> -----Original Message-----
> From: ffmpeg-devel <ffmpeg-devel-boun...@ffmpeg.org> On Behalf Of
> Shubhanshu Saxena
> Sent: 2021年7月5日 18:31
> To: ffmpeg-devel@ffmpeg.org
> Cc: Shubhanshu Saxena <shubhanshu....@gmail.com>
> Subject: [FFmpeg-devel] [PATCH V2 3/6] lavfi/dnn_backend_tf: Request-
> based Execution
>
> This commit uses TFRequestItem and the existing sync execution mechanism
> to use request-based execution. It will help in adding async functionality to
> the TensorFlow backend later.
>
> Signed-off-by: Shubhanshu Saxena <shubhanshu....@gmail.com>
> ---
> libavfilter/dnn/dnn_backend_common.h | 3 +
> libavfilter/dnn/dnn_backend_openvino.c | 2 +-
> libavfilter/dnn/dnn_backend_tf.c | 156 ++++++++++++++-----------
> 3 files changed, 91 insertions(+), 70 deletions(-)
>
> diff --git a/libavfilter/dnn/dnn_backend_common.h
> b/libavfilter/dnn/dnn_backend_common.h
> index df59615f40..5281fdfed1 100644
> --- a/libavfilter/dnn/dnn_backend_common.h
> +++ b/libavfilter/dnn/dnn_backend_common.h
> @@ -26,6 +26,9 @@
>
> #include "../dnn_interface.h"
>
> +#define DNN_BACKEND_COMMON_OPTIONS \
> + { "nireq", "number of request",
> OFFSET(options.nireq),
> AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INT_MAX, FLAGS },
> +
> // one task for one function call from dnn interface typedef struct TaskItem
> {
> void *model; // model for the backend diff --git
> a/libavfilter/dnn/dnn_backend_openvino.c
> b/libavfilter/dnn/dnn_backend_openvino.c
> index 3295fc79d3..f34b8150f5 100644
> --- a/libavfilter/dnn/dnn_backend_openvino.c
> +++ b/libavfilter/dnn/dnn_backend_openvino.c
> @@ -75,7 +75,7 @@ typedef struct RequestItem { #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 },
> + DNN_BACKEND_COMMON_OPTIONS
> { "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 }
> diff --git a/libavfilter/dnn/dnn_backend_tf.c
> b/libavfilter/dnn/dnn_backend_tf.c
> index 578748eb35..e8007406c8 100644
> --- a/libavfilter/dnn/dnn_backend_tf.c
> +++ b/libavfilter/dnn/dnn_backend_tf.c
> @@ -35,11 +35,13 @@
> #include "dnn_backend_native_layer_maximum.h"
> #include "dnn_io_proc.h"
> #include "dnn_backend_common.h"
> +#include "safe_queue.h"
> #include "queue.h"
> #include <tensorflow/c/c_api.h>
>
> typedef struct TFOptions{
> char *sess_config;
> + uint32_t nireq;
> } TFOptions;
>
> typedef struct TFContext {
> @@ -53,6 +55,7 @@ typedef struct TFModel{
> TF_Graph *graph;
> TF_Session *session;
> TF_Status *status;
> + SafeQueue *request_queue;
> Queue *inference_queue;
> } TFModel;
>
> @@ -77,12 +80,13 @@ typedef struct TFRequestItem { #define FLAGS
> AV_OPT_FLAG_FILTERING_PARAM static const AVOption
> dnn_tensorflow_options[] = {
> { "sess_config", "config for SessionOptions",
> OFFSET(options.sess_config),
> AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
> + DNN_BACKEND_COMMON_OPTIONS
> { NULL }
> };
>
> AVFILTER_DEFINE_CLASS(dnn_tensorflow);
>
> -static DNNReturnType execute_model_tf(Queue *inference_queue);
> +static DNNReturnType execute_model_tf(TFRequestItem *request, Queue
> +*inference_queue);
>
> static void free_buffer(void *data, size_t length) { @@ -237,6 +241,7 @@
> static DNNReturnType get_output_tf(void *model, const char *input_name,
> int inpu
> AVFrame *in_frame = av_frame_alloc();
> AVFrame *out_frame = NULL;
> TaskItem task;
> + TFRequestItem *request;
>
> if (!in_frame) {
> av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input
> frame\n"); @@ -267,7 +272,13 @@ static DNNReturnType
> get_output_tf(void *model, const char *input_name, int inpu
> return DNN_ERROR;
> }
>
> - ret = execute_model_tf(tf_model->inference_queue);
> + 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;
> + }
> +
> + ret = execute_model_tf(request, tf_model->inference_queue);
> *output_width = out_frame->width;
> *output_height = out_frame->height;
>
> @@ -771,6 +782,7 @@ DNNModel *ff_dnn_load_model_tf(const char
> *model_filename, DNNFunctionType func_ {
> DNNModel *model = NULL;
> TFModel *tf_model = NULL;
> + TFContext *ctx = NULL;
>
> model = av_mallocz(sizeof(DNNModel));
> if (!model){
> @@ -782,13 +794,14 @@ DNNModel *ff_dnn_load_model_tf(const char
> *model_filename, DNNFunctionType func_
> av_freep(&model);
> return NULL;
> }
> - tf_model->ctx.class = &dnn_tensorflow_class;
> tf_model->model = model;
> + ctx = &tf_model->ctx;
> + ctx->class = &dnn_tensorflow_class;
>
> //parse options
> - av_opt_set_defaults(&tf_model->ctx);
> - if (av_opt_set_from_string(&tf_model->ctx, options, NULL, "=", "&") < 0)
> {
> - av_log(&tf_model->ctx, AV_LOG_ERROR, "Failed to parse options
> \"%s\"\n", 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);
> av_freep(&tf_model);
> av_freep(&model);
> return NULL;
> @@ -803,6 +816,18 @@ DNNModel *ff_dnn_load_model_tf(const char
> *model_filename, DNNFunctionType func_
> }
> }
>
> + if (ctx->options.nireq <= 0) {
> + ctx->options.nireq = av_cpu_count() / 2 + 1;
> + }
> +
> + tf_model->request_queue = ff_safe_queue_create();
> +
> + for (int i = 0; i < ctx->options.nireq; i++) {
> + TFRequestItem *item = av_mallocz(sizeof(*item));
> + item->infer_request = tf_create_inference_request();
> + ff_safe_queue_push_back(tf_model->request_queue, item);
> + }
> +
> tf_model->inference_queue = ff_queue_create();
> model->model = tf_model;
> model->get_input = &get_input_tf;
> @@ -814,42 +839,42 @@ DNNModel *ff_dnn_load_model_tf(const char
> *model_filename, DNNFunctionType func_
> return model;
> }
>
> -static DNNReturnType execute_model_tf(Queue *inference_queue)
> +static DNNReturnType execute_model_tf(TFRequestItem *request, Queue
> +*inference_queue)
> {
> - TF_Output *tf_outputs;
> TFModel *tf_model;
> TFContext *ctx;
> + TFInferRequest *infer_request;
> InferenceItem *inference;
> TaskItem *task;
> DNNData input, *outputs;
> - TF_Tensor **output_tensors;
> - TF_Output tf_input;
> - TF_Tensor *input_tensor;
>
> inference = ff_queue_pop_front(inference_queue);
> av_assert0(inference);
> task = inference->task;
> tf_model = task->model;
> ctx = &tf_model->ctx;
> + request->inference = inference;
>
> if (get_input_tf(tf_model, &input, task->input_name) != DNN_SUCCESS)
> return DNN_ERROR;
>
> + infer_request = request->infer_request;
> input.height = task->in_frame->height;
> input.width = task->in_frame->width;
>
> - tf_input.oper = TF_GraphOperationByName(tf_model->graph, task-
> >input_name);
> - if (!tf_input.oper){
> + infer_request->tf_input = av_malloc(sizeof(TF_Output));
> + infer_request->tf_input->oper = TF_GraphOperationByName(tf_model-
> >graph, task->input_name);
> + if (!infer_request->tf_input->oper){
> av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", task-
> >input_name);
> return DNN_ERROR;
> }
> - tf_input.index = 0;
> - input_tensor = allocate_input_tensor(&input);
> - if (!input_tensor){
> + infer_request->tf_input->index = 0;
> + infer_request->input_tensor = allocate_input_tensor(&input);
> + if (!infer_request->input_tensor){
> av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input
> tensor\n");
> return DNN_ERROR;
> }
> - input.data = (float *)TF_TensorData(input_tensor);
> + input.data = (float *)TF_TensorData(infer_request->input_tensor);
>
> switch (tf_model->model->func_type) {
> case DFT_PROCESS_FRAME:
> @@ -869,60 +894,52 @@ static DNNReturnType execute_model_tf(Queue
> *inference_queue)
> break;
> }
>
> - tf_outputs = av_malloc_array(task->nb_output, sizeof(TF_Output));
> - if (tf_outputs == NULL) {
> - TF_DeleteTensor(input_tensor);
> - av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for
> *tf_outputs\n"); \
> + infer_request->tf_outputs = av_malloc_array(task->nb_output,
> sizeof(TF_Output));
> + if (infer_request->tf_outputs == NULL) {
> + av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for
> + *tf_outputs\n");
> return DNN_ERROR;
> }
>
> - output_tensors = av_mallocz_array(task->nb_output,
> sizeof(*output_tensors));
> - if (!output_tensors) {
> - TF_DeleteTensor(input_tensor);
> - av_freep(&tf_outputs);
> - av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output
> tensor\n"); \
> + infer_request->output_tensors = av_mallocz_array(task->nb_output,
> sizeof(*infer_request->output_tensors));
> + if (!infer_request->output_tensors) {
> + av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output
> + tensor\n");
> return DNN_ERROR;
> }
>
> for (int i = 0; i < task->nb_output; ++i) {
> - tf_outputs[i].oper = TF_GraphOperationByName(tf_model->graph,
> task->output_names[i]);
> - if (!tf_outputs[i].oper) {
> - TF_DeleteTensor(input_tensor);
> - av_freep(&tf_outputs);
> - av_freep(&output_tensors);
> - av_log(ctx, AV_LOG_ERROR, "Could not find output \"%s\" in
> model\n", task->output_names[i]); \
> + infer_request->output_tensors[i] = NULL;
> + infer_request->tf_outputs[i].oper =
> TF_GraphOperationByName(tf_model->graph, task->output_names[i]);
> + if (!infer_request->tf_outputs[i].oper) {
> + av_log(ctx, AV_LOG_ERROR, "Could not find output \"%s\" in
> + model\n", task->output_names[i]);
> return DNN_ERROR;
> }
> - tf_outputs[i].index = 0;
> + infer_request->tf_outputs[i].index = 0;
> }
>
> TF_SessionRun(tf_model->session, NULL,
> - &tf_input, &input_tensor, 1,
> - tf_outputs, output_tensors, task->nb_output,
> - NULL, 0, NULL, tf_model->status);
> + infer_request->tf_input, &infer_request->input_tensor, 1,
> + infer_request->tf_outputs, infer_request->output_tensors,
> + task->nb_output, NULL, 0, NULL,
> + tf_model->status);
> if (TF_GetCode(tf_model->status) != TF_OK) {
> - TF_DeleteTensor(input_tensor);
> - av_freep(&tf_outputs);
> - av_freep(&output_tensors);
> - av_log(ctx, AV_LOG_ERROR, "Failed to run session when executing
> model\n");
> - return DNN_ERROR;
> + tf_free_request(infer_request);
> + av_log(ctx, AV_LOG_ERROR, "Failed to run session when executing
> model\n");
> + return DNN_ERROR;
> }
>
> outputs = av_malloc_array(task->nb_output, sizeof(*outputs));
> if (!outputs) {
> - TF_DeleteTensor(input_tensor);
> - av_freep(&tf_outputs);
> - av_freep(&output_tensors);
> - av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for
> *outputs\n"); \
> + tf_free_request(infer_request);
> + av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for
> + *outputs\n");
> return DNN_ERROR;
> }
>
> for (uint32_t i = 0; i < task->nb_output; ++i) {
> - outputs[i].height = TF_Dim(output_tensors[i], 1);
> - outputs[i].width = TF_Dim(output_tensors[i], 2);
> - outputs[i].channels = TF_Dim(output_tensors[i], 3);
> - outputs[i].data = TF_TensorData(output_tensors[i]);
> - outputs[i].dt = TF_TensorType(output_tensors[i]);
> + outputs[i].height = TF_Dim(infer_request->output_tensors[i], 1);
> + outputs[i].width = TF_Dim(infer_request->output_tensors[i], 2);
> + outputs[i].channels = TF_Dim(infer_request->output_tensors[i], 3);
> + outputs[i].data = TF_TensorData(infer_request->output_tensors[i]);
> + outputs[i].dt =
> + TF_TensorType(infer_request->output_tensors[i]);
> }
> switch (tf_model->model->func_type) {
> case DFT_PROCESS_FRAME:
> @@ -946,30 +963,15 @@ static DNNReturnType execute_model_tf(Queue
> *inference_queue)
> tf_model->model->detect_post_proc(task->out_frame, outputs, task-
> >nb_output, tf_model->model->filter_ctx);
> break;
> default:
> - for (uint32_t i = 0; i < task->nb_output; ++i) {
> - if (output_tensors[i]) {
> - TF_DeleteTensor(output_tensors[i]);
> - }
> - }
> - TF_DeleteTensor(input_tensor);
> - av_freep(&output_tensors);
> - av_freep(&tf_outputs);
> - av_freep(&outputs);
> + tf_free_request(infer_request);
>
> av_log(ctx, AV_LOG_ERROR, "Tensorflow backend does not support this
> kind of dnn filter now\n");
> return DNN_ERROR;
> }
> - for (uint32_t i = 0; i < task->nb_output; ++i) {
> - if (output_tensors[i]) {
> - TF_DeleteTensor(output_tensors[i]);
> - }
> - }
> task->inference_done++;
> - TF_DeleteTensor(input_tensor);
> - av_freep(&output_tensors);
> - av_freep(&tf_outputs);
> + tf_free_request(infer_request);
> av_freep(&outputs);
> - return DNN_SUCCESS;
> + ff_safe_queue_push_back(tf_model->request_queue, request);
> return (task->inference_done == task->inference_todo) ? DNN_SUCCESS :
> DNN_ERROR; }
>
> @@ -978,6 +980,7 @@ DNNReturnType ff_dnn_execute_model_tf(const
> DNNModel *model, DNNExecBaseParams *
> 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;
> @@ -991,7 +994,14 @@ DNNReturnType ff_dnn_execute_model_tf(const
> DNNModel *model, DNNExecBaseParams *
> av_log(ctx, AV_LOG_ERROR, "unable to extract inference from
> task.\n");
> return DNN_ERROR;
> }
> - return execute_model_tf(tf_model->inference_queue);
> +
> + 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);
> }
>
> void ff_dnn_free_model_tf(DNNModel **model) @@ -1000,6 +1010,14
> @@ void ff_dnn_free_model_tf(DNNModel **model)
>
> if (*model){
> tf_model = (*model)->model;
> + while (ff_safe_queue_size(tf_model->request_queue) != 0) {
> + TFRequestItem *item = ff_safe_queue_pop_front(tf_model-
> >request_queue);
> + tf_free_request(item->infer_request);
> + av_freep(&item->infer_request);
> + av_freep(&item);
> + }
> + ff_safe_queue_destroy(tf_model->request_queue);
> +
> while (ff_queue_size(tf_model->inference_queue) != 0) {
> InferenceItem *item = ff_queue_pop_front(tf_model-
> >inference_queue);
> av_freep(&item);
LGTM, will push soon.
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