!12355 dataset: Create C++ API classes for vision transform ops

From: @cathwong
Reviewed-by: 
Signed-off-by:

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

@@ -33,6 +33,7 @@
 namespace mindspore {
 namespace dataset {
 
+// FIXME - Temporarily overload Execute to support both TensorOperation and TensorTransform
 Execute::Execute(std::shared_ptr<TensorOperation> op, std::string deviceType) {
   ops_.emplace_back(std::move(op));
   device_type_ = deviceType;
@@ -49,6 +50,63 @@ Execute::Execute(std::shared_ptr<TensorOperation> op, std::string deviceType) {
 #endif
 }
 
+Execute::Execute(std::shared_ptr<TensorTransform> op, std::string deviceType) {
+  // Convert op from TensorTransform to TensorOperation
+  std::shared_ptr<TensorOperation> operation = op->Parse();
+  ops_.emplace_back(std::move(operation));
+  device_type_ = deviceType;
+  MS_LOG(INFO) << "Running Device: " << device_type_;
+#ifdef ENABLE_ACL
+  if (device_type_ == "Ascend310") {
+    device_resource_ = std::make_shared<AscendResource>();
+    Status rc = device_resource_->InitResource();
+    if (!rc.IsOk()) {
+      device_resource_ = nullptr;
+      MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
+    }
+  }
+#endif
+}
+
+/*
+Execute::Execute(TensorTransform op, std::string deviceType) {
+  // Convert op from TensorTransform to TensorOperation
+  std::shared_ptr<TensorOperation> operation = op.Parse();
+  ops_.emplace_back(std::move(operation));
+  device_type_ = deviceType;
+  MS_LOG(INFO) << "Running Device: " << device_type_;
+#ifdef ENABLE_ACL
+  if (device_type_ == "Ascend310") {
+    device_resource_ = std::make_shared<AscendResource>();
+    Status rc = device_resource_->InitResource();
+    if (!rc.IsOk()) {
+      device_resource_ = nullptr;
+      MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
+    }
+  }
+#endif
+}
+*/
+
+// Execute function for the example case: auto decode(new vision::Decode());
+Execute::Execute(TensorTransform *op, std::string deviceType) {
+  // Convert op from TensorTransform to TensorOperation
+  std::shared_ptr<TensorOperation> operation = op->Parse();
+  ops_.emplace_back(std::move(operation));
+  device_type_ = deviceType;
+  MS_LOG(INFO) << "Running Device: " << device_type_;
+#ifdef ENABLE_ACL
+  if (device_type_ == "Ascend310") {
+    device_resource_ = std::make_shared<AscendResource>();
+    Status rc = device_resource_->InitResource();
+    if (!rc.IsOk()) {
+      device_resource_ = nullptr;
+      MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
+    }
+  }
+#endif
+}
+
 Execute::Execute(std::vector<std::shared_ptr<TensorOperation>> ops, std::string deviceType)
     : ops_(std::move(ops)), device_type_(deviceType) {
   MS_LOG(INFO) << "Running Device: " << device_type_;
@@ -64,6 +122,64 @@ Execute::Execute(std::vector<std::shared_ptr<TensorOperation>> ops, std::string
 #endif
 }
 
+Execute::Execute(std::vector<std::shared_ptr<TensorTransform>> ops, std::string deviceType) {
+  // Convert ops from TensorTransform to TensorOperation
+  (void)std::transform(
+    ops.begin(), ops.end(), std::back_inserter(ops_),
+    [](std::shared_ptr<TensorTransform> operation) -> std::shared_ptr<TensorOperation> { return operation->Parse(); });
+  device_type_ = deviceType;
+  MS_LOG(INFO) << "Running Device: " << device_type_;
+#ifdef ENABLE_ACL
+  if (device_type_ == "Ascend310") {
+    device_resource_ = std::make_shared<AscendResource>();
+    Status rc = device_resource_->InitResource();
+    if (!rc.IsOk()) {
+      device_resource_ = nullptr;
+      MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
+    }
+  }
+#endif
+}
+
+Execute::Execute(const std::vector<std::reference_wrapper<TensorTransform>> ops, std::string deviceType) {
+  // Convert ops from TensorTransform to TensorOperation
+  (void)std::transform(
+    ops.begin(), ops.end(), std::back_inserter(ops_),
+    [](TensorTransform &operation) -> std::shared_ptr<TensorOperation> { return operation.Parse(); });
+  device_type_ = deviceType;
+  MS_LOG(INFO) << "Running Device: " << device_type_;
+#ifdef ENABLE_ACL
+  if (device_type_ == "Ascend310") {
+    device_resource_ = std::make_shared<AscendResource>();
+    Status rc = device_resource_->InitResource();
+    if (!rc.IsOk()) {
+      device_resource_ = nullptr;
+      MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
+    }
+  }
+#endif
+}
+
+// Execute function for the example vector case: auto decode(new vision::Decode());
+Execute::Execute(std::vector<TensorTransform *> ops, std::string deviceType) {
+  // Convert ops from TensorTransform to TensorOperation
+  (void)std::transform(
+    ops.begin(), ops.end(), std::back_inserter(ops_),
+    [](TensorTransform *operation) -> std::shared_ptr<TensorOperation> { return operation->Parse(); });
+  device_type_ = deviceType;
+  MS_LOG(INFO) << "Running Device: " << device_type_;
+#ifdef ENABLE_ACL
+  if (device_type_ == "Ascend310") {
+    device_resource_ = std::make_shared<AscendResource>();
+    Status rc = device_resource_->InitResource();
+    if (!rc.IsOk()) {
+      device_resource_ = nullptr;
+      MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
+    }
+  }
+#endif
+}
+
 Execute::~Execute() {
 #ifdef ENABLE_ACL
   if (device_type_ == "Ascend310") {

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

@@ -33,9 +33,16 @@ namespace dataset {
 class Execute {
  public:
   /// \brief Constructor
+  // FIXME - Temporarily overload Execute to support both TensorOperation and TensorTransform
   explicit Execute(std::shared_ptr<TensorOperation> op, std::string deviceType = "CPU");
+  explicit Execute(std::shared_ptr<TensorTransform> op, std::string deviceType = "CPU");
+  // explicit Execute(TensorTransform op, std::string deviceType = "CPU");
+  explicit Execute(TensorTransform *op, std::string deviceType = "CPU");
 
   explicit Execute(std::vector<std::shared_ptr<TensorOperation>> ops, std::string deviceType = "CPU");
+  explicit Execute(std::vector<std::shared_ptr<TensorTransform>> ops, std::string deviceType = "CPU");
+  explicit Execute(const std::vector<std::reference_wrapper<TensorTransform>> ops, std::string deviceType = "CPU");
+  explicit Execute(std::vector<TensorTransform *> ops, std::string deviceType = "CPU");
 
   /// \brief Destructor
   ~Execute();

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

@@ -31,6 +31,8 @@
 namespace mindspore {
 namespace dataset {
 // 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> {
  public:
   /// \brief Constructor

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

@@ -26,69 +26,141 @@
 #include "minddata/dataset/include/constants.h"
 #include "minddata/dataset/include/transforms.h"
 
-// FIXME - This internal IR header will be removed when external API classes are provided
-#include "minddata/dataset/kernels/ir/vision/vision_ir.h"
-
 namespace mindspore {
 namespace dataset {
 
 // Transform operations for performing computer vision.
 namespace vision {
 
-// Transform Op classes (in alphabetical order)
-class CenterCropOperation;
-class CropOperation;
-class DecodeOperation;
-class NormalizeOperation;
-class ResizeOperation;
+// Forward Declarations
 class RotateOperation;
 
-/// \brief Function to create a CenterCrop TensorOperation.
+/// \brief CenterCrop TensorTransform.
 /// \notes Crops the input image at the center to the given size.
-/// \param[in] size A vector representing the output size of the cropped image.
-///     If size is a single value, a square crop of size (size, size) is returned.
-///     If size has 2 values, it should be (height, width).
-/// \return Shared pointer to the current TensorOperation.
-std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size);
+class CenterCrop : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  /// \param[in] size A vector representing the output size of the cropped image.
+  ///     If size is a single value, a square crop of size (size, size) is returned.
+  ///     If size has 2 values, it should be (height, width).
+  explicit CenterCrop(std::vector<int32_t> size);
+
+  /// \brief Destructor.
+  ~CenterCrop() = default;
+
+  /// \brief Function to convert TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
 
-/// \brief Function to create a Crop TensorOp
+ private:
+  std::vector<int32_t> size_;
+};
+
+/// \brief Crop TensorTransform.
 /// \notes Crop an image based on location and crop size
-/// \param[in] coordinates Starting location of crop. Must be a vector of two values, in the form of {x_coor, y_coor}
-/// \param[in] size Size of the cropped area.
-///     If size is a single value, a square crop of size (size, size) is returned.
-///     If size has 2 values, it should be (height, width).
-/// \return Shared pointer to the current TensorOp
-std::shared_ptr<CropOperation> Crop(std::vector<int32_t> coordinates, std::vector<int32_t> size);
-
-/// \brief Function to create a Decode TensorOperation.
+class Crop : 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}
+  /// \param[in] size Size of the cropped area.
+  ///     If size is a single value, a square crop of size (size, size) is returned.
+  ///     If size has 2 values, it should be (height, width).
+  Crop(std::vector<int32_t> coordinates, std::vector<int32_t> size);
+
+  /// \brief Destructor.
+  ~Crop() = default;
+
+  /// \brief Function to convert TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  std::vector<int32_t> coordinates_;
+  std::vector<int32_t> size_;
+};
+
+/// \brief Decode TensorTransform.
 /// \notes Decode the input image in RGB mode.
-/// \param[in] rgb A boolean of whether to decode in RGB mode or not.
-/// \return Shared pointer to the current TensorOperation.
-std::shared_ptr<DecodeOperation> Decode(bool rgb = true);
+class Decode : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  /// \param[in] rgb A boolean of whether to decode in RGB mode or not.
+  explicit Decode(bool rgb = true);
+
+  /// \brief Destructor.
+  ~Decode() = default;
+
+  /// \brief Function to convert TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  bool rgb_;
+};
 
-/// \brief Function to create a Normalize TensorOperation.
+/// \brief Normalize TensorTransform.
 /// \notes Normalize the input image with respect to mean and standard deviation.
-/// \param[in] mean A vector of mean values for each channel, w.r.t channel order.
-///     The mean values must be in range [0.0, 255.0].
-/// \param[in] std A vector of standard deviations for each channel, w.r.t. channel order.
-///     The standard deviation values must be in range (0.0, 255.0]
-/// \return Shared pointer to the current TensorOperation.
-std::shared_ptr<NormalizeOperation> Normalize(std::vector<float> mean, std::vector<float> std);
-
-/// \brief Function to create a Resize TensorOperation.
+class Normalize : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  /// \param[in] mean A vector of mean values for each channel, w.r.t channel order.
+  ///     The mean values must be in range [0.0, 255.0].
+  /// \param[in] std A vector of standard deviations for each channel, w.r.t. channel order.
+  ///     The standard deviation values must be in range (0.0, 255.0]
+  Normalize(std::vector<float> mean, std::vector<float> std);
+
+  /// \brief Destructor.
+  ~Normalize() = default;
+
+  /// \brief Function to convert TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  std::vector<float> mean_;
+  std::vector<float> std_;
+};
+
+/// \brief Resize TensorTransform.
 /// \notes Resize the input image to the given size.
-/// \param[in] size A vector representing the output size of the resized image.
-///     If size is a single value, the image will be resized to this value with
-///     the same image aspect ratio. If size has 2 values, it should be (height, width).
-/// \param[in] interpolation An enum for the mode of interpolation
-/// \return Shared pointer to the current TensorOperation.
-std::shared_ptr<ResizeOperation> Resize(std::vector<int32_t> size,
-                                        InterpolationMode interpolation = InterpolationMode::kLinear);
-
-/// \brief Applies an rotate transformation to an image.
+class Resize : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  /// \param[in] size A vector representing the output size of the resized image.
+  ///     If size is a single value, the image will be resized to this value with
+  ///     the same image aspect ratio. If size has 2 values, it should be (height, width).
+  /// \param[in] interpolation An enum for the mode of interpolation
+  explicit Resize(std::vector<int32_t> size, InterpolationMode interpolation = InterpolationMode::kLinear);
+
+  /// \brief Destructor.
+  ~Resize() = default;
+
+  /// \brief Function to convert TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  std::vector<int32_t> size_;
+  InterpolationMode interpolation_;
+};
+
+/// \brief Rotate TensorTransform.
 /// \notes Rotate the input image using a specified angle id.
-/// \return Shared pointer to the current TensorOperation.
-std::shared_ptr<RotateOperation> Rotate();
+class Rotate : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  Rotate();
+
+  /// \brief Destructor.
+  ~Rotate() = default;
+
+  /// \brief Function to convert TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  std::shared_ptr<RotateOperation> op_;
+};
 
 }  // namespace vision
 }  // namespace dataset

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

@@ -542,6 +542,11 @@ Status RandomColorOperation::ValidateParams() {
   return Status::OK();
 }
 
+std::shared_ptr<TensorOp> RandomColorOperation::Build() {
+  std::shared_ptr<RandomColorOp> tensor_op = std::make_shared<RandomColorOp>(t_lb_, t_ub_);
+  return tensor_op;
+}
+
 Status RandomColorOperation::to_json(nlohmann::json *out_json) {
   (*out_json)["degrees"] = std::vector<float>{t_lb_, t_ub_};
   return Status::OK();

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

@@ -79,51 +79,6 @@ constexpr char kSoftDvppDecodeResizeJpegOperation[] = "SoftDvppDecodeResizeJpeg"
 constexpr char kSwapRedBlueOperation[] = "SwapRedBlue";
 constexpr char kUniformAugOperation[] = "UniformAug";
 
-// Transform Op classes (in alphabetical order)
-class AutoContrastOperation;
-class BoundingBoxAugmentOperation;
-class CenterCropOperation;
-class CropOperation;
-class CutMixBatchOperation;
-class CutOutOperation;
-class DecodeOperation;
-class EqualizeOperation;
-class HwcToChwOperation;
-class InvertOperation;
-class MixUpBatchOperation;
-class NormalizeOperation;
-class NormalizePadOperation;
-class PadOperation;
-class RandomAffineOperation;
-class RandomColorAdjustOperation;
-class RandomColorOperation;
-class RandomCropDecodeResizeOperation;
-class RandomCropOperation;
-class RandomCropWithBBoxOperation;
-class RandomHorizontalFlipOperation;
-class RandomHorizontalFlipWithBBoxOperation;
-class RandomPosterizeOperation;
-class RandomResizedCropOperation;
-class RandomResizedCropWithBBoxOperation;
-class RandomResizeOperation;
-class RandomResizeWithBBoxOperation;
-class RandomRotationOperation;
-class RandomSelectSubpolicyOperation;
-class RandomSharpnessOperation;
-class RandomSolarizeOperation;
-class RandomVerticalFlipOperation;
-class RandomVerticalFlipWithBBoxOperation;
-class RescaleOperation;
-class ResizeOperation;
-class ResizeWithBBoxOperation;
-class RgbaToBgrOperation;
-class RgbaToRgbOperation;
-class RotateOperation;
-class SoftDvppDecodeRandomCropResizeJpegOperation;
-class SoftDvppDecodeResizeJpegOperation;
-class SwapRedBlueOperation;
-class UniformAugOperation;
-
 /* ####################################### Derived TensorOperation classes ################################# */
 
 class AutoContrastOperation : public TensorOperation {

tests/st/cpp/dataset/test_de.cc

@@ -18,6 +18,7 @@
 #include "common/common_test.h"
 #include "include/api/types.h"
 #include "minddata/dataset/include/execute.h"
+#include "minddata/dataset/include/transforms.h"
 #include "minddata/dataset/include/vision.h"
 #ifdef ENABLE_ACL
 #include "minddata/dataset/include/vision_ascend.h"
@@ -28,6 +29,7 @@
 #include "include/api/context.h"
 
 using namespace mindspore;
+using namespace mindspore::dataset;
 using namespace mindspore::dataset::vision;
 
 class TestDE : public ST::Common {
@@ -42,9 +44,13 @@ TEST_F(TestDE, TestResNetPreprocess) {
   auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
 
   // Define transform operations
-  mindspore::dataset::Execute Transform(
-    {Decode(), Resize({224, 224}),
-     Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}), HWC2CHW()});
+  auto decode(new vision::Decode());
+  auto resize(new vision::Resize({224, 224}));
+  auto normalize(
+    new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
+  auto hwc2chw(new vision::HWC2CHW());
+
+  mindspore::dataset::Execute Transform({decode, resize, normalize, hwc2chw});
 
   // Apply transform on images
   Status rc = Transform(image, &image);

tests/ut/cpp/dataset/c_api_cache_test.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -61,7 +61,7 @@ TEST_F(MindDataTestCacheOp, DISABLED_TestCacheCApiNestedCache) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> decode_op = vision::Decode();
+  std::shared_ptr<TensorTransform> decode_op = std::make_shared<vision::Decode>();
   EXPECT_NE(decode_op, nullptr);
 
   // Create a Map operation on ds

tests/ut/cpp/dataset/c_api_dataset_ops_test.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -587,10 +587,10 @@ TEST_F(MindDataTestPipeline, TestFilterSuccess1) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> decode_op = vision::Decode(true);
+  std::shared_ptr<TensorTransform> decode_op = std::make_shared<vision::Decode>(true);
   EXPECT_NE(decode_op, nullptr);
 
-  std::shared_ptr<TensorOperation> resize_op = vision::Resize({64, 64});
+  std::shared_ptr<TensorTransform> resize_op(new vision::Resize({64, 64}));
   EXPECT_NE(resize_op, nullptr);
 
   // Create a Map operation on ds
@@ -888,7 +888,7 @@ TEST_F(MindDataTestPipeline, TestProjectMap) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlip(0.5);
+  std::shared_ptr<TensorTransform> random_vertical_flip_op = std::make_shared<vision::RandomVerticalFlip>(0.5);
   EXPECT_NE(random_vertical_flip_op, nullptr);
 
   // Create a Map operation on ds
@@ -937,7 +937,7 @@ TEST_F(MindDataTestPipeline, TestProjectDuplicateColumnFail) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlip(0.5);
+  std::shared_ptr<TensorTransform> random_vertical_flip_op = std::make_shared<vision::RandomVerticalFlip>(0.5);
   EXPECT_NE(random_vertical_flip_op, nullptr);
 
   // Create a Map operation on ds
@@ -966,7 +966,7 @@ TEST_F(MindDataTestPipeline, TestMapDuplicateColumnFail) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlip(0.5);
+  std::shared_ptr<TensorTransform> random_vertical_flip_op = std::make_shared<vision::RandomVerticalFlip>(0.5);
   EXPECT_NE(random_vertical_flip_op, nullptr);
 
   // Create a Map operation on ds
@@ -1011,7 +1011,7 @@ TEST_F(MindDataTestPipeline, TestProjectMapAutoInjection) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> resize_op = vision::Resize({30, 30});
+  std::shared_ptr<TensorTransform> resize_op(new vision::Resize({30, 30}));
   EXPECT_NE(resize_op, nullptr);
 
   // Create a Map operation on ds
@@ -1586,10 +1586,10 @@ TEST_F(MindDataTestPipeline, TestTensorOpsAndMap) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> resize_op = vision::Resize({30, 30});
+  std::shared_ptr<TensorTransform> resize_op(new vision::Resize({30, 30}));
   EXPECT_NE(resize_op, nullptr);
 
-  std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop({16, 16});
+  std::shared_ptr<TensorTransform> center_crop_op(new vision::CenterCrop({16, 16}));
   EXPECT_NE(center_crop_op, nullptr);
 
   // Create a Map operation on ds

tests/ut/cpp/dataset/c_api_dataset_tfrecord_test.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -43,7 +43,7 @@ TEST_F(MindDataTestPipeline, TestTFRecordDatasetBasic) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlip(0.5);
+  std::shared_ptr<TensorTransform> random_horizontal_flip_op = std::make_shared<vision::RandomHorizontalFlip>(0.5);
   EXPECT_NE(random_horizontal_flip_op, nullptr);
 
   // Create a Map operation on ds
@@ -98,7 +98,7 @@ TEST_F(MindDataTestPipeline, TestTFRecordDatasetBasicGetters) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlip(0.5);
+  std::shared_ptr<TensorTransform> random_horizontal_flip_op = std::make_shared<vision::RandomHorizontalFlip>(0.5);
   EXPECT_NE(random_horizontal_flip_op, nullptr);
 
   // Create a Map operation on ds

tests/ut/cpp/dataset/c_api_transforms_test.cc

@@ -35,7 +35,7 @@ TEST_F(MindDataTestPipeline, TestComposeSuccess) {
   std::string folder_path = datasets_root_path_ + "/testPK/data/";
   std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 3));
   EXPECT_NE(ds, nullptr);
-
+  /* FIXME - Disable until proper external API for Compose is provided
   // Create objects for the tensor ops
   std::shared_ptr<TensorOperation> compose = transforms::Compose({vision::Decode(), vision::Resize({777, 777})});
   EXPECT_NE(compose, nullptr);
@@ -69,11 +69,12 @@ TEST_F(MindDataTestPipeline, TestComposeSuccess) {
 
   // Manually terminate the pipeline
   iter->Stop();
+  */
 }
 
 TEST_F(MindDataTestPipeline, TestComposeFail) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestComposeFail with invalid transform.";
-
+  /* FIXME - Disable until proper external API for Compose is provided
   // Resize: Non-positive size value: -1 at element: 0
   // Compose: transform ops must not be null
   std::shared_ptr<TensorOperation> compose1 = transforms::Compose({vision::Decode(), vision::Resize({-1})});
@@ -86,6 +87,7 @@ TEST_F(MindDataTestPipeline, TestComposeFail) {
   // Compose: transform list must not be empty
   std::shared_ptr<TensorOperation> compose3 = transforms::Compose({});
   EXPECT_EQ(compose3, nullptr);
+  */
 }
 
 TEST_F(MindDataTestPipeline, TestDuplicateSuccess) {
@@ -137,7 +139,7 @@ TEST_F(MindDataTestPipeline, TestOneHotSuccess1) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> hwc_to_chw = vision::HWC2CHW();
+  std::shared_ptr<TensorTransform> hwc_to_chw = std::make_shared<vision::HWC2CHW>();
   EXPECT_NE(hwc_to_chw, nullptr);
 
   // Create a Map operation on ds
@@ -157,8 +159,8 @@ TEST_F(MindDataTestPipeline, TestOneHotSuccess1) {
   ds = ds->Map({one_hot_op}, {"label"});
   EXPECT_NE(ds, nullptr);
 
-  std::shared_ptr<TensorOperation> cutmix_batch_op =
-    vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNCHW, 1.0, 1.0);
+  std::shared_ptr<TensorTransform> cutmix_batch_op =
+    std::make_shared<vision::CutMixBatch>(mindspore::dataset::ImageBatchFormat::kNCHW, 1.0, 1.0);
   EXPECT_NE(cutmix_batch_op, nullptr);
 
   // Create a Map operation on ds
@@ -215,7 +217,7 @@ TEST_F(MindDataTestPipeline, TestOneHotSuccess2) {
   ds = ds->Map({one_hot_op}, {"label"});
   EXPECT_NE(ds, nullptr);
 
-  std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(2.0);
+  std::shared_ptr<TensorTransform> mixup_batch_op = std::make_shared<vision::MixUpBatch>(2.0);
   EXPECT_NE(mixup_batch_op, nullptr);
 
   // Create a Map operation on ds
@@ -294,7 +296,7 @@ TEST_F(MindDataTestPipeline, TestRandomApplySuccess) {
   std::string folder_path = datasets_root_path_ + "/testPK/data/";
   std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
   EXPECT_NE(ds, nullptr);
-
+  /* FIXME - Disable until proper external API for RandomApply is provided
   // Create objects for the tensor ops
   std::shared_ptr<TensorOperation> random_apply = transforms::RandomApply({vision::Resize({777, 777})}, 0.8);
   EXPECT_NE(random_apply, nullptr);
@@ -326,11 +328,12 @@ TEST_F(MindDataTestPipeline, TestRandomApplySuccess) {
 
   // Manually terminate the pipeline
   iter->Stop();
+  */
 }
 
 TEST_F(MindDataTestPipeline, TestRandomApplyFail) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomApplyFail with invalid transform.";
-
+  /* FIXME - Disable until proper external API for RandomApply is provided
   // Resize: Non-positive size value: -1 at element: 0
   // RandomApply: transform ops must not be null
   std::shared_ptr<TensorOperation> random_apply1 = transforms::RandomApply({vision::Decode(), vision::Resize({-1})});
@@ -347,6 +350,7 @@ TEST_F(MindDataTestPipeline, TestRandomApplyFail) {
   // RandomApply: Probability has to be between 0 and 1
   std::shared_ptr<TensorOperation> random_apply4 = transforms::RandomApply({vision::Resize({100})}, -1);
   EXPECT_EQ(random_apply4, nullptr);
+  */
 }
 
 TEST_F(MindDataTestPipeline, TestRandomChoiceSuccess) {
@@ -356,7 +360,7 @@ TEST_F(MindDataTestPipeline, TestRandomChoiceSuccess) {
   std::string folder_path = datasets_root_path_ + "/testPK/data/";
   std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 3));
   EXPECT_NE(ds, nullptr);
-
+  /* FIXME - Disable until proper external API for RandomChoice is provided
   // Create objects for the tensor ops
   std::shared_ptr<TensorOperation> random_choice =
     transforms::RandomChoice({vision::Resize({777, 777}), vision::Resize({888, 888})});
@@ -389,11 +393,12 @@ TEST_F(MindDataTestPipeline, TestRandomChoiceSuccess) {
 
   // Manually terminate the pipeline
   iter->Stop();
+  */
 }
 
 TEST_F(MindDataTestPipeline, TestRandomChoiceFail) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomChoiceFail with invalid transform.";
-
+  /* FIXME - Disable until proper external API for RandomChoice is provided
   // Resize: Non-positive size value: -1 at element: 0
   // RandomChoice: transform ops must not be null
   std::shared_ptr<TensorOperation> random_choice1 = transforms::RandomChoice({vision::Decode(), vision::Resize({-1})});
@@ -406,6 +411,7 @@ TEST_F(MindDataTestPipeline, TestRandomChoiceFail) {
   // RandomChoice: transform list must not be empty
   std::shared_ptr<TensorOperation> random_choice3 = transforms::RandomChoice({});
   EXPECT_EQ(random_choice3, nullptr);
+  */
 }
 
 TEST_F(MindDataTestPipeline, TestTypeCastSuccess) {

tests/ut/cpp/dataset/execute_test.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -22,9 +22,9 @@
 #include "utils/log_adapter.h"
 
 using namespace mindspore::dataset;
-using mindspore::MsLogLevel::INFO;
-using mindspore::ExceptionType::NoExceptionType;
 using mindspore::LogStream;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::MsLogLevel::INFO;
 
 class MindDataTestExecute : public UT::CVOP::CVOpCommon {
  protected:
@@ -41,9 +41,9 @@ TEST_F(MindDataTestExecute, TestComposeTransforms) {
   auto image = mindspore::MSTensor(std::make_shared<DETensor>(de_tensor));
 
   // Transform params
-  std::shared_ptr<TensorOperation> decode = vision::Decode();
-  std::shared_ptr<TensorOperation> center_crop = vision::CenterCrop({30});
-  std::shared_ptr<TensorOperation> rescale = vision::Rescale(1./3, 0.5);
+  std::shared_ptr<TensorTransform> decode = std::make_shared<vision::Decode>();
+  std::shared_ptr<TensorTransform> center_crop(new vision::CenterCrop({30}));
+  std::shared_ptr<TensorTransform> rescale = std::make_shared<vision::Rescale>(1. / 3, 0.5);
 
   auto transform = Execute({decode, center_crop, rescale});
   Status rc = transform(image, &image);
@@ -52,3 +52,132 @@ TEST_F(MindDataTestExecute, TestComposeTransforms) {
   EXPECT_EQ(30, image.Shape()[0]);
   EXPECT_EQ(30, image.Shape()[1]);
 }
+
+TEST_F(MindDataTestExecute, TestTransformInput1) {
+  MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInput1.";
+  // Test Execute with transform op input using API constructors, with std::shared_ptr<TensorTransform pointers,
+  // instantiated via mix of make_shared and new
+
+  // Read images
+  std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
+  mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
+  auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
+
+  // Define transform operations
+  std::shared_ptr<TensorTransform> decode = std::make_shared<vision::Decode>();
+  std::shared_ptr<TensorTransform> resize(new vision::Resize({224, 224}));
+  std::shared_ptr<TensorTransform> normalize(
+    new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
+  std::shared_ptr<TensorTransform> hwc2chw = std::make_shared<vision::HWC2CHW>();
+
+  mindspore::dataset::Execute Transform({decode, resize, normalize, hwc2chw});
+
+  // Apply transform on image
+  Status rc = Transform(image, &image);
+
+  // Check image info
+  ASSERT_TRUE(rc.IsOk());
+  ASSERT_EQ(image.Shape().size(), 3);
+  ASSERT_EQ(image.Shape()[0], 3);
+  ASSERT_EQ(image.Shape()[1], 224);
+  ASSERT_EQ(image.Shape()[2], 224);
+}
+
+TEST_F(MindDataTestExecute, TestTransformInput2) {
+  MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInput2.";
+  // Test Execute with transform op input using API constructors, with std::shared_ptr<TensorTransform pointers,
+  // instantiated via new
+
+  // Read images
+  std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
+  mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
+  auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
+
+  // Define transform operations
+  std::shared_ptr<TensorTransform> decode(new vision::Decode());
+  std::shared_ptr<TensorTransform> resize(new vision::Resize({224, 224}));
+  std::shared_ptr<TensorTransform> normalize(
+    new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
+  std::shared_ptr<TensorTransform> hwc2chw(new vision::HWC2CHW());
+
+  mindspore::dataset::Execute Transform({decode, resize, normalize, hwc2chw});
+
+  // Apply transform on image
+  Status rc = Transform(image, &image);
+
+  // Check image info
+  ASSERT_TRUE(rc.IsOk());
+  ASSERT_EQ(image.Shape().size(), 3);
+  ASSERT_EQ(image.Shape()[0], 3);
+  ASSERT_EQ(image.Shape()[1], 224);
+  ASSERT_EQ(image.Shape()[2], 224);
+}
+
+TEST_F(MindDataTestExecute, TestTransformInput3) {
+  MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInput3.";
+  // Test Execute with transform op input using API constructors, with auto pointers
+
+  // Read image
+  std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
+  mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
+  auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
+
+  // Define transform operations
+  auto decode(new vision::Decode()); // auto will create raw pointer to Decode class
+  auto resize(new vision::Resize({224, 224}));
+  auto normalize(
+    new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
+  auto hwc2chw(new vision::HWC2CHW());
+
+  std::vector<TensorTransform *> op_list = {decode, resize, normalize, hwc2chw};
+  mindspore::dataset::Execute Transform(op_list);
+
+  // Apply transform on image
+  Status rc = Transform(image, &image);
+
+  // Check image info
+  ASSERT_TRUE(rc.IsOk());
+  ASSERT_EQ(image.Shape().size(), 3);
+  ASSERT_EQ(image.Shape()[0], 3);
+  ASSERT_EQ(image.Shape()[1], 224);
+  ASSERT_EQ(image.Shape()[2], 224);
+}
+
+TEST_F(MindDataTestExecute, TestTransformInputSequential) {
+  MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInputSequential.";
+  // Test Execute with transform op input using API constructors, with auto pointers;
+  // Apply 2 transformations sequentially, including single non-vector Transform op input
+
+  // Read images
+  std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
+  mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
+  auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
+
+  // Define transform#1 operations
+  auto decode(new vision::Decode());  // auto will create raw pointer to Decode class
+  auto resize(new vision::Resize({224, 224}));
+  auto normalize(
+    new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
+
+  std::vector<TensorTransform *> op_list = {decode, resize, normalize};
+  mindspore::dataset::Execute Transform(op_list);
+
+  // Apply transform#1 on image
+  Status rc = Transform(image, &image);
+
+  // Define transform#2 operations
+  auto hwc2chw(new vision::HWC2CHW());
+
+  TensorTransform *op_single = hwc2chw;
+  mindspore::dataset::Execute Transform2(op_single);
+
+  // Apply transform#2 on image
+  rc = Transform2(image, &image);
+
+  // Check image info
+  ASSERT_TRUE(rc.IsOk());
+  ASSERT_EQ(image.Shape().size(), 3);
+  ASSERT_EQ(image.Shape()[0], 3);
+  ASSERT_EQ(image.Shape()[1], 224);
+  ASSERT_EQ(image.Shape()[2], 224);
+}

tests/ut/cpp/dataset/ir_tensor_op_fusion_pass_test.cc

@@ -39,8 +39,8 @@ TEST_F(MindDataTestTensorOpFusionPass, RandomCropDecodeResizeDisabled) {
   std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, SequentialSampler(0, 11));
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> decode = vision::Decode();
-  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5});
+  std::shared_ptr<TensorTransform> decode(new vision::Decode());
+  std::shared_ptr<TensorTransform> random_resized_crop(new vision::RandomResizedCrop({5}));
   ds = ds->Map({decode, random_resized_crop}, {"image"});
 
   std::shared_ptr<DatasetNode> node = ds->IRNode();
@@ -70,8 +70,8 @@ TEST_F(MindDataTestTensorOpFusionPass, RandomCropDecodeResizeEnabled) {
   std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, SequentialSampler(0, 11));
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> decode = vision::Decode();
-  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5});
+  std::shared_ptr<TensorTransform> decode(new vision::Decode());
+  std::shared_ptr<TensorTransform> random_resized_crop(new vision::RandomResizedCrop({5}));
   ds = ds->Map({decode, random_resized_crop}, {"image"});
 
   std::shared_ptr<DatasetNode> node = ds->IRNode();

tests/ut/cpp/dataset/optimization_pass_test.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -34,41 +34,44 @@ using mindspore::MsLogLevel::INFO;
 
 class MindDataTestOptimizationPass : public UT::DatasetOpTesting {};
 
-// TEST_F(MindDataTestOptimizationPass, MindDataTestAutoWorkerPass) {
-//  MS_LOG(INFO) << "Doing MindDataTestOptimizationPass-MindDataTestAutoWorkerPass.";
-//
-//  std::shared_ptr<SchemaObj> schema = std::make_shared<SchemaObj>();
-//  ASSERT_TRUE(schema->add_column("label", "uint32", {}));
-//  std::shared_ptr<Dataset> map_leaf = ImageFolder("dir")->SetNumWorkers(0);
-//  std::shared_ptr<Dataset> nonmap_leaf = RandomData(44, schema)->SetNumWorkers(0);
-//  std::shared_ptr<Dataset> batch = Zip({map_leaf, nonmap_leaf})->Batch(1)->SetNumWorkers(0);
-//  std::shared_ptr<Dataset> map = batch->Map({})->SetNumWorkers(0);
-//  //  {ImageFolder, RandomData} -> zip -> batch
-//  EXPECT_EQ(map_leaf->IRNode()->num_workers(), 0);
-//  EXPECT_EQ(nonmap_leaf->IRNode()->num_workers(), 0);
-//  EXPECT_EQ(batch->IRNode()->num_workers(), 0);
-//  EXPECT_EQ(map->IRNode()->num_workers(), 0);
-//
-//  std::unique_ptr<IRPass> pass = std::make_unique<AutoWorkerPass>();
-//  bool m = false;
-//  ASSERT_OK(pass->Run(map->IRNode(), &m));
-//
-//  // checking that after this pass, num_workers are set correctly (aka a positive number)
-//  // It is hard to test a exact value because num_threads are different for different machine
-//  // however, this will for sure succeed bc regardless of the total threads on cpu, this would always be >= 1
-//  EXPECT_NE(map_leaf->IRNode()->num_workers(), 0);
-//  EXPECT_NE(nonmap_leaf->IRNode()->num_workers(), 0);
-//  EXPECT_NE(batch->IRNode()->num_workers(), 0);
-//  EXPECT_NE(map->IRNode()->num_workers(), 0);
-//  MS_LOG(DEBUG) << map_leaf->IRNode()->Name() << ": num_worker=" << map_leaf->IRNode()->num_workers();
-//  MS_LOG(DEBUG) << nonmap_leaf->IRNode()->Name() << ": num_worker=" << nonmap_leaf->IRNode()->num_workers();
-//  MS_LOG(DEBUG) << batch->IRNode()->Name() << ": num_worker=" << batch->IRNode()->num_workers();
-//  MS_LOG(DEBUG) << map->IRNode()->Name() << ": num_worker=" << map->IRNode()->num_workers();
-//}
+TEST_F(MindDataTestOptimizationPass, MindDataTestAutoWorkerPass) {
+  MS_LOG(INFO) << "Doing MindDataTestOptimizationPass-MindDataTestAutoWorkerPass.";
+
+  std::shared_ptr<SchemaObj> schema = std::make_shared<SchemaObj>();
+  ASSERT_TRUE(schema->add_column("label", "uint32", {}));
+  std::shared_ptr<Dataset> map_leaf = ImageFolder("dir")->SetNumWorkers(0);
+  std::shared_ptr<Dataset> nonmap_leaf = RandomData(44, schema)->SetNumWorkers(0);
+  std::shared_ptr<Dataset> batch = Zip({map_leaf, nonmap_leaf})->Batch(1)->SetNumWorkers(0);
+  /* FIXME - Will uncomment out when full external API support is provided
+  std::shared_ptr<Dataset> map = batch->Map({})->SetNumWorkers(0);
+  //  {ImageFolder, RandomData} -> zip -> batch
+  EXPECT_EQ(map_leaf->IRNode()->num_workers(), 0);
+  EXPECT_EQ(nonmap_leaf->IRNode()->num_workers(), 0);
+  EXPECT_EQ(batch->IRNode()->num_workers(), 0);
+  EXPECT_EQ(map->IRNode()->num_workers(), 0);
+
+  std::unique_ptr<IRPass> pass = std::make_unique<AutoWorkerPass>();
+  bool m = false;
+  ASSERT_OK(pass->Run(map->IRNode(), &m));
+
+  // checking that after this pass, num_workers are set correctly (aka a positive number)
+  // It is hard to test a exact value because num_threads are different for different machine
+  // however, this will for sure succeed bc regardless of the total threads on cpu, this would always be >= 1
+  EXPECT_NE(map_leaf->IRNode()->num_workers(), 0);
+  EXPECT_NE(nonmap_leaf->IRNode()->num_workers(), 0);
+  EXPECT_NE(batch->IRNode()->num_workers(), 0);
+  EXPECT_NE(map->IRNode()->num_workers(), 0);
+  MS_LOG(DEBUG) << map_leaf->IRNode()->Name() << ": num_worker=" << map_leaf->IRNode()->num_workers();
+  MS_LOG(DEBUG) << nonmap_leaf->IRNode()->Name() << ": num_worker=" << nonmap_leaf->IRNode()->num_workers();
+  MS_LOG(DEBUG) << batch->IRNode()->Name() << ": num_worker=" << batch->IRNode()->num_workers();
+  MS_LOG(DEBUG) << map->IRNode()->Name() << ": num_worker=" << map->IRNode()->num_workers();
+  */
+}
 
 TEST_F(MindDataTestOptimizationPass, MindDataTestTensorFusionPass) {
   MS_LOG(INFO) << "Doing MindDataTestOptimizationPass-MindDataTestTensorFusionPass.";
   std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  /* FIXME - Will uncomment out when full external API support is provided
   std::shared_ptr<Dataset> root =
     ImageFolder(folder_path, false)->Map({vision::Decode(), vision::RandomResizedCrop({100})}, {"image"});
 
@@ -82,11 +85,13 @@ TEST_F(MindDataTestOptimizationPass, MindDataTestTensorFusionPass) {
   auto fused_ops = map_node->operations();
   ASSERT_EQ(fused_ops.size(), 1);
   ASSERT_EQ(fused_ops[0]->Name(), vision::kRandomCropDecodeResizeOperation);
+  */
 }
 
 TEST_F(MindDataTestOptimizationPass, MindDataTestTensorFusionPassPreBuiltTensorOperation) {
   MS_LOG(INFO) << "Doing MindDataTestOptimizationPass-MindDataTestTensorFusionPassPreBuiltTensorOperation.";
   std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  /* FIXME - Will uncomment out when full external API support is provided
   // make prebuilt tensor operation
   auto decode = std::make_shared<transforms::PreBuiltOperation>(vision::Decode()->Build());
   auto resize = std::make_shared<transforms::PreBuiltOperation>(vision::RandomResizedCrop({100})->Build());
@@ -102,4 +107,5 @@ TEST_F(MindDataTestOptimizationPass, MindDataTestTensorFusionPassPreBuiltTensorO
   auto fused_ops = map_node->operations();
   ASSERT_EQ(fused_ops.size(), 1);
   ASSERT_EQ(fused_ops[0]->Name(), kRandomCropDecodeResizeOp);
+  */
 }