[WIP][coverity] fix coverity issues

wip

Signed-off-by: Seungbaek Hong <sb92.hong@samsung.com>

Author: baek2sm

nntrainer/layers/gru.cpp

@@ -94,6 +94,8 @@ void GRULayer::finalize(InitLayerContext &context) {
   const TensorDim &input_dim = context.getInputDimensions()[0];
   const unsigned int batch_size = input_dim.batch();
   const unsigned int max_timestep = input_dim.height();
+  NNTR_THROW_IF(max_timestep < 1, std::runtime_error)
+      << "max timestep must be greator than 0 in gru layer.";
   const unsigned int feature_size = input_dim.width();
 
   // if return_sequences == False :

nntrainer/layers/lstm.cpp

@@ -454,6 +454,8 @@ void LSTMLayer::finalize(InitLayerContext &context) {
   if (!std::get<props::MaxTimestep>(lstm_props).empty())
     max_timestep =
       std::max(max_timestep, std::get<props::MaxTimestep>(lstm_props).get());
+  NNTR_THROW_IF(max_timestep < 1, std::runtime_error)
+      << "max timestep must be greator than 0 in lstm layer.";
   std::get<props::MaxTimestep>(lstm_props).set(max_timestep);
   const unsigned int feature_size = input_dim.width();
 

nntrainer/layers/rnn.cpp

@@ -77,6 +77,8 @@ void RNNLayer::finalize(InitLayerContext &context) {
   const TensorDim &input_dim = context.getInputDimensions()[SINGLE_INOUT_IDX];
   const unsigned int batch_size = input_dim.batch();
   const unsigned int max_timestep = input_dim.height();
+  NNTR_THROW_IF(max_timestep < 1, std::runtime_error)
+      << "max timestep must be greator than 0 in rnn layer.";
   const unsigned int feature_size = input_dim.width();
 
   // output_dim = [ batch, 1, (return_sequences ? time_iteration : 1), unit ]

nntrainer/utils/util_func.cpp

@@ -210,7 +210,8 @@ char *getRealpath(const char *name, char *resolved) {
 #ifdef _WIN32
   return _fullpath(resolved, name, MAX_PATH_LENGTH);
 #else
-  return realpath(name, resolved);
+  resolved = realpath(name, nullptr);
+  return resolved;
 #endif
 }
 

test/unittest/compiler/unittest_tflite_export.cpp

@@ -23,6 +23,7 @@
 #include <node_exporter.h>
 #include <optimizer.h>
 #include <realizer.h>
+#include <stdlib.h>
 
 #include <nntrainer_test_util.h>
 
@@ -43,6 +44,8 @@ std::vector<float> ans;
 std::vector<float *> in_f;
 std::vector<float *> l_f;
 
+unsigned int seed = 0;
+
 /**
  * @brief make "key=value" from key and value
  *
@@ -155,7 +158,7 @@ TEST(nntrainerInterpreterTflite, simple_fc) {
   float *nntr_input = new float[data_size];
 
   for (unsigned int i = 0; i < data_size; i++) {
-    auto rand_float = static_cast<float>(std::rand() / (RAND_MAX + 1.0));
+    auto rand_float = static_cast<float>(rand_r(&seed) / (RAND_MAX + 1.0));
     input_data.push_back(rand_float);
     nntr_input[i] = rand_float;
   }
@@ -284,7 +287,7 @@ TEST(nntrainerInterpreterTflite, part_of_resnet_0) {
   float *nntr_input = new float[data_size];
 
   for (unsigned int i = 0; i < data_size; i++) {
-    auto rand_float = static_cast<float>(std::rand() / (RAND_MAX + 1.0));
+    auto rand_float = static_cast<float>(rand_r(&seed) / (RAND_MAX + 1.0));
     input_data.push_back(rand_float);
     nntr_input[i] = rand_float;
   }
@@ -365,7 +368,7 @@ TEST(nntrainerInterpreterTflite, MNIST_FULL_TEST) {
   float nntr_input[28 * 28];
 
   for (unsigned int i = 0; i < data_size; i++) {
-    auto rand_float = static_cast<float>(std::rand() / (RAND_MAX + 1.0));
+    auto rand_float = static_cast<float>(rand_r(&seed) / (RAND_MAX + 1.0));
     input_data.push_back(rand_float);
     nntr_input[i] = rand_float;
   }