fix missing return descriptions

mindspore/dataset/engine/serializer_deserializer.py

@@ -168,6 +168,17 @@ def create_node(node):
     # Find a matching Dataset class and call the constructor with the corresponding args.
     # When a new Dataset class is introduced, another if clause and parsing code needs to be added.
     # Dataset Source Ops (in alphabetical order)
+    pyobj = create_dataset_node(pyclass, node, dataset_op)
+    if not pyobj:
+        # Dataset Ops (in alphabetical order)
+        pyobj = create_dataset_operation_node(node, dataset_op)
+
+    return pyobj
+
+
+def create_dataset_node(pyclass, node, dataset_op):
+    """Parse the key, value in the dataset node dictionary and instantiate the Python Dataset object"""
+    pyobj = None
     if dataset_op == 'CelebADataset':
         sampler = construct_sampler(node.get('sampler'))
         num_samples = check_and_replace_input(node.get('num_samples'), 0, None)
@@ -189,7 +200,7 @@ def create_node(node):
 
     elif dataset_op == 'ClueDataset':
         shuffle = to_shuffle_mode(node.get('shuffle'))
-        if shuffle is not None and isinstance(shuffle, str):
+        if isinstance(shuffle, str):
             shuffle = de.Shuffle(shuffle)
         num_samples = check_and_replace_input(node.get('num_samples'), 0, None)
         pyobj = pyclass(node['dataset_files'], node.get('task'),
@@ -205,7 +216,7 @@ def create_node(node):
 
     elif dataset_op == 'CSVDataset':
         shuffle = to_shuffle_mode(node.get('shuffle'))
-        if shuffle is not None and isinstance(shuffle, str):
+        if isinstance(shuffle, str):
             shuffle = de.Shuffle(shuffle)
         num_samples = check_and_replace_input(node.get('num_samples'), 0, None)
         pyobj = pyclass(node['dataset_files'], node.get('field_delim'),
@@ -237,7 +248,7 @@ def create_node(node):
 
     elif dataset_op == 'TextFileDataset':
         shuffle = to_shuffle_mode(node.get('shuffle'))
-        if shuffle is not None and isinstance(shuffle, str):
+        if isinstance(shuffle, str):
             shuffle = de.Shuffle(shuffle)
         num_samples = check_and_replace_input(node.get('num_samples'), 0, None)
         pyobj = pyclass(node['dataset_files'], num_samples,
@@ -246,7 +257,7 @@ def create_node(node):
 
     elif dataset_op == 'TFRecordDataset':
         shuffle = to_shuffle_mode(node.get('shuffle'))
-        if shuffle is not None and isinstance(shuffle, str):
+        if isinstance(shuffle, str):
             shuffle = de.Shuffle(shuffle)
         num_samples = check_and_replace_input(node.get('num_samples'), 0, None)
         pyobj = pyclass(node['dataset_files'], node.get('schema'), node.get('columns_list'),
@@ -260,8 +271,13 @@ def create_node(node):
                         num_samples, node.get('num_parallel_workers'), node.get('shuffle'),
                         node.get('decode'), sampler, node.get('num_shards'), node.get('shard_id'))
 
-    # Dataset Ops (in alphabetical order)
-    elif dataset_op == 'Batch':
+    return pyobj
+
+
+def create_dataset_operation_node(node, dataset_op):
+    """Parse the key, value in the dataset operation node dictionary and instantiate the Python Dataset object"""
+    pyobj = None
+    if dataset_op == 'Batch':
         pyobj = de.Dataset().batch(node['batch_size'], node.get('drop_remainder'))
 
     elif dataset_op == 'Map':
@@ -292,7 +308,7 @@ def create_node(node):
         pyobj = de.Dataset().to_device(node.get('send_epoch_end'), node.get('create_data_info_queue'))
 
     elif dataset_op == 'Zip':
-        # Create ZipDataset instance, giving dummy input dataset that will be overrided in the caller.
+        # Create ZipDataset instance, giving dummy input dataset that will be overrode in the caller.
         pyobj = de.ZipDataset((de.Dataset(), de.Dataset()))
 
     else:

mindspore/dataset/text/utils.py

@@ -24,7 +24,6 @@ import mindspore._c_dataengine as cde
 from .validators import check_from_file, check_from_list, check_from_dict, check_from_dataset, \
     check_from_dataset_sentencepiece, check_from_file_sentencepiece, check_save_model
 
-
 __all__ = [
     "Vocab", "SentencePieceVocab", "to_str", "to_bytes"
 ]
@@ -66,7 +65,7 @@ class Vocab(cde.Vocab):
                 is specified and special_first is set to True, special_tokens will be prepended (default=True).
 
         Returns:
-            Vocab, Vocab object built from dataset.
+            Vocab, vocab built from the dataset.
         """
         return dataset.build_vocab(columns, freq_range, top_k, special_tokens, special_first)
 
@@ -82,6 +81,9 @@ class Vocab(cde.Vocab):
                 special_tokens=["<pad>","<unk>"] (default=None, no special tokens will be added).
             special_first(bool, optional): whether special_tokens will be prepended/appended to vocab, If special_tokens
                 is specified and special_first is set to True, special_tokens will be prepended (default=True).
+
+        Returns:
+            Vocab, vocab built from the `list`.
         """
         if special_tokens is None:
             special_tokens = []
@@ -103,6 +105,9 @@ class Vocab(cde.Vocab):
             special_first (bool, optional): whether special_tokens will be prepended/appended to vocab,
                 If special_tokens is specified and special_first is set to True,
                 special_tokens will be prepended (default=True).
+
+        Returns:
+            Vocab, vocab built from the file.
         """
         if vocab_size is None:
             vocab_size = -1
@@ -119,6 +124,9 @@ class Vocab(cde.Vocab):
         Args:
             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.
+
+        Returns:
+            Vocab, vocab built from the `dict`.
         """
 
         return super().from_dict(word_dict)
@@ -147,7 +155,7 @@ class SentencePieceVocab(cde.SentencePieceVocab):
             params(dict): A dictionary with no incoming parameters.
 
         Returns:
-            SentencePiece, SentencePiece object from dataset.
+            SentencePieceVocab, vocab built from the dataset.
         """
 
         return dataset.build_sentencepiece_vocab(col_names, vocab_size, character_coverage,
@@ -174,6 +182,9 @@ class SentencePieceVocab(cde.SentencePieceVocab):
 
                     input_sentence_size 0
                     max_sentencepiece_length 16
+
+        Returns:
+            SentencePieceVocab, vocab built from the file.
         """
         return super().from_file(file_path, vocab_size, character_coverage,
                                  DE_C_INTER_SENTENCEPIECE_MODE[model_type], params)
@@ -189,7 +200,7 @@ class SentencePieceVocab(cde.SentencePieceVocab):
             path(str): Path to store model.
             filename(str): The name of the file.
         """
-        return super().save_model(vocab, path, filename)
+        super().save_model(vocab, path, filename)
 
 
 def to_str(array, encoding='utf8'):

mindspore/mindrecord/filereader.py

@@ -38,6 +38,7 @@ class FileReader:
     Raises:
         ParamValueError: If file_name, num_consumer or columns is invalid.
     """
+
     def __init__(self, file_name, num_consumer=4, columns=None, operator=None):
         if isinstance(file_name, list):
             for f in file_name:
@@ -66,7 +67,6 @@ class FileReader:
         self._header = ShardHeader(self._reader.get_header())
         self._reader.launch()
 
-
     def get_next(self):
         """
         Yield a batch of data according to columns at a time.
@@ -85,4 +85,4 @@ class FileReader:
 
     def close(self):
         """Stop reader worker and close File."""
-        return self._reader.close()
+        self._reader.close()

mindspore/mindrecord/tools/tfrecord_to_mr.py

@@ -69,7 +69,7 @@ class TFRecordToMR:
 
     Args:
         source (str): the TFRecord file to be transformed.
-        destination (str): the MindRecord file path to tranform into.
+        destination (str): the MindRecord file path to transform into.
         feature_dict (dict): a dictionary that states the feature type, e.g.
             feature_dict = {"xxxx": tf.io.FixedLenFeature([], tf.string), \
                             "yyyy": tf.io.FixedLenFeature([], tf.int64)}
@@ -90,31 +90,14 @@ class TFRecordToMR:
         try:
             self.tf = import_module("tensorflow")  # just used to convert tfrecord to mindrecord
         except ModuleNotFoundError:
-            self.tf = None
-        if not self.tf:
             raise Exception("Module tensorflow is not found, please use pip install it.")
 
         if self.tf.__version__ < SupportedTensorFlowVersion:
             raise Exception("Module tensorflow version must be greater or equal {}.".format(SupportedTensorFlowVersion))
 
-        if not isinstance(source, str):
-            raise ValueError("Parameter source must be string.")
-        check_filename(source)
-
-        if not isinstance(destination, str):
-            raise ValueError("Parameter destination must be string.")
-        check_filename(destination)
-
+        self._check_input(source, destination, feature_dict)
         self.source = source
         self.destination = destination
-
-        if feature_dict is None or not isinstance(feature_dict, dict):
-            raise ValueError("Parameter feature_dict is None or not dict.")
-
-        for key, val in feature_dict.items():
-            if not isinstance(val, self.tf.io.FixedLenFeature):
-                raise ValueError("Parameter feature_dict: {} only support FixedLenFeature.".format(feature_dict))
-
         self.feature_dict = feature_dict
 
         bytes_fields_list = []
@@ -162,6 +145,23 @@ class TFRecordToMR:
                 mindrecord_schema[_cast_name(key)] = {"type": self._cast_type(val.dtype), "shape": [val.shape[0]]}
         self.mindrecord_schema = mindrecord_schema
 
+    def _check_input(self, source, destination, feature_dict):
+        """Validation check for inputs of init method"""
+        if not isinstance(source, str):
+            raise ValueError("Parameter source must be string.")
+        check_filename(source)
+
+        if not isinstance(destination, str):
+            raise ValueError("Parameter destination must be string.")
+        check_filename(destination)
+
+        if feature_dict is None or not isinstance(feature_dict, dict):
+            raise ValueError("Parameter feature_dict is None or not dict.")
+
+        for _, val in feature_dict.items():
+            if not isinstance(val, self.tf.io.FixedLenFeature):
+                raise ValueError("Parameter feature_dict: {} only support FixedLenFeature.".format(feature_dict))
+
     def _parse_record(self, example):
         """Returns features for a single example"""
         features = self.tf.io.parse_single_example(example, features=self.feature_dict)
@@ -206,6 +206,9 @@ class TFRecordToMR:
         """
         Yield a dict with key to be fields in schema, and value to be data.
         This function is for old version tensorflow whose version number < 2.1.0
+
+        Yields:
+            dict, data dictionary whose keys are the same as columns.
         """
         dataset = self.tf.data.TFRecordDataset(self.source)
         dataset = dataset.map(self._parse_record)
@@ -235,7 +238,12 @@ class TFRecordToMR:
                     raise ValueError("TFRecord feature_dict parameter error.")
 
     def tfrecord_iterator(self):
-        """Yield a dictionary whose keys are fields in schema."""
+        """
+        Yield a dictionary whose keys are fields in schema.
+
+        Yields:
+            dict, data dictionary whose keys are the same as columns.
+        """
         dataset = self.tf.data.TFRecordDataset(self.source)
         dataset = dataset.map(self._parse_record)
         iterator = dataset.__iter__()
@@ -265,7 +273,7 @@ class TFRecordToMR:
         Execute transformation from TFRecord to MindRecord.
 
         Returns:
-            MSRStatus, whether TFRecord is successfuly transformed to MindRecord.
+            MSRStatus, whether TFRecord is successfully transformed to MindRecord.
         """
         writer = FileWriter(self.destination)
         logger.info("Transformed MindRecord schema is: {}, TFRecord feature dict is: {}"