!42242 Fix the problem of alarm clearing

Merge pull request !42242 from 刘勇琪/master

mindspore/ccsrc/minddata/dataset/core/data_type.cc

@@ -124,10 +124,11 @@ DataType::DataType(const std::string &type_str) {
 }
 
 std::string DataType::ToString() const {
-  if (type_ < DataType::NUM_OF_TYPES)
+  if (type_ < DataType::NUM_OF_TYPES) {
     return kTypeInfo[type_].name_;
-  else
+  } else {
     return "unknown";
+  }
 }
 
 #ifdef ENABLE_PYTHON

mindspore/ccsrc/minddata/dataset/engine/datasetops/batch_op.cc

@@ -300,7 +300,9 @@ Status BatchOp::MapColumns(std::pair<std::unique_ptr<TensorQTable>, CBatchInfo>
   TensorTable in_cols(in_col_names_.size(), TensorRow(num_rows, nullptr)), out_cols;
 
   std::unordered_map<std::string, size_t> in_col_name_id;  // name of columns that need to be fed to per-batch_map
-  for (size_t i = 0; i < in_col_names_.size(); i++) in_col_name_id.insert({in_col_names_[i], i});
+  for (size_t i = 0; i < in_col_names_.size(); i++) {
+    in_col_name_id.insert({in_col_names_[i], i});
+  }
 
   for (const auto &itr : child_map_) {
     auto col_itr = in_col_name_id.find(itr.first);

mindspore/ccsrc/minddata/dataset/engine/datasetops/build_vocab_op.cc

@@ -135,7 +135,9 @@ Status BuildVocabOp::CollectorThread() {
     RETURN_IF_NOT_OK(collector_queue_->PopFront(&wrkr_map));
     RETURN_UNEXPECTED_IF_NULL(wrkr_map);
     if (!wrkr_map->empty()) {
-      for (const auto &wd : *wrkr_map) word_cnt_[wd.first] += wd.second;
+      for (const auto &wd : *wrkr_map) {
+        word_cnt_[wd.first] += wd.second;
+      }
     } else {
       ++num_quited_worker;
     }

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

@@ -35,8 +35,8 @@ const int32_t kQMnistLabelFileMagicNumber = 3074;
 const size_t kQMnistImageRows = 28;
 const size_t kQMnistImageCols = 28;
 const size_t kQMnistLabelLength = 8;
-uint32_t kNum4 = 4;
-uint32_t kNum12 = 12;
+const uint32_t kNum4 = 4;
+const uint32_t kNum12 = 12;
 
 QMnistOp::QMnistOp(const std::string &folder_path, const std::string &usage, bool compat,
                    std::unique_ptr<DataSchema> data_schema, std::shared_ptr<SamplerRT> sampler, int32_t num_workers,

mindspore/ccsrc/minddata/dataset/engine/opt/post/auto_worker_pass.cc

@@ -73,7 +73,9 @@ Status AutoWorkerPass::RunOnTree(std::shared_ptr<DatasetNode> root_ir, bool *con
     int32_t cur_node_num_worker = std::max(std::min(num_workers, cur_node_max), min_num_workers_);
 
     // if the num_worker to set is same as original, skip setting and printing the logs
-    if (cur_node_num_worker == p.first->NumWorkers()) continue;
+    if (cur_node_num_worker == p.first->NumWorkers()) {
+      continue;
+    }
     // log the change via warning msg so user can see what the num_worker is being set for which op
     MS_LOG(WARNING) << "AutoNumWorker enabled, num_workers in " << p.first->Name() << " is auto-adjusted from "
                     << std::to_string(p.first->NumWorkers()) + " to " + std::to_string(cur_node_num_worker);

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_jpeg_op.cc

@@ -122,8 +122,12 @@ Status DvppDecodeResizeJpegOp::OutputShape(const std::vector<TensorShape> &input
   if (inputs.size() < 1) {
     RETURN_STATUS_UNEXPECTED("DvppDecodeResizeJpegOp::OutputShape inputs is null");
   }
-  if (inputs[0].Rank() == 1) outputs.emplace_back(out);
-  if (!outputs.empty()) return Status::OK();
+  if (inputs[0].Rank() == 1) {
+    outputs.emplace_back(out);
+  }
+  if (!outputs.empty()) {
+    return Status::OK();
+  }
   return Status(StatusCode::kMDUnexpectedError, "Input has a wrong shape");
 }
 

mindspore/ccsrc/minddata/dataset/kernels/image/image_utils.cc

@@ -776,10 +776,11 @@ Status MaskWithTensor(const std::shared_ptr<Tensor> &sub_mat, std::shared_ptr<Te
 
 Status CopyTensorValue(const std::shared_ptr<Tensor> &source_tensor, std::shared_ptr<Tensor> *dest_tensor,
                        const std::vector<int64_t> &source_indx, const std::vector<int64_t> &dest_indx) {
-  if (source_tensor->type() != (*dest_tensor)->type())
+  if (source_tensor->type() != (*dest_tensor)->type()) {
     RETURN_STATUS_UNEXPECTED(
       "CutMixBatch: CopyTensorValue failed: "
       "source and destination tensor must have the same type.");
+  }
   if (source_tensor->type() == DataType::DE_UINT8) {
     uint8_t pixel_value = 0;
     RETURN_IF_NOT_OK(source_tensor->GetItemAt(&pixel_value, source_indx));

mindspore/ccsrc/minddata/dataset/kernels/image/rand_augment_op.cc

@@ -26,7 +26,7 @@ RandAugmentOp::RandAugmentOp(int32_t num_ops, int32_t magnitude, int32_t num_mag
       magnitude_(magnitude),
       num_magnitude_bins_(num_magnitude_bins),
       interpolation_(interpolation),
-      fill_value_(std::move(fill_value)) {
+      fill_value_(fill_value) {
   rnd_.seed(GetSeed());
 }
 

mindspore/ccsrc/minddata/mindrecord/io/shard_reader.cc

@@ -1441,8 +1441,7 @@ void ShardReader::ConsumerByRow(int consumer_id) {
       if (interrupt_) {
         return;
       }
-      delivery_map_[sample_id_pos] =
-        std::make_shared<std::vector<std::tuple<std::vector<uint8_t>, json>>>(std::move(batch));
+      delivery_map_[sample_id_pos] = std::make_shared<std::vector<std::tuple<std::vector<uint8_t>, json>>>(batch);
     }
     cv_iterator_.notify_one();
   }