avfilter/dnn: unify the layer execution function in native mode

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>

1 files changed, 6 insertions, 28 deletions

diff --git a/libavfilter/dnn/dnn_backend_native.c b/libavfilter/dnn/dnn_backend_native.c
index 97549d3077..c8fb956dec 100644
--- a/libavfilter/dnn/dnn_backend_native.c
+++ b/libavfilter/dnn/dnn_backend_native.c
@@ -29,6 +29,7 @@
 #include "dnn_backend_native_layer_conv2d.h"
 #include "dnn_backend_native_layer_depth2space.h"
 #include "dnn_backend_native_layer_maximum.h"
+#include "dnn_backend_native_layers.h"
 
 static DNNReturnType set_input_output_native(void *model, DNNInputData *input, const char *input_name, const char **output_names, uint32_t nb_output)
 {
@@ -331,10 +332,6 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
 {
     ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
     int32_t layer;
-    ConvolutionalParams *conv_params;
-    DepthToSpaceParams *depth_to_space_params;
-    LayerPadParams *pad_params;
-    DnnLayerMaximumParams *maximum_params;
     uint32_t nb = FFMIN(nb_output, network->nb_output);
 
     if (network->layers_num <= 0 || network->operands_num <= 0)
@@ -343,30 +340,11 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
         return DNN_ERROR;
 
     for (layer = 0; layer < network->layers_num; ++layer){
-        switch (network->layers[layer].type){
-        case DLT_CONV2D:
-            conv_params = (ConvolutionalParams *)network->layers[layer].params;
-            convolve(network->operands, network->layers[layer].input_operand_indexes,
-                     network->layers[layer].output_operand_index, conv_params);
-            break;
-        case DLT_DEPTH_TO_SPACE:
-            depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
-            depth_to_space(network->operands, network->layers[layer].input_operand_indexes,
-                           network->layers[layer].output_operand_index, depth_to_space_params->block_size);
-            break;
-        case DLT_MIRROR_PAD:
-            pad_params = (LayerPadParams *)network->layers[layer].params;
-            dnn_execute_layer_pad(network->operands, network->layers[layer].input_operand_indexes,
-                                  network->layers[layer].output_operand_index, pad_params);
-            break;
-        case DLT_MAXIMUM:
-            maximum_params = (DnnLayerMaximumParams *)network->layers[layer].params;
-            dnn_execute_layer_maximum(network->operands, network->layers[layer].input_operand_indexes,
-                                  network->layers[layer].output_operand_index, maximum_params);
-            break;
-        case DLT_INPUT:
-            return DNN_ERROR;
-        }
+        DNNLayerType layer_type = network->layers[layer].type;
+        layer_funcs[layer_type](network->operands,
+                                  network->layers[layer].input_operand_indexes,
+                                  network->layers[layer].output_operand_index,
+                                  network->layers[layer].params);
     }
 
     for (uint32_t i = 0; i < nb; ++i) {