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Code Issues Releases Activity

lavfi/dnn_backend_openvino: Return Specific Error Codes

Browse Source 
Switch to returning specific error codes or DNN_GENERIC_ERROR
when an error is encountered. For OpenVINO API errors, currently
DNN_GENERIC_ERROR is returned.

Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit is contained in:
Shubhanshu Saxena 2022-03-02 23:35:52 +05:30 committed by Guo Yejun
parent d0587daec2
commit 91af38f2b3
3 changed files with 89 additions and 63 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

138
libavfilter/dnn/dnn_backend_openvino.c
View File

@ -112,7 +112,7 @@ static int get_datatype_size(DNNDataType dt)
}
}
static DNNReturnType fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
static int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
{
dimensions_t dims;
precision_e precision;
@ -131,7 +131,7 @@ static DNNReturnType fill_model_input_ov(OVModel *ov_model, OVRequestItem *reque
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;
return DNN_GENERIC_ERROR;
}
status |= ie_blob_get_dims(input_blob, &dims);
@ -139,14 +139,14 @@ static DNNReturnType fill_model_input_ov(OVModel *ov_model, OVRequestItem *reque
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;
return DNN_GENERIC_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;
return DNN_GENERIC_ERROR;
}
input.height = dims.dims[2];
@ -301,8 +301,9 @@ static void infer_completion_callback(void *args)
}
}
static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, const char *output_name)
static int init_model_ov(OVModel *ov_model, const char *input_name, const char *output_name)
{
int ret = DNN_SUCCESS;
OVContext *ctx = &ov_model->ctx;
IEStatusCode status;
ie_available_devices_t a_dev;
@ -317,14 +318,18 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
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)
if (status != OK) {
ret = DNN_GENERIC_ERROR;
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)
if (status != OK) {
ret = DNN_GENERIC_ERROR;
goto err;
}
}
// The order of dims in the openvino is fixed and it is always NCHW for 4-D data.
@ -332,11 +337,13 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
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);
ret = DNN_GENERIC_ERROR;
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);
ret = DNN_GENERIC_ERROR;
goto err;
}
@ -350,6 +357,7 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
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);
ret = DNN_GENERIC_ERROR;
goto err;
}
}
@ -360,6 +368,7 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
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");
ret = DNN_GENERIC_ERROR;
goto err;
}
for (int i = 0; i < a_dev.num_devices; i++) {
@ -367,6 +376,7 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
}
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);
ret = AVERROR(ENODEV);
goto err;
}
@ -378,12 +388,14 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
ov_model->request_queue = ff_safe_queue_create();
if (!ov_model->request_queue) {
ret = AVERROR(ENOMEM);
goto err;
}
for (int i = 0; i < ctx->options.nireq; i++) {
OVRequestItem *item = av_mallocz(sizeof(*item));
if (!item) {
ret = AVERROR(ENOMEM);
goto err;
}
@ -391,16 +403,19 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
item->callback.args = item;
if (ff_safe_queue_push_back(ov_model->request_queue, item) < 0) {
av_freep(&item);
ret = AVERROR(ENOMEM);
goto err;
}
status = ie_exec_network_create_infer_request(ov_model->exe_network, &item->infer_request);
if (status != OK) {
ret = DNN_GENERIC_ERROR;
goto err;
}
item->lltasks = av_malloc_array(ctx->options.batch_size, sizeof(*item->lltasks));
if (!item->lltasks) {
ret = AVERROR(ENOMEM);
goto err;
}
item->lltask_count = 0;
@ -408,11 +423,13 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
ov_model->task_queue = ff_queue_create();
if (!ov_model->task_queue) {
ret = AVERROR(ENOMEM);
goto err;
}
ov_model->lltask_queue = ff_queue_create();
if (!ov_model->lltask_queue) {
ret = AVERROR(ENOMEM);
goto err;
}
@ -420,14 +437,14 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
err:
ff_dnn_free_model_ov(&ov_model->model);
return DNN_ERROR;
return ret;
}
static DNNReturnType execute_model_ov(OVRequestItem *request, Queue *inferenceq)
static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
{
IEStatusCode status;
DNNReturnType ret;
LastLevelTaskItem *lltask;
int ret = DNN_SUCCESS;
TaskItem *task;
OVContext *ctx;
OVModel *ov_model;
@ -451,11 +468,13 @@ static DNNReturnType execute_model_ov(OVRequestItem *request, Queue *inferenceq)
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");
ret = DNN_GENERIC_ERROR;
goto err;
}
status = ie_infer_request_infer_async(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start async inference\n");
ret = DNN_GENERIC_ERROR;
goto err;
}
return DNN_SUCCESS;
@ -467,20 +486,21 @@ static DNNReturnType execute_model_ov(OVRequestItem *request, Queue *inferenceq)
status = ie_infer_request_infer(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start synchronous model inference\n");
ret = DNN_GENERIC_ERROR;
goto err;
}
infer_completion_callback(request);
return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_ERROR;
return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_GENERIC_ERROR;
}
err:
if (ff_safe_queue_push_back(ov_model->request_queue, request) < 0) {
ie_infer_request_free(&request->infer_request);
av_freep(&request);
}
return DNN_ERROR;
return ret;
}
static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input_name)
static int get_input_ov(void *model, DNNData *input, const char *input_name)
{
OVModel *ov_model = model;
OVContext *ctx = &ov_model->ctx;
@ -495,14 +515,14 @@ static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input
status = ie_network_get_inputs_number(ov_model->network, &model_input_count);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input count\n");
return DNN_ERROR;
return DNN_GENERIC_ERROR;
}
for (size_t i = 0; i < model_input_count; i++) {
status = ie_network_get_input_name(ov_model->network, i, &model_input_name);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get No.%d input's name\n", (int)i);
return DNN_ERROR;
return DNN_GENERIC_ERROR;
}
if (strcmp(model_input_name, input_name) == 0) {
ie_network_name_free(&model_input_name);
@ -510,7 +530,7 @@ static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input
status |= ie_network_get_input_precision(ov_model->network, input_name, &precision);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get No.%d input's dims or precision\n", (int)i);
return DNN_ERROR;
return DNN_GENERIC_ERROR;
}
input->channels = dims.dims[1];
@ -527,7 +547,7 @@ static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input
}
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;
return AVERROR(EINVAL);
}
static int contain_valid_detection_bbox(AVFrame *frame)
@ -567,7 +587,7 @@ static int contain_valid_detection_bbox(AVFrame *frame)
return 1;
}
static DNNReturnType extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Queue *lltask_queue, DNNExecBaseParams *exec_params)
static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Queue *lltask_queue, DNNExecBaseParams *exec_params)
{
switch (func_type) {
case DFT_PROCESS_FRAME:
@ -575,14 +595,14 @@ static DNNReturnType extract_lltask_from_task(DNNFunctionType func_type, TaskIte
{
LastLevelTaskItem *lltask = av_malloc(sizeof(*lltask));
if (!lltask) {
return DNN_ERROR;
return AVERROR(ENOMEM);
}
task->inference_todo = 1;
task->inference_done = 0;
lltask->task = task;
if (ff_queue_push_back(lltask_queue, lltask) < 0) {
av_freep(&lltask);
return DNN_ERROR;
return AVERROR(ENOMEM);
}
return DNN_SUCCESS;
}
@ -615,28 +635,28 @@ static DNNReturnType extract_lltask_from_task(DNNFunctionType func_type, TaskIte
lltask = av_malloc(sizeof(*lltask));
if (!lltask) {
return DNN_ERROR;
return AVERROR(ENOMEM);
}
task->inference_todo++;
lltask->task = task;
lltask->bbox_index = i;
if (ff_queue_push_back(lltask_queue, lltask) < 0) {
av_freep(&lltask);
return DNN_ERROR;
return AVERROR(ENOMEM);
}
}
return DNN_SUCCESS;
}
default:
av_assert0(!"should not reach here");
return DNN_ERROR;
return AVERROR(EINVAL);
}
}
static DNNReturnType get_output_ov(void *model, const char *input_name, int input_width, int input_height,
static int 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)
{
DNNReturnType ret;
int ret;
OVModel *ov_model = model;
OVContext *ctx = &ov_model->ctx;
TaskItem task;
@ -653,7 +673,7 @@ static DNNReturnType get_output_ov(void *model, const char *input_name, int inpu
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;
return AVERROR(EINVAL);
}
if (ctx->options.input_resizable) {
@ -664,31 +684,33 @@ static DNNReturnType get_output_ov(void *model, const char *input_name, int inpu
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;
return DNN_GENERIC_ERROR;
}
}
if (!ov_model->exe_network) {
if (init_model_ov(ov_model, input_name, output_name) != DNN_SUCCESS) {
ret = init_model_ov(ov_model, input_name, output_name);
if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return DNN_ERROR;
return ret;
}
}
if (ff_dnn_fill_gettingoutput_task(&task, &exec_params, ov_model, input_height, input_width, ctx) != DNN_SUCCESS) {
return DNN_ERROR;
ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, ov_model, input_height, input_width, ctx);
if (ret != DNN_SUCCESS) {
goto err;
}
if (extract_lltask_from_task(ov_model->model->func_type, &task, ov_model->lltask_queue, NULL) != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
ret = DNN_ERROR;
ret = extract_lltask_from_task(ov_model->model->func_type, &task, ov_model->lltask_queue, NULL);
if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
goto err;
}
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
ret = DNN_ERROR;
ret = AVERROR(EINVAL);
goto err;
}
@ -758,45 +780,49 @@ err:
return NULL;
}
DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params)
int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params)
{
OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
OVRequestItem *request;
TaskItem *task;
DNNReturnType ret;
int ret;
if (ff_check_exec_params(ctx, DNN_OV, model->func_type, exec_params) != 0) {
return DNN_ERROR;
ret = ff_check_exec_params(ctx, DNN_OV, model->func_type, exec_params);
if (ret != 0) {
return ret;
}
if (!ov_model->exe_network) {
if (init_model_ov(ov_model, exec_params->input_name, exec_params->output_names[0]) != DNN_SUCCESS) {
ret = init_model_ov(ov_model, exec_params->input_name, exec_params->output_names[0]);
if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return DNN_ERROR;
return ret;
}
}
task = av_malloc(sizeof(*task));
if (!task) {
av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
return DNN_ERROR;
return AVERROR(ENOMEM);
}
if (ff_dnn_fill_task(task, exec_params, ov_model, ctx->options.async, 1) != DNN_SUCCESS) {
ret = ff_dnn_fill_task(task, exec_params, ov_model, ctx->options.async, 1);
if (ret != DNN_SUCCESS) {
av_freep(&task);
return DNN_ERROR;
return ret;
}
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;
return AVERROR(ENOMEM);
}
if (extract_lltask_from_task(model->func_type, task, ov_model->lltask_queue, exec_params) != DNN_SUCCESS) {
ret = extract_lltask_from_task(model->func_type, task, ov_model->lltask_queue, exec_params);
if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
return DNN_ERROR;
return ret;
}
if (ctx->options.async) {
@ -804,7 +830,7 @@ DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *
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 AVERROR(EINVAL);
}
ret = execute_model_ov(request, ov_model->lltask_queue);
@ -820,18 +846,18 @@ DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *
// Classification filter has not been completely
// tested with the sync mode. So, do not support now.
avpriv_report_missing_feature(ctx, "classify for sync execution");
return DNN_ERROR;
return AVERROR(ENOSYS);
}
if (ctx->options.batch_size > 1) {
avpriv_report_missing_feature(ctx, "batch mode for sync execution");
return DNN_ERROR;
return AVERROR(ENOSYS);
}
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 AVERROR(EINVAL);
}
return execute_model_ov(request, ov_model->lltask_queue);
}
@ -843,13 +869,13 @@ DNNAsyncStatusType ff_dnn_get_result_ov(const DNNModel *model, AVFrame **in, AVF
return ff_dnn_get_result_common(ov_model->task_queue, in, out);
}
DNNReturnType ff_dnn_flush_ov(const DNNModel *model)
int ff_dnn_flush_ov(const DNNModel *model)
{
OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
OVRequestItem *request;
IEStatusCode status;
DNNReturnType ret;
int ret;
if (ff_queue_size(ov_model->lltask_queue) == 0) {
// no pending task need to flush
@ -859,7 +885,7 @@ DNNReturnType ff_dnn_flush_ov(const DNNModel *model)
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 AVERROR(EINVAL);
}
ret = fill_model_input_ov(ov_model, request);
@ -870,12 +896,12 @@ DNNReturnType ff_dnn_flush_ov(const DNNModel *model)
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;
return DNN_GENERIC_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_GENERIC_ERROR;
}
return DNN_SUCCESS;

4
libavfilter/dnn/dnn_backend_openvino.h
View File

@ -31,9 +31,9 @@
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);
int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params);
DNNAsyncStatusType ff_dnn_get_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out);
DNNReturnType ff_dnn_flush_ov(const DNNModel *model);
int ff_dnn_flush_ov(const DNNModel *model);
void ff_dnn_free_model_ov(DNNModel **model);

10
libavfilter/dnn_interface.h
View File

@ -94,9 +94,9 @@ typedef struct DNNModel{
DNNFunctionType func_type;
// Gets model input information
// Just reuse struct DNNData here, actually the DNNData.data field is not needed.
DNNReturnType (*get_input)(void *model, DNNData *input, const char *input_name);
int (*get_input)(void *model, DNNData *input, const char *input_name);
// Gets model output width/height with given input w/h
DNNReturnType (*get_output)(void *model, const char *input_name, int input_width, int input_height,
int (*get_output)(void *model, const char *input_name, int input_width, int input_height,
const char *output_name, int *output_width, int *output_height);
// 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
@ -114,12 +114,12 @@ typedef struct DNNModel{
typedef struct DNNModule{
// Loads model and parameters from given file. Returns NULL if it is not possible.
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. Returns the error code otherwise.
int (*execute_model)(const DNNModel *model, DNNExecBaseParams *exec_params);
// Retrieve inference result.
DNNAsyncStatusType (*get_result)(const DNNModel *model, AVFrame **in, AVFrame **out);
// Flush all the pending tasks.
DNNReturnType (*flush)(const DNNModel *model);
int (*flush)(const DNNModel *model);
// Frees memory allocated for model.
void (*free_model)(DNNModel **model);
} DNNModule;