On Sun, Jul 11, 2021 at 6:25 PM Guo, Yejun <[email protected]> wrote:
> > > > -----Original Message----- > > From: ffmpeg-devel <[email protected]> On Behalf Of > > Shubhanshu Saxena > > Sent: 2021年7月5日 18:31 > > To: [email protected] > > Cc: Shubhanshu Saxena <[email protected]> > > 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 <[email protected]> > > --- > > 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. > > _______________________________________________ > ffmpeg-devel mailing list > [email protected] > https://ffmpeg.org/mailman/listinfo/ffmpeg-devel > > To unsubscribe, visit link above, or email > [email protected] with subject "unsubscribe". > Sure, thank you. _______________________________________________ ffmpeg-devel mailing list [email protected] https://ffmpeg.org/mailman/listinfo/ffmpeg-devel To unsubscribe, visit link above, or email [email protected] with subject "unsubscribe".
