[FFmpeg-cvslog] lavfi/dnn_backend_tf: Separate function for filling RequestItem
Shubhanshu Saxena
git at videolan.org
Sun Jul 11 16:23:21 EEST 2021
ffmpeg | branch: master | Shubhanshu Saxena <shubhanshu.e01 at gmail.com> | Mon Jul 5 16:18:56 2021 +0530| [b849228ae06fbcbf85b77e76dd46f63ea8c1406f] | committer: Guo Yejun
lavfi/dnn_backend_tf: Separate function for filling RequestItem
This commit rearranges the existing code to create separate function
for filling request with execution data.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01 at gmail.com>
> http://git.videolan.org/gitweb.cgi/ffmpeg.git/?a=commit;h=b849228ae06fbcbf85b77e76dd46f63ea8c1406f
---
libavfilter/dnn/dnn_backend_tf.c | 137 +++++++++++++++++++++++----------------
1 file changed, 80 insertions(+), 57 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_tf.c b/libavfilter/dnn/dnn_backend_tf.c
index e8007406c8..7f014d55fa 100644
--- a/libavfilter/dnn/dnn_backend_tf.c
+++ b/libavfilter/dnn/dnn_backend_tf.c
@@ -839,20 +839,16 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_
return model;
}
-static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *inference_queue)
-{
- TFModel *tf_model;
- TFContext *ctx;
- TFInferRequest *infer_request;
+static DNNReturnType fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
+ DNNData input;
InferenceItem *inference;
TaskItem *task;
- DNNData input, *outputs;
+ TFInferRequest *infer_request;
+ TFContext *ctx = &tf_model->ctx;
- inference = ff_queue_pop_front(inference_queue);
+ inference = ff_queue_pop_front(tf_model->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)
@@ -916,63 +912,90 @@ static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *inference_q
infer_request->tf_outputs[i].index = 0;
}
- TF_SessionRun(tf_model->session, NULL,
- 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_free_request(infer_request);
- av_log(ctx, AV_LOG_ERROR, "Failed to run session when executing model\n");
- return DNN_ERROR;
- }
+ return DNN_SUCCESS;
+}
- outputs = av_malloc_array(task->nb_output, sizeof(*outputs));
- if (!outputs) {
- tf_free_request(infer_request);
- av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *outputs\n");
+static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *inference_queue)
+{
+ TFModel *tf_model;
+ TFContext *ctx;
+ TFInferRequest *infer_request;
+ InferenceItem *inference;
+ TaskItem *task;
+ DNNData *outputs;
+
+ inference = ff_queue_peek_front(inference_queue);
+ task = inference->task;
+ tf_model = task->model;
+ ctx = &tf_model->ctx;
+
+ if (task->async) {
+ avpriv_report_missing_feature(ctx, "Async execution not supported");
return DNN_ERROR;
- }
+ } else {
+ if (fill_model_input_tf(tf_model, request) != DNN_SUCCESS) {
+ return DNN_ERROR;
+ }
- for (uint32_t i = 0; i < task->nb_output; ++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:
- //it only support 1 output if it's frame in & frame out
- if (task->do_ioproc) {
- if (tf_model->model->frame_post_proc != NULL) {
- tf_model->model->frame_post_proc(task->out_frame, outputs, tf_model->model->filter_ctx);
+ infer_request = request->infer_request;
+ TF_SessionRun(tf_model->session, NULL,
+ 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_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_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(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:
+ //it only support 1 output if it's frame in & frame out
+ if (task->do_ioproc) {
+ if (tf_model->model->frame_post_proc != NULL) {
+ tf_model->model->frame_post_proc(task->out_frame, outputs, tf_model->model->filter_ctx);
+ } else {
+ ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
+ }
} else {
- ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
+ task->out_frame->width = outputs[0].width;
+ task->out_frame->height = outputs[0].height;
}
- } else {
- task->out_frame->width = outputs[0].width;
- task->out_frame->height = outputs[0].height;
- }
- break;
- case DFT_ANALYTICS_DETECT:
- if (!tf_model->model->detect_post_proc) {
- av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n");
+ break;
+ case DFT_ANALYTICS_DETECT:
+ if (!tf_model->model->detect_post_proc) {
+ av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n");
+ return DNN_ERROR;
+ }
+ tf_model->model->detect_post_proc(task->out_frame, outputs, task->nb_output, tf_model->model->filter_ctx);
+ break;
+ default:
+ 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;
}
- tf_model->model->detect_post_proc(task->out_frame, outputs, task->nb_output, tf_model->model->filter_ctx);
- break;
- default:
+ task->inference_done++;
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;
+ av_freep(&outputs);
+ ff_safe_queue_push_back(tf_model->request_queue, request);
+ return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_ERROR;
}
- task->inference_done++;
- tf_free_request(infer_request);
- av_freep(&outputs);
- ff_safe_queue_push_back(tf_model->request_queue, request);
- return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_ERROR;
}
DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params)
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