fix waring

mindspore/ccsrc/minddata/dataset/audio/kernels/audio_utils.cc

@@ -1175,9 +1175,9 @@ Status Blackman(std::shared_ptr<Tensor> *output, int len) {
   RETURN_IF_NOT_OK(Tensor::CreateEmpty(TensorShape({len}), DataType(DataType::DE_FLOAT32), output));
   // Blackman window function.
   auto iter = (*output)->begin<float>();
-  float alpha = 0.42;
-  float half = 0.5;
-  float delta = 0.08;
+  const float alpha = 0.42;
+  const float half = 0.5;
+  const float delta = 0.08;
   for (ptrdiff_t i = 0; i < len; ++i) {
     *(iter + i) = alpha - half * std::cos(TWO * PI * i / len) + delta * std::cos(TWO * TWO * PI * i / len);
   }
@@ -1200,7 +1200,7 @@ Status Hann(std::shared_ptr<Tensor> *output, int len) {
   RETURN_IF_NOT_OK(Tensor::CreateEmpty(TensorShape({len}), DataType(DataType::DE_FLOAT32), output));
   // Hann window function.
   auto iter = (*output)->begin<float>();
-  float half = 0.5;
+  const float half = 0.5;
   for (ptrdiff_t i = 0; i < len; ++i) {
     *(iter + i) = half - half * std::cos(TWO * PI * i / len);
   }

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/iwslt_op.cc

@@ -306,10 +306,10 @@ Status IWSLTOp::CleanXmlFile(const std::string &src_file_path, const std::string
   std::ofstream new_file(new_file_path);
   CHECK_FAIL_RETURN_UNEXPECTED(new_file.is_open(), "Invalid file, failed to open file: " + new_file_path);
 
-  while (src_doc && target_doc) {
+  while (src_doc != nullptr && target_doc != nullptr) {
     XMLElement *src_seg = src_doc->FirstChildElement("seg");
     XMLElement *target_seg = target_doc->FirstChildElement("seg");
-    while (src_seg && target_seg) {
+    while (src_seg != nullptr && target_seg != nullptr) {
       src_content = src_seg->GetText();
       target_content = target_seg->GetText();
       RETURN_IF_NOT_OK(Trim(&src_content, " "));

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/tedlium_op.cc

@@ -211,7 +211,7 @@ Status TedliumOp::ReadSph(const Path &file_sph_path, double start_time, double e
   for (int32_t i = 0; i < size; ++i) {
     char bh = temp[2 * i];
     char bl = temp[2 * i + 1];
-    // SPH aduio files is big-endian, so we should convert the two bytes of data into int16_t based
+    // SPH audio files is big-endian, so we should convert the two bytes of data into int16_t based
     // on the high 8 bits and the low 8 bits.
     int16_t s = static_cast<int16_t>(((bh & 0x00FF) << 8) | (bl & 0x00FF));
     // Data normalization: Convert the data from the interval [-32768,32767] to the interval [-1,1].

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/ag_news_node.h

@@ -113,7 +113,6 @@ class AGNewsNode : public NonMappableSourceNode {
  private:
   std::string dataset_dir_;
   std::string usage_;
-  char field_delim_;
   std::vector<std::shared_ptr<CsvBase>> column_defaults_;
   std::vector<std::string> column_names_;
   int64_t num_samples_;

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/amazon_review_node.h

@@ -107,7 +107,6 @@ class AmazonReviewNode : public NonMappableSourceNode {
  private:
   std::string dataset_dir_;
   std::string usage_;
-  char field_delim_;
   std::vector<std::shared_ptr<CsvBase>> column_defaults_;
   std::vector<std::string> column_names_;
   int64_t num_samples_;

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/sogou_news_node.h

@@ -122,7 +122,6 @@ class SogouNewsNode : public NonMappableSourceNode {
  private:
   std::string dataset_dir_;
   std::string usage_;
-  char field_delim_;
   std::vector<std::shared_ptr<CsvBase>> column_defaults_;
   std::vector<std::string> column_names_;
   int64_t num_samples_;

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/yelp_review_node.h

@@ -122,7 +122,6 @@ class YelpReviewNode : public NonMappableSourceNode {
  private:
   std::string dataset_dir_;
   std::string usage_;
-  char field_delim_;
   std::vector<std::shared_ptr<CsvBase>> column_defaults_;
   std::vector<std::string> column_names_;
   int64_t num_samples_;

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/transfer_node.h

@@ -95,7 +95,6 @@ class TransferNode : public DatasetNode {
   std::string queue_name_;
   int32_t device_id_;
   std::string device_type_;
-  int32_t prefetch_size_;
   bool send_epoch_end_;
   int32_t total_batch_;
   bool create_data_info_queue_;

mindspore/ccsrc/minddata/dataset/engine/perf/auto_tune.cc

@@ -33,6 +33,7 @@ AutoTune::AutoTune(TreeAdapter *tree_adap, ProfilingManager *profiling_mgr)
       profiling_manager_(profiling_mgr),
       leaf_op_id_(-1),
       cur_epoch_(1),
+      mode_(0),
       skip_bool_(true),
       last_step_profiled_(0) {
   tree_modifier_ = std::make_unique<TreeModifier>(tree_adapter_);

mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.cc

@@ -101,9 +101,11 @@ static bool ResizeBilinear3C(const unsigned char *src, int src_width, int src_he
   int16_t *y_weight = reinterpret_cast<int16_t *>(x_weight + dst_width);
 
   if (!InitBilinearWeight(x_offset, x_weight, scale_width, dst_width, src_width, 3)) {
+    delete[] data_buf;
     return false;
   }
   if (!InitBilinearWeight(y_offset, y_weight, scale_height, dst_height, src_height, 1)) {
+    delete[] data_buf;
     return false;
   }
 
@@ -192,9 +194,11 @@ static bool ResizeBilinear1C(const unsigned char *src, int src_width, int src_he
   int16_t *y_weight = reinterpret_cast<int16_t *>(x_weight + dst_width);
 
   if (!InitBilinearWeight(x_offset, x_weight, scale_width, dst_width, src_width, 1)) {
+    delete[] data_buf;
     return false;
   }
   if (!InitBilinearWeight(y_offset, y_weight, scale_height, dst_height, src_height, 1)) {
+    delete[] data_buf;
     return false;
   }
 

mindspore/python/mindspore/dataset/engine/datasets_text.py

@@ -647,6 +647,7 @@ class EnWik9Dataset(SourceDataset):
         return cde.EnWik9Node(self.dataset_dir, self.num_samples, self.shuffle_flag, self.num_shards,
                               self.shard_id)
 
+
 class IMDBDataset(MappableDataset):
     """
     A source dataset for reading and parsing Internet Movie Database (IMDb).

mindspore/python/mindspore/dataset/engine/datasets_vision.py

@@ -2214,7 +2214,8 @@ class ImageFolderDataset(MappableDataset):
         >>> dataset = ds.ImageFolderDataset(dataset_dir=image_folder_dataset_dir,
         ...                                 class_indexing={"cat":0, "dog":1})
         >>>
-        >>> # 3) Read all samples (image files) in image_folder_dataset_dir with extensions .JPEG and .png (case sensitive)
+        >>> # 3) Read all samples (image files) in image_folder_dataset_dir with extensions .JPEG
+        >>> #    and .png (case sensitive)
         >>> dataset = ds.ImageFolderDataset(dataset_dir=image_folder_dataset_dir,
         ...                                 extensions=[".JPEG", ".png"])