dataset: Specify Sampler and TensorTransform C++ API classes as final

mindspore/ccsrc/minddata/dataset/include/samplers.h

@@ -68,7 +68,7 @@ class Sampler : std::enable_shared_from_this<Sampler> {
 
 /// \brief A class to represent a Distributed Sampler in the data pipeline.
 /// \notes A Sampler that accesses a shard of the dataset.
-class DistributedSampler : public Sampler {
+class DistributedSampler final : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -104,7 +104,7 @@ class DistributedSampler : public Sampler {
 /// \brief A class to represent a PK Sampler in the data pipeline.
 /// \notes Samples K elements for each P class in the dataset.
 ///        This will sample all classes.
-class PKSampler : public Sampler {
+class PKSampler final : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -130,7 +130,7 @@ class PKSampler : public Sampler {
 
 /// \brief A class to represent a Random Sampler in the data pipeline.
 /// \notes Samples the elements randomly.
-class RandomSampler : public Sampler {
+class RandomSampler final : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -154,7 +154,7 @@ class RandomSampler : public Sampler {
 
 /// \brief A class to represent a Sequential Sampler in the data pipeline.
 /// \notes Samples the dataset elements sequentially, same as not having a sampler.
-class SequentialSampler : public Sampler {
+class SequentialSampler final : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -201,7 +201,7 @@ class SubsetSampler : public Sampler {
 
 /// \brief A class to represent a Subset Random Sampler in the data pipeline.
 /// \notes Samples the elements randomly from a sequence of indices.
-class SubsetRandomSampler : public SubsetSampler {
+class SubsetRandomSampler final : public SubsetSampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -222,7 +222,7 @@ class SubsetRandomSampler : public SubsetSampler {
 /// \brief A class to represent a Weighted Random Sampler in the data pipeline.
 /// \notes Samples the elements from [0, len(weights) - 1] randomly with the given
 ///        weights (probabilities).
-class WeightedRandomSampler : public Sampler {
+class WeightedRandomSampler final : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:

mindspore/ccsrc/minddata/dataset/include/text.h

@@ -41,7 +41,7 @@ namespace text {
 #ifndef _WIN32
 /// \brief Tokenize a scalar tensor of UTF-8 string by specific rules.
 /// \notes BasicTokenizer is not supported on Windows platform yet.
-class BasicTokenizer : public TensorTransform {
+class BasicTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] lower_case If true, apply CaseFold, NormalizeUTF8(NFD mode), RegexReplace operation on input text to
@@ -72,7 +72,7 @@ class BasicTokenizer : public TensorTransform {
 
 /// \brief Tokenizer used for Bert text process.
 /// \notes BertTokenizer is not supported on Windows platform yet.
-class BertTokenizer : public TensorTransform {
+class BertTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] vocab A Vocab object.
@@ -117,7 +117,7 @@ class BertTokenizer : public TensorTransform {
 
 /// \brief Apply case fold operation on UTF-8 string tensor.
 /// \return Shared pointer to the current TensorOperation.
-class CaseFold : public TensorTransform {
+class CaseFold final : public TensorTransform {
  public:
   /// \brief Constructor.
   CaseFold();
@@ -134,7 +134,7 @@ class CaseFold : public TensorTransform {
 
 /// \brief Tokenize Chinese string into words based on dictionary.
 /// \notes The integrity of the HMMSEgment algorithm and MPSegment algorithm files must be confirmed.
-class JiebaTokenizer : public TensorTransform {
+class JiebaTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] hmm_path Dictionary file is used by HMMSegment algorithm. The dictionary can be obtained on the
@@ -169,7 +169,7 @@ class JiebaTokenizer : public TensorTransform {
 };
 
 /// \brief Look up a word into an id according to the input vocabulary table.
-class Lookup : public TensorTransform {
+class Lookup final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] vocab a Vocab object.
@@ -198,7 +198,7 @@ class Lookup : public TensorTransform {
 };
 
 /// \brief TensorOp to generate n-gram from a 1-D string Tensor.
-class Ngram : public TensorTransform {
+class Ngram final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] ngrams ngrams is a vector of positive integers. For example, if ngrams={4, 3}, then the result
@@ -231,7 +231,7 @@ class Ngram : public TensorTransform {
 
 #ifndef _WIN32
 /// \brief Apply normalize operation on UTF-8 string tensor.
-class NormalizeUTF8 : public TensorTransform {
+class NormalizeUTF8 final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] normalize_form Valid values can be any of [NormalizeForm::kNone,NormalizeForm::kNfc,
@@ -259,7 +259,7 @@ class NormalizeUTF8 : public TensorTransform {
 };
 
 /// \brief Replace UTF-8 string tensor with 'replace' according to regular expression 'pattern'.
-class RegexReplace : public TensorTransform {
+class RegexReplace final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] pattern The regex expression patterns.
@@ -285,7 +285,7 @@ class RegexReplace : public TensorTransform {
 };
 
 /// \brief Tokenize a scalar tensor of UTF-8 string by regex expression pattern.
-class RegexTokenizer : public TensorTransform {
+class RegexTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] delim_pattern The pattern of regex delimiters.
@@ -314,7 +314,7 @@ class RegexTokenizer : public TensorTransform {
 #endif
 
 /// \brief Tokenize scalar token or 1-D tokens to tokens by sentencepiece.
-class SentencePieceTokenizer : public TensorTransform {
+class SentencePieceTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] vocab a SentencePieceVocab object.
@@ -345,7 +345,7 @@ class SentencePieceTokenizer : public TensorTransform {
 
 /// \brief TensorOp to construct a tensor from data (only 1-D for now), where each element in the dimension
 ///   axis is a slice of data starting at the corresponding position, with a specified width.
-class SlidingWindow : public TensorTransform {
+class SlidingWindow final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] width The width of the window. It must be an integer and greater than zero.
@@ -371,7 +371,7 @@ class SlidingWindow : public TensorTransform {
 ///   https://en.cppreference.com/w/cpp/string/basic_string/stof,
 ///   https://en.cppreference.com/w/cpp/string/basic_string/stoul,
 ///   except that any strings which represent negative numbers cannot be cast to an unsigned integer type.
-class ToNumber : public TensorTransform {
+class ToNumber final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] data_type of the tensor to be casted to. Must be a numeric type.
@@ -393,7 +393,7 @@ class ToNumber : public TensorTransform {
 };
 
 /// \brief Truncate a pair of rank-1 tensors such that the total length is less than max_length.
-class TruncateSequencePair : public TensorTransform {
+class TruncateSequencePair final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] max_length Maximum length required.
@@ -413,7 +413,7 @@ class TruncateSequencePair : public TensorTransform {
 };
 
 /// \brief Tokenize a scalar tensor of UTF-8 string to Unicode characters.
-class UnicodeCharTokenizer : public TensorTransform {
+class UnicodeCharTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] with_offsets If or not output offsets of tokens (default=false).
@@ -434,7 +434,7 @@ class UnicodeCharTokenizer : public TensorTransform {
 
 #ifndef _WIN32
 /// \brief Tokenize a scalar tensor of UTF-8 string on Unicode script boundaries.
-class UnicodeScriptTokenizer : public TensorTransform {
+class UnicodeScriptTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] keep_whitespace If or not emit whitespace tokens (default=false).
@@ -455,7 +455,7 @@ class UnicodeScriptTokenizer : public TensorTransform {
 };
 
 /// \brief Tokenize a scalar tensor of UTF-8 string on ICU4C defined whitespaces.
-class WhitespaceTokenizer : public TensorTransform {
+class WhitespaceTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] with_offsets If or not output offsets of tokens (default=false).

mindspore/ccsrc/minddata/dataset/include/transforms.h

@@ -80,7 +80,7 @@ namespace transforms {
 
 /// \brief Compose Op.
 /// \notes Compose a list of transforms into a single transform.
-class Compose : public TensorTransform {
+class Compose final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] transforms A vector of raw pointers to TensorTransform objects to be applied.
@@ -108,7 +108,7 @@ class Compose : public TensorTransform {
 /// \brief Duplicate Op.
 /// \notes Duplicate the input tensor to a new output tensor.
 ///     The input tensor is carried over to the output list.
-class Duplicate : public TensorTransform {
+class Duplicate final : public TensorTransform {
  public:
   /// \brief Constructor.
   Duplicate();
@@ -124,7 +124,7 @@ class Duplicate : public TensorTransform {
 
 /// \brief OneHot Op.
 /// \notes Convert the labels into OneHot format.
-class OneHot : public TensorTransform {
+class OneHot final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] num_classes number of classes.
@@ -145,7 +145,7 @@ class OneHot : public TensorTransform {
 
 /// \brief RandomApply Op.
 /// \notes Randomly perform a series of transforms with a given probability.
-class RandomApply : public TensorTransform {
+class RandomApply final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] transforms A vector of raw pointers to TensorTransform objects to be applied.
@@ -175,7 +175,7 @@ class RandomApply : public TensorTransform {
 
 /// \brief RandomChoice Op.
 /// \notes Randomly selects one transform from a list of transforms to perform operation.
-class RandomChoice : public TensorTransform {
+class RandomChoice final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] transforms A vector of raw pointers to TensorTransform objects to be applied.
@@ -202,7 +202,7 @@ class RandomChoice : public TensorTransform {
 
 /// \brief TypeCast Op.
 /// \notes Tensor operation to cast to a given MindSpore data type.
-class TypeCast : public TensorTransform {
+class TypeCast final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] data_type mindspore.dtype to be cast to.
@@ -226,7 +226,7 @@ class TypeCast : public TensorTransform {
 /// \brief Unique Op.
 /// \notes Return an output tensor containing all the unique elements of the input tensor in
 ///     the same order that they occur in the input tensor.
-class Unique : public TensorTransform {
+class Unique final : public TensorTransform {
  public:
   /// \brief Constructor.
   Unique();

mindspore/ccsrc/minddata/dataset/include/vision.h

@@ -38,7 +38,7 @@ class TensorOperation;
 namespace vision {
 /// \brief AutoContrast TensorTransform.
 /// \notes Apply automatic contrast on input image.
-class AutoContrast : public TensorTransform {
+class AutoContrast final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] cutoff Percent of pixels to cut off from the histogram, the valid range of cutoff value is 0 to 100.
@@ -60,7 +60,7 @@ class AutoContrast : public TensorTransform {
 
 /// \brief BoundingBoxAugment TensorTransform.
 /// \notes  Apply a given image transform on a random selection of bounding box regions of a given image.
-class BoundingBoxAugment : public TensorTransform {
+class BoundingBoxAugment final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] transform Raw pointer to a TensorTransform operation.
@@ -93,7 +93,7 @@ class BoundingBoxAugment : public TensorTransform {
 /// \brief Constructor to apply CutMix on a batch of images
 /// \notes Masks a random section of each image with the corresponding part of another randomly
 ///     selected image in that batch
-class CutMixBatch : public TensorTransform {
+class CutMixBatch final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] image_batch_format The format of the batch
@@ -116,7 +116,7 @@ class CutMixBatch : public TensorTransform {
 
 /// \brief CutOut TensorOp
 /// \notes Randomly cut (mask) out a given number of square patches from the input image
-class CutOut : public TensorTransform {
+class CutOut final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] length Integer representing the side length of each square patch
@@ -138,7 +138,7 @@ class CutOut : public TensorTransform {
 
 /// \brief Equalize TensorTransform.
 /// \notes Apply histogram equalization on input image.
-class Equalize : public TensorTransform {
+class Equalize final : public TensorTransform {
  public:
   /// \brief Constructor.
   Equalize();
@@ -154,7 +154,7 @@ class Equalize : public TensorTransform {
 
 /// \brief HwcToChw TensorTransform.
 /// \notes Transpose the input image; shape (H, W, C) to shape (C, H, W).
-class HWC2CHW : public TensorTransform {
+class HWC2CHW final : public TensorTransform {
  public:
   /// \brief Constructor.
   HWC2CHW();
@@ -170,7 +170,7 @@ class HWC2CHW : public TensorTransform {
 
 /// \brief Invert TensorTransform.
 /// \notes Apply invert on input image in RGB mode.
-class Invert : public TensorTransform {
+class Invert final : public TensorTransform {
  public:
   /// \brief Constructor.
   Invert();
@@ -187,7 +187,7 @@ class Invert : public TensorTransform {
 /// \brief MixUpBatch TensorTransform.
 /// \notes Apply MixUp transformation on an input batch of images and labels. The labels must be in
 ///     one-hot format and Batch must be called before calling this function.
-class MixUpBatch : public TensorTransform {
+class MixUpBatch final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] alpha hyperparameter of beta distribution (default = 1.0)
@@ -209,7 +209,7 @@ class MixUpBatch : public TensorTransform {
 /// \brief NormalizePad TensorTransform.
 /// \notes Normalize the input image with respect to mean and standard deviation and pad an extra
 ///     channel with value zero.
-class NormalizePad : public TensorTransform {
+class NormalizePad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] mean A vector of mean values for each channel, w.r.t channel order.
@@ -239,7 +239,7 @@ class NormalizePad : public TensorTransform {
 
 /// \brief Pad TensorOp
 /// \notes Pads the image according to padding parameters
-class Pad : public TensorTransform {
+class Pad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] padding A vector representing the number of pixels to pad the image
@@ -277,7 +277,7 @@ class Pad : public TensorTransform {
 /// \brief Blends an image with its grayscale version with random weights
 ///        t and 1 - t generated from a given range. If the range is trivial
 ///        then the weights are determinate and t equals the bound of the interval
-class RandomColor : public TensorTransform {
+class RandomColor final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] t_lb Lower bound on the range of random weights
@@ -299,7 +299,7 @@ class RandomColor : public TensorTransform {
 
 /// \brief RandomColorAdjust TensorTransform.
 /// \brief Randomly adjust the brightness, contrast, saturation, and hue of the input image
-class RandomColorAdjust : public TensorTransform {
+class RandomColorAdjust final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] brightness Brightness adjustment factor. Must be a vector of one or two values
@@ -329,7 +329,7 @@ class RandomColorAdjust : public TensorTransform {
 
 /// \brief RandomCrop TensorTransform.
 /// \notes Crop the input image at a random location.
-class RandomCrop : public TensorTransform {
+class RandomCrop final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -365,7 +365,7 @@ class RandomCrop : public TensorTransform {
 
 /// \brief RandomCropDecodeResize TensorTransform.
 /// \notes Equivalent to RandomResizedCrop, but crops before decodes.
-class RandomCropDecodeResize : public TensorTransform {
+class RandomCropDecodeResize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -398,7 +398,7 @@ class RandomCropDecodeResize : public TensorTransform {
 
 /// \brief RandomCropWithBBox TensorTransform.
 /// \notes Crop the input image at a random location and adjust bounding boxes accordingly.
-class RandomCropWithBBox : public TensorTransform {
+class RandomCropWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -436,7 +436,7 @@ class RandomCropWithBBox : public TensorTransform {
 
 /// \brief RandomHorizontalFlip TensorTransform.
 /// \notes Tensor operation to perform random horizontal flip.
-class RandomHorizontalFlip : public TensorTransform {
+class RandomHorizontalFlip final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] prob A float representing the probability of flip.
@@ -457,7 +457,7 @@ class RandomHorizontalFlip : public TensorTransform {
 
 /// \brief RandomHorizontalFlipWithBBox TensorTransform.
 /// \notes Flip the input image horizontally, randomly with a given probability and adjust bounding boxes accordingly.
-class RandomHorizontalFlipWithBBox : public TensorTransform {
+class RandomHorizontalFlipWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] prob A float representing the probability of flip.
@@ -478,7 +478,7 @@ class RandomHorizontalFlipWithBBox : public TensorTransform {
 
 /// \brief RandomPosterize TensorTransform.
 /// \notes Tensor operation to perform random posterize.
-class RandomPosterize : public TensorTransform {
+class RandomPosterize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] bit_range - uint8_t vector representing the minimum and maximum bit in range. (Default={4, 8})
@@ -500,7 +500,7 @@ class RandomPosterize : public TensorTransform {
 /// \brief RandomResize TensorTransform.
 /// \notes Resize the input image using a randomly selected interpolation mode.
 //      the same image aspect ratio. If size has 2 values, it should be (height, width).
-class RandomResize : public TensorTransform {
+class RandomResize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the resized image.
@@ -523,7 +523,7 @@ class RandomResize : public TensorTransform {
 /// \brief RandomResizeWithBBox TensorTransform.
 /// \notes Resize the input image using a randomly selected interpolation mode and adjust
 ///     bounding boxes accordingly.
-class RandomResizeWithBBox : public TensorTransform {
+class RandomResizeWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the resized image.
@@ -546,7 +546,7 @@ class RandomResizeWithBBox : public TensorTransform {
 
 /// \brief RandomResizedCrop TensorTransform.
 /// \notes Crop the input image to a random size and aspect ratio.
-class RandomResizedCrop : public TensorTransform {
+class RandomResizedCrop final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -578,7 +578,7 @@ class RandomResizedCrop : public TensorTransform {
 
 /// \brief RandomResizedCropWithBBox TensorTransform.
 /// \notes Crop the input image to a random size and aspect ratio.
-class RandomResizedCropWithBBox : public TensorTransform {
+class RandomResizedCropWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -610,7 +610,7 @@ class RandomResizedCropWithBBox : public TensorTransform {
 
 /// \brief RandomRotation TensorOp
 /// \notes Rotates the image according to parameters
-class RandomRotation : public TensorTransform {
+class RandomRotation final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] degrees A float vector of size, representing the starting and ending degree
@@ -642,7 +642,7 @@ class RandomRotation : public TensorTransform {
 ///     (op, prob), where op is a TensorTransform operation and prob is the probability that this op will be applied.
 ///     Once a sub-policy is selected, each op within the sub-policy with be applied in sequence according to its
 ///     probability.
-class RandomSelectSubpolicy : public TensorTransform {
+class RandomSelectSubpolicy final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] policy Vector of sub-policies to choose from, in which the TensorTransform objects are raw pointers
@@ -672,7 +672,7 @@ class RandomSelectSubpolicy : public TensorTransform {
 
 /// \brief RandomSharpness TensorTransform.
 /// \notes Tensor operation to perform random sharpness.
-class RandomSharpness : public TensorTransform {
+class RandomSharpness final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] degrees A float vector of size 2, representing the starting and ending degree to uniformly
@@ -695,7 +695,7 @@ class RandomSharpness : public TensorTransform {
 /// \brief RandomSolarize TensorTransform.
 /// \notes Invert pixels randomly within specified range. If min=max, it is a single fixed magnitude operation
 ///     to inverts all pixel above that threshold
-class RandomSolarize : public TensorTransform {
+class RandomSolarize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] threshold A vector with two elements specifying the pixel range to invert.
@@ -716,7 +716,7 @@ class RandomSolarize : public TensorTransform {
 
 /// \brief RandomVerticalFlip TensorTransform.
 /// \notes Tensor operation to perform random vertical flip.
-class RandomVerticalFlip : public TensorTransform {
+class RandomVerticalFlip final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] prob A float representing the probability of flip.
@@ -737,7 +737,7 @@ class RandomVerticalFlip : public TensorTransform {
 
 /// \brief RandomVerticalFlipWithBBox TensorTransform.
 /// \notes Flip the input image vertically, randomly with a given probability and adjust bounding boxes accordingly.
-class RandomVerticalFlipWithBBox : public TensorTransform {
+class RandomVerticalFlipWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] prob A float representing the probability of flip.
@@ -758,7 +758,7 @@ class RandomVerticalFlipWithBBox : public TensorTransform {
 
 /// \brief RescaleOperation TensorTransform.
 /// \notes Tensor operation to rescale the input image.
-class Rescale : public TensorTransform {
+class Rescale final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] rescale Rescale factor.
@@ -780,7 +780,7 @@ class Rescale : public TensorTransform {
 
 /// \brief ResizeWithBBox TensorTransform.
 /// \notes Resize the input image to the given size and adjust bounding boxes accordingly.
-class ResizeWithBBox : public TensorTransform {
+class ResizeWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size The output size of the resized image.
@@ -804,7 +804,7 @@ class ResizeWithBBox : public TensorTransform {
 
 /// \brief RgbaToBgr TensorTransform.
 /// \notes Changes the input 4 channel RGBA tensor to 3 channel BGR.
-class RGBA2BGR : public TensorTransform {
+class RGBA2BGR final : public TensorTransform {
  public:
   /// \brief Constructor.
   RGBA2BGR();
@@ -820,7 +820,7 @@ class RGBA2BGR : public TensorTransform {
 
 /// \brief RgbaToRgb TensorTransform.
 /// \notes Changes the input 4 channel RGBA tensor to 3 channel RGB.
-class RGBA2RGB : public TensorTransform {
+class RGBA2RGB final : public TensorTransform {
  public:
   /// \brief Constructor.
   RGBA2RGB();
@@ -840,7 +840,7 @@ class RGBA2RGB : public TensorTransform {
 ///     The input image size should be in range [32*32, 8192*8192].
 ///     The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
 ///     Only images with an even resolution can be output. The output of odd resolution is not supported.
-class SoftDvppDecodeRandomCropResizeJpeg : public TensorTransform {
+class SoftDvppDecodeRandomCropResizeJpeg final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the resized image.
@@ -877,7 +877,7 @@ class SoftDvppDecodeRandomCropResizeJpeg : public TensorTransform {
 ///     and the input image size should be in range [32*32, 8192*8192].
 ///     The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
 ///     Only images with an even resolution can be output. The output of odd resolution is not supported.
-class SoftDvppDecodeResizeJpeg : public TensorTransform {
+class SoftDvppDecodeResizeJpeg final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the resized image.
@@ -900,7 +900,7 @@ class SoftDvppDecodeResizeJpeg : public TensorTransform {
 
 /// \brief SwapRedBlue TensorOp
 /// \notes Swaps the red and blue channels in image
-class SwapRedBlue : public TensorTransform {
+class SwapRedBlue final : public TensorTransform {
  public:
   /// \brief Constructor.
   SwapRedBlue();
@@ -916,7 +916,7 @@ class SwapRedBlue : public TensorTransform {
 
 /// \brief UniformAugment TensorTransform.
 /// \notes Tensor operation to perform randomly selected augmentation.
-class UniformAugment : public TensorTransform {
+class UniformAugment final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] transforms Raw pointer to vector of TensorTransform operations.

mindspore/ccsrc/minddata/dataset/include/vision_ascend.h

@@ -34,7 +34,7 @@ namespace vision {
 
 /* ##################################### API class ###########################################*/
 
-class DvppDecodeResizeJpeg : public TensorTransform {
+class DvppDecodeResizeJpeg final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] resize A vector of int value for each dimension, w.r.t H,W order.
@@ -55,7 +55,7 @@ class DvppDecodeResizeJpeg : public TensorTransform {
   std::shared_ptr<Data> data_;
 };
 
-class DvppDecodeResizeCropJpeg : public TensorTransform {
+class DvppDecodeResizeCropJpeg final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] crop A vector of int value for each dimension after final crop, w.r.t H,W order.
@@ -77,7 +77,7 @@ class DvppDecodeResizeCropJpeg : public TensorTransform {
   std::shared_ptr<Data> data_;
 };
 
-class DvppDecodePng : public TensorTransform {
+class DvppDecodePng final : public TensorTransform {
  public:
   /// \brief Constructor.
   DvppDecodePng();

mindspore/ccsrc/minddata/dataset/include/vision_lite.h

@@ -37,7 +37,7 @@ class RotateOperation;
 
 /// \brief Affine TensorTransform.
 /// \notes Apply affine transform on input image.
-class Affine : public TensorTransform {
+class Affine final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] degrees The degrees to rotate the image by
@@ -68,7 +68,7 @@ class Affine : public TensorTransform {
 
 /// \brief CenterCrop TensorTransform.
 /// \notes Crops the input image at the center to the given size.
-class CenterCrop : public TensorTransform {
+class CenterCrop final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -93,7 +93,7 @@ class CenterCrop : public TensorTransform {
 
 /// \brief Crop TensorTransform.
 /// \notes Crop an image based on location and crop size
-class Crop : public TensorTransform {
+class Crop final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] coordinates Starting location of crop. Must be a vector of two values, in the form of {x_coor, y_coor}
@@ -117,7 +117,7 @@ class Crop : public TensorTransform {
 
 /// \brief Decode TensorTransform.
 /// \notes Decode the input image in RGB mode.
-class Decode : public TensorTransform {
+class Decode final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] rgb A boolean of whether to decode in RGB mode or not.
@@ -140,7 +140,7 @@ class Decode : public TensorTransform {
 
 /// \brief Normalize TensorTransform.
 /// \notes Normalize the input image with respect to mean and standard deviation.
-class Normalize : public TensorTransform {
+class Normalize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] mean A vector of mean values for each channel, w.r.t channel order.
@@ -166,7 +166,7 @@ class Normalize : public TensorTransform {
 
 /// \brief RandomAffine TensorTransform.
 /// \notes Applies a Random Affine transformation on input image in RGB or Greyscale mode.
-class RandomAffine : public TensorTransform {
+class RandomAffine final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] degrees A float vector of size 2, representing the starting and ending degree
@@ -204,7 +204,7 @@ class RandomAffine : public TensorTransform {
 
 /// \brief Resize TensorTransform.
 /// \notes Resize the input image to the given size.
-class Resize : public TensorTransform {
+class Resize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the resized image.
@@ -230,7 +230,7 @@ class Resize : public TensorTransform {
 
 /// \brief Rotate TensorTransform.
 /// \notes Rotate the input image using a specified angle id.
-class Rotate : public TensorTransform {
+class Rotate final : public TensorTransform {
  public:
   /// \brief Constructor.
   Rotate();

tests/ut/cpp/dataset/c_api_samplers_test.cc

@@ -149,7 +149,7 @@ TEST_F(MindDataTestPipeline, TestDistributedSamplerSuccess2) {
   // Test basic setting of distributed_sampler
 
   // num_shards=4, shard_id=0, shuffle=false, num_samplers=0, seed=0, offset=-1, even_dist=true
-  Sampler *sampler = new DistributedSampler(4, 0, false, 0, 0, -1, true);
+  auto sampler(new DistributedSampler(4, 0, false, 0, 0, -1, true));
   // Note that with new, we have to explicitly delete the allocated object as shown below.
   // Note: No need to check for output after calling API class constructor
 
@@ -232,7 +232,7 @@ TEST_F(MindDataTestPipeline, TestDistributedSamplerFail2) {
 
   // num_shards=4, shard_id=0, shuffle=false, num_samplers=0, seed=0, offset=5, even_dist=true
   // offset=5 which is greater than num_shards=4 --> will fail later
-  Sampler *sampler = new DistributedSampler(4, 0, false, 0, 0, 5, false);
+  auto sampler(new DistributedSampler(4, 0, false, 0, 0, 5, false));
   // Note that with new, we have to explicitly delete the allocated object as shown below.
   // Note: No need to check for output after calling API class constructor
 

tests/ut/cpp/dataset/execute_test.cc

@@ -173,7 +173,6 @@ TEST_F(MindDataTestExecute, TestTransformInput3) {
 TEST_F(MindDataTestExecute, TestTransformInputSequential) {
   MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInputSequential.";
   // Test Execute with transform op input using API constructors, with auto pointers;
-  // Note that with auto and new, we have to explicitly delete the allocated object as shown below.
   // Apply 2 transformations sequentially, including single non-vector Transform op input
 
   // Read image, construct MSTensor from dataset tensor