[svace] fix svace issues

fixed all svace issues on main branch

**Self-evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test:   [X]Passed [ ]Failed [ ]Skipped

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

Applications/utils/jni/bitmap_helpers.cpp

@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 @file	bitmat_helpers.cpp
 @brief  bitmap_helpers from tensorflow
+@author TensorFlow Authors
+@bug there are no known bugs
 
 ==============================================================================*/
 
@@ -89,7 +91,7 @@ uint8_t *read_bmp(const std::string &input_bmp_name, int *width, int *height,
 
   const uint8_t *img_bytes = new uint8_t[len];
   file.seekg(0, std::ios::beg);
-  file.read((char *)img_bytes, len);
+  file.read((char *)img_bytes, static_cast<std::streamsize>(len));
   const int32_t header_size =
     *(reinterpret_cast<const int32_t *>(img_bytes + 10));
   *width = *(reinterpret_cast<const int32_t *>(img_bytes + 18));

nntrainer/dataset/raw_file_data_producer.cpp

@@ -67,8 +67,9 @@ RawFileDataProducer::finalize(const std::vector<TensorDim> &input_dims,
                                  std::vector<Tensor> &labels) {
     NNTR_THROW_IF(idx >= sz, std::range_error)
       << "given index is out of bound, index: " << idx << " size: " << sz;
-    file.seekg(idx * sample_size * RawFileDataProducer::pixel_size,
-               std::ios_base::beg);
+    std::streamoff offset = static_cast<std::streamoff>(
+      idx * sample_size * RawFileDataProducer::pixel_size);
+    file.seekg(offset, std::ios_base::beg);
     for (auto &input : inputs) {
       input.read(file);
     }
@@ -107,7 +108,8 @@ RawFileDataProducer::size(const std::vector<TensorDim> &input_dims,
   //   << " Given file does not align with the given sample size, sample size: "
   //   << sample_size << " file_size: " << file_size;
 
-  return file_size / (sample_size * RawFileDataProducer::pixel_size);
+  return static_cast<unsigned int>(file_size) /
+         (sample_size * RawFileDataProducer::pixel_size);
 }
 
 void RawFileDataProducer::exportTo(

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;
   }