dnn/native: rename struct ConvolutionalNetwork to NativeModel

Signed-off-by: Ting Fu <ting.fu@intel.com>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>

libavfilter/dnn/dnn_backend_native.c

@@ -30,10 +30,10 @@
 
 static DNNReturnType get_input_native(void *model, DNNData *input, const char *input_name)
 {
-    ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
+    NativeModel *native_model = (NativeModel *)model;
 
-    for (int i = 0; i < network->operands_num; ++i) {
+    for (int i = 0; i < native_model->operands_num; ++i) {
-        DnnOperand *oprd = &network->operands[i];
+        DnnOperand *oprd = &native_model->operands[i];
         if (strcmp(oprd->name, input_name) == 0) {
             if (oprd->type != DOT_INPUT)
                 return DNN_ERROR;
@@ -52,15 +52,15 @@ static DNNReturnType get_input_native(void *model, DNNData *input, const char *i
 
 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;
+    NativeModel *native_model = (NativeModel *)model;
     DnnOperand *oprd = NULL;
 
-    if (network->layers_num <= 0 || network->operands_num <= 0)
+    if (native_model->layers_num <= 0 || native_model->operands_num <= 0)
         return DNN_ERROR;
 
     /* inputs */
-    for (int i = 0; i < network->operands_num; ++i) {
+    for (int i = 0; i < native_model->operands_num; ++i) {
-        oprd = &network->operands[i];
+        oprd = &native_model->operands[i];
         if (strcmp(oprd->name, input_name) == 0) {
             if (oprd->type != DOT_INPUT)
                 return DNN_ERROR;
@@ -88,24 +88,24 @@ static DNNReturnType set_input_output_native(void *model, DNNData *input, const
     input->data = oprd->data;
 
     /* outputs */
-    network->nb_output = 0;
+    native_model->nb_output = 0;
-    av_freep(&network->output_indexes);
+    av_freep(&native_model->output_indexes);
-    network->output_indexes = av_mallocz_array(nb_output, sizeof(*network->output_indexes));
+    native_model->output_indexes = av_mallocz_array(nb_output, sizeof(*native_model->output_indexes));
-    if (!network->output_indexes)
+    if (!native_model->output_indexes)
         return DNN_ERROR;
 
     for (uint32_t i = 0; i < nb_output; ++i) {
         const char *output_name = output_names[i];
-        for (int j = 0; j < network->operands_num; ++j) {
+        for (int j = 0; j < native_model->operands_num; ++j) {
-            oprd = &network->operands[j];
+            oprd = &native_model->operands[j];
             if (strcmp(oprd->name, output_name) == 0) {
-                network->output_indexes[network->nb_output++] = j;
+                native_model->output_indexes[native_model->nb_output++] = j;
                 break;
             }
         }
     }
 
-    if (network->nb_output != nb_output)
+    if (native_model->nb_output != nb_output)
         return DNN_ERROR;
 
     return DNN_SUCCESS;
@@ -122,7 +122,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *optio
     char *buf;
     size_t size;
     int version, header_size, major_version_expected = 1;
-    ConvolutionalNetwork *network = NULL;
+    NativeModel *native_model = NULL;
     AVIOContext *model_file_context;
     int file_size, dnn_size, parsed_size;
     int32_t layer;
@@ -167,29 +167,29 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *optio
     dnn_size += 4;
     header_size = dnn_size;
 
-    network = av_mallocz(sizeof(ConvolutionalNetwork));
+    native_model = av_mallocz(sizeof(NativeModel));
-    if (!network){
+    if (!native_model){
         goto fail;
     }
-    model->model = (void *)network;
+    model->model = (void *)native_model;
 
     avio_seek(model_file_context, file_size - 8, SEEK_SET);
-    network->layers_num = (int32_t)avio_rl32(model_file_context);
+    native_model->layers_num = (int32_t)avio_rl32(model_file_context);
-    network->operands_num = (int32_t)avio_rl32(model_file_context);
+    native_model->operands_num = (int32_t)avio_rl32(model_file_context);
     dnn_size += 8;
     avio_seek(model_file_context, header_size, SEEK_SET);
 
-    network->layers = av_mallocz(network->layers_num * sizeof(Layer));
+    native_model->layers = av_mallocz(native_model->layers_num * sizeof(Layer));
-    if (!network->layers){
+    if (!native_model->layers){
         goto fail;
     }
 
-    network->operands = av_mallocz(network->operands_num * sizeof(DnnOperand));
+    native_model->operands = av_mallocz(native_model->operands_num * sizeof(DnnOperand));
-    if (!network->operands){
+    if (!native_model->operands){
         goto fail;
     }
 
-    for (layer = 0; layer < network->layers_num; ++layer){
+    for (layer = 0; layer < native_model->layers_num; ++layer){
         layer_type = (int32_t)avio_rl32(model_file_context);
         dnn_size += 4;
 
@@ -197,25 +197,25 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *optio
             goto fail;
         }
 
-        network->layers[layer].type = layer_type;
+        native_model->layers[layer].type = layer_type;
-        parsed_size = layer_funcs[layer_type].pf_load(&network->layers[layer], model_file_context, file_size, network->operands_num);
+        parsed_size = layer_funcs[layer_type].pf_load(&native_model->layers[layer], model_file_context, file_size, native_model->operands_num);
         if (!parsed_size) {
             goto fail;
         }
         dnn_size += parsed_size;
     }
 
-    for (int32_t i = 0; i < network->operands_num; ++i){
+    for (int32_t i = 0; i < native_model->operands_num; ++i){
         DnnOperand *oprd;
         int32_t name_len;
         int32_t operand_index = (int32_t)avio_rl32(model_file_context);
         dnn_size += 4;
 
-        if (operand_index >= network->operands_num) {
+        if (operand_index >= native_model->operands_num) {
             goto fail;
         }
 
-        oprd = &network->operands[operand_index];
+        oprd = &native_model->operands[operand_index];
         name_len = (int32_t)avio_rl32(model_file_context);
         dnn_size += 4;
 
@@ -257,25 +257,25 @@ fail:
 
 DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
 {
-    ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
+    NativeModel *native_model = (NativeModel *)model->model;
     int32_t layer;
-    uint32_t nb = FFMIN(nb_output, network->nb_output);
+    uint32_t nb = FFMIN(nb_output, native_model->nb_output);
 
-    if (network->layers_num <= 0 || network->operands_num <= 0)
+    if (native_model->layers_num <= 0 || native_model->operands_num <= 0)
         return DNN_ERROR;
-    if (!network->operands[0].data)
+    if (!native_model->operands[0].data)
         return DNN_ERROR;
 
-    for (layer = 0; layer < network->layers_num; ++layer){
+    for (layer = 0; layer < native_model->layers_num; ++layer){
-        DNNLayerType layer_type = network->layers[layer].type;
+        DNNLayerType layer_type = native_model->layers[layer].type;
-        layer_funcs[layer_type].pf_exec(network->operands,
+        layer_funcs[layer_type].pf_exec(native_model->operands,
-                                  network->layers[layer].input_operand_indexes,
+                                        native_model->layers[layer].input_operand_indexes,
-                                  network->layers[layer].output_operand_index,
+                                        native_model->layers[layer].output_operand_index,
-                                  network->layers[layer].params);
+                                        native_model->layers[layer].params);
     }
 
     for (uint32_t i = 0; i < nb; ++i) {
-        DnnOperand *oprd = &network->operands[network->output_indexes[i]];
+        DnnOperand *oprd = &native_model->operands[native_model->output_indexes[i]];
         outputs[i].data = oprd->data;
         outputs[i].height = oprd->dims[1];
         outputs[i].width = oprd->dims[2];
@@ -309,34 +309,34 @@ int32_t calculate_operand_data_length(const DnnOperand* oprd)
 
 void ff_dnn_free_model_native(DNNModel **model)
 {
-    ConvolutionalNetwork *network;
+    NativeModel *native_model;
     ConvolutionalParams *conv_params;
     int32_t layer;
 
     if (*model)
     {
         if ((*model)->model) {
-            network = (ConvolutionalNetwork *)(*model)->model;
+            native_model = (NativeModel *)(*model)->model;
-            if (network->layers) {
+            if (native_model->layers) {
-                for (layer = 0; layer < network->layers_num; ++layer){
+                for (layer = 0; layer < native_model->layers_num; ++layer){
-                    if (network->layers[layer].type == DLT_CONV2D){
+                    if (native_model->layers[layer].type == DLT_CONV2D){
-                        conv_params = (ConvolutionalParams *)network->layers[layer].params;
+                        conv_params = (ConvolutionalParams *)native_model->layers[layer].params;
                         av_freep(&conv_params->kernel);
                         av_freep(&conv_params->biases);
                     }
-                    av_freep(&network->layers[layer].params);
+                    av_freep(&native_model->layers[layer].params);
                 }
-                av_freep(&network->layers);
+                av_freep(&native_model->layers);
             }
 
-            if (network->operands) {
+            if (native_model->operands) {
-                for (uint32_t operand = 0; operand < network->operands_num; ++operand)
+                for (uint32_t operand = 0; operand < native_model->operands_num; ++operand)
-                    av_freep(&network->operands[operand].data);
+                    av_freep(&native_model->operands[operand].data);
-                av_freep(&network->operands);
+                av_freep(&native_model->operands);
             }
 
-            av_freep(&network->output_indexes);
+            av_freep(&native_model->output_indexes);
-            av_freep(&network);
+            av_freep(&native_model);
         }
         av_freep(model);
     }

libavfilter/dnn/dnn_backend_native.h

@@ -107,14 +107,14 @@ typedef struct InputParams{
 } InputParams;
 
 // Represents simple feed-forward convolutional network.
-typedef struct ConvolutionalNetwork{
+typedef struct NativeModel{
     Layer *layers;
     int32_t layers_num;
     DnnOperand *operands;
     int32_t operands_num;
     int32_t *output_indexes;
     uint32_t nb_output;
-} ConvolutionalNetwork;
+} NativeModel;
 
 DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *options);
 

libavfilter/dnn/dnn_backend_tf.c

@@ -487,15 +487,15 @@ static DNNReturnType load_native_model(TFModel *tf_model, const char *model_file
     int64_t transpose_perm_shape[] = {4};
     int64_t input_shape[] = {1, -1, -1, -1};
     DNNReturnType layer_add_res;
-    DNNModel *native_model = NULL;
+    DNNModel *model = NULL;
-    ConvolutionalNetwork *conv_network;
+    NativeModel *native_model;
 
-    native_model = ff_dnn_load_model_native(model_filename, NULL);
+    model = ff_dnn_load_model_native(model_filename, NULL);
-    if (!native_model){
+    if (!model){
         return DNN_ERROR;
     }
 
-    conv_network = (ConvolutionalNetwork *)native_model->model;
+    native_model = (NativeModel *)model->model;
     tf_model->graph = TF_NewGraph();
     tf_model->status = TF_NewStatus();
 
@@ -528,26 +528,26 @@ static DNNReturnType load_native_model(TFModel *tf_model, const char *model_file
     }
     transpose_op = TF_FinishOperation(op_desc, tf_model->status);
 
-    for (layer = 0; layer < conv_network->layers_num; ++layer){
+    for (layer = 0; layer < native_model->layers_num; ++layer){
-        switch (conv_network->layers[layer].type){
+        switch (native_model->layers[layer].type){
         case DLT_INPUT:
             layer_add_res = DNN_SUCCESS;
             break;
         case DLT_CONV2D:
             layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
-                                           (ConvolutionalParams *)conv_network->layers[layer].params, layer);
+                                           (ConvolutionalParams *)native_model->layers[layer].params, layer);
             break;
         case DLT_DEPTH_TO_SPACE:
             layer_add_res = add_depth_to_space_layer(tf_model, &op,
-                                                     (DepthToSpaceParams *)conv_network->layers[layer].params, layer);
+                                                     (DepthToSpaceParams *)native_model->layers[layer].params, layer);
             break;
         case DLT_MIRROR_PAD:
             layer_add_res = add_pad_layer(tf_model, &op,
-                                          (LayerPadParams *)conv_network->layers[layer].params, layer);
+                                          (LayerPadParams *)native_model->layers[layer].params, layer);
             break;
         case DLT_MAXIMUM:
             layer_add_res = add_maximum_layer(tf_model, &op,
-                                          (DnnLayerMaximumParams *)conv_network->layers[layer].params, layer);
+                                          (DnnLayerMaximumParams *)native_model->layers[layer].params, layer);
             break;
         default:
             CLEANUP_ON_ERROR(tf_model);
@@ -567,7 +567,7 @@ static DNNReturnType load_native_model(TFModel *tf_model, const char *model_file
         CLEANUP_ON_ERROR(tf_model);
     }
 
-    ff_dnn_free_model_native(&native_model);
+    ff_dnn_free_model_native(&model);
 
     return DNN_SUCCESS;
 }