fix asan issue

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

@@ -232,7 +232,8 @@ std::vector<dsize_t> TensorShape::Strides() const { return std::vector<dsize_t>{
 Status TensorShape::ToFlatIndex(const std::vector<dsize_t> &index, dsize_t *flat_index) const {
   *flat_index = 0;
   for (size_t k = 0; k < index.size(); k++) {
-    *flat_index += index[k] * strides_[k + 1];  // skip the first element of strides_ which is numOfElements
+    *flat_index +=
+      (index[k] == 0) ? 0 : index[k] * strides_[k + 1];  // skip the first element of strides_ which is numOfElements
   }
   CHECK_FAIL_RETURN_UNEXPECTED(*flat_index < NumOfElements(), "Not a valid index");
   return Status::OK();

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

@@ -394,8 +394,10 @@ Status Crop(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *outpu
   }
   try {
     TensorShape shape{h, w};
-    int num_channels = input_cv->shape()[2];
+    if (input_cv->Rank() == 3) {
-    if (input_cv->Rank() == 3) shape = shape.AppendDim(num_channels);
+      int num_channels = input_cv->shape()[2];
+      shape = shape.AppendDim(num_channels);
+    }
     std::shared_ptr<CVTensor> output_cv;
     RETURN_IF_NOT_OK(CVTensor::CreateEmpty(shape, input_cv->type(), &output_cv));
     cv::Rect roi(x, y, w, h);

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

@@ -1581,13 +1581,11 @@ bool GetPerspectiveTransform(std::vector<Point> src_point, std::vector<Point> ds
     n[i + 4] = dst_point[i].y;
   }
 
-  double x[9] = {0};
+  M.Init(3, 3, LDataType(LDataType::DOUBLE));
-  LiteMat dst(1, 8, x, LDataType(LDataType::DOUBLE));
+  LiteMat dst(1, 8, M.data_ptr_, LDataType(LDataType::DOUBLE));
 
   GetPerspectiveTransformImpl(src1, src2, dst);
   dst.ptr<double>(8)[0] = 1;
-  M.Init(3, 3, dst.data_ptr_, dst.data_type_);
-
   return true;
 }
 

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

@@ -1129,12 +1129,12 @@ Status RandomRotationOperation::ValidateParams() {
     MS_LOG(ERROR) << "RandomRotation: degrees must be a vector of one or two values, got: " << degrees_;
     RETURN_STATUS_SYNTAX_ERROR(err_msg);
   }
-  if ((degrees_[1] < degrees_[0]) && (degrees_.size() == 2)) {
+  if ((degrees_.size() == 2) && (degrees_[1] < degrees_[0])) {
     std::string err_msg = "RandomRotation: degrees must be in the format of (min, max), got: (" +
                           std::to_string(degrees_[0]) + ", " + std::to_string(degrees_[1]) + ")";
     MS_LOG(ERROR) << err_msg;
     RETURN_STATUS_SYNTAX_ERROR(err_msg);
-  } else if ((degrees_[0] < 0) && degrees_.size() == 1) {
+  } else if ((degrees_.size() == 1) && (degrees_[0] < 0)) {
     std::string err_msg =
       "RandomRotation: if degrees only has one value, it must be greater than or equal to 0, got: " +
       std::to_string(degrees_[0]);

tests/ut/cpp/dataset/image_process_test.cc

@@ -239,8 +239,7 @@ TEST_F(MindDataImageProcess, testNV21ToBGR) {
   bool ret = ReadYUV(filename, w, h, &yuv_data);
   ASSERT_TRUE(ret == true);
 
-  cv::Mat yuvimg(h * 3 / 2, w, CV_8UC1);
+  cv::Mat yuvimg(h * 3 / 2, w, CV_8UC1, yuv_data);
-  memcpy(yuvimg.data, yuv_data, w * h * 3 / 2);
   cv::Mat rgbimage;
 
   cv::cvtColor(yuvimg, rgbimage, cv::COLOR_YUV2BGR_NV21);
@@ -250,6 +249,7 @@ TEST_F(MindDataImageProcess, testNV21ToBGR) {
   ret = InitFromPixel(yuv_data, LPixelType::NV212BGR, LDataType::UINT8, w, h, lite_mat_bgr);
   ASSERT_TRUE(ret == true);
   cv::Mat dst_image(lite_mat_bgr.height_, lite_mat_bgr.width_, CV_8UC3, lite_mat_bgr.data_ptr_);
+  free(yuv_data);
 }
 
 TEST_F(MindDataImageProcess, testNV12ToBGR) {
@@ -270,6 +270,7 @@ TEST_F(MindDataImageProcess, testNV12ToBGR) {
   ret = InitFromPixel(yuv_data, LPixelType::NV122BGR, LDataType::UINT8, w, h, lite_mat_bgr);
   ASSERT_TRUE(ret == true);
   cv::Mat dst_image(lite_mat_bgr.height_, lite_mat_bgr.width_, CV_8UC3, lite_mat_bgr.data_ptr_);
+  free(yuv_data);
 }
 
 TEST_F(MindDataImageProcess, testExtractChannel) {

tests/ut/cpp/dataset/optimization_pass_test.cc

@@ -93,7 +93,7 @@ TEST_F(MindDataTestOptimizationPass, MindDataTestTensorFusionPassPreBuiltTensorO
   // make prebuilt tensor operation
   auto decode = std::make_shared<transforms::PreBuiltOperation>(vision::DecodeOperation(true).Build());
   auto resize = std::make_shared<transforms::PreBuiltOperation>(
-    vision::RandomResizedCropOperation({100}, {0.5}, {0.1}, InterpolationMode::kNearestNeighbour, 5).Build());
+    vision::RandomResizedCropOperation({100, 100}, {0.5, 1.0}, {0.1, 0.2}, InterpolationMode::kNearestNeighbour, 5).Build());
   std::vector<std::shared_ptr<TensorOperation>> op_list = {decode, resize};
   std::vector<std::string> op_name = {"image"};
   std::shared_ptr<DatasetNode> root = ImageFolder(folder_path, false)->IRNode();