[FFmpeg-cvslog] libavfilter/dnn: support multiple outputs for tensorflow model
Guo, Yejun
git at videolan.org
Wed May 8 18:39:21 EEST 2019
ffmpeg | branch: master | Guo, Yejun <yejun.guo at intel.com> | Thu Apr 25 10:14:33 2019 +0800| [25c1cd909fa6c8b6b778dc24192dc3ec780324b0] | committer: Pedro Arthur
libavfilter/dnn: support multiple outputs for tensorflow model
some models such as ssd, yolo have more than one output.
the clean up code in this patch is a little complex, it is because
that set_input_output_tf could be called for many times together
with ff_dnn_execute_model_tf, we have to clean resources for the
case that the two interfaces are called interleaved.
Signed-off-by: Guo, Yejun <yejun.guo at intel.com>
Signed-off-by: Pedro Arthur <bygrandao at gmail.com>
> http://git.videolan.org/gitweb.cgi/ffmpeg.git/?a=commit;h=25c1cd909fa6c8b6b778dc24192dc3ec780324b0
---
libavfilter/dnn_backend_native.c | 15 +++++---
libavfilter/dnn_backend_native.h | 2 +-
libavfilter/dnn_backend_tf.c | 80 ++++++++++++++++++++++++++++++++--------
libavfilter/dnn_backend_tf.h | 2 +-
libavfilter/dnn_interface.h | 6 ++-
libavfilter/vf_sr.c | 11 +++---
6 files changed, 85 insertions(+), 31 deletions(-)
diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_native.c
index 18735c025c..8a83c63c73 100644
--- a/libavfilter/dnn_backend_native.c
+++ b/libavfilter/dnn_backend_native.c
@@ -25,7 +25,7 @@
#include "dnn_backend_native.h"
-static DNNReturnType set_input_output_native(void *model, DNNData *input, const char *input_name, const char *output_name)
+static DNNReturnType set_input_output_native(void *model, DNNData *input, const char *input_name, const char **output_names, uint32_t nb_output)
{
ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
InputParams *input_params;
@@ -275,7 +275,7 @@ static void depth_to_space(const float *input, float *output, int block_size, in
}
}
-DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output)
+DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
{
ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
int cur_width, cur_height, cur_channels;
@@ -317,10 +317,13 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
}
}
- output->data = network->layers[network->layers_num - 1].output;
- output->height = cur_height;
- output->width = cur_width;
- output->channels = cur_channels;
+ // native mode does not support multiple outputs yet
+ if (nb_output > 1)
+ return DNN_ERROR;
+ outputs[0].data = network->layers[network->layers_num - 1].output;
+ outputs[0].height = cur_height;
+ outputs[0].width = cur_width;
+ outputs[0].channels = cur_channels;
return DNN_SUCCESS;
}
diff --git a/libavfilter/dnn_backend_native.h b/libavfilter/dnn_backend_native.h
index adaf4a75e2..e13a68a168 100644
--- a/libavfilter/dnn_backend_native.h
+++ b/libavfilter/dnn_backend_native.h
@@ -63,7 +63,7 @@ typedef struct ConvolutionalNetwork{
DNNModel *ff_dnn_load_model_native(const char *model_filename);
-DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output);
+DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output);
void ff_dnn_free_model_native(DNNModel **model);
diff --git a/libavfilter/dnn_backend_tf.c b/libavfilter/dnn_backend_tf.c
index be8401e524..ca6472d445 100644
--- a/libavfilter/dnn_backend_tf.c
+++ b/libavfilter/dnn_backend_tf.c
@@ -26,6 +26,7 @@
#include "dnn_backend_tf.h"
#include "dnn_backend_native.h"
#include "libavformat/avio.h"
+#include "libavutil/avassert.h"
#include <tensorflow/c/c_api.h>
@@ -33,9 +34,11 @@ typedef struct TFModel{
TF_Graph *graph;
TF_Session *session;
TF_Status *status;
- TF_Output input, output;
+ TF_Output input;
TF_Tensor *input_tensor;
- TF_Tensor *output_tensor;
+ TF_Output *outputs;
+ TF_Tensor **output_tensors;
+ uint32_t nb_output;
} TFModel;
static void free_buffer(void *data, size_t length)
@@ -76,7 +79,7 @@ static TF_Buffer *read_graph(const char *model_filename)
return graph_buf;
}
-static DNNReturnType set_input_output_tf(void *model, DNNData *input, const char *input_name, const char *output_name)
+static DNNReturnType set_input_output_tf(void *model, DNNData *input, const char *input_name, const char **output_names, uint32_t nb_output)
{
TFModel *tf_model = (TFModel *)model;
int64_t input_dims[] = {1, input->height, input->width, input->channels};
@@ -100,11 +103,38 @@ static DNNReturnType set_input_output_tf(void *model, DNNData *input, const char
input->data = (float *)TF_TensorData(tf_model->input_tensor);
// Output operation
- tf_model->output.oper = TF_GraphOperationByName(tf_model->graph, output_name);
- if (!tf_model->output.oper){
+ if (nb_output == 0)
+ return DNN_ERROR;
+
+ av_freep(&tf_model->outputs);
+ tf_model->outputs = av_malloc_array(nb_output, sizeof(*tf_model->outputs));
+ if (!tf_model->outputs)
+ return DNN_ERROR;
+ for (int i = 0; i < nb_output; ++i) {
+ tf_model->outputs[i].oper = TF_GraphOperationByName(tf_model->graph, output_names[i]);
+ if (!tf_model->outputs[i].oper){
+ av_freep(&tf_model->outputs);
+ return DNN_ERROR;
+ }
+ tf_model->outputs[i].index = 0;
+ }
+
+ if (tf_model->output_tensors) {
+ for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
+ if (tf_model->output_tensors[i]) {
+ TF_DeleteTensor(tf_model->output_tensors[i]);
+ tf_model->output_tensors[i] = NULL;
+ }
+ }
+ }
+ av_freep(&tf_model->output_tensors);
+ tf_model->output_tensors = av_mallocz_array(nb_output, sizeof(*tf_model->output_tensors));
+ if (!tf_model->output_tensors) {
+ av_freep(&tf_model->outputs);
return DNN_ERROR;
}
- tf_model->output.index = 0;
+
+ tf_model->nb_output = nb_output;
if (tf_model->session){
TF_CloseSession(tf_model->session, tf_model->status);
@@ -484,25 +514,36 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename)
-DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData *output)
+DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
{
TFModel *tf_model = (TFModel *)model->model;
- if (tf_model->output_tensor)
- TF_DeleteTensor(tf_model->output_tensor);
+ uint32_t nb = FFMIN(nb_output, tf_model->nb_output);
+ if (nb == 0)
+ return DNN_ERROR;
+
+ av_assert0(tf_model->output_tensors);
+ for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
+ if (tf_model->output_tensors[i]) {
+ TF_DeleteTensor(tf_model->output_tensors[i]);
+ tf_model->output_tensors[i] = NULL;
+ }
+ }
TF_SessionRun(tf_model->session, NULL,
&tf_model->input, &tf_model->input_tensor, 1,
- &tf_model->output, &tf_model->output_tensor, 1,
+ tf_model->outputs, tf_model->output_tensors, nb,
NULL, 0, NULL, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
- output->height = TF_Dim(tf_model->output_tensor, 1);
- output->width = TF_Dim(tf_model->output_tensor, 2);
- output->channels = TF_Dim(tf_model->output_tensor, 3);
- output->data = TF_TensorData(tf_model->output_tensor);
+ for (uint32_t i = 0; i < nb; ++i) {
+ outputs[i].height = TF_Dim(tf_model->output_tensors[i], 1);
+ outputs[i].width = TF_Dim(tf_model->output_tensors[i], 2);
+ outputs[i].channels = TF_Dim(tf_model->output_tensors[i], 3);
+ outputs[i].data = TF_TensorData(tf_model->output_tensors[i]);
+ }
return DNN_SUCCESS;
}
@@ -526,9 +567,16 @@ void ff_dnn_free_model_tf(DNNModel **model)
if (tf_model->input_tensor){
TF_DeleteTensor(tf_model->input_tensor);
}
- if (tf_model->output_tensor){
- TF_DeleteTensor(tf_model->output_tensor);
+ if (tf_model->output_tensors) {
+ for (uint32_t i = 0; i < tf_model->nb_output; ++i) {
+ if (tf_model->output_tensors[i]) {
+ TF_DeleteTensor(tf_model->output_tensors[i]);
+ tf_model->output_tensors[i] = NULL;
+ }
+ }
}
+ av_freep(&tf_model->outputs);
+ av_freep(&tf_model->output_tensors);
av_freep(&tf_model);
av_freep(model);
}
diff --git a/libavfilter/dnn_backend_tf.h b/libavfilter/dnn_backend_tf.h
index 47a24ec7b7..07877b1209 100644
--- a/libavfilter/dnn_backend_tf.h
+++ b/libavfilter/dnn_backend_tf.h
@@ -31,7 +31,7 @@
DNNModel *ff_dnn_load_model_tf(const char *model_filename);
-DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData *output);
+DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData *outputs, uint32_t nb_output);
void ff_dnn_free_model_tf(DNNModel **model);
diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
index 822f6e5b68..73d226ec91 100644
--- a/libavfilter/dnn_interface.h
+++ b/libavfilter/dnn_interface.h
@@ -26,6 +26,8 @@
#ifndef AVFILTER_DNN_INTERFACE_H
#define AVFILTER_DNN_INTERFACE_H
+#include <stdint.h>
+
typedef enum {DNN_SUCCESS, DNN_ERROR} DNNReturnType;
typedef enum {DNN_NATIVE, DNN_TF} DNNBackendType;
@@ -40,7 +42,7 @@ typedef struct DNNModel{
void *model;
// Sets model input and output.
// Should be called at least once before model execution.
- DNNReturnType (*set_input_output)(void *model, DNNData *input, const char *input_name, const char *output_name);
+ DNNReturnType (*set_input_output)(void *model, DNNData *input, const char *input_name, const char **output_names, uint32_t nb_output);
} DNNModel;
// Stores pointers to functions for loading, executing, freeing DNN models for one of the backends.
@@ -48,7 +50,7 @@ typedef struct DNNModule{
// Loads model and parameters from given file. Returns NULL if it is not possible.
DNNModel *(*load_model)(const char *model_filename);
// Executes model with specified input and output. Returns DNN_ERROR otherwise.
- DNNReturnType (*execute_model)(const DNNModel *model, DNNData *output);
+ DNNReturnType (*execute_model)(const DNNModel *model, DNNData *outputs, uint32_t nb_output);
// Frees memory allocated for model.
void (*free_model)(DNNModel **model);
} DNNModule;
diff --git a/libavfilter/vf_sr.c b/libavfilter/vf_sr.c
index 12804d8bdf..0145511d11 100644
--- a/libavfilter/vf_sr.c
+++ b/libavfilter/vf_sr.c
@@ -116,18 +116,19 @@ static int config_props(AVFilterLink *inlink)
AVFilterLink *outlink = context->outputs[0];
DNNReturnType result;
int sws_src_h, sws_src_w, sws_dst_h, sws_dst_w;
+ const char *model_output_name = "y";
sr_context->input.width = inlink->w * sr_context->scale_factor;
sr_context->input.height = inlink->h * sr_context->scale_factor;
sr_context->input.channels = 1;
- result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, "x", "y");
+ result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, "x", &model_output_name, 1);
if (result != DNN_SUCCESS){
av_log(context, AV_LOG_ERROR, "could not set input and output for the model\n");
return AVERROR(EIO);
}
- result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output);
+ result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output, 1);
if (result != DNN_SUCCESS){
av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
return AVERROR(EIO);
@@ -136,12 +137,12 @@ static int config_props(AVFilterLink *inlink)
if (sr_context->input.height != sr_context->output.height || sr_context->input.width != sr_context->output.width){
sr_context->input.width = inlink->w;
sr_context->input.height = inlink->h;
- result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, "x", "y");
+ result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, "x", &model_output_name, 1);
if (result != DNN_SUCCESS){
av_log(context, AV_LOG_ERROR, "could not set input and output for the model\n");
return AVERROR(EIO);
}
- result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output);
+ result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output, 1);
if (result != DNN_SUCCESS){
av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
return AVERROR(EIO);
@@ -256,7 +257,7 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in)
}
av_frame_free(&in);
- dnn_result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output);
+ dnn_result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output, 1);
if (dnn_result != DNN_SUCCESS){
av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
return AVERROR(EIO);
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