!12239 [MD] fix bugs of execute & update doc description in minddata

From: @luoyang42
Reviewed-by: @liucunwei
Signed-off-by: @liucunwei

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

@@ -51,12 +51,19 @@ Status Execute::operator()(const mindspore::MSTensor &input, mindspore::MSTensor
   Status rc = dataset::Tensor::CreateFromMemory(dataset::TensorShape(input.Shape()),
                                                 MSTypeToDEType(static_cast<TypeId>(input.DataType())),
                                                 (const uchar *)(input.Data().get()), input.DataSize(), &de_tensor);
-  RETURN_IF_NOT_OK(rc);
+  if (rc.IsError()) {
+    MS_LOG(ERROR) << rc;
+    RETURN_IF_NOT_OK(rc);
+  }
 
   // Apply transforms on tensor
   for (auto &t : transforms) {
     std::shared_ptr<dataset::Tensor> de_output;
-    RETURN_IF_NOT_OK(t->Compute(de_tensor, &de_output));
+    Status rc_ = t->Compute(de_tensor, &de_output);
+    if (rc_.IsError()) {
+      MS_LOG(ERROR) << rc_;
+      RETURN_IF_NOT_OK(rc_);
+    }
 
     // For next transform
     de_tensor = std::move(de_output);
@@ -90,14 +97,21 @@ Status Execute::operator()(const std::vector<MSTensor> &input_tensor_list, std::
     Status rc = dataset::Tensor::CreateFromMemory(dataset::TensorShape(tensor.Shape()),
                                                   MSTypeToDEType(static_cast<TypeId>(tensor.DataType())),
                                                   (const uchar *)(tensor.Data().get()), tensor.DataSize(), &de_tensor);
-    RETURN_IF_NOT_OK(rc);
+    if (rc.IsError()) {
+      MS_LOG(ERROR) << rc;
+      RETURN_IF_NOT_OK(rc);
+    }
     de_tensor_list.emplace_back(std::move(de_tensor));
   }
 
   // Apply transforms on tensor
   for (auto &t : transforms) {
     TensorRow de_output_list;
-    RETURN_IF_NOT_OK(t->Compute(de_tensor_list, &de_output_list));
+    Status rc = t->Compute(de_tensor_list, &de_output_list);
+    if (rc.IsError()) {
+      MS_LOG(ERROR) << rc;
+      RETURN_IF_NOT_OK(rc);
+    }
     // For next transform
     de_tensor_list = std::move(de_output_list);
   }

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/include/execute_binding.cc

@@ -31,12 +31,7 @@ PYBIND_REGISTER(Execute, 0, ([](const py::module *m) {
                     .def("__call__",
                          [](Execute &self, const std::shared_ptr<Tensor> &de_tensor) {
                            auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(de_tensor));
-                           Status rc = self(ms_tensor, &ms_tensor);
-                           if (rc.IsError()) {
-                             THROW_IF_ERROR([&rc]() {
-                               RETURN_STATUS_UNEXPECTED("Failed to execute transform op, " + rc.ToString());
-                             }());
-                           }
+                           THROW_IF_ERROR(self(ms_tensor, &ms_tensor));
                            std::shared_ptr<dataset::Tensor> de_output_tensor;
                            dataset::Tensor::CreateFromMemory(dataset::TensorShape(ms_tensor.Shape()),
                                                              MSTypeToDEType(static_cast<TypeId>(ms_tensor.DataType())),
@@ -51,11 +46,7 @@ PYBIND_REGISTER(Execute, 0, ([](const py::module *m) {
                         auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(tensor));
                         ms_input_tensor_list.emplace_back(std::move(ms_tensor));
                       }
-                      Status rc = self(ms_input_tensor_list, &ms_output_tensor_list);
-                      if (rc.IsError()) {
-                        THROW_IF_ERROR(
-                          [&rc]() { RETURN_STATUS_UNEXPECTED("Failed to execute transform op, " + rc.ToString()); }());
-                      }
+                      THROW_IF_ERROR(self(ms_input_tensor_list, &ms_output_tensor_list));
                       std::vector<std::shared_ptr<dataset::Tensor>> de_output_tensor_list;
                       for (auto &tensor : ms_output_tensor_list) {
                         std::shared_ptr<dataset::Tensor> de_output_tensor;

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

@@ -825,8 +825,8 @@ std::shared_ptr<TensorOp> PadOperation::Build() {
       break;
     case 2:
       pad_left = padding_[0];
-      pad_top = padding_[1];
-      pad_right = padding_[0];
+      pad_top = padding_[0];
+      pad_right = padding_[1];
       pad_bottom = padding_[1];
       break;
     default:
@@ -1096,8 +1096,8 @@ std::shared_ptr<TensorOp> RandomCropOperation::Build() {
       break;
     case 2:
       pad_left = padding_[0];
-      pad_top = padding_[1];
-      pad_right = padding_[0];
+      pad_top = padding_[0];
+      pad_right = padding_[1];
       pad_bottom = padding_[1];
       break;
     default:
@@ -1215,8 +1215,8 @@ std::shared_ptr<TensorOp> RandomCropWithBBoxOperation::Build() {
       break;
     case 2:
       pad_left = padding_[0];
-      pad_top = padding_[1];
-      pad_right = padding_[0];
+      pad_top = padding_[0];
+      pad_right = padding_[1];
       pad_bottom = padding_[1];
       break;
     default:

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

@@ -141,7 +141,7 @@ std::shared_ptr<JiebaTokenizerOperation> JiebaTokenizer(const std::string &hmm_p
                                                         const JiebaMode &mode = JiebaMode::kMix,
                                                         bool with_offsets = false);
 
-/// \brief Lookup operator that looks up a word to an id.
+/// \brief Look up a word into an id according to the input vocabulary table.
 /// \param[in] vocab a Vocab object.
 /// \param[in] unknown_token word to use for lookup if the word being looked up is out of Vocabulary (oov).
 ///   If unknown_token is oov, runtime error will be thrown. If unknown_token is {}, which means that not to

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

@@ -200,8 +200,8 @@ std::shared_ptr<NormalizePadOperation> NormalizePad(const std::vector<float> &me
 /// \notes Pads the image according to padding parameters
 /// \param[in] padding A vector representing the number of pixels to pad the image
 ///    If vector has one value, it pads all sides of the image with that value.
-///    If vector has two values, it pads left and right with the first and
-///    top and bottom with the second value.
+///    If vector has two values, it pads left and top with the first and
+///    right and bottom with the second value.
 ///    If vector has four values, it pads left, top, right, and bottom with
 ///    those values respectively.
 /// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is
@@ -270,8 +270,12 @@ std::shared_ptr<RandomColorAdjustOperation> RandomColorAdjust(std::vector<float>
 /// \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).
-/// \param[in] padding A vector with the value of pixels to pad the image. If 4 values are provided,
-///     it pads the left, top, right and bottom respectively.
+/// \param[in] padding A vector representing the number of pixels to pad the image
+///    If vector has one value, it pads all sides of the image with that value.
+///    If vector has two values, it pads left and top with the first and
+///    right and bottom with the second value.
+///    If vector has four values, it pads left, top, right, and bottom with
+///    those values respectively.
 /// \param[in] pad_if_needed A boolean whether to pad the image if either side is smaller than
 ///     the given output size.
 /// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is
@@ -304,8 +308,12 @@ std::shared_ptr<RandomCropDecodeResizeOperation> RandomCropDecodeResize(
 /// \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).
-/// \param[in] padding A vector with the value of pixels to pad the image. If 4 values are provided,
-///     it pads the left, top, right and bottom respectively.
+/// \param[in] padding A vector representing the number of pixels to pad the image
+///    If vector has one value, it pads all sides of the image with that value.
+///    If vector has two values, it pads left and top with the first and
+///    right and bottom with the second value.
+///    If vector has four values, it pads left, top, right, and bottom with
+///    those values respectively.
 /// \param[in] pad_if_needed A boolean whether to pad the image if either side is smaller than
 ///     the given output size.
 /// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is

mindspore/ccsrc/minddata/dataset/kernels/data/duplicate_op.cc

@@ -24,7 +24,7 @@ namespace dataset {
 
 Status DuplicateOp::Compute(const TensorRow &input, TensorRow *output) {
   IO_CHECK_VECTOR(input, output);
-  CHECK_FAIL_RETURN_UNEXPECTED(input.size() == 1, "Duplicate: only support one input.");
+  CHECK_FAIL_RETURN_UNEXPECTED(input.size() == 1, "Duplicate: only supports transform one column each time.");
   std::shared_ptr<Tensor> out;
   RETURN_IF_NOT_OK(Tensor::CreateFromTensor(input[0], &out));
   output->push_back(input[0]);

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

@@ -103,8 +103,12 @@ Status Resize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *out
     RETURN_STATUS_UNEXPECTED("Resize: load image failed.");
   }
   if (input_cv->Rank() != 3 && input_cv->Rank() != 2) {
-    RETURN_STATUS_UNEXPECTED("Resize: input is not in shape of <H,W,C> or <H,W>");
+    RETURN_STATUS_UNEXPECTED("Resize: input tensor is not in shape of <H,W,C> or <H,W>");
   }
+  if (input_cv->shape()[2] != 3 && input_cv->shape()[2] != 1) {
+    RETURN_STATUS_UNEXPECTED("Resize: channel of input tesnor is not in 1 or 3.");
+  }
+
   cv::Mat in_image = input_cv->mat();
   // resize image too large or too small
   if (output_height > in_image.rows * 1000 || output_width > in_image.cols * 1000) {

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

@@ -41,7 +41,7 @@ Status SharpnessOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_pt
     /// Get number of channels and image matrix
     std::size_t num_of_channels = input_cv->shape()[2];
     if (num_of_channels != 1 && num_of_channels != 3) {
-      RETURN_STATUS_UNEXPECTED("Sharpness: image shape is not <H,W,C>.");
+      RETURN_STATUS_UNEXPECTED("Sharpness: image channel is not 1 or 3.");
     }
 
     /// creating a smoothing filter. 1, 1, 1,

mindspore/dataset/engine/datasets.py

@@ -579,7 +579,7 @@ class Dataset:
             python_multiprocessing (bool, optional): Parallelize Python operations with multiple worker processes. This
                 option could be beneficial if the Python operation is computational heavy (default=False).
             cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-                (default=None which means no cache is used).
+                (default=None, which means no cache is used).
             callbacks: (DSCallback, list[DSCallback], optional): List of Dataset callbacks to be called (Default=None).
 
 
@@ -2288,7 +2288,7 @@ class MapDataset(Dataset):
         python_multiprocessing (bool, optional): Parallelize Python operations with multiple worker process. This
             option could be beneficial if the Python operation is computational heavy (default=False).
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
         callbacks: (DSCallback, list[DSCallback], optional): List of Dataset callbacks to be called (Default=None)
 
         Raises:
@@ -2843,7 +2843,7 @@ class ImageFolderDataset(MappableDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Raises:
         RuntimeError: If sampler and shuffle are specified at the same time.
@@ -2951,7 +2951,7 @@ class MnistDataset(MappableDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Raises:
         RuntimeError: If sampler and shuffle are specified at the same time.
@@ -2981,7 +2981,7 @@ class MnistDataset(MappableDataset):
 
 class MindDataset(MappableDataset):
     """
-    A source dataset that reads MindRecord files.
+    A source dataset for reading and parsing MindRecord dataset.
 
     Args:
         dataset_file (Union[str, list[str]]): If dataset_file is a str, it represents for
@@ -3505,7 +3505,7 @@ class GeneratorDataset(MappableDataset):
 
 class TFRecordDataset(SourceDataset):
     """
-    A source dataset that reads and parses datasets stored on disk in TFData format.
+    A source dataset for reading and parsing datasets stored on disk in TFData format.
 
     Args:
         dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search for a
@@ -3537,7 +3537,7 @@ class TFRecordDataset(SourceDataset):
         shard_equal_rows (bool, optional): Get equal rows for all shards(default=False). If shard_equal_rows
             is false, number of rows of each shard may be not equal.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Examples:
         >>> import mindspore.common.dtype as mstype
@@ -3581,7 +3581,7 @@ class TFRecordDataset(SourceDataset):
 
 class ManifestDataset(MappableDataset):
     """
-    A source dataset that reads images from a manifest file.
+    A source dataset for reading images from a Manifest file.
 
     The generated dataset has two columns ['image', 'label'].
     The shape of the image column is [image_size] if decode flag is False, or [H,W,C]
@@ -3637,7 +3637,7 @@ class ManifestDataset(MappableDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Raises:
         RuntimeError: If sampler and shuffle are specified at the same time.
@@ -3690,7 +3690,7 @@ class ManifestDataset(MappableDataset):
 
 class Cifar10Dataset(MappableDataset):
     """
-    A source dataset that reads cifar10 data.
+    A source dataset for reading and parsing Cifar10 dataset.
 
     The generated dataset has two columns ['image', 'label'].
     The type of the image tensor is uint8. The label is a scalar uint32 tensor.
@@ -3756,7 +3756,7 @@ class Cifar10Dataset(MappableDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Raises:
         RuntimeError: If sampler and shuffle are specified at the same time.
@@ -3793,7 +3793,7 @@ class Cifar10Dataset(MappableDataset):
 
 class Cifar100Dataset(MappableDataset):
     """
-    A source dataset that reads cifar100 data.
+    A source dataset for reading and parsing Cifar100 dataset.
 
     The generated dataset has three columns ['image', 'coarse_label', 'fine_label'].
     The type of the image tensor is uint8. The coarse and fine labels are each a scalar uint32 tensor.
@@ -3861,7 +3861,7 @@ class Cifar100Dataset(MappableDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Raises:
         RuntimeError: If sampler and shuffle are specified at the same time.
@@ -3906,7 +3906,7 @@ class RandomDataset(SourceDataset):
         num_parallel_workers (int, optional): Number of workers to read the data
             (default=None, number set in the config).
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
         shuffle (bool, optional): Whether or not to perform shuffle on the dataset
             (default=None, expected order behavior shown in the table).
         num_shards (int, optional): Number of shards that the dataset will be divided
@@ -4125,7 +4125,7 @@ class VOCDataset(MappableDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Raises:
         RuntimeError: If xml of Annotations is an invalid format.
@@ -4280,7 +4280,7 @@ class CocoDataset(MappableDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Raises:
         RuntimeError: If sampler and shuffle are specified at the same time.
@@ -4391,7 +4391,7 @@ class CelebADataset(MappableDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Examples:
         >>> dataset = ds.CelebADataset(dataset_dir=celeba_dataset_dir, usage='train')
@@ -4465,7 +4465,7 @@ class CLUEDataset(SourceDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Examples:
         >>> clue_dataset_dir = ["/path/to/clue_dataset_file"] # contains 1 or multiple text files
@@ -4592,7 +4592,7 @@ class CSVDataset(SourceDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
 
     Examples:
@@ -4642,7 +4642,7 @@ class TextFileDataset(SourceDataset):
         shard_id (int, optional): The shard ID within num_shards (default=None). This
             argument can only be specified when num_shards is also specified.
         cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
-            (default=None which means no cache is used).
+            (default=None, which means no cache is used).
 
     Examples:
         >>> # contains 1 or multiple text files

mindspore/dataset/engine/samplers.py

@@ -586,7 +586,7 @@ class SubsetSampler(BuiltinSampler):
 
         for i, item in enumerate(indices):
             if not isinstance(item, numbers.Number):
-                raise TypeError("type of weights element should be number, "
+                raise TypeError("type of indices element should be number, "
                                 "but got w[{}]: {}, type: {}.".format(i, item, type(item)))
 
         self.indices = indices

mindspore/dataset/text/transforms.py

@@ -275,7 +275,7 @@ class JiebaTokenizer(TextTensorOperation):
 
 class Lookup(TextTensorOperation):
     """
-    Lookup operator that looks up a word to an id.
+    Look up a word into an id according to the input vocabulary table.
 
     Args:
         vocab (Vocab): A vocabulary object.

mindspore/dataset/transforms/c_transforms.py

@@ -39,13 +39,14 @@ class OneHot(cde.OneHotOp):
     Tensor operation to apply one hot encoding.
 
     Args:
-        num_classes (int): Number of classes of the label.
+        num_classes (int): Number of classes of objects in dataset.
             It should be larger than the largest label number in the dataset.
 
     Raises:
         RuntimeError: feature size is bigger than num_classes.
 
     Examples:
+        >>> # Assume that dataset has 10 classes, thus the label ranges from 0 to 9
         >>> onehot_op = c_transforms.OneHot(num_classes=10)
         >>> mnist_dataset = mnist_dataset.map(operations=onehot_op, input_columns=["label"])
     """
@@ -114,13 +115,13 @@ class _SliceOption(cde.SliceOption):
     Internal class SliceOption to be used with SliceOperation
 
     Args:
-        _SliceOption(Union[int, list(int), slice, None, Ellipses, bool, _SliceOption]):
+        _SliceOption(Union[int, list(int), slice, None, Ellipsis, bool, _SliceOption]):
 
             1.  :py:obj:`int`: Slice this index only along the dimension. Negative index is supported.
             2.  :py:obj:`list(int)`: Slice these indices along the dimension. Negative indices are supported.
             3.  :py:obj:`slice`: Slice the generated indices from the slice object along the dimension.
             4.  :py:obj:`None`: Slice the whole dimension. Similar to `:` in Python indexing.
-            5.  :py:obj:`Ellipses`: Slice the whole dimension. Similar to `:` in Python indexing.
+            5.  :py:obj:`Ellipsis`: Slice the whole dimension. Similar to `:` in Python indexing.
             6.  :py:obj:`boolean`: Slice the whole dimension. Similar to `:` in Python indexing.
     """
 
@@ -143,16 +144,16 @@ class Slice(cde.SliceOp):
     (Currently only rank-1 tensors are supported).
 
     Args:
-        *slices(Union[int, list(int), slice, None, Ellipses]):
+        *slices(Union[int, list(int), slice, None, Ellipsis]):
             Maximum `n` number of arguments to slice a tensor of rank `n`.
             One object in slices can be one of:
 
             1.  :py:obj:`int`: Slice this index only along the first dimension. Negative index is supported.
             2.  :py:obj:`list(int)`: Slice these indices along the first dimension. Negative indices are supported.
             3.  :py:obj:`slice`: Slice the generated indices from the slice object along the first dimension.
-                Similar to `start:stop:step`.
+                Similar to start:stop:step.
             4.  :py:obj:`None`: Slice the whole dimension. Similar to `:` in Python indexing.
-            5.  :py:obj:`Ellipses`: Slice the whole dimension. Similar to `:` in Python indexing.
+            5.  :py:obj:`Ellipsis`: Slice the whole dimension. Similar to `:` in Python indexing.
 
     Examples:
         >>> # Data before
@@ -232,7 +233,7 @@ class Mask(cde.MaskOp):
 
 class PadEnd(cde.PadEndOp):
     """
-    Pad input tensor according to `pad_shape`, need to have same rank.
+    Pad input tensor according to pad_shape, need to have same rank.
 
     Args:
         pad_shape (list(int)): List of integers representing the shape needed. Dimensions that set to `None` will
@@ -295,7 +296,7 @@ class Concatenate(cde.ConcatenateOp):
 
 class Duplicate(cde.DuplicateOp):
     """
-    Duplicate the input tensor to a new output tensor. The input tensor is carried over to the output list.
+    Duplicate the input tensor to output, only support transform one column each time.
 
     Examples:
         >>> # Data before
@@ -405,7 +406,7 @@ class RandomApply():
 
 class RandomChoice():
     """
-    Randomly selects one transform from a list of transforms to perform operation.
+    Randomly select one transform from a list of transforms to perform operation.
 
     Args:
         transforms (list): List of transformations to be chosen from to apply.

mindspore/dataset/transforms/py_transforms.py

@@ -26,11 +26,13 @@ class OneHotOp:
     Apply one hot encoding transformation to the input label, make label be more smoothing and continuous.
 
     Args:
-        num_classes (int): Number of classes of objects in dataset. Value must be larger than 0.
+        num_classes (int): Number of classes of objects in dataset.
+            It should be larger than the largest label number in the dataset.
         smoothing_rate (float, optional): Adjustable hyperparameter for label smoothing level.
             (Default=0.0 means no smoothing is applied.)
 
     Examples:
+        >>> # Assume that dataset has 10 classes, thus the label ranges from 0 to 9
         >>> transforms_list = [py_transforms.OneHotOp(num_classes=10, smoothing_rate=0.1)]
         >>> transform = py_transforms.Compose(transforms_list)
         >>> mnist_dataset = mnist_dataset(input_columns=["label"], operations=transform)
@@ -83,14 +85,14 @@ class Compose:
         >>>
         >>> # Compose is also be invoked implicitly, by just passing in a list of ops
         >>> # the above example then becomes:
-        >>> transform_list = [py_vision.Decode(),
-        ...                   py_vision.RandomHorizontalFlip(0.5),
-        ...                   py_vision.ToTensor(),
-        ...                   py_vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
-        ...                   py_vision.RandomErasing()]
+        >>> transforms_list = [py_vision.Decode(),
+        ...                    py_vision.RandomHorizontalFlip(0.5),
+        ...                    py_vision.ToTensor(),
+        ...                    py_vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
+        ...                    py_vision.RandomErasing()]
         >>>
         >>> # apply the transform to the dataset through dataset.map()
-        >>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transform_list, input_columns=["image"])
+        >>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list, input_columns=["image"])
         >>>
         >>> # Certain C++ and Python ops can be combined, but not all of them
         >>> # An example of combined operations
@@ -163,9 +165,9 @@ class RandomApply:
 
     Examples:
         >>> from mindspore.dataset.transforms.py_transforms import Compose
-        >>> transform_list = [py_vision.RandomHorizontalFlip(0.5),
-        ...                   py_vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
-        ...                   py_vision.RandomErasing()]
+        >>> transforms_list = [py_vision.RandomHorizontalFlip(0.5),
+        ...                    py_vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
+        ...                    py_vision.RandomErasing()]
         >>> transforms = Compose([py_vision.Decode(),
         ...                       py_transforms.RandomApply(transforms_list, prob=0.6),
         ...                       py_vision.ToTensor()])
@@ -199,11 +201,11 @@ class RandomChoice:
 
     Examples:
         >>> from mindspore.dataset.transforms.py_transforms import Compose, RandomChoice
-        >>> transform_list = [py_vision.RandomHorizontalFlip(0.5),
-        ...                   py_vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
-        ...                   py_vision.RandomErasing()]
+        >>> transforms_list = [py_vision.RandomHorizontalFlip(0.5),
+        ...                    py_vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
+        ...                    py_vision.RandomErasing()]
         >>> transforms = Compose([py_vision.Decode(),
-        ...                       py_transforms.RandomChoice(transform_list),
+        ...                       py_transforms.RandomChoice(transforms_list),
         ...                       py_vision.ToTensor()])
         >>> image_folder_dataset = image_folder_dataset.map(operations=transforms, input_columns=["image"])
     """
@@ -234,9 +236,9 @@ class RandomOrder:
 
     Examples:
         >>> from mindspore.dataset.transforms.py_transforms import Compose
-        >>> transform_list = [py_vision.RandomHorizontalFlip(0.5),
-        ...                   py_vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
-        ...                   py_vision.RandomErasing()]
+        >>> transforms_list = [py_vision.RandomHorizontalFlip(0.5),
+        ...                    py_vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
+        ...                    py_vision.RandomErasing()]
         >>> transforms = Compose([py_vision.Decode(),
         ...                       py_transforms.RandomOrder(transforms_list),
         ...                       py_vision.ToTensor()])

mindspore/dataset/vision/c_transforms.py

@@ -108,8 +108,8 @@ def parse_padding(padding):
     if isinstance(padding, numbers.Number):
         padding = [padding] * 4
     if len(padding) == 2:
-        left = right = padding[0]
-        top = bottom = padding[1]
+        left = top = padding[0]
+        right = bottom = padding[1]
         padding = (left, top, right, bottom,)
     if isinstance(padding, list):
         padding = tuple(padding)
@@ -438,8 +438,8 @@ class Pad(ImageTensorOperation):
     Args:
         padding (Union[int, sequence]): The number of pixels to pad the image.
             If a single number is provided, it pads all borders with this value.
-            If a tuple or list of 2 values are provided, it pads left and right
-            with the first value and top and bottom with the second value.
+            If a tuple or list of 2 values are provided, it pads the (left and top)
+            with the first value and (right and bottom) with the second value.
             If 4 values are provided as a list or tuple,
             it pads the left, top, right and bottom respectively.
         fill_value (Union[int, tuple], optional): The pixel intensity of the borders, only valid for
@@ -674,8 +674,8 @@ class RandomCrop(ImageTensorOperation):
         padding (Union[int, sequence], optional): The number of pixels to pad the image (default=None).
             If padding is not None, pad image firstly with padding values.
             If a single number is provided, pad all borders with this value.
-            If a tuple or list of 2 values are provided, it pads left and right
-            with the first value and top and bottom with the second value.
+            If a tuple or list of 2 values are provided, pad the (left and top)
+            with the first value and (right and bottom) with the second value.
             If 4 values are provided as a list or tuple,
             pad the left, top, right and bottom respectively.
         pad_if_needed (bool, optional): Pad the image if either side is smaller than
@@ -790,8 +790,8 @@ class RandomCropWithBBox(ImageTensorOperation):
         padding (Union[int, sequence], optional): The number of pixels to pad the image (default=None).
             If padding is not None, first pad image with padding values.
             If a single number is provided, pad all borders with this value.
-            If a tuple or list of 2 values are provided, it pads left and right
-            with the first value and top and bottom with the second value.
+            If a tuple or list of 2 values are provided, pad the (left and top)
+            with the first value and (right and bottom) with the second value.
             If 4 values are provided as a list or tuple, pad the left, top, right and bottom respectively.
         pad_if_needed (bool, optional): Pad the image if either side is smaller than
             the given output size (default=False).
@@ -845,7 +845,7 @@ class RandomCropWithBBox(ImageTensorOperation):
 
 class RandomHorizontalFlip(ImageTensorOperation):
     """
-    Flip the input image horizontally, randomly with a given probability.
+    Randomly flip the input image horizontally with a given probability.
 
     Args:
         prob (float, optional): Probability of the image being flipped (default=0.5).
@@ -1202,12 +1202,12 @@ class RandomSharpness(ImageTensorOperation):
 
 class RandomSolarize(ImageTensorOperation):
     """
-    Invert all pixel values above a threshold.
+    Invert all pixel values with given range.
 
     Args:
         threshold (tuple, optional): Range of random solarize threshold. Threshold values should always be
-            in the range (0, 255), include at least one integer value in the given range and
-            be in (min, max) format. If min=max, then it is a single fixed magnitude operation (default=(0, 255)).
+            in the range (0, 255), include at least one integer value in the given range and be in
+            (min, max) format. If min=max, then invert all pixel values above min(max) (default=(0, 255)).
 
     Examples:
         >>> transforms_list = [c_vision.Decode(), c_vision.RandomSolarize(threshold=(10,100))]
@@ -1225,7 +1225,7 @@ class RandomSolarize(ImageTensorOperation):
 
 class RandomVerticalFlip(ImageTensorOperation):
     """
-    Flip the input image vertically, randomly with a given probability.
+    Randomly flip the input image vertically with a given probability.
 
     Args:
         prob (float, optional): Probability of the image being flipped (default=0.5).

mindspore/dataset/vision/py_transforms.py

@@ -1357,7 +1357,8 @@ class RandomColor:
 
 class RandomSharpness:
     """
-    Adjust the sharpness of the input PIL image by a random degree.
+    Adjust the sharpness of the input PIL image by a fixed or random degree. Degree of 0.0 gives a blurred image,
+    degree of 1.0 gives the original image, and degree of 2.0 gives a sharpened image.
 
     Args:
         degrees (sequence): Range of random sharpness adjustment degrees.

mindspore/dataset/vision/validators.py

@@ -499,7 +499,8 @@ def check_cutout(method):
     @wraps(method)
     def new_method(self, *args, **kwargs):
         [length, num_patches], _ = parse_user_args(method, *args, **kwargs)
-
+        type_check(length, (int,), "length")
+        type_check(num_patches, (int,), "num_patches")
         check_value(length, (1, FLOAT_MAX_INTEGER))
         check_value(num_patches, (1, FLOAT_MAX_INTEGER))
 
@@ -613,7 +614,10 @@ def check_uniform_augment_cpp(method):
                 parsed_transforms.append(op.parse())
             else:
                 parsed_transforms.append(op)
-        type_check_list(parsed_transforms, (TensorOp, TensorOperation), "transforms")
+        type_check(parsed_transforms, (list, tuple,), "transforms")
+        for index, arg in enumerate(parsed_transforms):
+            if not isinstance(arg, (TensorOp, TensorOperation)):
+                raise TypeError("Type of Transforms[{0}] must be c_transform, but got {1}".format(index, type(arg)))
 
         return method(self, *args, **kwargs)
 

tests/ut/python/dataset/test_pad.py

@@ -72,6 +72,26 @@ def test_pad_op():
         assert mse < 0.01
 
 
+def test_pad_op2():
+    """
+    Test Pad op2
+    """
+    logger.info("test padding parameter with size 2")
+
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    decode_op = c_vision.Decode()
+    resize_op = c_vision.Resize([90, 90])
+    pad_op = c_vision.Pad((100, 9,))
+    ctrans = [decode_op, resize_op, pad_op]
+
+    data1 = data1.map(operations=ctrans, input_columns=["image"])
+    for data in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
+        logger.info(data["image"].shape)
+        # It pads left, top with 100 and right, bottom with 9,
+        # so the final size of image is 90 + 100 + 9 = 199
+        assert data["image"].shape[0] == 199
+        assert data["image"].shape[1] == 199
+
 
 def test_pad_grayscale():
     """

tests/ut/python/dataset/test_slice_op.py

@@ -145,6 +145,28 @@ def test_slice_multiple_rows():
         np.testing.assert_array_equal(exp_d, d['col'])
 
 
+def test_slice_none_and_ellipsis():
+    """
+    Test passing None and Ellipsis to Slice
+    """
+    dataset = [[1], [3, 4, 5], [1, 2], [1, 2, 3, 4, 5, 6, 7]]
+    exp_dataset = [[1], [3, 4, 5], [1, 2], [1, 2, 3, 4, 5, 6, 7]]
+
+    def gen():
+        for row in dataset:
+            yield (np.array(row),)
+
+    data = ds.GeneratorDataset(gen, column_names=["col"])
+    data = data.map(operations=ops.Slice(None))
+    for (d, exp_d) in zip(data.create_dict_iterator(output_numpy=True), exp_dataset):
+        np.testing.assert_array_equal(exp_d, d['col'])
+
+    data = ds.GeneratorDataset(gen, column_names=["col"])
+    data = data.map(operations=ops.Slice(Ellipsis))
+    for (d, exp_d) in zip(data.create_dict_iterator(output_numpy=True), exp_dataset):
+        np.testing.assert_array_equal(exp_d, d['col'])
+
+
 def test_slice_obj_neg():
     slice_compare([1, 2, 3, 4, 5], slice(-1, -5, -1), [5, 4, 3, 2])
     slice_compare([1, 2, 3, 4, 5], slice(-1), [1, 2, 3, 4])

tests/ut/python/dataset/test_uniform_augment.py

@@ -186,10 +186,7 @@ def test_cpp_uniform_augment_exception_pyops(num_ops=2):
         C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
 
     logger.info("Got an exception in DE: {}".format(str(e)))
-    assert "Argument transforms[5] with value" \
-           " <mindspore.dataset.vision.py_transforms.Invert" in str(e.value)
-    assert "is not of type (<class 'mindspore._c_dataengine.TensorOp'>,"\
-           " <class 'mindspore._c_dataengine.TensorOperation'>)" in str(e.value)
+    assert "Type of Transforms[5] must be c_transform" in str(e.value)
 
 
 def test_cpp_uniform_augment_exception_large_numops(num_ops=6):