Fix code clean

docs/api/api_python/dataset/dataset_method/operation/mindspore.dataset.TextBaseDataset.build_sentencepiece_vocab.rst

@@ -6,6 +6,8 @@ mindspore.dataset.TextBaseDataset.build_sentencepiece_vocab
     迭代源数据集对象获取数据并构建SentencePiece词汇表。
     源数据集要求的是文本类数据集。
 
+    .. note:: mindspore.dataset.Dataset.build_sentencepiece_vocab 从2.0版本开始弃用。请使用mindspore.dataset.text.SentencePieceVocab.from_dataset代替。
+
     参数：
         - **columns** (list[str]) - 指定 `build_sentencepiece_vocab` 操作的输入列，会从该列获取数据构造词汇表。
         - **vocab_size** (int) - 词汇表的容量。

docs/api/api_python/dataset/dataset_method/operation/mindspore.dataset.TextBaseDataset.build_vocab.rst

@@ -8,6 +8,8 @@ mindspore.dataset.TextBaseDataset.build_vocab
 
     收集数据集中所有的不重复单词。返回 `top_k` 个最常见的单词组成的词汇表（如果指定了 `top_k` ）。
 
+    .. note:: mindspore.dataset.Dataset.build_vocab 从2.0版本开始弃用。请使用mindspore.dataset.text.Vocab.from_dataset代替。
+
     参数：
         - **columns** (Union[str, list[str]]) - 指定 `build_vocab` 操作的输入列，会从该列获取数据构造词汇表。
         - **freq_range** (tuple[int]) - 由(min_frequency, max_frequency)组成的整数元组，代表词汇出现的频率范围，在这个频率范围的词汇会被保存下来。

mindspore/ccsrc/minddata/dataset/engine/datasetops/batch_op.cc

@@ -263,7 +263,7 @@ Status BatchOp::WorkerEntry(int32_t workerId) {
       RETURN_IF_NOT_OK(worker_out_queues_[workerId]->EmplaceBack(TensorRow(TensorRow::TensorRowFlags::kFlagEOF)));
     } else if (table_pair.second.ctrl_ == batchCtrl::kWait) {
       RETURN_IF_NOT_OK(worker_out_queues_[workerId]->EmplaceBack(TensorRow(TensorRow::TensorRowFlags::kFlagWait)));
-      TaskManager::FindMe()->Wait();  // wait for auto tune update workers successful
+      RETURN_IF_NOT_OK(TaskManager::FindMe()->Wait());  // wait for auto tune update workers successful
       TaskManager::FindMe()->Clear();
     } else if (table_pair.second.ctrl_ == batchCtrl::kNoCtrl) {
       TensorRow new_row;

mindspore/ccsrc/minddata/dataset/engine/datasetops/map_op/map_op.cc

@@ -214,7 +214,7 @@ Status MapOp::WorkerEntry(int32_t worker_id) {
       }
       RETURN_IF_NOT_OK(worker_out_queues_[worker_id]->EmplaceBack(std::move(in_row)));
       if (in_row.wait()) {
-        TaskManager::FindMe()->Wait();  // wait for auto tune update workers successful
+        RETURN_IF_NOT_OK(TaskManager::FindMe()->Wait());  // wait for auto tune update workers successful
         TaskManager::FindMe()->Clear();
       }
     } else {

mindspore/ccsrc/minddata/dataset/engine/datasetops/parallel_op.h

@@ -368,7 +368,7 @@ class ParallelOp : public DatasetOp {
         // If num_worker signals are received, wakes up the main thread
         if (++num_workers_paused_ == num_workers_) {
           wait_for_workers_post_.Set();
-          wait_for_collector_.Wait();
+          RETURN_IF_NOT_OK(wait_for_collector_.Wait());
           wait_for_collector_.Clear();
           num_rows = 0;
         }

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/cifar_op.cc

@@ -58,7 +58,7 @@ Status CifarOp::LoadTensorRow(row_id_type index, TensorRow *trow) {
   std::shared_ptr<Tensor> fine_label;
   std::shared_ptr<Tensor> ori_image = cifar_image_label_pairs_[index].first;
   std::shared_ptr<Tensor> copy_image;
-  uint64_t path_index = std::ceil(index / kCifarBlockImageNum);
+  uint64_t path_index = static_cast<uint64_t>(std::ceil(index / kCifarBlockImageNum));
   RETURN_IF_NOT_OK(Tensor::CreateFromTensor(ori_image, &copy_image));
   RETURN_IF_NOT_OK(Tensor::CreateScalar(cifar_image_label_pairs_[index].second[0], &label));
 

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/mappable_leaf_op.cc

@@ -135,7 +135,7 @@ Status MappableLeafOp::WorkerEntry(int32_t worker_id) {
   while (io_block != nullptr) {
     if (io_block->wait()) {
       RETURN_IF_NOT_OK(worker_out_queues_[worker_id]->EmplaceBack(TensorRow(TensorRow::TensorRowFlags::kFlagWait)));
-      TaskManager::FindMe()->Wait();  // wait for auto tune update workers successful
+      RETURN_IF_NOT_OK(TaskManager::FindMe()->Wait());  // wait for auto tune update workers successful
       TaskManager::FindMe()->Clear();
     } else if (io_block->eoe()) {
       RETURN_IF_NOT_OK(worker_out_queues_[worker_id]->EmplaceBack(TensorRow(TensorRow::TensorRowFlags::kFlagEOE)));

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/mindrecord_op.cc

@@ -148,7 +148,7 @@ Status MindRecordOp::WorkerEntry(int32_t worker_id) {
   while (io_block != nullptr) {
     if (io_block->wait()) {
       RETURN_IF_NOT_OK(worker_out_queues_[worker_id]->EmplaceBack(TensorRow(TensorRow::TensorRowFlags::kFlagWait)));
-      TaskManager::FindMe()->Wait();  // wait for auto tune update workers successful
+      RETURN_IF_NOT_OK(TaskManager::FindMe()->Wait());  // wait for auto tune update workers successful
       TaskManager::FindMe()->Clear();
     } else if (io_block->eoe()) {
       RETURN_IF_NOT_OK(worker_out_queues_[worker_id]->EmplaceBack(TensorRow(TensorRow::TensorRowFlags::kFlagEOE)));

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/speech_commands_op.cc

@@ -180,7 +180,7 @@ Status SpeechCommandsOp::GetFileInfo(const std::string &file_path, std::string *
   std::smatch result;
   {
     std::unique_lock<std::mutex> _lock(mux_);
-    regex_match(filename, result, std::regex("(.*)_nohash_(\\d+)\\.wav"));
+    (void)regex_match(filename, result, std::regex("(.*)_nohash_(\\d+)\\.wav"));
   }
   CHECK_FAIL_RETURN_UNEXPECTED(!(result[0] == "" || result[1] == ""),
                                "Invalid file name, failed to get file info: " + filename);

mindspore/ccsrc/minddata/mindrecord/io/shard_writer.cc

@@ -1104,7 +1104,7 @@ Status ShardWriter::SerializeRawData(std::map<uint64_t, std::vector<json>> &raw_
     thread_num = kThreadNumber;
   }
   // Set the number of samples processed by each thread
-  int group_num = ceil(row_count * 1.0 / thread_num);
+  int group_num = static_cast<int>(ceil(row_count * 1.0 / thread_num));
   std::vector<std::thread> thread_set(thread_num);
   int work_thread_num = 0;
   for (uint32_t x = 0; x < thread_num; ++x) {

mindspore/ccsrc/plugin/device/cpu/kernel/max_pool_with_argmax_cpu_kernel.cc

@@ -151,7 +151,9 @@ bool MaxPoolWithArgmaxCpuKernelMod::LaunchKernel(const std::vector<kernel::Addre
   MS_EXCEPTION_IF_NULL(output);
   auto *mask = reinterpret_cast<int32_t *>(outputs.at(kIndex1)->addr);
   MS_EXCEPTION_IF_NULL(mask);
-  int cWeight, hWeight, wWeight;
+  int cWeight;
+  int hWeight;
+  int wWeight;
   if (data_format_ == Format::NHWC) {
     cWeight = 1;
     wWeight = channel_ * cWeight;

mindspore/python/mindspore/dataset/engine/datasets.py

@@ -1900,6 +1900,7 @@ class VisionBaseDataset(Dataset):
         raise NotImplementedError("Dataset has to implement parse method.")
 
 
+# pylint: disable=abstract-method
 class TextBaseDataset(Dataset):
     """
     Abstract class to represent a text source dataset which produces content to the data pipeline.
@@ -1919,6 +1920,10 @@ class TextBaseDataset(Dataset):
         Build a vocab from a dataset. This would collect all the unique words in a dataset and return a vocab
         which contains top_k most frequent words (if top_k is specified).
 
+        Note:
+            mindspore.dataset.Dataset.build_vocab is deprecated from version 2.0
+            and will be removed in a future version. Use mindspore.dataset.text.Vocab.from_dataset instead.
+
         Args:
             columns(Union[str, list[str]]): Column names to get words from.
             freq_range(tuple[int]): A tuple of integers (min_frequency, max_frequency). Words within the frequency
@@ -1950,6 +1955,67 @@ class TextBaseDataset(Dataset):
             ...                               special_first=True)
 
         """
+        raise NotImplementedError("mindspore.dataset.Dataset.build_vocab is deprecated from version 2.0 "
+                                  "and will be removed in a future version. "
+                                  "Use mindspore.dataset.text.Vocab.from_dataset instead.")
+
+    def build_sentencepiece_vocab(self, columns, vocab_size, character_coverage, model_type, params):
+        """
+        Function to create a SentencePieceVocab from source dataset.
+        Desired source dataset is a text type dataset.
+
+        Note:
+            mindspore.dataset.Dataset.build_sentencepiece_vocab is deprecated from version 2.0
+            and will be removed in a future version. Use mindspore.dataset.text.SentencePieceVocab.from_dataset instead.
+
+        Args:
+            columns(list[str]): Column names to get words from.
+            vocab_size(int): Vocabulary size.
+            character_coverage(float): Percentage of characters covered by the model, must be between
+                0.98 and 1.0 Good defaults are: 0.9995 for languages with rich character sets like
+                Japanese or Chinese character sets, and 1.0 for other languages with small character sets
+                like English or Latin.
+            model_type(SentencePieceModel): Model type. Choose from unigram (default), bpe, char, or word.
+                The input sentence must be pretokenized when using word type.
+            params(dict): Any extra optional parameters of sentencepiece library according to your raw data
+
+        Returns:
+            SentencePieceVocab, vocab built from the dataset.
+
+        Examples:
+            >>> from mindspore.dataset.text import SentencePieceModel
+            >>>
+            >>> # You can construct any text dataset as source, take TextFileDataset as example.
+            >>> dataset = ds.TextFileDataset("/path/to/sentence/piece/vocab/file", shuffle=False)
+            >>> dataset = dataset.build_sentencepiece_vocab(["text"], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
+        """
+        raise NotImplementedError("mindspore.dataset.Dataset.build_sentencepiece_vocab is deprecated from version 2.0 "
+                                  "and will be removed in a future version. "
+                                  "Use mindspore.dataset.text.SentencePieceVocab.from_dataset instead.")
+
+    def _build_vocab(self, columns, freq_range, top_k, special_tokens, special_first):
+        """
+        Function to create a Vocab from source dataset.
+        Desired source dataset is a text type dataset.
+
+        Build a vocab from a dataset. This would collect all the unique words in a dataset and return a vocab
+        which contains top_k most frequent words (if top_k is specified).
+
+        Args:
+            columns(Union[str, list[str]]): Column names to get words from.
+            freq_range(tuple[int]): A tuple of integers (min_frequency, max_frequency). Words within the frequency
+                range will be stored.
+                Naturally 0 <= min_frequency <= max_frequency <= total_words. min_frequency/max_frequency
+                can be set to default, which corresponds to 0/total_words separately.
+            top_k(int): Number of words to be built into vocab. top_k most frequent words are
+                taken. The top_k is taken after freq_range. If not enough top_k, all words will be taken
+            special_tokens(list[str]): A list of strings, each one is a special token.
+            special_first(bool): Whether special_tokens will be prepended/appended to vocab, If special_tokens
+                is specified and special_first is set to default, special_tokens will be prepended.
+
+        Returns:
+            Vocab, vocab built from the dataset.
+        """
         vocab = cde.Vocab()
         columns = replace_none(columns, [])
         if not isinstance(columns, list):
@@ -1981,7 +2047,7 @@ class TextBaseDataset(Dataset):
 
         return vocab
 
-    def build_sentencepiece_vocab(self, columns, vocab_size, character_coverage, model_type, params):
+    def _build_sentencepiece_vocab(self, columns, vocab_size, character_coverage, model_type, params):
         """
         Function to create a SentencePieceVocab from source dataset.
         Desired source dataset is a text type dataset.
@@ -1999,13 +2065,6 @@ class TextBaseDataset(Dataset):
 
         Returns:
             SentencePieceVocab, vocab built from the dataset.
-
-        Examples:
-            >>> from mindspore.dataset.text import SentencePieceModel
-            >>>
-            >>> # You can construct any text dataset as source, take TextFileDataset as example.
-            >>> dataset = ds.TextFileDataset("/path/to/sentence/piece/vocab/file", shuffle=False)
-            >>> dataset = dataset.build_sentencepiece_vocab(["text"], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
         """
         if not isinstance(model_type, SentencePieceModel):
             raise TypeError("Argument model_type with value {0} is not of type SentencePieceModel, but got {1}." \

mindspore/python/mindspore/dataset/text/utils.py

@@ -246,9 +246,10 @@ class SentencePieceVocab:
         """
 
         sentence_piece_vocab = cls()
-        sentence_piece_vocab.c_sentence_piece_vocab = dataset.build_sentencepiece_vocab(col_names, vocab_size,
-                                                                                        character_coverage,
-                                                                                        model_type, params)
+        # pylint: disable=protected-access
+        sentence_piece_vocab.c_sentence_piece_vocab = dataset._build_sentencepiece_vocab(col_names, vocab_size,
+                                                                                         character_coverage,
+                                                                                         model_type, params)
         return sentence_piece_vocab
 
     @classmethod
@@ -428,7 +429,8 @@ class Vocab:
         """
 
         vocab = cls()
-        vocab.c_vocab = dataset.build_vocab(columns, freq_range, top_k, special_tokens, special_first)
+        # pylint: disable=protected-access
+        vocab.c_vocab = dataset._build_vocab(columns, freq_range, top_k, special_tokens, special_first)
         return vocab
 
     @classmethod