Em qua, 24 de abr de 2019 às 23:14, Guo, Yejun <yejun....@intel.com> escreveu:
>
> 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....@intel.com>
> ---
> 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 18735c0..8a83c63 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 adaf4a7..e13a68a 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 be8401e..ca6472d 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 47a24ec..07877b1 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 822f6e5..73d226e 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 53bd8ea..b4d4165 100644
> --- a/libavfilter/vf_sr.c
> +++ b/libavfilter/vf_sr.c
> @@ -117,18 +117,19 @@ 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);
> @@ -137,12 +138,12 @@ 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);
> @@ -259,7 +260,7 @@ 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);
> --
> 2.7.4
>
LGTM.
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