Em qua, 24 de abr de 2019 às 23:14, Guo, Yejun <yejun....@intel.com> escreveu:
>
> Currently, within interface set_input_output, the dims/memory of the
> tensorflow
> dnn model output is determined by executing the model with zero input,
> actually, the output dims might vary with different input data for networks
> such as object detection models faster-rcnn, ssd and yolo.
>
> This patch moves the logic from set_input_output to execute_model which
> is suitable for all the cases. Since interface changed, and so
> dnn_backend_native
> also changes.
>
> In vf_sr.c, it knows it's srcnn or espcn by executing the model with zero
> input,
> so execute_model has to be called in function config_props
>
> Signed-off-by: Guo, Yejun <yejun....@intel.com>
> ---
> libavfilter/dnn_backend_native.c | 14 +++++-----
> libavfilter/dnn_backend_native.h | 2 +-
> libavfilter/dnn_backend_tf.c | 56
> ++++++++++++++++------------------------
> libavfilter/dnn_backend_tf.h | 2 +-
> libavfilter/dnn_interface.h | 6 ++---
> libavfilter/vf_sr.c | 20 +++++++++++---
> 6 files changed, 51 insertions(+), 49 deletions(-)
>
> diff --git a/libavfilter/dnn_backend_native.c
> b/libavfilter/dnn_backend_native.c
> index fe43116..18735c0 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, DNNData *output, const char *output_name)
> +static DNNReturnType set_input_output_native(void *model, DNNData *input,
> const char *input_name, const char *output_name)
> {
> ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
> InputParams *input_params;
> @@ -81,11 +81,6 @@ static DNNReturnType set_input_output_native(void *model,
> DNNData *input, const
> }
> }
>
> - output->data = network->layers[network->layers_num - 1].output;
> - output->height = cur_height;
> - output->width = cur_width;
> - output->channels = cur_channels;
> -
> return DNN_SUCCESS;
> }
>
> @@ -280,7 +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)
> +DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData
> *output)
> {
> ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
> int cur_width, cur_height, cur_channels;
> @@ -322,6 +317,11 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel
> *model)
> }
> }
>
> + output->data = network->layers[network->layers_num - 1].output;
> + output->height = cur_height;
> + output->width = cur_width;
> + output->channels = cur_channels;
> +
> return DNN_SUCCESS;
> }
>
> diff --git a/libavfilter/dnn_backend_native.h
> b/libavfilter/dnn_backend_native.h
> index 51d4cac..adaf4a7 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);
> +DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData
> *output);
>
> void ff_dnn_free_model_native(DNNModel **model);
>
> diff --git a/libavfilter/dnn_backend_tf.c b/libavfilter/dnn_backend_tf.c
> index a838907..7bee45c 100644
> --- a/libavfilter/dnn_backend_tf.c
> +++ b/libavfilter/dnn_backend_tf.c
> @@ -35,7 +35,6 @@ typedef struct TFModel{
> TF_Status *status;
> TF_Output input, output;
> TF_Tensor *input_tensor;
> - DNNData *output_data;
> } TFModel;
>
> static void free_buffer(void *data, size_t length)
> @@ -76,13 +75,12 @@ 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, DNNData *output, const char *output_name)
> +static DNNReturnType set_input_output_tf(void *model, DNNData *input, const
> char *input_name, const char *output_name)
> {
> TFModel *tf_model = (TFModel *)model;
> int64_t input_dims[] = {1, input->height, input->width, input->channels};
> TF_SessionOptions *sess_opts;
> const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph,
> "init");
> - TF_Tensor *output_tensor;
>
> // Input operation
> tf_model->input.oper = TF_GraphOperationByName(tf_model->graph,
> input_name);
> @@ -132,26 +130,6 @@ static DNNReturnType set_input_output_tf(void *model,
> DNNData *input, const char
> }
> }
>
> - // Execute network to get output height, width and number of channels
> - TF_SessionRun(tf_model->session, NULL,
> - &tf_model->input, &tf_model->input_tensor, 1,
> - &tf_model->output, &output_tensor, 1,
> - NULL, 0, NULL, tf_model->status);
> - if (TF_GetCode(tf_model->status) != TF_OK){
> - return DNN_ERROR;
> - }
> - else{
> - output->height = TF_Dim(output_tensor, 1);
> - output->width = TF_Dim(output_tensor, 2);
> - output->channels = TF_Dim(output_tensor, 3);
> - output->data = av_malloc(output->height * output->width *
> output->channels * sizeof(float));
> - if (!output->data){
> - return DNN_ERROR;
> - }
> - tf_model->output_data = output;
> - TF_DeleteTensor(output_tensor);
> - }
> -
> return DNN_SUCCESS;
> }
>
> @@ -489,7 +467,6 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename)
> }
> tf_model->session = NULL;
> tf_model->input_tensor = NULL;
> - tf_model->output_data = NULL;
>
> if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
> if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
> @@ -508,10 +485,12 @@ DNNModel *ff_dnn_load_model_tf(const char
> *model_filename)
>
>
>
> -DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model)
> +DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData *output)
> {
> TFModel *tf_model = (TFModel *)model->model;
> TF_Tensor *output_tensor;
> + uint64_t count;
> + uint64_t old_count = output->height * output->width * output->channels *
> sizeof(float);
>
> TF_SessionRun(tf_model->session, NULL,
> &tf_model->input, &tf_model->input_tensor, 1,
> @@ -521,14 +500,26 @@ DNNReturnType ff_dnn_execute_model_tf(const DNNModel
> *model)
> if (TF_GetCode(tf_model->status) != TF_OK){
> return DNN_ERROR;
> }
> - else{
> - memcpy(tf_model->output_data->data, TF_TensorData(output_tensor),
> - tf_model->output_data->height * tf_model->output_data->width *
> - tf_model->output_data->channels * sizeof(float));
> - TF_DeleteTensor(output_tensor);
>
> - return DNN_SUCCESS;
> + output->height = TF_Dim(output_tensor, 1);
> + output->width = TF_Dim(output_tensor, 2);
> + output->channels = TF_Dim(output_tensor, 3);
> + count = output->height * output->width * output->channels *
> sizeof(float);
> + if (output->data) {
> + if (count > old_count) {
> + av_freep(&output->data);
> + }
> + }
> + if (!output->data) {
> + output->data = av_malloc(count);
> + if (!output->data){
> + return DNN_ERROR;
> + }
> }
> + memcpy(output->data, TF_TensorData(output_tensor), count);
> + TF_DeleteTensor(output_tensor);
> +
> + return DNN_SUCCESS;
> }
>
> void ff_dnn_free_model_tf(DNNModel **model)
> @@ -550,9 +541,6 @@ void ff_dnn_free_model_tf(DNNModel **model)
> if (tf_model->input_tensor){
> TF_DeleteTensor(tf_model->input_tensor);
> }
> - if (tf_model->output_data){
> - av_freep(&tf_model->output_data->data);
> - }
> av_freep(&tf_model);
> av_freep(model);
> }
> diff --git a/libavfilter/dnn_backend_tf.h b/libavfilter/dnn_backend_tf.h
> index 7ba84f4..47a24ec 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);
> +DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData
> *output);
>
> void ff_dnn_free_model_tf(DNNModel **model);
>
> diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
> index 0390e39..822f6e5 100644
> --- a/libavfilter/dnn_interface.h
> +++ b/libavfilter/dnn_interface.h
> @@ -38,9 +38,9 @@ typedef struct DNNData{
> typedef struct DNNModel{
> // Stores model that can be different for different backends.
> void *model;
> - // Sets model input and output, while allocating additional memory for
> intermediate calculations.
> + // 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, DNNData *output, const char *output_name);
> + DNNReturnType (*set_input_output)(void *model, DNNData *input, const
> char *input_name, const char *output_name);
> } DNNModel;
>
> // Stores pointers to functions for loading, executing, freeing DNN models
> for one of the backends.
> @@ -48,7 +48,7 @@ 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);
> + DNNReturnType (*execute_model)(const DNNModel *model, DNNData *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 085ac19..7c92730 100644
> --- a/libavfilter/vf_sr.c
> +++ b/libavfilter/vf_sr.c
> @@ -122,20 +122,31 @@ static int config_props(AVFilterLink *inlink)
> 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", &sr_context->output, "y");
> + result = (sr_context->model->set_input_output)(sr_context->model->model,
> &sr_context->input, "x", "y");
> 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);
> + if (result != DNN_SUCCESS){
> + av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
> + return AVERROR(EIO);
> + }
> +
> 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", &sr_context->output, "y");
> + result =
> (sr_context->model->set_input_output)(sr_context->model->model,
> &sr_context->input, "x", "y");
> 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);
> + if (result != DNN_SUCCESS){
> + av_log(context, AV_LOG_ERROR, "failed to execute loaded
> model\n");
> + return AVERROR(EIO);
> + }
> sr_context->scale_factor = 0;
> }
> outlink->h = sr_context->output.height;
> @@ -248,7 +259,7 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in)
> }
> av_frame_free(&in);
>
> - dnn_result = (sr_context->dnn_module->execute_model)(sr_context->model);
> + dnn_result = (sr_context->dnn_module->execute_model)(sr_context->model,
> &sr_context->output);
> if (dnn_result != DNN_SUCCESS){
> av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
> return AVERROR(EIO);
> @@ -266,6 +277,9 @@ static av_cold void uninit(AVFilterContext *context)
> int i;
> SRContext *sr_context = context->priv;
>
> + if (sr_context->backend_type == DNN_TF)
> + av_freep(&sr_context->output.data);
> +
> if (sr_context->dnn_module){
> (sr_context->dnn_module->free_model)(&sr_context->model);
> av_freep(&sr_context->dnn_module);
> --
> 2.7.4
>
LGTM.
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