!4421 dataset: modify some error in api comment

Merge pull request !4421 from ms_yan/api_comment

mindspore/dataset/engine/datasets.py

@@ -267,9 +267,9 @@ class Dataset:
                 be dropped and not propagated to the child node.
             num_parallel_workers (int, optional): Number of workers to process the Dataset in parallel (default=None).
             per_batch_map (callable, optional): Per batch map callable. A callable which takes
-                (list[Tensor], list[Tensor], ..., BatchInfo) as input parameters. Each list[Tensor] represent a batch of
-                Tensors on a given column. The number of lists should match with number of entries in input_columns. The
-                last parameter of the callable should always be a BatchInfo object.
+                (list[Tensor], list[Tensor], ..., BatchInfo) as input parameters. Each list[Tensor] represents a batch
+                of Tensors on a given column. The number of lists should match with number of entries in input_columns.
+                The last parameter of the callable should always be a BatchInfo object.
             input_columns (list[str], optional): List of names of the input columns. The size of the list should
                 match with signature of per_batch_map callable.
             pad_info (dict, optional): Whether to perform padding on selected columns. pad_info={"col1":([224,224],0)}
@@ -2821,7 +2821,7 @@ class MnistDataset(MappableDataset):
         num_samples (int, optional): The number of images to be included in the dataset
             (default=None, all images).
         num_parallel_workers (int, optional): Number of workers to read the data
-            (default=value, set in the config).
+            (default=None, set in the config).
         shuffle (bool, optional): Whether or not to perform shuffle on the dataset
             (default=None, expected order behavior shown in the table).
         sampler (Sampler, optional): Object used to choose samples from the
@@ -2898,7 +2898,7 @@ class MnistDataset(MappableDataset):
 
 class MindDataset(MappableDataset):
     """
-    A source dataset that reads from shard files and database.
+    A source dataset that reads MindRecord files.
 
     Args:
         dataset_file (Union[str, list[str]]): One of file names or file list in dataset.
@@ -3225,7 +3225,7 @@ class _GeneratorWorker(multiprocessing.Process):
 
 class GeneratorDataset(MappableDataset):
     """
-    A source dataset that generate data from python by invoking python data source each epoch.
+    A source dataset that generates data from python by invoking python data source each epoch.
 
     This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table
     below shows what input args are allowed and their expected behavior.
@@ -3437,7 +3437,7 @@ class TFRecordDataset(SourceDataset):
 
     Args:
         dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search for a
-        pattern of files. The list will be sorted in a lexicographical order.
+            pattern of files. The list will be sorted in a lexicographical order.
         schema (Union[str, Schema], optional): Path to the json schema file or schema object (default=None).
             If the schema is not provided, the meta data from the TFData file is considered the schema.
         columns_list (list[str], optional): List of columns to be read (default=None, read all columns)
@@ -3620,7 +3620,7 @@ class ManifestDataset(MappableDataset):
 
     Args:
         dataset_file (str): File to be read.
-        usage (str, optional): Need train, eval or inference data (default="train").
+        usage (str, optional): acceptable usages include train, eval and inference (default="train").
         num_samples (int, optional): The number of images to be included in the dataset.
             (default=None, all images).
         num_parallel_workers (int, optional): Number of workers to read the data

mindspore/dataset/text/transforms.py

@@ -72,6 +72,7 @@ class Lookup(cde.LookupOp):
     def __init__(self, vocab, unknown_token=None):
         super().__init__(vocab, unknown_token)
 
+
 class SlidingWindow(cde.SlidingWindowOp):
     """
     TensorOp to construct a tensor from data (only 1-D for now), where each element in the dimension axis
@@ -101,6 +102,7 @@ class SlidingWindow(cde.SlidingWindowOp):
     def __init__(self, width, axis=0):
         super().__init__(width=width, axis=axis)
 
+
 class Ngram(cde.NgramOp):
     """
     TensorOp to generate n-gram from a 1-D string Tensor.
@@ -511,8 +513,8 @@ if platform.system().lower() != 'windows':
                 on input text to make the text to lower case and strip accents characters; If False, only apply
                 NormalizeUTF8('normalization_form' mode) operation on input text(default=False).
             keep_whitespace(bool, optional): If True, the whitespace will be kept in out tokens(default=False).
-            normalization_form(NormalizeForm, optional): Used to specify a specific normlaize mode,
-                only effective when 'lower_case' is False. See NormalizeUTF8 for details(default='NONE').
+            normalization_form(NormalizeForm, optional): Used to specify a specific normalize mode,
+                only effective when 'lower_case' is False. See NormalizeUTF8 for details(default=NormalizeForm.NONE).
             preserve_unused_token(bool, optional): If True, do not split special tokens like
                 '[CLS]', '[SEP]', '[UNK]', '[PAD]', '[MASK]'(default=True).
             with_offsets (bool, optional): If or not output offsets of tokens (default=False).

mindspore/dataset/text/utils.py

@@ -132,12 +132,13 @@ class Vocab(cde.Vocab):
         Build a vocab object from a dict.
 
         Args:
-            word_dict (dict): dict contains word, id pairs where word should be str and id int. id is recommended to
-                start from 0 and be continuous. ValueError will be raised if id is negative.
+            word_dict (dict): dict contains word and id pairs, where word should be str and id be int. id is recommended
+                to start from 0 and be continuous. ValueError will be raised if id is negative.
         """
 
         return super().from_dict(word_dict)
 
+
 class SentencePieceVocab(cde.SentencePieceVocab):
     """
     SentencePiece obiect that is used to segmentate words
@@ -151,9 +152,9 @@ class SentencePieceVocab(cde.SentencePieceVocab):
         Args:
             dataset(Dataset): Dataset to build sentencepiece.
             col_names(list): The list of the col name.
-            vocab_size(int): Vocabulary size, the type of uint32_t.
+            vocab_size(int): Vocabulary size.
             character_coverage(float): Amount of characters covered by the model, good defaults are: 0.9995 for
-                languages. with rich character set like Japanse or Chinese and 1.0 for other languages with small
+                languages. with rich character set like Japanese or Chinese and 1.0 for other languages with small
                 character set.
             model_type(SentencePieceModel): Choose from unigram (default), bpe, char, or word. The input sentence
                 must be pretokenized when using word type.
@@ -261,6 +262,7 @@ class NormalizeForm(IntEnum):
     NFD = 3
     NFKD = 4
 
+
 class SentencePieceModel(IntEnum):
     """An enumeration for SentencePieceModel, effective enumeration types are UNIGRAM, BPE, CHAR, WORD."""
     UNIGRAM = 0
@@ -275,11 +277,13 @@ DE_C_INTER_SENTENCEPIECE_MODE = {
     SentencePieceModel.WORD: cde.SentencePieceModel.DE_SENTENCE_PIECE_WORD
 }
 
+
 class SPieceTokenizerOutType(IntEnum):
     """An enumeration for SPieceTokenizerOutType, effective enumeration types are STRING, INT."""
     STRING = 0
     INT = 1
 
+
 class SPieceTokenizerLoadType(IntEnum):
     """An enumeration for SPieceTokenizerLoadType, effective enumeration types are FILE, MODEL."""
     FILE = 0

mindspore/dataset/transforms/c_transforms.py

@@ -204,7 +204,7 @@ class Concatenate(cde.ConcatenateOp):
     Tensor operation that concatenates all columns into a single tensor.
 
     Args:
-        axis (int, optional): axis to concatenate the tensors along (Default=0).
+        axis (int, optional): concatenate the tensors along given axis (Default=0).
         prepend (numpy.array, optional): numpy array to be prepended to the already concatenated tensors (Default=None).
         append (numpy.array, optional): numpy array to be appended to the already concatenated tensors (Default=None).
     """

mindspore/dataset/transforms/vision/c_transforms.py

@@ -188,8 +188,8 @@ class Normalize(cde.NormalizeOp):
     Normalize the input image with respect to mean and standard deviation.
 
     Args:
-        mean (sequence): List or tuple of mean values for each channel, w.r.t channel order.
-        std (sequence): List or tuple of standard deviations for each channel, w.r.t. channel order.
+        mean (sequence): List or tuple of mean values for each channel, with respect to channel order.
+        std (sequence): List or tuple of standard deviations for each channel, with respect to channel order.
     """
 
     @check_normalize_c

mindspore/mindrecord/filereader.py

@@ -23,6 +23,7 @@ from .common.exceptions import ParamValueError, ParamTypeError
 
 __all__ = ['FileReader']
 
+
 class FileReader:
     """
     Class to read MindRecord File series.
@@ -31,7 +32,7 @@ class FileReader:
        file_name (str, list[str]): One of MindRecord File or file list.
        num_consumer(int, optional): Number of consumer threads which load data to memory (default=4).
            It should not be smaller than 1 or larger than the number of CPU.
-       columns (list[str], optional): List of fields which correspond data would be read (default=None).
+       columns (list[str], optional): List of fields which corresponding data would be read (default=None).
        operator(int, optional): Reserved parameter for operators (default=None).
 
     Raises:

mindspore/mindrecord/filewriter.py

@@ -275,7 +275,7 @@ class FileWriter:
 
     def commit(self):
         """
-        Flush data to disk and generate the correspond db files.
+        Flush data to disk and generate the corresponding db files.
 
         Returns:
             MSRStatus, SUCCESS or FAILED.

mindspore/mindrecord/tools/imagenet_to_mr.py

@@ -25,12 +25,13 @@ from ..shardutils import check_filename
 
 __all__ = ['ImageNetToMR']
 
+
 class ImageNetToMR:
     """
     Class is for transformation from imagenet to MindRecord.
 
     Args:
-        map_file (str): the map file which indicate label.
+        map_file (str): the map file which indicates label.
                   the map file content should like this:
 
                   .. code-block::

mindspore/mindrecord/tools/mnist_to_mr.py

@@ -37,7 +37,7 @@ class MnistToMR:
     Class is for transformation from Mnist to MindRecord.
 
     Args:
-        source (str): directory which contain t10k-images-idx3-ubyte.gz,
+        source (str): directory which contains t10k-images-idx3-ubyte.gz,
                       train-images-idx3-ubyte.gz, t10k-labels-idx1-ubyte.gz,
                       train-labels-idx1-ubyte.gz.
         destination (str): the MindRecord file directory to transform into.