[FFmpeg-devel] [PATCH V2 1/6] lavfi/dnn_backend_tf: TaskItem Based Inference
Shubhanshu Saxena
shubhanshu.e01 at gmail.com
Mon Jul 5 13:30:53 EEST 2021
This commit uses the common TaskItem and InferenceItem typedefs
for execution in TensorFlow backend.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01 at gmail.com>
---
libavfilter/dnn/dnn_backend_tf.c | 134 ++++++++++++++++++++++---------
1 file changed, 94 insertions(+), 40 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_tf.c b/libavfilter/dnn/dnn_backend_tf.c
index 4c16c2bdb0..8762211ebc 100644
--- a/libavfilter/dnn/dnn_backend_tf.c
+++ b/libavfilter/dnn/dnn_backend_tf.c
@@ -35,6 +35,7 @@
#include "dnn_backend_native_layer_maximum.h"
#include "dnn_io_proc.h"
#include "dnn_backend_common.h"
+#include "queue.h"
#include <tensorflow/c/c_api.h>
typedef struct TFOptions{
@@ -52,6 +53,7 @@ typedef struct TFModel{
TF_Graph *graph;
TF_Session *session;
TF_Status *status;
+ Queue *inference_queue;
} TFModel;
#define OFFSET(x) offsetof(TFContext, x)
@@ -63,15 +65,29 @@ static const AVOption dnn_tensorflow_options[] = {
AVFILTER_DEFINE_CLASS(dnn_tensorflow);
-static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_name, AVFrame *in_frame,
- const char **output_names, uint32_t nb_output, AVFrame *out_frame,
- int do_ioproc);
+static DNNReturnType execute_model_tf(Queue *inference_queue);
static void free_buffer(void *data, size_t length)
{
av_freep(&data);
}
+static DNNReturnType extract_inference_from_task(TaskItem *task, Queue *inference_queue)
+{
+ InferenceItem *inference = av_malloc(sizeof(*inference));
+ if (!inference) {
+ return DNN_ERROR;
+ }
+ task->inference_todo = 1;
+ task->inference_done = 0;
+ inference->task = task;
+ if (ff_queue_push_back(inference_queue, inference) < 0) {
+ av_freep(&inference);
+ return DNN_ERROR;
+ }
+ return DNN_SUCCESS;
+}
+
static TF_Buffer *read_graph(const char *model_filename)
{
TF_Buffer *graph_buf;
@@ -171,6 +187,7 @@ static DNNReturnType get_output_tf(void *model, const char *input_name, int inpu
TFContext *ctx = &tf_model->ctx;
AVFrame *in_frame = av_frame_alloc();
AVFrame *out_frame = NULL;
+ TaskItem task;
if (!in_frame) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input frame\n");
@@ -187,7 +204,21 @@ static DNNReturnType get_output_tf(void *model, const char *input_name, int inpu
in_frame->width = input_width;
in_frame->height = input_height;
- ret = execute_model_tf(tf_model->model, input_name, in_frame, &output_name, 1, out_frame, 0);
+ task.do_ioproc = 0;
+ task.async = 0;
+ task.input_name = input_name;
+ task.in_frame = in_frame;
+ task.output_names = &output_name;
+ task.out_frame = out_frame;
+ task.model = tf_model;
+ task.nb_output = 1;
+
+ if (extract_inference_from_task(&task, tf_model->inference_queue) != DNN_SUCCESS) {
+ av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
+ return DNN_ERROR;
+ }
+
+ ret = execute_model_tf(tf_model->inference_queue);
*output_width = out_frame->width;
*output_height = out_frame->height;
@@ -723,6 +754,7 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_
}
}
+ tf_model->inference_queue = ff_queue_create();
model->model = tf_model;
model->get_input = &get_input_tf;
model->get_output = &get_output_tf;
@@ -733,26 +765,33 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_
return model;
}
-static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_name, AVFrame *in_frame,
- const char **output_names, uint32_t nb_output, AVFrame *out_frame,
- int do_ioproc)
+static DNNReturnType execute_model_tf(Queue *inference_queue)
{
TF_Output *tf_outputs;
- TFModel *tf_model = model->model;
- TFContext *ctx = &tf_model->ctx;
+ TFModel *tf_model;
+ TFContext *ctx;
+ InferenceItem *inference;
+ TaskItem *task;
DNNData input, *outputs;
TF_Tensor **output_tensors;
TF_Output tf_input;
TF_Tensor *input_tensor;
- if (get_input_tf(tf_model, &input, input_name) != DNN_SUCCESS)
+ inference = ff_queue_pop_front(inference_queue);
+ av_assert0(inference);
+ task = inference->task;
+ tf_model = task->model;
+ ctx = &tf_model->ctx;
+
+ if (get_input_tf(tf_model, &input, task->input_name) != DNN_SUCCESS)
return DNN_ERROR;
- input.height = in_frame->height;
- input.width = in_frame->width;
- tf_input.oper = TF_GraphOperationByName(tf_model->graph, input_name);
+ 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){
- av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
+ av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", task->input_name);
return DNN_ERROR;
}
tf_input.index = 0;
@@ -765,30 +804,30 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n
switch (tf_model->model->func_type) {
case DFT_PROCESS_FRAME:
- if (do_ioproc) {
+ if (task->do_ioproc) {
if (tf_model->model->frame_pre_proc != NULL) {
- tf_model->model->frame_pre_proc(in_frame, &input, tf_model->model->filter_ctx);
+ tf_model->model->frame_pre_proc(task->in_frame, &input, tf_model->model->filter_ctx);
} else {
- ff_proc_from_frame_to_dnn(in_frame, &input, ctx);
+ ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
}
}
break;
case DFT_ANALYTICS_DETECT:
- ff_frame_to_dnn_detect(in_frame, &input, ctx);
+ ff_frame_to_dnn_detect(task->in_frame, &input, ctx);
break;
default:
avpriv_report_missing_feature(ctx, "model function type %d", tf_model->model->func_type);
break;
}
- tf_outputs = av_malloc_array(nb_output, sizeof(*tf_outputs));
+ 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"); \
return DNN_ERROR;
}
- output_tensors = av_mallocz_array(nb_output, sizeof(*output_tensors));
+ output_tensors = av_mallocz_array(task->nb_output, sizeof(*output_tensors));
if (!output_tensors) {
TF_DeleteTensor(input_tensor);
av_freep(&tf_outputs);
@@ -796,13 +835,13 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n
return DNN_ERROR;
}
- for (int i = 0; i < nb_output; ++i) {
- tf_outputs[i].oper = TF_GraphOperationByName(tf_model->graph, output_names[i]);
+ 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", output_names[i]); \
+ 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;
@@ -810,7 +849,7 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n
TF_SessionRun(tf_model->session, NULL,
&tf_input, &input_tensor, 1,
- tf_outputs, output_tensors, nb_output,
+ tf_outputs, output_tensors, task->nb_output,
NULL, 0, NULL, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK) {
TF_DeleteTensor(input_tensor);
@@ -820,7 +859,7 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n
return DNN_ERROR;
}
- outputs = av_malloc_array(nb_output, sizeof(*outputs));
+ outputs = av_malloc_array(task->nb_output, sizeof(*outputs));
if (!outputs) {
TF_DeleteTensor(input_tensor);
av_freep(&tf_outputs);
@@ -829,36 +868,36 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n
return DNN_ERROR;
}
- for (uint32_t i = 0; i < nb_output; ++i) {
+ 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]);
}
- switch (model->func_type) {
+ switch (tf_model->model->func_type) {
case DFT_PROCESS_FRAME:
//it only support 1 output if it's frame in & frame out
- if (do_ioproc) {
+ if (task->do_ioproc) {
if (tf_model->model->frame_post_proc != NULL) {
- tf_model->model->frame_post_proc(out_frame, outputs, tf_model->model->filter_ctx);
+ tf_model->model->frame_post_proc(task->out_frame, outputs, tf_model->model->filter_ctx);
} else {
- ff_proc_from_dnn_to_frame(out_frame, outputs, ctx);
+ ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
}
} else {
- out_frame->width = outputs[0].width;
- out_frame->height = outputs[0].height;
+ task->out_frame->width = outputs[0].width;
+ task->out_frame->height = outputs[0].height;
}
break;
case DFT_ANALYTICS_DETECT:
- if (!model->detect_post_proc) {
+ if (!tf_model->model->detect_post_proc) {
av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n");
return DNN_ERROR;
}
- model->detect_post_proc(out_frame, outputs, nb_output, model->filter_ctx);
+ 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 < nb_output; ++i) {
+ for (uint32_t i = 0; i < task->nb_output; ++i) {
if (output_tensors[i]) {
TF_DeleteTensor(output_tensors[i]);
}
@@ -871,30 +910,39 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n
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 < nb_output; ++i) {
+ 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);
av_freep(&outputs);
return DNN_SUCCESS;
+ return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_ERROR;
}
DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params)
{
TFModel *tf_model = model->model;
TFContext *ctx = &tf_model->ctx;
+ TaskItem task;
if (ff_check_exec_params(ctx, DNN_TF, model->func_type, exec_params) != 0) {
- return DNN_ERROR;
+ return DNN_ERROR;
}
- return execute_model_tf(model, exec_params->input_name, exec_params->in_frame,
- exec_params->output_names, exec_params->nb_output, exec_params->out_frame, 1);
+ if (ff_dnn_fill_task(&task, exec_params, tf_model, 0, 1) != DNN_SUCCESS) {
+ return DNN_ERROR;
+ }
+
+ if (extract_inference_from_task(&task, tf_model->inference_queue) != DNN_SUCCESS) {
+ av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
+ return DNN_ERROR;
+ }
+ return execute_model_tf(tf_model->inference_queue);
}
void ff_dnn_free_model_tf(DNNModel **model)
@@ -903,6 +951,12 @@ void ff_dnn_free_model_tf(DNNModel **model)
if (*model){
tf_model = (*model)->model;
+ while (ff_queue_size(tf_model->inference_queue) != 0) {
+ InferenceItem *item = ff_queue_pop_front(tf_model->inference_queue);
+ av_freep(&item);
+ }
+ ff_queue_destroy(tf_model->inference_queue);
+
if (tf_model->graph){
TF_DeleteGraph(tf_model->graph);
}
--
2.25.1
More information about the ffmpeg-devel
mailing list