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

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

1 files changed, 12 insertions, 102 deletions

diff --git a/libavfilter/dnn/dnn_backend_native.c b/libavfilter/dnn/dnn_backend_native.c
index c8fb956dec..06b010d90e 100644
--- a/libavfilter/dnn/dnn_backend_native.c
+++ b/libavfilter/dnn/dnn_backend_native.c
@@ -25,10 +25,7 @@
 
 #include "dnn_backend_native.h"
 #include "libavutil/avassert.h"
-#include "dnn_backend_native_layer_pad.h"
 #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)
@@ -104,13 +101,9 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
     int version, header_size, major_version_expected = 0;
     ConvolutionalNetwork *network = NULL;
     AVIOContext *model_file_context;
-    int file_size, dnn_size, kernel_size, i;
+    int file_size, dnn_size, parsed_size;
     int32_t layer;
     DNNLayerType layer_type;
-    ConvolutionalParams *conv_params;
-    DepthToSpaceParams *depth_to_space_params;
-    LayerPadParams *pad_params;
-    DnnLayerMaximumParams *maximum_params;
 
     model = av_malloc(sizeof(DNNModel));
     if (!model){
@@ -189,104 +182,21 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
     for (layer = 0; layer < network->layers_num; ++layer){
         layer_type = (int32_t)avio_rl32(model_file_context);
         dnn_size += 4;
+
+        if (layer_type >= DLT_COUNT) {
+            avio_closep(&model_file_context);
+            ff_dnn_free_model_native(&model);
+            return NULL;
+        }
+
         network->layers[layer].type = layer_type;
-        switch (layer_type){
-        case DLT_CONV2D:
-            conv_params = av_malloc(sizeof(ConvolutionalParams));
-            if (!conv_params){
-                avio_closep(&model_file_context);
-                ff_dnn_free_model_native(&model);
-                return NULL;
-            }
-            conv_params->dilation = (int32_t)avio_rl32(model_file_context);
-            conv_params->padding_method = (int32_t)avio_rl32(model_file_context);
-            conv_params->activation = (int32_t)avio_rl32(model_file_context);
-            conv_params->input_num = (int32_t)avio_rl32(model_file_context);
-            conv_params->output_num = (int32_t)avio_rl32(model_file_context);
-            conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
-            kernel_size = conv_params->input_num * conv_params->output_num *
-                          conv_params->kernel_size * conv_params->kernel_size;
-            dnn_size += 24 + (kernel_size + conv_params->output_num << 2);
-            if (dnn_size > file_size || conv_params->input_num <= 0 ||
-                conv_params->output_num <= 0 || conv_params->kernel_size <= 0){
-                avio_closep(&model_file_context);
-                av_freep(&conv_params);
-                ff_dnn_free_model_native(&model);
-                return NULL;
-            }
-            conv_params->kernel = av_malloc(kernel_size * sizeof(float));
-            conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
-            if (!conv_params->kernel || !conv_params->biases){
-                avio_closep(&model_file_context);
-                av_freep(&conv_params->kernel);
-                av_freep(&conv_params->biases);
-                av_freep(&conv_params);
-                ff_dnn_free_model_native(&model);
-                return NULL;
-            }
-            for (i = 0; i < kernel_size; ++i){
-                conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
-            }
-            for (i = 0; i < conv_params->output_num; ++i){
-                conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
-            }
-            network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
-            network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
-            dnn_size += 8;
-            network->layers[layer].params = conv_params;
-            break;
-        case DLT_DEPTH_TO_SPACE:
-            depth_to_space_params = av_malloc(sizeof(DepthToSpaceParams));
-            if (!depth_to_space_params){
-                avio_closep(&model_file_context);
-                ff_dnn_free_model_native(&model);
-                return NULL;
-            }
-            depth_to_space_params->block_size = (int32_t)avio_rl32(model_file_context);
-            dnn_size += 4;
-            network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
-            network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
-            dnn_size += 8;
-            network->layers[layer].params = depth_to_space_params;
-            break;
-        case DLT_MIRROR_PAD:
-            pad_params = av_malloc(sizeof(LayerPadParams));
-            if (!pad_params){
-                avio_closep(&model_file_context);
-                ff_dnn_free_model_native(&model);
-                return NULL;
-            }
-            pad_params->mode = (int32_t)avio_rl32(model_file_context);
-            dnn_size += 4;
-            for (i = 0; i < 4; ++i) {
-                pad_params->paddings[i][0] = avio_rl32(model_file_context);
-                pad_params->paddings[i][1] = avio_rl32(model_file_context);
-                dnn_size += 8;
-            }
-            network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
-            network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
-            dnn_size += 8;
-            network->layers[layer].params = pad_params;
-            break;
-        case DLT_MAXIMUM:
-            maximum_params = av_malloc(sizeof(*maximum_params));
-            if (!maximum_params){
-                avio_closep(&model_file_context);
-                ff_dnn_free_model_native(&model);
-                return NULL;
-            }
-            maximum_params->val.u32 = avio_rl32(model_file_context);
-            dnn_size += 4;
-            network->layers[layer].params = maximum_params;
-            network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
-            network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
-            dnn_size += 8;
-            break;
-        default:
+        parsed_size = layer_funcs[layer_type].pf_load(&network->layers[layer], model_file_context, file_size);
+        if (!parsed_size) {
             avio_closep(&model_file_context);
             ff_dnn_free_model_native(&model);
             return NULL;
         }
+        dnn_size += parsed_size;
     }
 
     for (int32_t i = 0; i < network->operands_num; ++i){
@@ -341,7 +251,7 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
 
     for (layer = 0; layer < network->layers_num; ++layer){
         DNNLayerType layer_type = network->layers[layer].type;
-        layer_funcs[layer_type](network->operands,
+        layer_funcs[layer_type].pf_exec(network->operands,
                                   network->layers[layer].input_operand_indexes,
                                   network->layers[layer].output_operand_index,
                                   network->layers[layer].params);