fix code review

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

@@ -34,7 +34,7 @@ Status AmplitudeToDBOp::Compute(const std::shared_ptr<Tensor> &input, std::share
 
   float top_db = top_db_;
   float multiplier = stype_ == ScaleType::kPower ? 10.0 : 20.0;
-  float amin = 1e-10;
+  const float amin = 1e-10;
   float db_multiplier = std::log10(std::max(amin_, ref_value_));
 
   // typecast

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

@@ -481,6 +481,7 @@ Status Decoding(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *o
 
   while (itr != end) {
     auto x_mu = *itr;
+    CHECK_FAIL_RETURN_SYNTAX_ERROR(mu != 0, "mu can not be zero.");
     x_mu = ((x_mu) / mu) * 2 - 1.0;
     x_mu = sgn(x_mu) * expm1(fabs(x_mu) * log1p(mu)) / mu;
     *itr_out = x_mu;

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

@@ -50,6 +50,7 @@ VOCOp::VOCOp(const TaskType &task_type, const std::string &task_mode, const std:
              std::unique_ptr<DataSchema> data_schema, std::shared_ptr<SamplerRT> sampler, bool extra_metadata)
     : MappableLeafOp(num_workers, queue_size, std::move(sampler)),
       decode_(decode),
+      row_cnt_(0),
       task_type_(task_type),
       usage_(task_mode),
       folder_path_(folder_path),

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/div2k_node.cc

@@ -126,7 +126,6 @@ Status DIV2KNode::GetDatasetSize(const std::shared_ptr<DatasetSizeGetter> &size_
   std::shared_ptr<SamplerRT> sampler_rt = nullptr;
   RETURN_IF_NOT_OK(sampler_->SamplerBuild(&sampler_rt));
   sample_size = sampler_rt->CalculateNumSamples(num_rows);
-
   if (sample_size == -1) {
     RETURN_IF_NOT_OK(size_getter->DryRun(shared_from_this(), &sample_size));
   }

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

@@ -41,7 +41,7 @@ Status CenterCropOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_p
                    ", crop width: " + std::to_string(crop_wid_) + "\t"
                : "";
 
-  if (err_msg.length() != 0) RETURN_STATUS_UNEXPECTED(err_head + err_msg);
+  CHECK_FAIL_RETURN_SYNTAX_ERROR(err_msg.length() == 0, err_head + err_msg);
 
   int32_t top = crop_het_ - input->shape()[0];  // number of pixels to pad (top and bottom)
   int32_t left = crop_wid_ - input->shape()[1];

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

@@ -37,6 +37,7 @@ LiteMat::LiteMat() {
   data_type_ = LDataType::UINT8;
   ref_count_ = nullptr;
   setSteps(0, 0, 0);
+  release_flag = false;
 }
 
 LiteMat::LiteMat(int width, LDataType data_type) {
@@ -51,6 +52,7 @@ LiteMat::LiteMat(int width, LDataType data_type) {
   ref_count_ = nullptr;
   size_ = 0;
   setSteps(0, 0, 0);
+  release_flag = false;
   Init(width, data_type);
 }
 
@@ -66,6 +68,7 @@ LiteMat::LiteMat(int width, int height, LDataType data_type) {
   ref_count_ = nullptr;
   size_ = 0;
   setSteps(0, 0, 0);
+  release_flag = false;
   Init(width, height, data_type);
 }
 
@@ -81,6 +84,7 @@ LiteMat::LiteMat(int width, int height, void *p_data, LDataType data_type) {
   ref_count_ = nullptr;
   size_ = 0;
   setSteps(0, 0, 0);
+  release_flag = false;
   Init(width, height, p_data, data_type);
 }
 
@@ -96,6 +100,7 @@ LiteMat::LiteMat(int width, int height, int channel, LDataType data_type) {
   ref_count_ = nullptr;
   size_ = 0;
   setSteps(0, 0, 0);
+  release_flag = false;
   Init(width, height, channel, data_type);
 }
 
@@ -111,6 +116,7 @@ LiteMat::LiteMat(int width, int height, int channel, void *p_data, LDataType dat
   ref_count_ = nullptr;
   size_ = 0;
   setSteps(0, 0, 0);
+  release_flag = false;
   Init(width, height, channel, p_data, data_type);
 }
 
@@ -133,6 +139,7 @@ LiteMat::LiteMat(const LiteMat &m) {
   data_type_ = m.data_type_;
   ref_count_ = m.ref_count_;
   size_ = m.size_;
+  release_flag = m.release_flag;
   setSteps(m.steps_[0], m.steps_[1], m.steps_[2]);
   if (ref_count_) {
     addRef(ref_count_, 1);

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

@@ -34,7 +34,7 @@ static const std::vector<uint8_t> _clip8_table = []() {
 
 static const uint8_t *clip8_table = &_clip8_table[640];
 
-static inline uint8_t clip8(int input) { return clip8_table[input >> PrecisionBits]; }
+static inline uint8_t clip8(unsigned int input) { return clip8_table[input >> PrecisionBits]; }
 
 static inline double cubic_interp(double x) {
   double a = -0.5;
@@ -60,6 +60,10 @@ int calc_coeff(int input_size, int out_size, int input0, int input1, struct inte
   double threshold, scale, interp_scale;
   int kernel_size;
 
+  if (out_size == 0) {
+    MS_LOG(ERROR) << "out_size can not be zero.";
+    return 0;
+  }
   scale = static_cast<double>((input1 - input0)) / out_size;
   if (scale < 1.0) {
     interp_scale = 1.0;
@@ -133,7 +137,7 @@ void normalize_coeff(int out_size, int kernel_size, const std::vector<double> &p
 Status ImagingHorizontalInterp(LiteMat &output, LiteMat input, int offset, int kernel_size,
                                const std::vector<int> &regions, const std::vector<double> &prekk) {
   int ss0, ss1, ss2;
-  int32_t *k;
+  int32_t *k = nullptr;
 
   // normalize previous calculated coefficients
   std::vector<int> kk(prekk.begin(), prekk.end());
@@ -202,7 +206,7 @@ Status ImagingVerticalInterp(LiteMat &output, LiteMat input, int offset, int ker
 }
 
 bool ImageInterpolation(LiteMat input, LiteMat &output, int x_size, int y_size, struct interpolation *interp,
-                        int rect[4]) {
+                        const int rect[4]) {
   int horizontal_interp, vertical_interp, horiz_kernel, vert_kernel, rect_y0, rect_y1;
   std::vector<int> horiz_region, vert_region;
   std::vector<double> horiz_coeff, vert_coeff;
@@ -269,7 +273,11 @@ bool ResizeCubic(const LiteMat &input, LiteMat &dst, int dst_w, int dst_h) {
   struct interpolation interp = {cubic_interp, 2.0};
   bool res = ImageInterpolation(input, output, x_size, y_size, &interp, rect);
 
-  memcpy_s(dst.data_ptr_, output.size_, output.data_ptr_, output.size_);
+  auto ret_code = memcpy_s(dst.data_ptr_, output.size_, output.data_ptr_, output.size_);
+  if (ret_code != 0) {
+    MS_LOG(ERROR) << "memcpy_s failed when copying tensor.";
+    return false;
+  }
   return res;
 }
 }  // namespace dataset

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

@@ -47,7 +47,7 @@ Status ImagingVerticalInterp(LiteMat &output, LiteMat input, int offset, int ker
 
 /// \brief Mainly logic of Cubic interpolation
 bool ImageInterpolation(LiteMat input, LiteMat &output, int x_size, int y_size, struct interpolation *interp,
-                        int rect[4]);
+                        const int rect[4]);
 
 /// \brief Apply cubic interpolation on input image and obtain the output image
 /// \param[in] input Input image

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

@@ -31,11 +31,20 @@ const uint8_t RotateOp::kDefFillR = 0;
 const uint8_t RotateOp::kDefFillG = 0;
 const uint8_t RotateOp::kDefFillB = 0;
 
-RotateOp::RotateOp(int angle_id) : angle_id_(angle_id) {}
+RotateOp::RotateOp(int angle_id)
+    : angle_id_(angle_id),
+      degrees_(0),
+      center_({}),
+      interpolation_(InterpolationMode::kLinear),
+      expand_(false),
+      fill_r_(0),
+      fill_g_(0),
+      fill_b_(0) {}
 
 RotateOp::RotateOp(float degrees, InterpolationMode resample, bool expand, std::vector<float> center, uint8_t fill_r,
                    uint8_t fill_g, uint8_t fill_b)
-    : degrees_(degrees),
+    : angle_id_(0),
+      degrees_(degrees),
       center_(center),
       interpolation_(resample),
       expand_(expand),

mindspore/ccsrc/minddata/dataset/kernels/ir/vision/rotate_ir.cc

@@ -22,11 +22,22 @@ namespace mindspore {
 namespace dataset {
 namespace vision {
 // RotateOperation
-RotateOperation::RotateOperation(FixRotationAngle angle) : angle_id_(static_cast<uint64_t>(angle)) {}
+RotateOperation::RotateOperation(FixRotationAngle angle)
+    : angle_id_(static_cast<uint64_t>(angle)),
+      degrees_(0),
+      interpolation_mode_(InterpolationMode::kLinear),
+      expand_(false),
+      center_({}),
+      fill_value_({}) {}
 
 RotateOperation::RotateOperation(float degrees, InterpolationMode resample, bool expand, std::vector<float> center,
                                  std::vector<uint8_t> fill_value)
-    : degrees_(degrees), interpolation_mode_(resample), expand_(expand), center_(center), fill_value_(fill_value) {}
+    : angle_id_(0),
+      degrees_(degrees),
+      interpolation_mode_(resample),
+      expand_(expand),
+      center_(center),
+      fill_value_(fill_value) {}
 
 RotateOperation::~RotateOperation() = default;
 

mindspore/ccsrc/minddata/dataset/kernels/plugin_op.cc

@@ -45,8 +45,8 @@ Status PluginOp::TensorRowToPlugin(const TensorRow &in_row, std::vector<plugin::
         int ret_code = memcpy_s(tensor.buffer_.data(), tensor.buffer_.size(), in_row[ind]->GetBuffer(), buffer_size);
         CHECK_FAIL_RETURN_UNEXPECTED(ret_code == 0, "Failed to copy data into plugin tensor.");
       } else {
-        auto ret_code = std::memcpy(tensor.buffer_.data(), in_row[ind]->GetBuffer(), buffer_size);
-        CHECK_FAIL_RETURN_UNEXPECTED(ret_code == tensor.buffer_.data(), "Failed to copy data into plugin tensor.");
+        int ret_code = memcpy_s(tensor.buffer_.data(), buffer_size, in_row[ind]->GetBuffer(), buffer_size);
+        CHECK_FAIL_RETURN_UNEXPECTED(ret_code == 0, "Failed to copy data into plugin tensor.");
       }
     } else {  // string tensor, for now, only tensor with 1 string is supported!
       CHECK_FAIL_RETURN_UNEXPECTED(in_row[ind]->shape().NumOfElements() == 1,

mindspore/ccsrc/minddata/dataset/util/btree.h

@@ -316,7 +316,7 @@ class BPlusTree {
 
   void Init() {
     typename LeafNode::alloc_type alloc(alloc_);
-    LeafNode *p;
+    LeafNode *p = nullptr;
     try {
       p = alloc.allocate(1);
     } catch (std::bad_alloc &e) {

mindspore/dataset/engine/datasets.py

@@ -3693,7 +3693,7 @@ class SamplerFn:
         if multi_process is True:
             try:
                 self.eof = multiprocessing.Event()
-            except:
+            except Exception:
                 raise RuntimeError("Init multiprocessing.Event() failed, This might be caused by insufficient shm,"
                                    + " and the recommended shm size is at least 5 GB.")
         else:

mindspore/dataset/vision/py_transforms_util.py

@@ -581,7 +581,7 @@ def to_type(img, output_type):
 
     try:
         return img.astype(output_type)
-    except:
+    except Exception:
         raise RuntimeError("output_type: " + str(output_type) + " is not a valid datatype.")