVContext *ctx = &ov_model->ctx;
+ IEStatusCode status;
+ ie_available_devices_t a_dev;
+ ie_config_t config = {NULL, NULL, NULL};
+ char *all_dev_names = NULL;
+
+ // 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;
+ }
+
+ // The order of dims in the openvino is fixed and it is always NCHW for 4-D data.
+ // while we pass NHWC data from FFmpeg to openvino
+ status = ie_network_set_input_layout(ov_model->network, input_name, NHWC);
+ if (status != OK) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to set layout as NHWC for input %s\n", input_name);
+ goto err;
+ }
+ status = ie_network_set_output_layout(ov_model->network, output_name, NHWC);
+ if (status != OK) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to set layout as NHWC for output %s\n", output_name);
+ goto err;
+ }
+
+ // all models in openvino open model zoo use BGR with range [0.0f, 255.0f] as input,
+ // we don't have a AVPixelFormat to describe it, so we'll use AV_PIX_FMT_BGR24 and
+ // ask openvino to do the conversion internally.
+ // the current supported SR model (frame processing) is generated from tensorflow model,
+ // and its input is Y channel as float with range [0.0f, 1.0f], so do not set for this case.
+ // TODO: we need to get a final clear&general solution with all backends/formats considered.
+ if (ov_model->model->func_type != DFT_PROCESS_FRAME) {
+ status = ie_network_set_input_precision(ov_model->network, input_name, U8);
+ if (status != OK) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to set input precision as U8 for %s\n", input_name);
+ 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 load OpenVINO model network\n");
+ status = ie_core_get_available_devices(ov_model->core, &a_dev);
+ if (status != OK) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to get available devices\n");
+ goto err;
+ }
+ for (int i = 0; i < a_dev.num_devices; i++) {
+ APPEND_STRING(all_dev_names, a_dev.devices[i])
+ }
+ av_log(ctx, AV_LOG_ERROR,"device %s may not be supported, all available devices are: \"%s\"\n",
+ ctx->options.device_type, all_dev_names);
+ goto err;
+ }
+
+ // create infer_request for sync execution
+ status = ie_exec_network_create_infer_request(ov_model->exe_network, &ov_model->infer_request);
+ if (status != OK)
+ goto err;
+
+ // create infer_requests for async execution
+ if (ctx->options.nireq <= 0) {
+ // the default value is a rough estimation
+ ctx->options.nireq = av_cpu_count() / 2 + 1;
+ }
+
+ ov_model->request_queue = ff_safe_queue_create();
+ if (!ov_model->request_queue) {
+ goto err;
+ }
+
+ for (int i = 0; i < ctx->options.nireq; i++) {
+ RequestItem *item = av_mallocz(sizeof(*item));
+ if (!item) {
+ goto err;
+ }
+
+ item->callback.completeCallBackFunc = infer_completion_callback;
+ item->callback.args = item;
+ if (ff_safe_queue_push_back(ov_model->request_queue, item) < 0) {
+ av_freep(&item);
+ goto err;
+ }
+
+ status = ie_exec_network_create_infer_request(ov_model->exe_network, &item->infer_request);
+ if (status != OK) {
+ goto err;
+ }
+
+ item->tasks = av_malloc_array(ctx->options.batch_size, sizeof(*item->tasks));
+ if (!item->tasks) {
+ goto err;
+ }
+ item->task_count = 0;
+ }
+
+ ov_model->task_queue = ff_queue_create();
+ if (!ov_model->task_queue) {
+ goto err;
+ }
+
+ return DNN_SUCCESS;
+
+err:
+ ff_dnn_free_model_ov(&ov_model->model);
+ return DNN_ERROR;
+}
+
+static DNNReturnType execute_model_ov(RequestItem *request)
+{
+ IEStatusCode status;
+ DNNReturnType ret;
+ TaskItem *task = request->tasks[0];
+ OVContext *ctx = &task->ov_model->ctx;
+
+ if (task->async) {
+ 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");
+ 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;
+ } 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;
+ }
+ infer_completion_callback(request);
+ return task->done ? DNN_SUCCESS : DNN_ERROR;
+ }
+}
+
static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input_name)
{
- OVModel *ov_model = (OVModel *)model;
+ OVModel *ov_model = model;
OVContext *ctx = &ov_model->ctx;
char *model_input_name = NULL;
+ char *all_input_names = NULL;
IEStatusCode status;
size_t model_input_count = 0;
dimensions_t dims;
precision_e precision;
+ int input_resizable = ctx->options.input_resizable;
status = ie_network_get_inputs_number(ov_model->network, &model_input_count);
if (status != OK) {
return DNN_ERROR;
}
- // The order of dims in the openvino is fixed and it is always NCHW for 4-D data.
- // while we pass NHWC data from FFmpeg to openvino
- status = ie_network_set_input_layout(ov_model->network, input_name, NHWC);
- if (status != OK) {
- av_log(ctx, AV_LOG_ERROR, "Input \"%s\" does not match layout NHWC\n", input_name);
- return DNN_ERROR;
- }
-
input->channels = dims.dims[1];
- input->height = dims.dims[2];
- input->width = dims.dims[3];
+ input->height = input_resizable ? -1 : dims.dims[2];
+ input->width = input_resizable ? -1 : dims.dims[3];
input->dt = precision_to_datatype(precision);
return DNN_SUCCESS;
+ } else {
+ //incorrect input name
+ APPEND_STRING(all_input_names, model_input_name)
}
ie_network_name_free(&model_input_name);
}
- av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", model_input_name);
+ av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model, all input(s) are: \"%s\"\n", input_name, all_input_names);
return DNN_ERROR;
}
-static DNNReturnType set_input_ov(void *model, DNNData *input, const char *input_name)
+static DNNReturnType get_output_ov(void *model, const char *input_name, int input_width, int input_height,
+ const char *output_name, int *output_width, int *output_height)
{
- OVModel *ov_model = (OVModel *)model;
+ DNNReturnType ret;
+ OVModel *ov_model = model;
OVContext *ctx = &ov_model->ctx;
+ TaskItem task;
+ RequestItem request;
+ AVFrame *in_frame = NULL;
+ AVFrame *out_frame = NULL;
+ TaskItem *ptask = &task;
IEStatusCode status;
- dimensions_t dims;
- precision_e precision;
- ie_blob_buffer_t blob_buffer;
-
- status = ie_exec_network_create_infer_request(ov_model->exe_network, &ov_model->infer_request);
- if (status != OK)
- goto err;
+ input_shapes_t input_shapes;
- status = ie_infer_request_get_blob(ov_model->infer_request, input_name, &ov_model->input_blob);
- if (status != OK)
- goto err;
+ if (ov_model->model->func_type != DFT_PROCESS_FRAME) {
+ av_log(ctx, AV_LOG_ERROR, "Get output dim only when processing frame.\n");
+ return DNN_ERROR;
+ }
- status |= ie_blob_get_dims(ov_model->input_blob, &dims);
- status |= ie_blob_get_precision(ov_model->input_blob, &precision);
- if (status != OK)
- goto err;
+ if (ctx->options.input_resizable) {
+ status = ie_network_get_input_shapes(ov_model->network, &input_shapes);
+ input_shapes.shapes->shape.dims[2] = input_height;
+ input_shapes.shapes->shape.dims[3] = input_width;
+ status |= ie_network_reshape(ov_model->network, input_shapes);
+ ie_network_input_shapes_free(&input_shapes);
+ if (status != OK) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to reshape input size for %s\n", input_name);
+ return DNN_ERROR;
+ }
+ }
- av_assert0(input->channels == dims.dims[1]);
- av_assert0(input->height == dims.dims[2]);
- av_assert0(input->width == dims.dims[3]);
- av_assert0(input->dt == precision_to_datatype(precision));
+ if (!ov_model->exe_network) {
+ if (init_model_ov(ov_model, input_name, output_name) != DNN_SUCCESS) {
+ av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
+ return DNN_ERROR;
+ }
+ }
- status = ie_blob_get_buffer(ov_model->input_blob, &blob_buffer);
- if (status != OK)
- goto err;
- input->data = blob_buffer.buffer;
+ in_frame = av_frame_alloc();
+ if (!in_frame) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input frame\n");
+ return DNN_ERROR;
+ }
+ in_frame->width = input_width;
+ in_frame->height = input_height;
- return DNN_SUCCESS;
+ out_frame = av_frame_alloc();
+ if (!out_frame) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output frame\n");
+ av_frame_free(&in_frame);
+ return DNN_ERROR;
+ }
-err:
- if (ov_model->input_blob)
- ie_blob_free(&ov_model->input_blob);
- if (ov_model->infer_request)
- ie_infer_request_free(&ov_model->infer_request);
- av_log(ctx, AV_LOG_ERROR, "Failed to create inference instance or get input data/dims/precision/memory\n");
- return DNN_ERROR;
+ task.done = 0;
+ task.do_ioproc = 0;
+ task.async = 0;
+ task.input_name = input_name;
+ task.in_frame = in_frame;
+ task.output_name = output_name;
+ task.out_frame = out_frame;
+ task.ov_model = ov_model;
+
+ request.infer_request = ov_model->infer_request;
+ request.task_count = 1;
+ request.tasks = &ptask;
+
+ ret = execute_model_ov(&request);
+ *output_width = out_frame->width;
+ *output_height = out_frame->height;
+
+ av_frame_free(&out_frame);
+ av_frame_free(&in_frame);
+ return ret;
}
-DNNModel *ff_dnn_load_model_ov(const char *model_filename, const char *options)
+DNNModel *ff_dnn_load_model_ov(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx)
{
DNNModel *model = NULL;
OVModel *ov_model = NULL;
+ OVContext *ctx = NULL;
IEStatusCode status;
- ie_config_t config = {NULL, NULL, NULL};
- model = av_malloc(sizeof(DNNModel));
+ model = av_mallocz(sizeof(DNNModel));
if (!model){
return NULL;
}
ov_model = av_mallocz(sizeof(OVModel));
- if (!ov_model)
- goto err;
+ if (!ov_model) {
+ av_freep(&model);
+ return NULL;
+ }
+ model->model = ov_model;
+ ov_model->model = model;
ov_model->ctx.class = &dnn_openvino_class;
+ ctx = &ov_model->ctx;
- status = ie_core_create("", &ov_model->core);
- if (status != OK)
+ //parse options
+ av_opt_set_defaults(ctx);
+ if (av_opt_set_from_string(ctx, options, NULL, "=", "&") < 0) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to parse options \"%s\"\n", options);
goto err;
+ }
- status = ie_core_read_network(ov_model->core, model_filename, NULL, &ov_model->network);
+ status = ie_core_create("", &ov_model->core);
if (status != OK)
goto err;
- status = ie_core_load_network(ov_model->core, ov_model->network, "CPU", &config, &ov_model->exe_network);
- if (status != OK)
+ status = ie_core_read_network(ov_model->core, model_filename, NULL, &ov_model->network);
+ if (status != OK) {
+ ie_version_t ver;
+ ver = ie_c_api_version();
+ av_log(ctx, AV_LOG_ERROR, "Failed to read the network from model file %s,\n"
+ "Please check if the model version matches the runtime OpenVINO %s\n",
+ model_filename, ver.api_version);
+ ie_version_free(&ver);
goto err;
+ }
- model->model = (void *)ov_model;
- model->set_input = &set_input_ov;
model->get_input = &get_input_ov;
+ model->get_output = &get_output_ov;
model->options = options;
+ model->filter_ctx = filter_ctx;
+ model->func_type = func_type;
return model;
err:
- if (model)
- av_freep(&model);
- if (ov_model) {
- if (ov_model->exe_network)
- ie_exec_network_free(&ov_model->exe_network);
- if (ov_model->network)
- ie_network_free(&ov_model->network);
- if (ov_model->core)
- ie_core_free(&ov_model->core);
- av_freep(&ov_model);
- }
+ ff_dnn_free_model_ov(&model);
return NULL;
}
-DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNData *outputs, const char **output_names, uint32_t nb_output)
+DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, const char *input_name, AVFrame *in_frame,
+ const char **output_names, uint32_t nb_output, AVFrame *out_frame)
{
- dimensions_t dims;
- precision_e precision;
- ie_blob_buffer_t blob_buffer;
- OVModel *ov_model = (OVModel *)model->model;
+ OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
- IEStatusCode status = ie_infer_request_infer(ov_model->infer_request);
- if (status != OK) {
- av_log(ctx, AV_LOG_ERROR, "Failed to start synchronous model inference\n");
+ TaskItem task;
+ RequestItem request;
+ TaskItem *ptask = &task;
+
+ if (!in_frame) {
+ av_log(ctx, AV_LOG_ERROR, "in frame is NULL when execute model.\n");
return DNN_ERROR;
}
- for (uint32_t i = 0; i < nb_output; ++i) {
- const char *output_name = output_names[i];
- ie_blob_t *output_blob = NULL;
- status = ie_infer_request_get_blob(ov_model->infer_request, output_name, &output_blob);
- if (status != OK) {
- av_log(ctx, AV_LOG_ERROR, "Failed to get model output data\n");
+ if (!out_frame && model->func_type == DFT_PROCESS_FRAME) {
+ av_log(ctx, AV_LOG_ERROR, "out frame is NULL when execute model.\n");
+ return DNN_ERROR;
+ }
+
+ if (nb_output != 1) {
+ // currently, the filter does not need multiple outputs,
+ // so we just pending the support until we really need it.
+ avpriv_report_missing_feature(ctx, "multiple outputs");
+ 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, input_name, output_names[0]) != DNN_SUCCESS) {
+ av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return DNN_ERROR;
}
+ }
- status = ie_blob_get_buffer(output_blob, &blob_buffer);
- if (status != OK) {
- av_log(ctx, AV_LOG_ERROR, "Failed to access output memory\n");
+ task.done = 0;
+ task.do_ioproc = 1;
+ task.async = 0;
+ task.input_name = input_name;
+ task.in_frame = in_frame;
+ task.output_name = output_names[0];
+ task.out_frame = out_frame;
+ task.ov_model = ov_model;
+
+ request.infer_request = ov_model->infer_request;
+ request.task_count = 1;
+ request.tasks = &ptask;
+
+ return execute_model_ov(&request);
+}
+
+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)
+{
+ OVModel *ov_model = model->model;
+ OVContext *ctx = &ov_model->ctx;
+ RequestItem *request;
+ TaskItem *task;
+
+ if (!in_frame) {
+ av_log(ctx, AV_LOG_ERROR, "in frame is NULL when async execute model.\n");
+ return DNN_ERROR;
+ }
+
+ if (!out_frame && model->func_type == DFT_PROCESS_FRAME) {
+ av_log(ctx, AV_LOG_ERROR, "out frame is NULL when async execute model.\n");
+ return DNN_ERROR;
+ }
+
+ if (!ov_model->exe_network) {
+ if (init_model_ov(ov_model, input_name, output_names[0]) != DNN_SUCCESS) {
+ av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return DNN_ERROR;
}
+ }
- status |= ie_blob_get_dims(output_blob, &dims);
- status |= ie_blob_get_precision(output_blob, &precision);
- if (status != OK) {
- av_log(ctx, AV_LOG_ERROR, "Failed to get dims or precision of output\n");
+ task = av_malloc(sizeof(*task));
+ if (!task) {
+ av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
+ return DNN_ERROR;
+ }
+
+ task->done = 0;
+ task->do_ioproc = 1;
+ task->async = 1;
+ task->input_name = input_name;
+ task->in_frame = in_frame;
+ task->output_name = output_names[0];
+ task->out_frame = out_frame;
+ task->ov_model = ov_model;
+ if (ff_queue_push_back(ov_model->task_queue, task) < 0) {
+ av_freep(&task);
+ av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\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;
+ }
+
+ 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)
+{
+ OVModel *ov_model = model->model;
+ TaskItem *task = ff_queue_peek_front(ov_model->task_queue);
+
+ if (!task) {
+ return DAST_EMPTY_QUEUE;
+ }
+
+ if (!task->done) {
+ return DAST_NOT_READY;
+ }
+
+ *in = task->in_frame;
+ *out = task->out_frame;
+ ff_queue_pop_front(ov_model->task_queue);
+ av_freep(&task);
+
+ return DAST_SUCCESS;
+}
+
+DNNReturnType ff_dnn_flush_ov(const DNNModel *model)
+{
+ OVModel *ov_model = 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;
+ }
- outputs[i].channels = dims.dims[1];
- outputs[i].height = dims.dims[2];
- outputs[i].width = dims.dims[3];
- outputs[i].dt = precision_to_datatype(precision);
- outputs[i].data = blob_buffer.buffer;
+ 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){
- OVModel *ov_model = (OVModel *)(*model)->model;
- if (ov_model->input_blob)
- ie_blob_free(&ov_model->input_blob);
+ OVModel *ov_model = (*model)->model;
+ while (ff_safe_queue_size(ov_model->request_queue) != 0) {
+ RequestItem *item = ff_safe_queue_pop_front(ov_model->request_queue);
+ 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);
+
if (ov_model->infer_request)
ie_infer_request_free(&ov_model->infer_request);
if (ov_model->exe_network)
projects / ffmpeg / blobdiff