Modify alarm

mindspore/ccsrc/debug/debugger/debugger.cc

@@ -149,7 +149,7 @@ void Debugger::EnableDebugger() {
     last_overflow_bin_ = 0;
     DIR *d;
     d = opendir(overflow_bin_path_.c_str());
-    if (d) {
+    if (d != nullptr) {
       struct dirent *dir;
       while ((dir = readdir(d)) != NULL) {
         if (dir->d_type == DT_REG) {
@@ -718,9 +718,9 @@ std::vector<std::string> Debugger::CheckOpOverflow() {
   std::vector<double> bin_list;
   std::vector<std::string> op_names;
   DIR *d;
-  struct dirent *dir;
+  struct dirent *dir = nullptr;
   d = opendir(overflow_bin_path_.c_str());
-  if (d) {
+  if (d != nullptr) {
     while ((dir = readdir(d)) != NULL) {
       if (dir->d_type == DT_REG) {
         std::string file_path = overflow_bin_path_;

mindspore/ccsrc/minddata/dataset/api/datasets.cc

@@ -609,6 +609,7 @@ Status ComputeShuffleSize(int64_t num_files, int64_t num_devices, int64_t num_ro
   }
 
   // get the average per file
+  CHECK_FAIL_RETURN_UNEXPECTED(num_files != 0, "The size of dataset_files must greater than 0.");
   avg_rows_per_file = num_rows / num_files;
 
   *shuffle_size = std::max(avg_rows_per_file * average_files_multiplier, shuffle_max);

mindspore/ccsrc/minddata/dataset/engine/gnn/graph_loader.h

@@ -35,6 +35,7 @@
 #include "minddata/dataset/engine/gnn/node.h"
 #include "minddata/dataset/util/status.h"
 #include "minddata/mindrecord/include/shard_reader.h"
+#include "minddata/dataset/engine/gnn/graph_data_impl.h"
 namespace mindspore {
 namespace dataset {
 namespace gnn {
@@ -49,8 +50,6 @@ using EdgeFeatureMap = std::unordered_map<EdgeType, std::unordered_set<FeatureTy
 using DefaultNodeFeatureMap = std::unordered_map<FeatureType, std::shared_ptr<Feature>>;
 using DefaultEdgeFeatureMap = std::unordered_map<FeatureType, std::shared_ptr<Feature>>;
 
-class GraphDataImpl;
-
 // this class interfaces with the underlying storage format (mindrecord)
 // it returns raw nodes and edges via GetNodesAndEdges
 // it is then the responsibility of graph to construct itself based on the nodes and edges

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

@@ -32,7 +32,7 @@ CutMixBatchOp::CutMixBatchOp(ImageBatchFormat image_batch_format, float alpha, f
 }
 
 void CutMixBatchOp::GetCropBox(int height, int width, float lam, int *x, int *y, int *crop_width, int *crop_height) {
-  float cut_ratio = 1 - lam;
+  const float cut_ratio = 1 - lam;
   int cut_w = static_cast<int>(width * cut_ratio);
   int cut_h = static_cast<int>(height * cut_ratio);
   std::uniform_int_distribution<int> width_uniform_distribution(0, width);
@@ -116,7 +116,6 @@ Status CutMixBatchOp::Compute(const TensorRow &input, TensorRow *output) {
         RETURN_IF_NOT_OK(Crop(rand_image, &cropped, x, y, crop_width, crop_height));
         RETURN_IF_NOT_OK(MaskWithTensor(cropped, &images[i], x, y, crop_width, crop_height, ImageFormat::HWC));
         label_lam = 1 - (crop_width * crop_height / static_cast<float>(image_shape[1] * image_shape[2]));
-
       } else {
         // NCHW Format
         GetCropBox(static_cast<int32_t>(image_shape[2]), static_cast<int32_t>(image_shape[3]), lam, &x, &y, &crop_width,

mindspore/ccsrc/minddata/dataset/kernels/image/random_color_op.h

@@ -39,6 +39,8 @@ namespace dataset {
 class RandomColorOp : public TensorOp {
  public:
   RandomColorOp() = default;
+
+  ~RandomColorOp() = default;
   /// \brief Constructor
   /// \param[in] t_lb lower bound for the random weights
   /// \param[in] t_ub upper bound for the random weights