add ms_api

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

@@ -38,7 +38,7 @@ class TensorOperation;
 namespace audio {
 
 /// \brief Compute the angle of complex tensor input.
-class Angle final : public TensorTransform {
+class MS_API Angle final : public TensorTransform {
  public:
   /// \brief Constructor.
   Angle();
@@ -52,7 +52,7 @@ class Angle final : public TensorTransform {
 };
 
 /// \brief Design two-pole band filter.
-class BandBiquad final : public TensorTransform {
+class MS_API BandBiquad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be zero.
@@ -75,7 +75,7 @@ class BandBiquad final : public TensorTransform {
 };
 
 /// \brief Design two-pole allpass filter. Similar to SoX implementation.
-class AllpassBiquad final : public TensorTransform {
+class MS_API AllpassBiquad final : public TensorTransform {
  public:
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be zero.
   /// \param[in] central_freq Central frequency (in Hz).
@@ -97,7 +97,7 @@ class AllpassBiquad final : public TensorTransform {
 
 /// \brief AmplitudeToDB TensorTransform.
 /// \notes Turn a tensor from the power/amplitude scale to the decibel scale.
-class AmplitudeToDB final : public TensorTransform {
+class MS_API AmplitudeToDB final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] stype Scale of input tensor, must be one of [ScaleType::kPower, ScaleType::kMagnitude] (Default:
@@ -122,7 +122,7 @@ class AmplitudeToDB final : public TensorTransform {
 };
 
 /// \brief Design two-pole band-pass filter.
-class BandpassBiquad final : public TensorTransform {
+class MS_API BandpassBiquad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be zero.
@@ -146,7 +146,7 @@ class BandpassBiquad final : public TensorTransform {
 };
 
 /// \brief Design two-pole band-reject filter. Similar to SoX implementation.
-class BandrejectBiquad final : public TensorTransform {
+class MS_API BandrejectBiquad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be zero.
@@ -168,7 +168,7 @@ class BandrejectBiquad final : public TensorTransform {
 };
 
 /// \brief Design a bass tone-control effect.
-class BassBiquad final : public TensorTransform {
+class MS_API BassBiquad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be zero.
@@ -191,7 +191,7 @@ class BassBiquad final : public TensorTransform {
 };
 
 /// \brief Perform a biquad filter of input tensor.
-class Biquad final : public TensorTransform {
+class MS_API Biquad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] b0 Numerator coefficient of current input, x[n].
@@ -217,7 +217,7 @@ class Biquad final : public TensorTransform {
 
 /// \brief ComplexNorm TensorTransform.
 /// \notes Compute the norm of complex tensor input.
-class ComplexNorm final : public TensorTransform {
+class MS_API ComplexNorm final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] power Power of the norm, which must be non-negative (Default: 1.0).
@@ -238,7 +238,7 @@ class ComplexNorm final : public TensorTransform {
 
 /// \brief ComputeDeltas Transform.
 /// \note Compute delta coefficients of a spectrogram.
-class ComputeDeltas final : public TensorTransform {
+class MS_API ComputeDeltas final : public TensorTransform {
  public:
   /// \brief Construct a new Compute Deltas object.
   /// \param[in] win_length The window length used for computing delta, must be no less than 3 (Default: 5).
@@ -259,7 +259,7 @@ class ComputeDeltas final : public TensorTransform {
 };
 
 /// \brief Apply contrast effect.
-class Contrast final : public TensorTransform {
+class MS_API Contrast final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] enhancement_amount Controls the amount of the enhancement (Default: 75.0).
@@ -279,7 +279,7 @@ class Contrast final : public TensorTransform {
 };
 
 /// \brief Turn a waveform from the decibel scale to the power/amplitude scale.
-class DBToAmplitude final : public TensorTransform {
+class MS_API DBToAmplitude final : public TensorTransform {
  public:
   /// \brief Constructor
   /// \param[in] ref Reference which the output will be scaled by.
@@ -300,7 +300,7 @@ class DBToAmplitude final : public TensorTransform {
 };
 
 /// \brief Apply a DC shift to the audio.
-class DCShift : public TensorTransform {
+class MS_API DCShift : public TensorTransform {
  public:
   /// \brief Constructor
   /// \param[in] shift Indicates the amount to shift the audio, the value must be in the range [-2.0, 2.0].
@@ -332,7 +332,7 @@ class DCShift : public TensorTransform {
 Status CreateDct(mindspore::MSTensor *output, int32_t n_mfcc, int32_t n_mels, NormMode norm = NormMode::kNone);
 
 /// \brief Design two-pole deemph filter. Similar to SoX implementation.
-class DeemphBiquad final : public TensorTransform {
+class MS_API DeemphBiquad final : public TensorTransform {
  public:
   /// \param[in] sample_rate Sampling rate of the waveform, the value can only be 44100 (Hz) or 48000(hz).
   explicit DeemphBiquad(int32_t sample_rate);
@@ -351,7 +351,7 @@ class DeemphBiquad final : public TensorTransform {
 };
 
 /// \brief Detect pitch frequency.
-class DetectPitchFrequency final : public TensorTransform {
+class MS_API DetectPitchFrequency final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be zero.
@@ -378,7 +378,7 @@ class DetectPitchFrequency final : public TensorTransform {
 };
 
 /// \brief EqualizerBiquad TensorTransform. Apply highpass biquad filter on audio.
-class EqualizerBiquad final : public TensorTransform {
+class MS_API EqualizerBiquad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be zero.
@@ -401,7 +401,7 @@ class EqualizerBiquad final : public TensorTransform {
 };
 
 /// \brief Add fade in or/and fade out on the input audio.
-class Fade final : public TensorTransform {
+class MS_API Fade final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] fade_in_len Length of fade-in (time frames), which must be non-negative
@@ -425,7 +425,7 @@ class Fade final : public TensorTransform {
 };
 
 /// \brief Apply a flanger effect to the audio.
-class Flanger final : public TensorTransform {
+class MS_API Flanger final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz).
@@ -458,7 +458,7 @@ class Flanger final : public TensorTransform {
 
 /// \brief FrequencyMasking TensorTransform.
 /// \notes Apply masking to a spectrogram in the frequency domain.
-class FrequencyMasking final : public TensorTransform {
+class MS_API FrequencyMasking final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] iid_masks Whether to apply different masks to each example.
@@ -484,7 +484,7 @@ class FrequencyMasking final : public TensorTransform {
 };
 
 /// \brief HighpassBiquad TensorTransform. Apply highpass biquad filter on audio.
-class HighpassBiquad final : public TensorTransform {
+class MS_API HighpassBiquad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be zero.
@@ -506,7 +506,7 @@ class HighpassBiquad final : public TensorTransform {
 };
 
 /// \brief Design filter. Similar to SoX implementation.
-class LFilter final : public TensorTransform {
+class MS_API LFilter final : public TensorTransform {
  public:
   /// \param[in] a_coeffs Numerator coefficients of difference equation of dimension of (n_order + 1).
   ///     Lower delays coefficients are first, e.g. [a0, a1, a2, ...].
@@ -531,7 +531,7 @@ class LFilter final : public TensorTransform {
 };
 
 /// \brief Design biquad lowpass filter and perform filtering. Similar to SoX implementation.
-class LowpassBiquad final : public TensorTransform {
+class MS_API LowpassBiquad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be zero.
@@ -553,7 +553,7 @@ class LowpassBiquad final : public TensorTransform {
 };
 
 /// \brief Separate a complex-valued spectrogram with shape (..., 2) into its magnitude and phase.
-class Magphase final : public TensorTransform {
+class MS_API Magphase final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] power Power of the norm, which must be non-negative (Default: 1.0).
@@ -574,7 +574,7 @@ class Magphase final : public TensorTransform {
 
 /// \brief MuLawDecoding TensorTransform.
 /// \note Decode mu-law encoded signal.
-class MuLawDecoding final : public TensorTransform {
+class MS_API MuLawDecoding final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] quantization_channels Number of channels, which must be positive (Default: 256).
@@ -595,7 +595,7 @@ class MuLawDecoding final : public TensorTransform {
 
 /// \brief MuLawEncoding TensorTransform.
 /// \note Encode signal based on mu-law companding.
-class MuLawEncoding final : public TensorTransform {
+class MS_API MuLawEncoding final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] quantization_channels Number of channels, which must be positive (Default: 256).
@@ -615,7 +615,7 @@ class MuLawEncoding final : public TensorTransform {
 };
 
 /// \brief Overdrive TensorTransform.
-class Overdrive final : public TensorTransform {
+class MS_API Overdrive final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] gain Coefficient of overload in dB, in range of [0, 100] (Default: 20.0).
@@ -636,7 +636,7 @@ class Overdrive final : public TensorTransform {
 };
 
 /// \brief Phaser TensorTransform.
-class Phaser final : public TensorTransform {
+class MS_API Phaser final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz).
@@ -666,7 +666,7 @@ class Phaser final : public TensorTransform {
 };
 
 /// \brief Apply RIAA vinyl playback equalization.
-class RiaaBiquad final : public TensorTransform {
+class MS_API RiaaBiquad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz),
@@ -687,7 +687,7 @@ class RiaaBiquad final : public TensorTransform {
 };
 
 /// \brief Apply sliding-window cepstral mean (and optionally variance) normalization per utterance.
-class SlidingWindowCmn final : public TensorTransform {
+class MS_API SlidingWindowCmn final : public TensorTransform {
  public:
   /// \brief Constructor of SlidingWindowCmnOp.
   /// \param[in] cmn_window The window in frames for running average CMN computation (Default: 600).
@@ -714,7 +714,7 @@ class SlidingWindowCmn final : public TensorTransform {
 
 /// \brief TimeMasking TensorTransform.
 /// \notes Apply masking to a spectrogram in the time domain.
-class TimeMasking final : public TensorTransform {
+class MS_API TimeMasking final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] iid_masks Whether to apply different masks to each example.
@@ -741,7 +741,7 @@ class TimeMasking final : public TensorTransform {
 
 /// \brief TimeStretch TensorTransform
 /// \notes Stretch STFT in time at a given rate, without changing the pitch.
-class TimeStretch final : public TensorTransform {
+class MS_API TimeStretch final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] hop_length Length of hop between STFT windows (Default: None, will use ((n_freq - 1) * 2) // 2).
@@ -765,7 +765,7 @@ class TimeStretch final : public TensorTransform {
 };
 
 /// \brief Design a treble tone-control effect.
-class TrebleBiquad final : public TensorTransform {
+class MS_API TrebleBiquad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] sample_rate Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be zero.
@@ -789,7 +789,7 @@ class TrebleBiquad final : public TensorTransform {
 
 /// \brief Vol TensorTransform.
 /// \notes Add a volume to an waveform.
-class Vol final : public TensorTransform {
+class MS_API Vol final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] gain Gain value, varies according to the value of gain_type. If gain_type is GainType::kAmplitude,

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

@@ -37,7 +37,7 @@ namespace config {
 ///     // Operations with randomness will use the seed value to generate random values.
 ///     bool rc = config::set_seed(5);
 /// \endcode
-bool set_seed(int32_t seed);
+bool MS_API set_seed(int32_t seed);
 
 /// \brief A function to get the seed.
 /// \return The seed set in the configuration.
@@ -47,7 +47,7 @@ bool set_seed(int32_t seed);
 ///     // If set_seed() is never called before, the default value(std::mt19937::default_seed) will be returned.
 ///     uint32_t seed = config::get_seed();
 /// \endcode
-uint32_t get_seed();
+uint32_t MS_API get_seed();
 
 /// \brief A function to set the number of rows to be prefetched.
 /// \param[in] prefetch_size Total number of rows to be prefetched.
@@ -57,7 +57,7 @@ uint32_t get_seed();
 ///     // Set a new global configuration value for the prefetch size.
 ///     bool rc = config::set_prefetch_size(1000);
 /// \endcode
-bool set_prefetch_size(int32_t prefetch_size);
+bool MS_API set_prefetch_size(int32_t prefetch_size);
 
 /// \brief A function to get the prefetch size in number of rows.
 /// \return Total number of rows to be prefetched.
@@ -67,7 +67,7 @@ bool set_prefetch_size(int32_t prefetch_size);
 ///     // If set_prefetch_size() is never called before, the default value(16) will be returned.
 ///     int32_t prefetch_size = config::get_prefetch_size();
 /// \endcode
-int32_t get_prefetch_size();
+int32_t MS_API get_prefetch_size();
 
 /// \brief A function to set the default number of parallel workers.
 /// \param[in] num_parallel_workers Number of parallel workers to be used as the default for each operation.
@@ -78,7 +78,7 @@ int32_t get_prefetch_size();
 ///     // Now parallel dataset operators will run with 16 workers.
 ///     bool rc = config::set_num_parallel_workers(16);
 /// \endcode
-bool set_num_parallel_workers(int32_t num_parallel_workers);
+bool MS_API set_num_parallel_workers(int32_t num_parallel_workers);
 
 /// \brief A function to get the default number of parallel workers.
 /// \return Number of parallel workers to be used as the default for each operation.
@@ -88,7 +88,7 @@ bool set_num_parallel_workers(int32_t num_parallel_workers);
 ///     // If set_num_parallel_workers() is never called before, the default value(8) will be returned.
 ///     int32_t parallel_workers = config::get_num_parallel_workers();
 /// \endcode
-int32_t get_num_parallel_workers();
+int32_t MS_API get_num_parallel_workers();
 
 /// \brief A function to set the default interval (in milliseconds) for monitor sampling.
 /// \param[in] interval Interval (in milliseconds) to be used for performance monitor sampling.
@@ -98,7 +98,7 @@ int32_t get_num_parallel_workers();
 ///     // Set a new global configuration value for the monitor sampling interval.
 ///     bool rc = config::set_monitor_sampling_interval(100);
 /// \endcode
-bool set_monitor_sampling_interval(int32_t interval);
+bool MS_API set_monitor_sampling_interval(int32_t interval);
 
 /// \brief A function to get the default interval of performance monitor sampling.
 /// \return Interval (in milliseconds) for performance monitor sampling.
@@ -108,7 +108,7 @@ bool set_monitor_sampling_interval(int32_t interval);
 ///     // If set_monitor_sampling_interval() is never called before, the default value(1000) will be returned.
 ///     int32_t sampling_interval = config::get_monitor_sampling_interval();
 /// \endcode
-int32_t get_monitor_sampling_interval();
+int32_t MS_API get_monitor_sampling_interval();
 
 /// \brief A function to set the default timeout (in seconds) for DSWaitedCallback. In case of a deadlock, the wait
 ///    function will exit after the timeout period.
@@ -119,7 +119,7 @@ int32_t get_monitor_sampling_interval();
 ///     // Set a new global configuration value for the timeout value.
 ///     bool rc = config::set_callback_timeout(100);
 /// \endcode
-bool set_callback_timeout(int32_t timeout);
+bool MS_API set_callback_timeout(int32_t timeout);
 
 /// \brief A function to get the default timeout for DSWaitedCallback. In case of a deadback, the wait function
 ///    will exit after the timeout period.
@@ -130,14 +130,14 @@ bool set_callback_timeout(int32_t timeout);
 ///     // If set_callback_timeout() is never called before, the default value(60) will be returned.
 ///     int32_t callback_timeout = config::get_callback_timeout();
 /// \endcode
-int32_t get_callback_timeout();
+int32_t MS_API get_callback_timeout();
 
 /// \brief A function to load the configuration from a file.
 /// \param[in] file Path of the configuration file to be loaded.
 /// \return The config file is loaded successfully or not.
 /// \note The reason for using this API is that std::string will be constrained by the
 ///    compiler option '_GLIBCXX_USE_CXX11_ABI' while char is free of this restriction.
-bool load(const std::vector<char> &file);
+bool MS_API load(const std::vector<char> &file);
 
 /// \brief A function to load the configuration from a file.
 /// \param[in] file Path of the configuration file to be loaded.
@@ -155,7 +155,7 @@ bool load(const std::vector<char> &file);
 ///     std::string config_file = "/path/to/config/file";
 ///     bool rc = config::load(config_file);
 /// \endcode
-inline bool load(std::string file) { return load(StringToChar(file)); }
+inline bool MS_API load(std::string file) { return load(StringToChar(file)); }
 
 }  // namespace config
 }  // namespace dataset

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

@@ -20,6 +20,8 @@
 #include <limits>
 #include <random>
 
+#include "include/api/types.h"
+
 namespace mindspore {
 namespace dataset {
 // Various type defines for convenience
@@ -27,26 +29,26 @@ using uchar = unsigned char;
 using dsize_t = int64_t;
 
 /// \brief The modulation in Flanger
-enum class Modulation {
+enum class MS_API Modulation {
   kSinusoidal = 0,  ///< Use sinusoidal modulation.
   kTriangular = 1   ///< Use triangular modulation.
 };
 
 /// \brief The interpolation in Flanger
-enum class Interpolation {
+enum class MS_API Interpolation {
   kLinear = 0,    ///< Use linear for delay-line interpolation.
   kQuadratic = 1  ///< Use quadratic for delay-line interpolation.
 };
 
 /// \brief The dataset auto augment policy in AutoAugment
-enum class AutoAugmentPolicy {
+enum class MS_API AutoAugmentPolicy {
   kImageNet = 0,  ///< AutoAugment policy learned on the ImageNet dataset.
   kCifar10 = 1,   ///< AutoAugment policy learned on the Cifar10 dataset.
   kSVHN = 2       ///< AutoAugment policy learned on the SVHN dataset.
 };
 
 /// \brief The color conversion code
-enum class ConvertMode {
+enum class MS_API ConvertMode {
   COLOR_BGR2BGRA = 0,                 ///< Add alpha channel to BGR image.
   COLOR_RGB2RGBA = COLOR_BGR2BGRA,    ///< Add alpha channel to RGB image.
   COLOR_BGRA2BGR = 1,                 ///< Remove alpha channel to BGR image.
@@ -70,20 +72,20 @@ enum class ConvertMode {
 };
 
 /// \brief Values of norm in CreateDct.
-enum class NormMode {
+enum class MS_API NormMode {
   kNone = 0,  ///< None type norm.
   kOrtho = 1  ///< Ortho type norm.
 };
 
 /// \brief Target devices to perform map operation.
-enum class MapTargetDevice {
+enum class MS_API MapTargetDevice {
   kCpu,       ///< CPU Device.
   kGpu,       ///< Gpu Device.
   kAscend310  ///< Ascend310 Device.
 };
 
 /// \brief The initial type of tensor implementation.
-enum class TensorImpl {
+enum class MS_API TensorImpl {
   kNone,      ///< None type tensor.
   kFlexible,  ///< Flexible type tensor, can be converted to any type.
   kCv,        ///< CV type tensor.
@@ -91,7 +93,7 @@ enum class TensorImpl {
 };
 
 /// \brief The mode for shuffling data.
-enum class ShuffleMode {
+enum class MS_API ShuffleMode {
   kFalse = 0,   ///< No shuffling is performed.
   kFiles = 1,   ///< Shuffle files only.
   kGlobal = 2,  ///< Shuffle both the files and samples.
@@ -99,20 +101,20 @@ enum class ShuffleMode {
 };
 
 /// \brief Possible scale for input audio.
-enum class ScaleType {
+enum class MS_API ScaleType {
   kMagnitude = 0,  ///< Audio scale is magnitude.
   kPower = 1,      ///< Audio scale is power.
 };
 
 /// \brief The scale for gain type.
-enum class GainType {
+enum class MS_API GainType {
   kAmplitude = 0,  ///< Audio gain type is amplitude.
   kPower = 1,      ///< Audio gain type is power.
   kDb = 2,         ///< Audio gain type is db.
 };
 
 /// \brief The method of padding.
-enum class BorderType {
+enum class MS_API BorderType {
   kConstant = 0,  ///< Fill the border with constant values.
   kEdge = 1,      ///< Fill the border with the last value on the edge.
   kReflect = 2,   ///< Reflect the values on the edge omitting the last value of edge.
@@ -120,7 +122,7 @@ enum class BorderType {
 };
 
 /// \brief Possible fix rotation angle for Rotate Op.
-enum class FixRotationAngle {
+enum class MS_API FixRotationAngle {
   k0Degree = 1,             ///< Rotate 0 degree.
   k0DegreeAndMirror = 2,    ///< Rotate 0 degree and apply horizontal flip.
   k180Degree = 3,           ///< Rotate 180 degree.
@@ -132,20 +134,20 @@ enum class FixRotationAngle {
 };
 
 /// \brief Possible options for Image format types in a batch.
-enum class ImageBatchFormat {
+enum class MS_API ImageBatchFormat {
   kNHWC = 0,  ///< Indicate the input batch is of NHWC format.
   kNCHW = 1   ///< Indicate the input batch is of NCHW format.
 };
 
 /// \brief Possible options for Image format types.
-enum class ImageFormat {
+enum class MS_API ImageFormat {
   HWC = 0,  ///< Indicate the input batch is of NHWC format
   CHW = 1,  ///< Indicate the input batch is of NHWC format
   HW = 2    ///< Indicate the input batch is of NHWC format
 };
 
 /// \brief Possible options for interpolation method.
-enum class InterpolationMode {
+enum class MS_API InterpolationMode {
   kLinear = 0,            ///< Interpolation method is linear interpolation.
   kNearestNeighbour = 1,  ///< Interpolation method is nearest-neighbor interpolation.
   kCubic = 2,             ///< Interpolation method is bicubic interpolation.
@@ -154,26 +156,26 @@ enum class InterpolationMode {
 };
 
 /// \brief Possible tokenize modes for JiebaTokenizer.
-enum class JiebaMode {
+enum class MS_API JiebaMode {
   kMix = 0,  ///< Tokenize with MPSegment algorithm.
   kMp = 1,   ///< Tokenize with Hiddel Markov Model Segment algorithm.
   kHmm = 2   ///< Tokenize with a mix of MPSegment and HMMSegment algorithm.
 };
 
 /// \brief Possible options for SPieceTokenizerOutType.
-enum class SPieceTokenizerOutType {
+enum class MS_API SPieceTokenizerOutType {
   kString = 0,  ///< Output of sentencepiece tokenizer is string type.
   kInt = 1      ///< Output of sentencepiece tokenizer is int type.
 };
 
 /// \brief Possible options for SPieceTokenizerLoadType.
-enum class SPieceTokenizerLoadType {
+enum class MS_API SPieceTokenizerLoadType {
   kFile = 0,  ///< Load sentencepiece tokenizer from local sentencepiece vocab file.
   kModel = 1  ///< Load sentencepiece tokenizer from sentencepiece vocab instance.
 };
 
 /// \brief Type options for SentencePiece Model.
-enum class SentencePieceModel {
+enum class MS_API SentencePieceModel {
   kUnigram = 0,  ///< Based on Unigram model.
   kBpe = 1,      ///< Based on Byte Pair Encoding (BPE) model.
   kChar = 2,     ///< Based on Char model.
@@ -181,7 +183,7 @@ enum class SentencePieceModel {
 };
 
 /// \brief Possible options to specify a specific normalize mode.
-enum class NormalizeForm {
+enum class MS_API NormalizeForm {
   kNone = 0,  ///< Keep the input string tensor unchanged.
   kNfc,       ///< Normalize with Normalization Form C.
   kNfkc,      ///< Normalize with Normalization Form KC.
@@ -190,7 +192,7 @@ enum class NormalizeForm {
 };
 
 /// \brief Possible options for Mask.
-enum class RelationalOp {
+enum class MS_API RelationalOp {
   kEqual = 0,     ///< equal to `==`
   kNotEqual,      ///< equal to `!=`
   kLess,          ///< equal to `<`
@@ -200,26 +202,26 @@ enum class RelationalOp {
 };
 
 /// \brief Possible modes for slice patches.
-enum class SliceMode {
+enum class MS_API SliceMode {
   kPad = 0,   ///< Pad some pixels before slice to patches.
   kDrop = 1,  ///< Drop remainder pixels before slice to patches.
 };
 
 /// \brief Possible options for SamplingStrategy.
-enum class SamplingStrategy {
+enum class MS_API SamplingStrategy {
   kRandom = 0,     ///< Random sampling with replacement.
   kEdgeWeight = 1  ///< Sampling with edge weight as probability.
 };
 
 /// \brief Possible values for output format in get all neighbors function of gnn dataset
-enum class OutputFormat {
+enum class MS_API OutputFormat {
   kNormal = 0,  ///< Normal format.
   kCoo = 1,     ///< COO format.
   kCsr = 2      ///< CSR format.
 };
 
 /// \brief Possible options for fade shape.
-enum class FadeShape {
+enum class MS_API FadeShape {
   kLinear = 0,       ///< Fade shape is linear mode.
   kExponential = 1,  ///< Fade shape is exponential mode.
   kLogarithmic = 2,  ///< Fade shape is logarithmic mode.
@@ -231,12 +233,12 @@ enum class FadeShape {
 /// \param[in] bits a 32bit int to be tested
 /// \param[in] bitMask a 32bit int representing bit mask
 /// \return bool Result for the check
-inline bool BitTest(uint32_t bits, uint32_t bitMask) { return (bits & bitMask) == bitMask; }
+inline bool MS_API BitTest(uint32_t bits, uint32_t bitMask) { return (bits & bitMask) == bitMask; }
 
 /// \brief Convenience function to set bitmask for a 32bit int
 /// \param[in] bits a 32bit int to deal with
 /// \param[in] bitMask a 32bit int representing bit mask
-inline void BitSet(uint32_t *bits, uint32_t bitMask) {
+inline void MS_API BitSet(uint32_t *bits, uint32_t bitMask) {
   if (bits == nullptr) {
     return;
   }
@@ -246,7 +248,7 @@ inline void BitSet(uint32_t *bits, uint32_t bitMask) {
 /// \brief Convenience function to clear bitmask from a 32bit int
 /// \param[in] bits a 32bit int to deal with
 /// \param[in] bitMask a 32bit int representing bit mask
-inline void BitClear(uint32_t *bits, uint32_t bitMask) {
+inline void MS_API BitClear(uint32_t *bits, uint32_t bitMask) {
   if (bits == nullptr) {
     return;
   }

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

@@ -33,7 +33,7 @@ namespace mindspore {
 namespace dataset {
 
 /// \brief Simple class to do data manipulation, contains helper function to update json files in dataset
-class DataHelper {
+class MS_API DataHelper {
  public:
   /// \brief constructor
   DataHelper() {}

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

@@ -30,7 +30,7 @@ namespace mindspore {
 namespace dataset {
 class DeviceResource;
 // class to run tensor operations in eager mode
-class Execute {
+class MS_API Execute {
  public:
   /// \brief Constructor.
   /// \param[in] op TensorOperation to be applied in Eager mode, it accepts operation in type of shared pointer.

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

@@ -45,7 +45,7 @@ using MSTensorMapChar = std::map<std::vector<char>, mindspore::MSTensor>;
 using MSTensorVec = std::vector<mindspore::MSTensor>;
 
 // Abstract class for iterating over the dataset.
-class Iterator {
+class MS_API Iterator {
  public:
   /// \brief Constructor.
   Iterator();
@@ -146,7 +146,7 @@ class Iterator {
   IteratorConsumer *consumer_;
 };
 
-class PullIterator : public Iterator {
+class MS_API PullIterator : public Iterator {
  public:
   /// \brief Constructor.
   PullIterator();

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

@@ -20,6 +20,8 @@
 #include <memory>
 #include <vector>
 
+#include "include/api/types.h"
+
 namespace mindspore {
 namespace dataset {
 
@@ -29,7 +31,7 @@ class SamplerObj;
 // Abstract class to represent a sampler in the data pipeline.
 /// \class Sampler samplers.h
 /// \brief An abstract base class to represent a sampler in the data pipeline.
-class Sampler : std::enable_shared_from_this<Sampler> {
+class MS_API Sampler : std::enable_shared_from_this<Sampler> {
   friend class AlbumDataset;
   friend class CelebADataset;
   friend class Cifar10Dataset;
@@ -81,7 +83,7 @@ class Sampler : std::enable_shared_from_this<Sampler> {
 
 /// \brief A class to represent a Distributed Sampler in the data pipeline.
 /// \note A Sampler that accesses a shard of the dataset.
-class DistributedSampler final : public Sampler {
+class MS_API DistributedSampler final : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -123,7 +125,7 @@ class DistributedSampler final : public Sampler {
 /// \brief A class to represent a PK Sampler in the data pipeline.
 /// \note Samples K elements for each P class in the dataset.
 ///        This will sample all classes.
-class PKSampler final : public Sampler {
+class MS_API PKSampler final : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -155,7 +157,7 @@ class PKSampler final : public Sampler {
 
 /// \brief A class to represent a Random Sampler in the data pipeline.
 /// \note Samples the elements randomly.
-class RandomSampler final : public Sampler {
+class MS_API RandomSampler final : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -185,7 +187,7 @@ class RandomSampler final : public Sampler {
 
 /// \brief A class to represent a Sequential Sampler in the data pipeline.
 /// \note Samples the dataset elements sequentially, same as not having a sampler.
-class SequentialSampler final : public Sampler {
+class MS_API SequentialSampler final : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -215,7 +217,7 @@ class SequentialSampler final : public Sampler {
 
 /// \brief A class to represent a Subset Sampler in the data pipeline.
 /// \note Samples the elements from a sequence of indices.
-class SubsetSampler : public Sampler {
+class MS_API SubsetSampler : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -244,7 +246,7 @@ class SubsetSampler : public Sampler {
 
 /// \brief A class to represent a Subset Random Sampler in the data pipeline.
 /// \note Samples the elements randomly from a sequence of indices.
-class SubsetRandomSampler final : public SubsetSampler {
+class MS_API SubsetRandomSampler final : public SubsetSampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:
@@ -271,7 +273,7 @@ class SubsetRandomSampler final : public SubsetSampler {
 /// \brief A class to represent a Weighted Random Sampler in the data pipeline.
 /// \note Samples the elements from [0, len(weights) - 1] randomly with the given
 ///        weights (probabilities).
-class WeightedRandomSampler final : public Sampler {
+class MS_API WeightedRandomSampler final : public Sampler {
   friend std::shared_ptr<SamplerObj> SelectSampler(int64_t, bool, int32_t, int32_t);
 
  public:

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

@@ -41,7 +41,7 @@ namespace text {
 #ifndef _WIN32
 /// \brief Tokenize a scalar tensor of UTF-8 string by specific rules.
 /// \note BasicTokenizer is not supported on the Windows platform yet.
-class BasicTokenizer final : public TensorTransform {
+class MS_API BasicTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] lower_case If true, apply CaseFold, NormalizeUTF8 (NFD mode) and RegexReplace operations to
@@ -81,7 +81,7 @@ class BasicTokenizer final : public TensorTransform {
 
 /// \brief A tokenizer used for Bert text process.
 /// \note BertTokenizer is not supported on the Windows platform yet.
-class BertTokenizer final : public TensorTransform {
+class MS_API BertTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] vocab A Vocab object.
@@ -153,7 +153,7 @@ class BertTokenizer final : public TensorTransform {
 };
 
 /// \brief Apply case fold operation on UTF-8 string tensors.
-class CaseFold final : public TensorTransform {
+class MS_API CaseFold final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -179,7 +179,7 @@ class CaseFold final : public TensorTransform {
 
 /// \brief Tokenize a Chinese string into words based on the dictionary.
 /// \note The integrity of the HMMSegment algorithm and MPSegment algorithm files must be confirmed.
-class JiebaTokenizer final : public TensorTransform {
+class MS_API JiebaTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] hmm_path Dictionary file is used by the HMMSegment algorithm. The dictionary can be obtained on the
@@ -291,7 +291,7 @@ class JiebaTokenizer final : public TensorTransform {
 };
 
 /// \brief Look up a word into an id according to the input vocabulary table.
-class Lookup final : public TensorTransform {
+class MS_API Lookup final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] vocab a Vocab object.
@@ -345,7 +345,7 @@ class Lookup final : public TensorTransform {
 };
 
 /// \brief Generate n-gram from a 1-D string Tensor.
-class Ngram final : public TensorTransform {
+class MS_API 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
@@ -396,7 +396,7 @@ class Ngram final : public TensorTransform {
 
 #ifndef _WIN32
 /// \brief Apply normalize operation to UTF-8 string tensors.
-class NormalizeUTF8 final : public TensorTransform {
+class MS_API NormalizeUTF8 final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] normalize_form Valid values can be any of [NormalizeForm::kNone,NormalizeForm::kNfc,
@@ -432,7 +432,7 @@ class NormalizeUTF8 final : public TensorTransform {
 };
 
 /// \brief Replace a UTF-8 string tensor with 'replace' according to regular expression 'pattern'.
-class RegexReplace final : public TensorTransform {
+class MS_API RegexReplace final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] pattern The regex expression patterns.
@@ -472,7 +472,7 @@ class RegexReplace final : public TensorTransform {
 };
 
 /// \brief Tokenize a scalar tensor of UTF-8 string by the regex expression pattern.
-class RegexTokenizer final : public TensorTransform {
+class MS_API RegexTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] delim_pattern The pattern of regex delimiters.
@@ -510,7 +510,7 @@ class RegexTokenizer final : public TensorTransform {
 #endif
 
 /// \brief Tokenize a scalar token or a 1-D token to tokens by sentencepiece.
-class SentencePieceTokenizer final : public TensorTransform {
+class MS_API SentencePieceTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] vocab a SentencePieceVocab object.
@@ -566,7 +566,7 @@ class SentencePieceTokenizer final : public TensorTransform {
 
 /// \brief 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 final : public TensorTransform {
+class MS_API SlidingWindow final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] width The width of the window. It must be an integer and greater than zero.
@@ -601,7 +601,7 @@ class SlidingWindow final : 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 final : public TensorTransform {
+class MS_API ToNumber final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] data_type mindspore::DataType of the tensor to be cast to. Must be a numeric type, excluding bool.
@@ -630,7 +630,7 @@ class ToNumber final : public TensorTransform {
 };
 
 /// \brief Truncate a pair of rank-1 tensors such that the total length is less than max_length.
-class TruncateSequencePair final : public TensorTransform {
+class MS_API TruncateSequencePair final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] max_length Maximum length required.
@@ -659,7 +659,7 @@ class TruncateSequencePair final : public TensorTransform {
 };
 
 /// \brief Tokenize a scalar tensor of UTF-8 string to Unicode characters.
-class UnicodeCharTokenizer final : public TensorTransform {
+class MS_API UnicodeCharTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] with_offsets whether to output offsets of tokens (default=false).
@@ -688,7 +688,7 @@ class UnicodeCharTokenizer final : public TensorTransform {
 };
 
 /// \brief Tokenize scalar token or 1-D tokens to 1-D sub-word tokens.
-class WordpieceTokenizer final : public TensorTransform {
+class MS_API WordpieceTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] vocab A Vocab object.
@@ -734,7 +734,7 @@ class WordpieceTokenizer final : public TensorTransform {
 
 #ifndef _WIN32
 /// \brief Tokenize a scalar tensor of UTF-8 string on Unicode script boundaries.
-class UnicodeScriptTokenizer final : public TensorTransform {
+class MS_API UnicodeScriptTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] keep_whitespace whether to emit whitespace tokens (default=false).
@@ -764,7 +764,7 @@ class UnicodeScriptTokenizer final : public TensorTransform {
 };
 
 /// \brief Tokenize a scalar tensor of UTF-8 string on ICU4C defined whitespaces.
-class WhitespaceTokenizer final : public TensorTransform {
+class MS_API WhitespaceTokenizer final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] with_offsets whether to output offsets of tokens (default=false).

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

@@ -48,7 +48,7 @@ class UniformAugment;
 // Abstract class to represent a tensor transform operation in the data pipeline.
 /// \class TensorTransform transforms.h
 /// \brief A base class to represent a tensor transform operation in the data pipeline.
-class TensorTransform : public std::enable_shared_from_this<TensorTransform> {
+class MS_API TensorTransform : public std::enable_shared_from_this<TensorTransform> {
   friend class Dataset;
   friend class Execute;
   friend class transforms::Compose;
@@ -77,7 +77,7 @@ class TensorTransform : public std::enable_shared_from_this<TensorTransform> {
 };
 
 /// \brief Slice object used in SliceOption.
-class Slice {
+class MS_API Slice {
  public:
   /// \brief Constructor, with start, stop and step default to 0.
   Slice() : start_(0), stop_(0), step_(0) {}
@@ -104,7 +104,7 @@ class Slice {
 };
 
 /// \brief SliceOption used in Slice TensorTransform.
-class SliceOption {
+class MS_API SliceOption {
  public:
   /// \param[in] all Slice the whole dimension
   explicit SliceOption(bool all) : all_(all) {}
@@ -128,7 +128,7 @@ class SliceOption {
 namespace transforms {
 
 /// \brief Compose a list of transforms into a single transform.
-class Compose final : public TensorTransform {
+class MS_API Compose final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] transforms A vector of raw pointers to TensorTransform objects to be applied.
@@ -191,7 +191,7 @@ class Compose final : public TensorTransform {
 };
 
 /// \brief Concatenate all tensors into a single tensor.
-class Concatenate final : public TensorTransform {
+class MS_API Concatenate final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] axis Concatenate the tensors along given axis, only support 0 or -1 so far (default=0).
@@ -225,7 +225,7 @@ class Concatenate final : public TensorTransform {
 
 /// \brief Duplicate the input tensor to a new output tensor.
 ///     The input tensor is carried over to the output list.
-class Duplicate final : public TensorTransform {
+class MS_API Duplicate final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -251,7 +251,7 @@ class Duplicate final : public TensorTransform {
 
 /// \brief Fill all elements in the tensor with the specified value.
 ///    The output tensor will have the same shape and type as the input tensor.
-class Fill final : public TensorTransform {
+class MS_API Fill final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] fill_value Scalar value to fill the tensor with.
@@ -284,7 +284,7 @@ class Fill final : public TensorTransform {
 
 /// \brief Mask content of the input tensor with the given predicate.
 ///     Any element of the tensor that matches the predicate will be evaluated to True, otherwise False.
-class Mask final : public TensorTransform {
+class MS_API Mask final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] op One of the relational operators: EQ, NE LT, GT, LE or GE.
@@ -319,7 +319,7 @@ class Mask final : public TensorTransform {
 };
 
 /// \brief Convert the labels into OneHot format.
-class OneHot final : public TensorTransform {
+class MS_API OneHot final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] num_classes number of classes.
@@ -349,7 +349,7 @@ class OneHot final : public TensorTransform {
 };
 
 /// \brief Pad input tensor according to pad_shape
-class PadEnd final : public TensorTransform {
+class MS_API PadEnd final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] pad_shape List of integers representing the shape needed, need to have same rank with input tensor.
@@ -382,7 +382,7 @@ class PadEnd final : public TensorTransform {
 };
 
 /// \brief Randomly perform a series of transforms with a given probability.
-class RandomApply final : public TensorTransform {
+class MS_API RandomApply final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] transforms A vector of raw pointers to TensorTransform objects to be applied.
@@ -448,7 +448,7 @@ class RandomApply final : public TensorTransform {
 };
 
 /// \brief Randomly select one transform from a list of transforms to perform on the input tensor.
-class RandomChoice final : public TensorTransform {
+class MS_API RandomChoice final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] transforms A vector of raw pointers to TensorTransform objects to be applied.
@@ -513,7 +513,7 @@ class RandomChoice final : public TensorTransform {
 /// \brief Extract a tensor out using the given n slices.
 ///     The functionality of Slice is similar to the feature of indexing of NumPy.
 ///     (Currently only rank-1 tensors are supported).
-class Slice final : public TensorTransform {
+class MS_API Slice final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] slice_input Vector of SliceOption.
@@ -543,7 +543,7 @@ class Slice final : public TensorTransform {
 };
 
 /// \brief Cast the MindSpore data type of a tensor to another.
-class TypeCast final : public TensorTransform {
+class MS_API TypeCast final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] data_type mindspore::DataType to be cast to.
@@ -573,7 +573,7 @@ class TypeCast final : public TensorTransform {
 
 /// \brief Return an output tensor that contains all the unique elements of the input tensor in
 ///     the same order as they appear in the input tensor.
-class Unique final : public TensorTransform {
+class MS_API Unique final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example

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

@@ -39,7 +39,7 @@ namespace vision {
 
 /// \brief AdjustGamma TensorTransform.
 /// \note Apply gamma correction on input image.
-class AdjustGamma final : public TensorTransform {
+class MS_API AdjustGamma final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] gamma Non negative real number, which makes the output image pixel value
@@ -71,7 +71,7 @@ class AdjustGamma final : public TensorTransform {
 };
 
 /// \brief Apply AutoAugment data augmentation method.
-class AutoAugment final : public TensorTransform {
+class MS_API AutoAugment final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] policy An enum for the data auto augmentation policy (default=AutoAugmentPolicy::kImageNet).
@@ -111,7 +111,7 @@ class AutoAugment final : public TensorTransform {
 };
 
 /// \brief Apply automatic contrast on the input image.
-class AutoContrast final : public TensorTransform {
+class MS_API 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 50.
@@ -143,7 +143,7 @@ class AutoContrast final : public TensorTransform {
 
 /// \brief BoundingBoxAugment TensorTransform.
 /// \note  Apply a given image transform on a random selection of bounding box regions of a given image.
-class BoundingBoxAugment final : public TensorTransform {
+class MS_API BoundingBoxAugment final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] transform Raw pointer to the TensorTransform operation.
@@ -204,7 +204,7 @@ class BoundingBoxAugment final : public TensorTransform {
 };
 
 /// \brief Change the color space of the image.
-class ConvertColor final : public TensorTransform {
+class MS_API ConvertColor final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] convert_mode The mode of image channel conversion.
@@ -232,7 +232,7 @@ class ConvertColor final : public TensorTransform {
 
 /// \brief Mask a random section of each image with the corresponding part of another randomly
 ///     selected image in that batch.
-class CutMixBatch final : public TensorTransform {
+class MS_API CutMixBatch final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] image_batch_format The format of the batch.
@@ -261,7 +261,7 @@ class CutMixBatch final : public TensorTransform {
 };
 
 /// \brief Randomly cut (mask) out a given number of square patches from the input image.
-class CutOut final : public TensorTransform {
+class MS_API CutOut final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] length Integer representing the side length of each square patch.
@@ -289,7 +289,7 @@ class CutOut final : public TensorTransform {
 };
 
 /// \brief Apply histogram equalization on the input image.
-class Equalize final : public TensorTransform {
+class MS_API Equalize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -311,7 +311,7 @@ class Equalize final : public TensorTransform {
 };
 
 /// \brief Flip the input image horizontally.
-class HorizontalFlip final : public TensorTransform {
+class MS_API HorizontalFlip final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -333,7 +333,7 @@ class HorizontalFlip final : public TensorTransform {
 };
 
 /// \brief Transpose the input image; shape (H, W, C) to shape (C, H, W).
-class HWC2CHW final : public TensorTransform {
+class MS_API HWC2CHW final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -355,7 +355,7 @@ class HWC2CHW final : public TensorTransform {
 };
 
 /// \brief Apply invert on the input image in RGB mode.
-class Invert final : public TensorTransform {
+class MS_API Invert final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -378,7 +378,7 @@ class Invert final : public TensorTransform {
 
 /// \brief 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 final : public TensorTransform {
+class MS_API MixUpBatch final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] alpha hyperparameter of beta distribution (default = 1.0).
@@ -406,7 +406,7 @@ class MixUpBatch final : public TensorTransform {
 
 /// \brief Normalize the input image with respect to mean and standard deviation and pads an extra
 ///     channel with value zero.
-class NormalizePad final : public TensorTransform {
+class MS_API NormalizePad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] mean A vector of mean values for each channel, with respect to channel order.
@@ -444,7 +444,7 @@ class NormalizePad final : public TensorTransform {
 };
 
 /// \brief Pad the image according to padding parameters.
-class Pad final : public TensorTransform {
+class MS_API Pad final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] padding A vector representing the number of pixels to pad the image.
@@ -490,7 +490,7 @@ class Pad final : public TensorTransform {
 };
 
 /// \brief Automatically adjust the contrast of the image with a given probability.
-class RandomAutoContrast final : public TensorTransform {
+class MS_API RandomAutoContrast final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] cutoff Percent of the lightest and darkest pixels to be cut off from
@@ -525,7 +525,7 @@ class RandomAutoContrast final : public TensorTransform {
 };
 
 /// \brief Randomly adjust the sharpness of the input image with a given probability.
-class RandomAdjustSharpness final : public TensorTransform {
+class MS_API RandomAdjustSharpness final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] degree A float representing sharpness adjustment degree, which must be non negative.
@@ -559,7 +559,7 @@ class RandomAdjustSharpness final : public TensorTransform {
 /// \brief Blend 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 to the bound of the interval.
-class RandomColor final : public TensorTransform {
+class MS_API RandomColor final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] t_lb Lower bound random weights.
@@ -590,7 +590,7 @@ class RandomColor final : public TensorTransform {
 };
 
 /// \brief Randomly adjust the brightness, contrast, saturation, and hue of the input image.
-class RandomColorAdjust final : public TensorTransform {
+class MS_API RandomColorAdjust final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] brightness Brightness adjustment factor. Must be a vector of one or two values
@@ -629,7 +629,7 @@ class RandomColorAdjust final : public TensorTransform {
 };
 
 /// \brief Crop the input image at a random location.
-class RandomCrop final : public TensorTransform {
+class MS_API RandomCrop final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -681,7 +681,7 @@ class RandomCrop final : public TensorTransform {
 };
 
 /// \brief Equivalent to RandomResizedCrop TensorTransform, but crop the image before decoding.
-class RandomCropDecodeResize final : public TensorTransform {
+class MS_API RandomCropDecodeResize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -728,7 +728,7 @@ class RandomCropDecodeResize final : public TensorTransform {
 
 /// \brief Crop the input image at a random location and adjust bounding boxes accordingly.
 ///        If the cropped area is out of bbox, the returned bbox will be empty.
-class RandomCropWithBBox final : public TensorTransform {
+class MS_API RandomCropWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -778,7 +778,7 @@ class RandomCropWithBBox final : public TensorTransform {
 };
 
 /// \brief Randomly apply histogram equalization on the input image with a given probability.
-class RandomEqualize final : public TensorTransform {
+class MS_API RandomEqualize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] prob A float representing the probability of equalization, which
@@ -809,7 +809,7 @@ class RandomEqualize final : public TensorTransform {
 };
 
 /// \brief Randomly flip the input image horizontally with a given probability.
-class RandomHorizontalFlip final : public TensorTransform {
+class MS_API RandomHorizontalFlip final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] prob A float representing the probability of flip.
@@ -839,7 +839,7 @@ class RandomHorizontalFlip final : public TensorTransform {
 };
 
 /// \brief Randomly flip the input image horizontally with a given probability and adjust bounding boxes accordingly.
-class RandomHorizontalFlipWithBBox final : public TensorTransform {
+class MS_API RandomHorizontalFlipWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] prob A float representing the probability of flip.
@@ -868,7 +868,7 @@ class RandomHorizontalFlipWithBBox final : public TensorTransform {
 };
 
 /// \brief Randomly invert the input image with a given probability.
-class RandomInvert final : public TensorTransform {
+class MS_API RandomInvert final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] prob A float representing the probability of the image being inverted, which
@@ -899,7 +899,7 @@ class RandomInvert final : public TensorTransform {
 };
 
 /// \brief Add AlexNet-style PCA-based noise to an image.
-class RandomLighting final : public TensorTransform {
+class MS_API RandomLighting final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] alpha A float representing the intensity of the image (default=0.05).
@@ -929,7 +929,7 @@ class RandomLighting final : public TensorTransform {
 };
 
 /// \brief Reduce the number of bits for each color channel randomly.
-class RandomPosterize final : public TensorTransform {
+class MS_API RandomPosterize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] bit_range Range of random posterize to compress image.
@@ -960,7 +960,7 @@ class RandomPosterize final : public TensorTransform {
 };
 
 /// \brief Resize the input image using a randomly selected interpolation mode.
-class RandomResize final : public TensorTransform {
+class MS_API RandomResize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the resized image.
@@ -993,7 +993,7 @@ class RandomResize final : public TensorTransform {
 
 /// \brief Resize the input image using a randomly selected interpolation mode and adjust
 ///     bounding boxes accordingly.
-class RandomResizeWithBBox final : public TensorTransform {
+class MS_API RandomResizeWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the resized image.
@@ -1024,7 +1024,7 @@ class RandomResizeWithBBox final : public TensorTransform {
 };
 
 /// \brief Crop the input image to a random size and aspect ratio.
-class RandomResizedCrop final : public TensorTransform {
+class MS_API RandomResizedCrop final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -1072,7 +1072,7 @@ class RandomResizedCrop final : public TensorTransform {
 
 /// \brief Crop the input image to a random size and aspect ratio.
 ///        If cropped area is out of bbox, the return bbox will be empty.
-class RandomResizedCropWithBBox final : public TensorTransform {
+class MS_API RandomResizedCropWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -1117,7 +1117,7 @@ class RandomResizedCropWithBBox final : public TensorTransform {
 };
 
 /// \brief Rotate the image according to parameters.
-class RandomRotation final : public TensorTransform {
+class MS_API RandomRotation final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] degrees A float vector of size 2, representing the starting and ending degrees.
@@ -1163,7 +1163,7 @@ class RandomRotation final : public TensorTransform {
 ///     (operation, prob), where operation is a TensorTransform operation and prob is the probability that this
 ///     operation will be applied. Once a sub-policy is selected, each operation within the sub-policy with be
 ///     applied in sequence according to its probability.
-class RandomSelectSubpolicy final : public TensorTransform {
+class MS_API 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.
@@ -1234,7 +1234,7 @@ class RandomSelectSubpolicy final : public TensorTransform {
 };
 
 /// \brief Adjust the sharpness of the input image by a fixed or random degree.
-class RandomSharpness final : public TensorTransform {
+class MS_API RandomSharpness final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] degrees A float vector of size 2, representing the range of random sharpness
@@ -1266,7 +1266,7 @@ class RandomSharpness final : public TensorTransform {
 };
 
 /// \brief Invert pixels randomly within a specified range.
-class RandomSolarize final : public TensorTransform {
+class MS_API RandomSolarize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] threshold A vector with two elements specifying the pixel range to invert.
@@ -1298,7 +1298,7 @@ class RandomSolarize final : public TensorTransform {
 };
 
 /// \brief Randomly flip the input image vertically with a given probability.
-class RandomVerticalFlip final : public TensorTransform {
+class MS_API RandomVerticalFlip final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] prob A float representing the probability of flip.
@@ -1328,7 +1328,7 @@ class RandomVerticalFlip final : public TensorTransform {
 };
 
 /// \brief Randomly flip the input image vertically with a given probability and adjust bounding boxes accordingly.
-class RandomVerticalFlipWithBBox final : public TensorTransform {
+class MS_API RandomVerticalFlipWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] prob A float representing the probability of flip.
@@ -1357,7 +1357,7 @@ class RandomVerticalFlipWithBBox final : public TensorTransform {
 };
 
 /// \brief Rescale the pixel value of input image.
-class Rescale final : public TensorTransform {
+class MS_API Rescale final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] rescale Rescale factor.
@@ -1388,7 +1388,7 @@ class Rescale final : public TensorTransform {
 };
 
 /// \brief Resize the input image to the given size and adjust bounding boxes accordingly.
-class ResizeWithBBox final : public TensorTransform {
+class MS_API ResizeWithBBox final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size The output size of the resized image.
@@ -1425,7 +1425,7 @@ class ResizeWithBBox final : public TensorTransform {
 };
 
 /// \brief Change the format of input tensor from 4-channel RGBA to 3-channel BGR.
-class RGBA2BGR final : public TensorTransform {
+class MS_API RGBA2BGR final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -1450,7 +1450,7 @@ class RGBA2BGR final : public TensorTransform {
 };
 
 /// \brief Change the input 4 channel RGBA tensor to 3 channel RGB.
-class RGBA2RGB final : public TensorTransform {
+class MS_API RGBA2RGB final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -1475,7 +1475,7 @@ class RGBA2RGB final : public TensorTransform {
 };
 
 /// \note Slice the tensor to multiple patches in horizontal and vertical directions.
-class SlicePatches final : public TensorTransform {
+class MS_API SlicePatches final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] num_height The number of patches in vertical direction (default=1).
@@ -1516,7 +1516,7 @@ class SlicePatches final : 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 be 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 final : public TensorTransform {
+class MS_API SoftDvppDecodeRandomCropResizeJpeg final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the resized image.
@@ -1561,7 +1561,7 @@ class SoftDvppDecodeRandomCropResizeJpeg final : 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 be 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 final : public TensorTransform {
+class MS_API SoftDvppDecodeResizeJpeg final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the resized image.
@@ -1592,7 +1592,7 @@ class SoftDvppDecodeResizeJpeg final : public TensorTransform {
 };
 
 /// \brief Swap the red and blue channels of the input image.
-class SwapRedBlue final : public TensorTransform {
+class MS_API SwapRedBlue final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -1617,7 +1617,7 @@ class SwapRedBlue final : public TensorTransform {
 };
 
 /// \brief Randomly perform transformations, as selected from input transform list, on the input tensor.
-class UniformAugment final : public TensorTransform {
+class MS_API UniformAugment final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] transforms Raw pointer to vector of TensorTransform operations.
@@ -1684,7 +1684,7 @@ class UniformAugment final : public TensorTransform {
 };
 
 /// \brief Flip the input image vertically.
-class VerticalFlip final : public TensorTransform {
+class MS_API VerticalFlip final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example

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

@@ -36,7 +36,7 @@ namespace vision {
 
 /// \brief Decode and resize JPEG image using the hardware algorithm of
 ///     Ascend series chip DVPP module.
-class DvppDecodeResizeJpeg final : public TensorTransform {
+class MS_API DvppDecodeResizeJpeg final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] resize Parameter vector of two integers for each dimension, with respect to H,W order.
@@ -68,7 +68,7 @@ class DvppDecodeResizeJpeg final : public TensorTransform {
 
 /// \brief Decode, resize and crop JPEG image using the hardware algorithm of
 ///     Ascend series chip DVPP module.
-class DvppDecodeResizeCropJpeg final : public TensorTransform {
+class MS_API DvppDecodeResizeCropJpeg final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] crop Parameter vector of two integers for each dimension after final crop, with respect to H,W order.
@@ -101,7 +101,7 @@ class DvppDecodeResizeCropJpeg final : public TensorTransform {
 
 /// \brief Decode PNG image using the hardware algorithm of
 ///     Ascend series chip DVPP module.
-class DvppDecodePng final : public TensorTransform {
+class MS_API DvppDecodePng final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example

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

@@ -36,7 +36,7 @@ namespace vision {
 class RotateOperation;
 
 /// \brief Apply affine transform on the input image.
-class Affine final : public TensorTransform {
+class MS_API Affine final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] degrees The degrees to rotate the image.
@@ -80,7 +80,7 @@ class Affine final : public TensorTransform {
 };
 
 /// \brief Crop the input image at the center to the given size.
-class CenterCrop final : public TensorTransform {
+class MS_API CenterCrop final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the cropped image.
@@ -114,7 +114,7 @@ class CenterCrop final : public TensorTransform {
 };
 
 /// \brief Crop an image based on location and crop size.
-class Crop final : public TensorTransform {
+class MS_API 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}.
@@ -147,7 +147,7 @@ class Crop final : public TensorTransform {
 };
 
 /// \brief Decode the input image in RGB mode.
-class Decode final : public TensorTransform {
+class MS_API Decode final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] rgb A boolean indicating whether to decode the image in RGB mode or not.
@@ -178,7 +178,7 @@ class Decode final : public TensorTransform {
 };
 
 /// \brief Blur the input image with the specified Gaussian kernel.
-class GaussianBlur final : public TensorTransform {
+class MS_API GaussianBlur final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] kernel_size A vector of Gaussian kernel size for width and height. The value must be positive and odd.
@@ -210,7 +210,7 @@ class GaussianBlur final : public TensorTransform {
 };
 
 /// \brief Normalize the input image with respect to mean and standard deviation.
-class Normalize final : public TensorTransform {
+class MS_API Normalize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] mean A vector of mean values for each channel, with respect to channel order.
@@ -245,7 +245,7 @@ class Normalize final : public TensorTransform {
 };
 
 /// \brief Apply a Random Affine transformation on the input image in RGB or Greyscale mode.
-class RandomAffine final : public TensorTransform {
+class MS_API RandomAffine final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] degrees A float vector of size 2, representing the starting and ending degree.
@@ -296,7 +296,7 @@ class RandomAffine final : public TensorTransform {
 };
 
 /// \brief Resize the input image to the given size.
-class Resize final : public TensorTransform {
+class MS_API Resize final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] size A vector representing the output size of the resized image.
@@ -336,7 +336,7 @@ class Resize final : public TensorTransform {
 };
 
 /// \brief Keep the original picture ratio and fills the rest.
-class ResizePreserveAR final : public TensorTransform {
+class MS_API ResizePreserveAR final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \param[in] height The height of image output value after resizing.
@@ -377,7 +377,7 @@ class ResizePreserveAR final : public TensorTransform {
 
 /// \brief RGB2BGR TensorTransform.
 /// \notes Convert the format of input image from RGB to BGR.
-class RGB2BGR final : public TensorTransform {
+class MS_API RGB2BGR final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -404,7 +404,7 @@ class RGB2BGR final : public TensorTransform {
 /// \brief RGB2GRAY TensorTransform.
 /// \note Convert RGB image or color image to grayscale image.
 /// \brief Convert a RGB image or color image to a grayscale one.
-class RGB2GRAY final : public TensorTransform {
+class MS_API RGB2GRAY final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \par Example
@@ -429,7 +429,7 @@ class RGB2GRAY final : public TensorTransform {
 };
 
 /// \brief Rotate the input image according to parameters.
-class Rotate final : public TensorTransform {
+class MS_API Rotate final : public TensorTransform {
  public:
   /// \brief Constructor.
   /// \note This api is only used in Lite, the interpolation mode is bilinear.

mindspore/ccsrc/minddata/dataset/liteapi/include/datasets.h

@@ -63,7 +63,7 @@ class DSCallback;
 
 /// \class Dataset datasets.h
 /// \brief A base class to represent a dataset in the data pipeline.
-class Dataset : public std::enable_shared_from_this<Dataset> {
+class MS_API Dataset : public std::enable_shared_from_this<Dataset> {
  public:
   // need friend class so they can access the children_ field
   friend class Iterator;
@@ -388,7 +388,7 @@ class Dataset : public std::enable_shared_from_this<Dataset> {
   bool SaveCharIF(const std::vector<char> &dataset_path, int32_t num_files, const std::vector<char> &dataset_type);
 };
 
-class SchemaObj {
+class MS_API SchemaObj {
  public:
   /// \brief Constructor
   explicit SchemaObj(const std::string &schema_file = "") : SchemaObj(StringToChar(schema_file)) {}
@@ -490,13 +490,13 @@ class SchemaObj {
   std::shared_ptr<Data> data_;
 };
 
-class BatchDataset : public Dataset {
+class MS_API BatchDataset : public Dataset {
  public:
   BatchDataset(std::shared_ptr<Dataset> input, int32_t batch_size, bool drop_remainder = false);
   ~BatchDataset() = default;
 };
 
-class MapDataset : public Dataset {
+class MS_API MapDataset : public Dataset {
  public:
   MapDataset(std::shared_ptr<Dataset> input, std::vector<std::shared_ptr<TensorOperation>> operations,
              const std::vector<std::vector<char>> &input_columns, const std::vector<std::vector<char>> &output_columns,
@@ -505,13 +505,13 @@ class MapDataset : public Dataset {
   ~MapDataset() = default;
 };
 
-class ProjectDataset : public Dataset {
+class MS_API ProjectDataset : public Dataset {
  public:
   ProjectDataset(std::shared_ptr<Dataset> input, const std::vector<std::vector<char>> &columns);
   ~ProjectDataset() = default;
 };
 
-class ShuffleDataset : public Dataset {
+class MS_API ShuffleDataset : public Dataset {
  public:
   ShuffleDataset(std::shared_ptr<Dataset> input, int32_t buffer_size);
   ~ShuffleDataset() = default;
@@ -522,16 +522,16 @@ class ShuffleDataset : public Dataset {
 /// \note The reason for using this API is that std::string will be constrained by the
 ///    compiler option '_GLIBCXX_USE_CXX11_ABI' while char is free of this restriction.
 /// \return Shared pointer to the current schema.
-std::shared_ptr<SchemaObj> SchemaCharIF(const std::vector<char> &schema_file);
+std::shared_ptr<SchemaObj> MS_API SchemaCharIF(const std::vector<char> &schema_file);
 
 /// \brief Function to create a SchemaObj.
 /// \param[in] schema_file Path of schema file.
 /// \return Shared pointer to the current schema.
-inline std::shared_ptr<SchemaObj> Schema(const std::string &schema_file = "") {
+inline std::shared_ptr<SchemaObj> MS_API Schema(const std::string &schema_file = "") {
   return SchemaCharIF(StringToChar(schema_file));
 }
 
-class AlbumDataset : public Dataset {
+class MS_API AlbumDataset : public Dataset {
  public:
   /// \brief Constructor of AlbumDataset.
   /// \param[in] dataset_dir Path to the root directory that contains the dataset.
@@ -599,10 +599,10 @@ class AlbumDataset : public Dataset {
 ///      /* Note: As we defined before, each data dictionary owns keys "image", "label" and "id" */
 ///      auto image = row["image"];
 /// \endcode
-inline std::shared_ptr<AlbumDataset> Album(const std::string &dataset_dir, const std::string &data_schema,
-                                           const std::vector<std::string> &column_names = {}, bool decode = false,
-                                           const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
-                                           const std::shared_ptr<DatasetCache> &cache = nullptr) {
+inline std::shared_ptr<AlbumDataset> MS_API
+Album(const std::string &dataset_dir, const std::string &data_schema, const std::vector<std::string> &column_names = {},
+      bool decode = false, const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
+      const std::shared_ptr<DatasetCache> &cache = nullptr) {
   return std::make_shared<AlbumDataset>(StringToChar(dataset_dir), StringToChar(data_schema),
                                         VectorStringToChar(column_names), decode, sampler, cache);
 }
@@ -615,10 +615,10 @@ inline std::shared_ptr<AlbumDataset> Album(const std::string &dataset_dir, const
 /// \param[in] sampler Raw pointer to a sampler object used to choose samples from the dataset.
 /// \param[in] cache Tensor cache to use. (default=nullptr which means no cache is used).
 /// \return Shared pointer to the current Dataset
-inline std::shared_ptr<AlbumDataset> Album(const std::string &dataset_dir, const std::string &data_schema,
-                                           const std::vector<std::string> &column_names, bool decode,
-                                           const Sampler *sampler,
-                                           const std::shared_ptr<DatasetCache> &cache = nullptr) {
+inline std::shared_ptr<AlbumDataset> MS_API Album(const std::string &dataset_dir, const std::string &data_schema,
+                                                  const std::vector<std::string> &column_names, bool decode,
+                                                  const Sampler *sampler,
+                                                  const std::shared_ptr<DatasetCache> &cache = nullptr) {
   return std::make_shared<AlbumDataset>(StringToChar(dataset_dir), StringToChar(data_schema),
                                         VectorStringToChar(column_names), decode, sampler, cache);
 }
@@ -631,15 +631,15 @@ inline std::shared_ptr<AlbumDataset> Album(const std::string &dataset_dir, const
 /// \param[in] sampler Sampler object used to choose samples from the dataset.
 /// \param[in] cache Tensor cache to use. (default=nullptr which means no cache is used).
 /// \return Shared pointer to the current Dataset
-inline std::shared_ptr<AlbumDataset> Album(const std::string &dataset_dir, const std::string &data_schema,
-                                           const std::vector<std::string> &column_names, bool decode,
-                                           const std::reference_wrapper<Sampler> sampler,
-                                           const std::shared_ptr<DatasetCache> &cache = nullptr) {
+inline std::shared_ptr<AlbumDataset> MS_API Album(const std::string &dataset_dir, const std::string &data_schema,
+                                                  const std::vector<std::string> &column_names, bool decode,
+                                                  const std::reference_wrapper<Sampler> sampler,
+                                                  const std::shared_ptr<DatasetCache> &cache = nullptr) {
   return std::make_shared<AlbumDataset>(StringToChar(dataset_dir), StringToChar(data_schema),
                                         VectorStringToChar(column_names), decode, sampler, cache);
 }
 
-class MnistDataset : public Dataset {
+class MS_API MnistDataset : public Dataset {
  public:
   /// \brief Constructor of MnistDataset.
   /// \param[in] dataset_dir Path to the root directory that contains the dataset.
@@ -692,9 +692,10 @@ class MnistDataset : public Dataset {
 ///      /* Note: In MNIST dataset, each dictionary has keys "image" and "label" */
 ///      auto image = row["image"];
 /// \endcode
-inline std::shared_ptr<MnistDataset> Mnist(const std::string &dataset_dir, const std::string &usage = "all",
-                                           const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
-                                           const std::shared_ptr<DatasetCache> &cache = nullptr) {
+inline std::shared_ptr<MnistDataset> MS_API
+Mnist(const std::string &dataset_dir, const std::string &usage = "all",
+      const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
+      const std::shared_ptr<DatasetCache> &cache = nullptr) {
   return std::make_shared<MnistDataset>(StringToChar(dataset_dir), StringToChar(usage), sampler, cache);
 }
 
@@ -705,9 +706,9 @@ inline std::shared_ptr<MnistDataset> Mnist(const std::string &dataset_dir, const
 /// \param[in] sampler Raw pointer to a sampler object used to choose samples from the dataset.
 /// \param[in] cache Tensor cache to use. (default=nullptr which means no cache is used).
 /// \return Shared pointer to the current MnistDataset
-inline std::shared_ptr<MnistDataset> Mnist(const std::string &dataset_dir, const std::string &usage,
-                                           const Sampler *sampler,
-                                           const std::shared_ptr<DatasetCache> &cache = nullptr) {
+inline std::shared_ptr<MnistDataset> MS_API Mnist(const std::string &dataset_dir, const std::string &usage,
+                                                  const Sampler *sampler,
+                                                  const std::shared_ptr<DatasetCache> &cache = nullptr) {
   return std::make_shared<MnistDataset>(StringToChar(dataset_dir), StringToChar(usage), sampler, cache);
 }
 
@@ -718,9 +719,9 @@ inline std::shared_ptr<MnistDataset> Mnist(const std::string &dataset_dir, const
 /// \param[in] sampler Sampler object used to choose samples from the dataset.
 /// \param[in] cache Tensor cache to use. (default=nullptr which means no cache is used).
 /// \return Shared pointer to the current MnistDataset
-inline std::shared_ptr<MnistDataset> Mnist(const std::string &dataset_dir, const std::string &usage,
-                                           const std::reference_wrapper<Sampler> sampler,
-                                           const std::shared_ptr<DatasetCache> &cache = nullptr) {
+inline std::shared_ptr<MnistDataset> MS_API Mnist(const std::string &dataset_dir, const std::string &usage,
+                                                  const std::reference_wrapper<Sampler> sampler,
+                                                  const std::shared_ptr<DatasetCache> &cache = nullptr) {
   return std::make_shared<MnistDataset>(StringToChar(dataset_dir), StringToChar(usage), sampler, cache);
 }
 }  // namespace dataset