[Filter] util to get nth info

Use util function to get nth tensor-info ptr.
Also, remove unnecessary header include in filter subplugin.

Signed-off-by: Jaeyun Jung <jy1210.jung@samsung.com>

diff --git a/nnstreamer/tensor_filter/tensor_filter_nntrainer.cc b/nnstreamer/tensor_filter/tensor_filter_nntrainer.cc
index 94900d735f5646a7def54fb154c8f2cebeebb907..78516beaa1634f491d958b9c070dae851a90cb3b 100644 (file)
--- a/nnstreamer/tensor_filter/tensor_filter_nntrainer.cc
+++ b/nnstreamer/tensor_filter/tensor_filter_nntrainer.cc
@@ -23,8 +23,6 @@
 
 #include <nntrainer_error.h>
 
-#include "ml-api-common.h"
-#include "nnstreamer.h"
 #include "nnstreamer_plugin_api.h"
 #include "tensor_filter_nntrainer.hh"
 
@@ -77,9 +75,6 @@ to_nnst_tensor_dim(const ml::train::TensorDim &dim) {
   for (unsigned int i = 0; i < ml::train::TensorDim::MAXDIM; ++i) {
     info->dimension[i] = dim.getTensorDim(ml::train::TensorDim::MAXDIM - i - 1);
   }
-  for (unsigned int i = ml::train::TensorDim::MAXDIM; i < NNS_TENSOR_RANK_LIMIT;
-       ++i)
-    info->dimension[i] = 1;
 
   return info;
 }
@@ -90,8 +85,7 @@ static ml::train::TensorDim to_nntr_tensor_dim(const GstTensorInfo *info) {
 }
 
 NNTrainerInference::NNTrainerInference(const std::string &model_config_) :
-  batch_size(1),
-  model_config(model_config_) {
+  batch_size(1), model_config(model_config_) {
   loadModel();
   model->compile();
   model->initialize();
@@ -219,6 +213,7 @@ static int nntrainer_setInputDim(const GstTensorFilterProperties *prop,
                                  GstTensorsInfo *out_info) {
   NNTrainerInference *nntrainer =
     static_cast<NNTrainerInference *>(*private_data);
+  GstTensorInfo *_info;
   g_return_val_if_fail(prop && nntrainer && in_info && out_info, -EINVAL);
 
   auto model_inputs = nntrainer->getInputDimension();
@@ -232,13 +227,14 @@ static int nntrainer_setInputDim(const GstTensorFilterProperties *prop,
     for (unsigned int i = 0u; i < model_inputs.size(); ++i) {
       auto default_dim = model_inputs[i];
       default_dim.batch(1);
-      mutable_in_info->info[i].type = _NNS_FLOAT32;
-      gst_tensor_info_copy(mutable_in_info->info + i,
-                           to_nnst_tensor_dim(default_dim).get());
+      _info = gst_tensors_info_get_nth_info(mutable_in_info, i);
+      _info->type = _NNS_FLOAT32;
+      gst_tensor_info_copy(_info, to_nnst_tensor_dim(default_dim).get());
     }
   }
 
-  auto batch_size = in_info->info[0].dimension[3];
+  _info = gst_tensors_info_get_nth_info((GstTensorsInfo *)in_info, 0);
+  auto batch_size = _info->dimension[3];
 
   /// this does not allocate the memory for the inference, so setting batch here
   /// does not have a large effect on the first inference call as of now.
@@ -253,9 +249,9 @@ static int nntrainer_setInputDim(const GstTensorFilterProperties *prop,
   /// check each in dimension matches
   for (unsigned int i = 0; i < in_info->num_tensors; ++i) {
     model_inputs[i].batch(batch_size);
-    g_return_val_if_fail(in_info->info[i].type == _NNS_FLOAT32, -EINVAL);
-    g_return_val_if_fail(
-      model_inputs[i] == to_nntr_tensor_dim(in_info->info + i), -EINVAL);
+    _info = gst_tensors_info_get_nth_info((GstTensorsInfo *)in_info, i);
+    g_return_val_if_fail(_info->type == _NNS_FLOAT32, -EINVAL);
+    g_return_val_if_fail(model_inputs[i] == to_nntr_tensor_dim(_info), -EINVAL);
   }
 
   auto model_outputs = nntrainer->getOutputDimension();
@@ -264,9 +260,9 @@ static int nntrainer_setInputDim(const GstTensorFilterProperties *prop,
   out_info->num_tensors = model_outputs.size();
   for (unsigned int i = 0; i < out_info->num_tensors; ++i) {
     model_outputs[i].batch(batch_size);
-    out_info->info[i].type = _NNS_FLOAT32;
-    gst_tensor_info_copy(out_info->info + i,
-                         to_nnst_tensor_dim(model_outputs[i]).get());
+    _info = gst_tensors_info_get_nth_info(out_info, i);
+    _info->type = _NNS_FLOAT32;
+    gst_tensor_info_copy(_info, to_nnst_tensor_dim(model_outputs[i]).get());
   }
 
   return 0;