Clean up work for text python package

mindspore/ccsrc/dataset/CMakeLists.txt

@@ -52,7 +52,7 @@ add_subdirectory(core)
 add_subdirectory(kernels)
 add_subdirectory(engine)
 add_subdirectory(api)
-add_subdirectory(nlp)
+add_subdirectory(text)
 ######################################################################
 
 ################### Create _c_dataengine Library ######################
@@ -62,15 +62,14 @@ set(submodules
     $<TARGET_OBJECTS:kernels>
     $<TARGET_OBJECTS:kernels-image>
     $<TARGET_OBJECTS:kernels-data>
-    $<TARGET_OBJECTS:kernels-text>
     $<TARGET_OBJECTS:APItoPython>
     $<TARGET_OBJECTS:engine-datasetops-source>
     $<TARGET_OBJECTS:engine-datasetops-source-sampler>
     $<TARGET_OBJECTS:engine-datasetops>
     $<TARGET_OBJECTS:engine-opt>
     $<TARGET_OBJECTS:engine>
-    $<TARGET_OBJECTS:nlp>
-    $<TARGET_OBJECTS:nlp-kernels>
+    $<TARGET_OBJECTS:text>
+    $<TARGET_OBJECTS:text-kernels>
     )
 
 if (ENABLE_TDTQUE)

mindspore/ccsrc/dataset/api/python_bindings.cc

@@ -38,10 +38,6 @@
 #include "dataset/kernels/image/resize_op.h"
 #include "dataset/kernels/image/uniform_aug_op.h"
 #include "dataset/kernels/data/type_cast_op.h"
-#include "dataset/kernels/text/jieba_tokenizer_op.h"
-#include "dataset/kernels/text/unicode_char_tokenizer_op.h"
-#include "dataset/nlp/vocab.h"
-#include "dataset/nlp/kernels/lookup_op.h"
 #include "dataset/engine/datasetops/source/cifar_op.h"
 #include "dataset/engine/datasetops/source/image_folder_op.h"
 #include "dataset/engine/datasetops/source/io_block.h"
@@ -62,6 +58,10 @@
 #include "dataset/engine/datasetops/source/text_file_op.h"
 #include "dataset/engine/datasetops/source/voc_op.h"
 #include "dataset/kernels/data/to_float16_op.h"
+#include "dataset/text/kernels/jieba_tokenizer_op.h"
+#include "dataset/text/kernels/unicode_char_tokenizer_op.h"
+#include "dataset/text/vocab.h"
+#include "dataset/text/kernels/lookup_op.h"
 #include "dataset/util/random.h"
 #include "mindrecord/include/shard_operator.h"
 #include "mindrecord/include/shard_pk_sample.h"
@@ -549,9 +549,9 @@ PYBIND11_MODULE(_c_dataengine, m) {
     .value("TEXTFILE", OpName::kTextFile);
 
   (void)py::enum_<JiebaMode>(m, "JiebaMode", py::arithmetic())
-    .value("DE_INTER_JIEBA_MIX", JiebaMode::kMix)
-    .value("DE_INTER_JIEBA_MP", JiebaMode::kMp)
-    .value("DE_INTER_JIEBA_HMM", JiebaMode::kHmm)
+    .value("DE_JIEBA_MIX", JiebaMode::kMix)
+    .value("DE_JIEBA_MP", JiebaMode::kMp)
+    .value("DE_JIEBA_HMM", JiebaMode::kHmm)
     .export_values();
 
   (void)py::enum_<InterpolationMode>(m, "InterpolationMode", py::arithmetic())

mindspore/ccsrc/dataset/kernels/CMakeLists.txt

@@ -2,7 +2,6 @@ add_subdirectory(image)
 add_subdirectory(data)
 file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc")
 set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
-add_subdirectory(text)
 add_library(kernels OBJECT
     py_func_op.cc
     tensor_op.cc)

mindspore/ccsrc/dataset/nlp/kernels/CMakeLists.txt

-add_library(nlp-kernels OBJECT
-        lookup_op.cc
-        )

mindspore/ccsrc/dataset/nlp/CMakeLists.txt → mindspore/ccsrc/dataset/text/CMakeLists.txt

 add_subdirectory(kernels)
 
-add_library(nlp OBJECT
+add_library(text OBJECT
         vocab.cc
         )
 
-add_dependencies(nlp nlp-kernels)
\ No newline at end of file
+add_dependencies(text text-kernels)
\ No newline at end of file

mindspore/ccsrc/dataset/kernels/text/CMakeLists.txt → mindspore/ccsrc/dataset/text/kernels/CMakeLists.txt

 file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc")
 set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
-add_library(kernels-text OBJECT
-    jieba_tokenizer_op.cc
-    unicode_char_tokenizer_op.cc
-)
\ No newline at end of file
+add_library(text-kernels OBJECT
+        lookup_op.cc
+        jieba_tokenizer_op.cc
+        unicode_char_tokenizer_op.cc
+        )

mindspore/ccsrc/dataset/kernels/text/jieba_tokenizer_op.cc → mindspore/ccsrc/dataset/text/kernels/jieba_tokenizer_op.cc

@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "dataset/kernels/text/jieba_tokenizer_op.h"
+#include "dataset/text/kernels/jieba_tokenizer_op.h"
 
 #include <vector>
 #include <memory>

mindspore/ccsrc/dataset/nlp/kernels/lookup_op.cc → mindspore/ccsrc/dataset/text/kernels/lookup_op.cc

@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "dataset/nlp/kernels/lookup_op.h"
+#include "dataset/text/kernels/lookup_op.h"
 
 #include <string>
 

mindspore/ccsrc/dataset/nlp/kernels/lookup_op.h → mindspore/ccsrc/dataset/text/kernels/lookup_op.h

@@ -24,7 +24,7 @@
 #include "dataset/core/tensor.h"
 #include "dataset/kernels/tensor_op.h"
 #include "dataset/util/status.h"
-#include "dataset/nlp/vocab.h"
+#include "dataset/text/vocab.h"
 
 namespace mindspore {
 namespace dataset {

mindspore/ccsrc/dataset/kernels/text/unicode_char_tokenizer_op.cc → mindspore/ccsrc/dataset/text/kernels/unicode_char_tokenizer_op.cc

@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "dataset/kernels/text/unicode_char_tokenizer_op.h"
+#include "dataset/text/kernels/unicode_char_tokenizer_op.h"
 #include <memory>
 #include <string>
 #include <string_view>

mindspore/ccsrc/dataset/nlp/vocab.cc → mindspore/ccsrc/dataset/text/vocab.cc

@@ -17,7 +17,7 @@
 #include <map>
 #include <utility>
 
-#include "dataset/nlp/vocab.h"
+#include "dataset/text/vocab.h"
 
 namespace mindspore {
 namespace dataset {

mindspore/dataset/engine/datasets.py

@@ -284,10 +284,10 @@ class Dataset:
 
         Examples:
             >>> import mindspore.dataset as ds
-            >>> import mindspore.dataset.transforms.text.utils as text
+            >>> import mindspore.dataset.text as text
             >>> # declare a function which returns a Dataset object
             >>> def flat_map_func(x):
-            >>>     data_dir = text.as_text(x[0])
+            >>>     data_dir = text.to_str(x[0])
             >>>     d = ds.ImageFolderDatasetV2(data_dir)
             >>>     return d
             >>> # data is a Dataset object

mindspore/dataset/text/__init__.py

@@ -15,5 +15,5 @@
 """
 mindspore.dataset.text
 """
-
-from .c_transforms import *
+from .transforms import Lookup, JiebaTokenizer, UnicodeCharTokenizer
+from .utils import to_str, to_bytes, JiebaMode, Vocab

mindspore/dataset/transforms/text/c_transforms.py → mindspore/dataset/text/transforms.py

@@ -11,20 +11,40 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# ==============================================================================
 """
-This module c_transforms provides common nlp operations.
+c transforms for all text related operators
 """
+
 import os
 import re
+
 import mindspore._c_dataengine as cde
+
 from .utils import JiebaMode
-from .validators import check_jieba_add_dict, check_jieba_add_word, check_jieba_init
+from .validators import check_lookup, check_jieba_add_dict, \
+    check_jieba_add_word, check_jieba_init
+
+
+class Lookup(cde.LookupOp):
+    """
+        Lookup operator that looks up a word to an id
+    Args:
+        vocab(Vocab): a Vocab object
+        unknown(None,int): default id to lookup a word that is out of vocab
+    """
+
+    @check_lookup
+    def __init__(self, vocab, unknown=None):
+        if unknown is None:
+            super().__init__(vocab)
+        else:
+            super().__init__(vocab, unknown)
+
 
 DE_C_INTER_JIEBA_MODE = {
-    JiebaMode.MIX: cde.JiebaMode.DE_INTER_JIEBA_MIX,
-    JiebaMode.MP: cde.JiebaMode.DE_INTER_JIEBA_MP,
-    JiebaMode.HMM: cde.JiebaMode.DE_INTER_JIEBA_HMM
+    JiebaMode.MIX: cde.JiebaMode.DE_JIEBA_MIX,
+    JiebaMode.MP: cde.JiebaMode.DE_JIEBA_MP,
+    JiebaMode.HMM: cde.JiebaMode.DE_JIEBA_HMM
 }
 
 
@@ -41,6 +61,7 @@ class JiebaTokenizer(cde.JiebaTokenizerOp):
             "HMM" mode will tokenize with Hiddel Markov Model Segment algorithm,
             "MIX" model will tokenize with a mix of MPSegment and HMMSegment algorithm.
     """
+
     @check_jieba_init
     def __init__(self, hmm_path, mp_path, mode=JiebaMode.MIX):
         self.mode = mode

mindspore/dataset/text/c_transforms.py → mindspore/dataset/text/utils.py

@@ -12,11 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-c transforms for all text related operators
+Some basic function for nlp
 """
+from enum import IntEnum
 
 import mindspore._c_dataengine as cde
-from .validators import check_lookup, check_from_list, check_from_dict, check_from_file
+import numpy as np
+
+from .validators import check_from_file, check_from_list, check_from_dict
 
 
 class Vocab(cde.Vocab):
@@ -61,17 +64,43 @@ class Vocab(cde.Vocab):
         return super().from_dict(word_dict)
 
 
-class Lookup(cde.LookupOp):
+def to_str(array, encoding='utf8'):
     """
-        Lookup operator that looks up a word to an id
+    Convert numpy array of `bytes` to array of `str` by decoding each element based on charset `encoding`.
+
     Args:
-        vocab(Vocab): a Vocab object
-        unknown(None,int): default id to lookup a word that is out of vocab
+        array (numpy array): Array of type `bytes` representing strings.
+        encoding (string): Indicating the charset for decoding.
+    Returns:
+        Numpy array of `str`.
+
+    """
+
+    if not isinstance(array, np.ndarray):
+        raise ValueError('input should be a numpy array')
+
+    return np.char.decode(array, encoding)
+
+
+def to_bytes(array, encoding='utf8'):
     """
+    Convert numpy array of `str` to array of `bytes` by encoding each element based on charset `encoding`.
+
+    Args:
+        array (numpy array): Array of type `str` representing strings.
+        encoding (string): Indicating the charset for encoding.
+    Returns:
+        Numpy array of `bytes`.
+
+    """
+
+    if not isinstance(array, np.ndarray):
+        raise ValueError('input should be a numpy array')
+
+    return np.char.encode(array, encoding)
+
 
-    @check_lookup
-    def __init__(self, vocab, unknown=None):
-        if unknown is None:
-            super().__init__(vocab)
-        else:
-            super().__init__(vocab, unknown)
+class JiebaMode(IntEnum):
+    MIX = 0
+    MP = 1
+    HMM = 2

mindspore/dataset/text/validators.py

@@ -17,8 +17,11 @@ validators for text ops
 """
 
 from functools import wraps
+
 import mindspore._c_dataengine as cde
 
+from ..transforms.validators import check_uint32
+
 
 def check_lookup(method):
     """A wrapper that wrap a parameter checker to the original function(crop operation)."""
@@ -106,3 +109,67 @@ def check_from_dict(method):
         return method(self, **kwargs)
 
     return new_method
+
+
+def check_jieba_init(method):
+    """Wrapper method to check the parameters of jieba add word."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        hmm_path, mp_path, model = (list(args) + 3 * [None])[:3]
+
+        if "hmm_path" in kwargs:
+            hmm_path = kwargs.get("hmm_path")
+        if "mp_path" in kwargs:
+            mp_path = kwargs.get("mp_path")
+        if hmm_path is None:
+            raise ValueError(
+                "the dict of HMMSegment in cppjieba is not provided")
+        kwargs["hmm_path"] = hmm_path
+        if mp_path is None:
+            raise ValueError(
+                "the dict of MPSegment in cppjieba is not provided")
+        kwargs["mp_path"] = mp_path
+        if model is not None:
+            kwargs["model"] = model
+        return method(self, **kwargs)
+
+    return new_method
+
+
+def check_jieba_add_word(method):
+    """Wrapper method to check the parameters of jieba add word."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        word, freq = (list(args) + 2 * [None])[:2]
+
+        if "word" in kwargs:
+            word = kwargs.get("word")
+        if "freq" in kwargs:
+            freq = kwargs.get("freq")
+        if word is None:
+            raise ValueError("word is not provided")
+        kwargs["word"] = word
+        if freq is not None:
+            check_uint32(freq)
+            kwargs["freq"] = freq
+        return method(self, **kwargs)
+
+    return new_method
+
+
+def check_jieba_add_dict(method):
+    """Wrapper method to check the parameters of add dict"""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        user_dict = (list(args) + [None])[0]
+        if "user_dict" in kwargs:
+            user_dict = kwargs.get("user_dict")
+        if user_dict is None:
+            raise ValueError("user_dict is not provided")
+        kwargs["user_dict"] = user_dict
+        return method(self, **kwargs)
+
+    return new_method

mindspore/dataset/transforms/text/__init__.py

-# Copyright 2020 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.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-This module is to support nlp augmentations. It includes two parts:
-c_transforms and py_transforms. C_transforms is a high performance
-image augmentation module which is developed with c++ opencv. Py_transforms
-provide more kinds of image augmentations which is developed with python PIL.
-"""
-from .utils import as_text, JiebaMode
-from . import c_transforms

mindspore/dataset/transforms/text/utils.py

-# Copyright 2020 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.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Some basic function for nlp
-"""
-from enum import IntEnum
-import numpy as np
-
-
-def as_text(array, encoding='utf8'):
-    """
-    Convert data of array to unicode.
-
-    Args:
-        array (numpy array): Data of array should be ASCII values of each character after converted.
-        encoding (string): Indicating the charset for decoding.
-    Returns:
-        A 'str' object.
-
-    """
-
-    if not isinstance(array, np.ndarray):
-        raise ValueError('input should be a numpy array')
-
-    decode = np.vectorize(lambda x: x.decode(encoding))
-    return decode(array)
-
-
-class JiebaMode(IntEnum):
-    MIX = 0
-    MP = 1
-    HMM = 2

mindspore/dataset/transforms/text/validators.py

-# Copyright 2020 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.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Validators for TensorOps.
-"""
-from functools import wraps
-from ...transforms.validators import check_uint32
-
-
-def check_jieba_init(method):
-    """Wrapper method to check the parameters of jieba add word."""
-
-    @wraps(method)
-    def new_method(self, *args, **kwargs):
-        hmm_path, mp_path, model = (list(args) + 3 * [None])[:3]
-
-        if "hmm_path" in kwargs:
-            hmm_path = kwargs.get("hmm_path")
-        if "mp_path" in kwargs:
-            mp_path = kwargs.get("mp_path")
-        if hmm_path is None:
-            raise ValueError(
-                "the dict of HMMSegment in cppjieba is not provided")
-        kwargs["hmm_path"] = hmm_path
-        if mp_path is None:
-            raise ValueError(
-                "the dict of MPSegment in cppjieba is not provided")
-        kwargs["mp_path"] = mp_path
-        if model is not None:
-            kwargs["model"] = model
-        return method(self, **kwargs)
-    return new_method
-
-
-def check_jieba_add_word(method):
-    """Wrapper method to check the parameters of jieba add word."""
-
-    @wraps(method)
-    def new_method(self, *args, **kwargs):
-        word, freq = (list(args) + 2 * [None])[:2]
-
-        if "word" in kwargs:
-            word = kwargs.get("word")
-        if "freq" in kwargs:
-            freq = kwargs.get("freq")
-        if word is None:
-            raise ValueError("word is not provided")
-        kwargs["word"] = word
-        if freq is not None:
-            check_uint32(freq)
-            kwargs["freq"] = freq
-        return method(self, **kwargs)
-    return new_method
-
-
-def check_jieba_add_dict(method):
-    """Wrapper method to check the parameters of add dict"""
-
-    @wraps(method)
-    def new_method(self, *args, **kwargs):
-        user_dict = (list(args) + [None])[0]
-        if "user_dict" in kwargs:
-            user_dict = kwargs.get("user_dict")
-        if user_dict is None:
-            raise ValueError("user_dict is not provided")
-        kwargs["user_dict"] = user_dict
-        return method(self, **kwargs)
-    return new_method

tests/ut/cpp/dataset/jieba_tokenizer_op_test.cc

@@ -18,7 +18,7 @@
 #include <string_view>
 
 #include "common/common.h"
-#include "dataset/kernels/text/jieba_tokenizer_op.h"
+#include "dataset/text/kernels/jieba_tokenizer_op.h"
 #include "gtest/gtest.h"
 #include "utils/log_adapter.h"
 

tests/ut/cpp/dataset/tokenizer_op_test.cc

@@ -18,7 +18,7 @@
 #include <string_view>
 
 #include "common/common.h"
-#include "dataset/kernels/text/unicode_char_tokenizer_op.h"
+#include "dataset/text/kernels/unicode_char_tokenizer_op.h"
 #include "gtest/gtest.h"
 #include "utils/log_adapter.h"
 

tests/ut/data/dataset/testVocab/lines.txt

+home is behind the world ahead
+is behind home ahead world the

tests/ut/python/dataset/test_datasets_textfileop.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ==============================================================================
 import mindspore.dataset as ds
-import mindspore.dataset.transforms.text.utils as nlp
 from mindspore import log as logger
 
 DATA_FILE = "../data/dataset/testTextFileDataset/1.txt"

tests/ut/python/dataset/test_flat_map.py

@@ -24,7 +24,6 @@ def test_flat_map_1():
     '''
     DATA_FILE records the path of image folders, load the images from them.
     '''
-    import mindspore.dataset.transforms.text.utils as nlp
 
     def flat_map_func(x):
         data_dir = x[0].item().decode('utf8')
@@ -45,7 +44,6 @@ def test_flat_map_2():
     '''
     Flatten 3D structure data
     '''
-    import mindspore.dataset.transforms.text.utils as nlp
 
     def flat_map_func_1(x):
         data_dir = x[0].item().decode('utf8')

tests/ut/python/dataset/test_minddataset_sampler.py

@@ -27,7 +27,7 @@ import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore import log as logger
 from mindspore.dataset.transforms.vision import Inter
-from mindspore.dataset.transforms.text import as_text
+from mindspore.dataset.text import to_str
 from mindspore.mindrecord import FileWriter
 
 FILES_NUM = 4
@@ -73,7 +73,7 @@ def test_cv_minddataset_pk_sample_no_column(add_and_remove_cv_file):
     for item in data_set.create_dict_iterator():
         logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
-                {}------------------------".format(as_text(item["file_name"])))
+                {}------------------------".format(to_str(item["file_name"])))
         logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
         num_iter += 1
 
@@ -91,7 +91,7 @@ def test_cv_minddataset_pk_sample_basic(add_and_remove_cv_file):
     for item in data_set.create_dict_iterator():
         logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
-                {}------------------------".format(as_text(item["file_name"])))
+                {}------------------------".format(to_str(item["file_name"])))
         logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
         num_iter += 1
 
@@ -109,7 +109,7 @@ def test_cv_minddataset_pk_sample_shuffle(add_and_remove_cv_file):
     for item in data_set.create_dict_iterator():
         logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
-                {}------------------------".format(as_text(item["file_name"])))
+                {}------------------------".format(to_str(item["file_name"])))
         logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
         num_iter += 1
 
@@ -126,7 +126,7 @@ def test_cv_minddataset_pk_sample_out_of_range(add_and_remove_cv_file):
     for item in data_set.create_dict_iterator():
         logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
         logger.info("-------------- item[file_name]: \
-                {}------------------------".format(as_text(item["file_name"])))
+                {}------------------------".format(to_str(item["file_name"])))
         logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
         num_iter += 1
 

tests/ut/python/dataset/test_nlp.py

+# Copyright 2019 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.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import numpy as np
+
+import mindspore.dataset as ds
+import mindspore.dataset.text as text
+
+# this file contains "home is behind the world head" each word is 1 line
+DATA_FILE = "../data/dataset/testVocab/words.txt"
+VOCAB_FILE = "../data/dataset/testVocab/vocab_list.txt"
+HMM_FILE = "../data/dataset/jiebadict/hmm_model.utf8"
+MP_FILE = "../data/dataset/jiebadict/jieba.dict.utf8"
+
+
+def test_on_tokenized_line():
+    data = ds.TextFileDataset("../data/dataset/testVocab/lines.txt", shuffle=False)
+    jieba_op = text.JiebaTokenizer(HMM_FILE, MP_FILE, mode=text.JiebaMode.MP)
+    with open(VOCAB_FILE, 'r') as f:
+        for line in f:
+            word = line.split(',')[0]
+            jieba_op.add_word(word)
+    data = data.map(input_columns=["text"], operations=jieba_op)
+    vocab = text.Vocab.from_file(VOCAB_FILE, ",")
+    lookup = text.Lookup(vocab)
+    data = data.map(input_columns=["text"], operations=lookup)
+    res = np.array([[10, 1, 11, 1, 12, 1, 15, 1, 13, 1, 14],
+                    [11, 1, 12, 1, 10, 1, 14, 1, 13, 1, 15]], dtype=np.int32)
+    for i, d in enumerate(data.create_dict_iterator()):
+        np.testing.assert_array_equal(d["text"], res[i]), i
+
+
+if __name__ == '__main__':
+    test_on_tokenized_line()

tests/ut/python/dataset/test_nlp_jieop.py

@@ -14,8 +14,8 @@
 # ==============================================================================
 import numpy as np
 import mindspore.dataset as ds
-from mindspore.dataset.transforms.text.c_transforms import JiebaTokenizer
-from mindspore.dataset.transforms.text.utils import JiebaMode, as_text
+from mindspore.dataset.text import JiebaTokenizer
+from mindspore.dataset.text import JiebaMode, to_str
 
 DATA_FILE = "../data/dataset/testJiebaDataset/3.txt"
 DATA_ALL_FILE = "../data/dataset/testJiebaDataset/*"
@@ -33,7 +33,7 @@ def test_jieba_1():
     expect = ['今天天气', '太好了', '我们', '一起', '去', '外面', '玩吧']
     ret = []
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 
@@ -46,7 +46,7 @@ def test_jieba_1_1():
                     operations=jieba_op, num_parallel_workers=1)
     expect = ['今天', '天气', '太', '好', '了', '我们', '一起', '去', '外面', '玩', '吧']
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 
@@ -59,7 +59,7 @@ def test_jieba_1_2():
                     operations=jieba_op, num_parallel_workers=1)
     expect = ['今天天气', '太好了', '我们', '一起', '去', '外面', '玩吧']
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 
@@ -74,7 +74,7 @@ def test_jieba_2():
     data = data.map(input_columns=["text"],
                     operations=jieba_op, num_parallel_workers=2)
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 
@@ -89,7 +89,7 @@ def test_jieba_2_1():
                     operations=jieba_op, num_parallel_workers=2)
     expect = ['男默女泪', '市', '长江大桥']
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 
@@ -113,7 +113,7 @@ def test_jieba_2_3():
                     operations=jieba_op, num_parallel_workers=2)
     expect = ['江州', '市长', '江大桥', '参加', '了', '长江大桥', '的', '通车', '仪式']
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 
@@ -131,7 +131,7 @@ def test_jieba_3():
                     operations=jieba_op, num_parallel_workers=1)
     expect = ['男默女泪', '市', '长江大桥']
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 
@@ -150,7 +150,7 @@ def test_jieba_3_1():
                     operations=jieba_op, num_parallel_workers=1)
     expect = ['男默女泪', '市长', '江大桥']
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 
@@ -166,7 +166,7 @@ def test_jieba_4():
                     operations=jieba_op, num_parallel_workers=1)
     expect = ['今天天气', '太好了', '我们', '一起', '去', '外面', '玩吧']
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 
@@ -192,7 +192,7 @@ def test_jieba_5():
                     operations=jieba_op, num_parallel_workers=1)
     expect = ['江州', '市长', '江大桥', '参加', '了', '长江大桥', '的', '通车', '仪式']
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 
@@ -203,7 +203,7 @@ def gen():
 
 
 def pytoken_op(input_data):
-    te = str(as_text(input_data))
+    te = str(to_str(input_data))
     tokens = []
     tokens.append(te[:5].encode("UTF8"))
     tokens.append(te[5:10].encode("UTF8"))
@@ -217,7 +217,7 @@ def test_jieba_6():
                     operations=pytoken_op, num_parallel_workers=1)
     expect = ['今天天气太', '好了我们一', '起去外面玩吧']
     for i in data.create_dict_iterator():
-        ret = as_text(i["text"])
+        ret = to_str(i["text"])
         for index, item in enumerate(ret):
             assert item == expect[index]
 

tests/ut/python/dataset/test_tensor_string.py

@@ -16,6 +16,8 @@ import mindspore._c_dataengine as cde
 import numpy as np
 import pytest
 
+from mindspore.dataset.text import to_str, to_bytes
+
 import mindspore.dataset as ds
 import mindspore.common.dtype as mstype
 
@@ -64,7 +66,8 @@ def test_map():
     data = ds.GeneratorDataset(gen, column_names=["col"])
 
     def split(b):
-        splits = b.item().decode("utf8").split()
+        s = to_str(b)
+        splits = s.item().split()
         return np.array(splits, dtype='S')
 
     data = data.map(input_columns=["col"], operations=split)
@@ -73,11 +76,20 @@ def test_map():
         np.testing.assert_array_equal(d[0], expected)
 
 
-def as_str(arr):
-    def decode(s): return s.decode("utf8")
+def test_map2():
+    def gen():
+        yield np.array(["ab cde 121"], dtype='S'),
+
+    data = ds.GeneratorDataset(gen, column_names=["col"])
+
+    def upper(b):
+        out = np.char.upper(b)
+        return out
 
-    decode_v = np.vectorize(decode)
-    return decode_v(arr)
+    data = data.map(input_columns=["col"], operations=upper)
+    expected = np.array(["AB CDE 121"], dtype='S')
+    for d in data:
+        np.testing.assert_array_equal(d[0], expected)
 
 
 line = np.array(["This is a text file.",
@@ -105,9 +117,9 @@ def test_tfrecord1():
         assert d["line"].shape == line[i].shape
         assert d["words"].shape == words[i].shape
         assert d["chinese"].shape == chinese[i].shape
-        np.testing.assert_array_equal(line[i], as_str(d["line"]))
-        np.testing.assert_array_equal(words[i], as_str(d["words"]))
-        np.testing.assert_array_equal(chinese[i], as_str(d["chinese"]))
+        np.testing.assert_array_equal(line[i], to_str(d["line"]))
+        np.testing.assert_array_equal(words[i], to_str(d["words"]))
+        np.testing.assert_array_equal(chinese[i], to_str(d["chinese"]))
 
 
 def test_tfrecord2():
@@ -117,9 +129,9 @@ def test_tfrecord2():
         assert d["line"].shape == line[i].shape
         assert d["words"].shape == words[i].shape
         assert d["chinese"].shape == chinese[i].shape
-        np.testing.assert_array_equal(line[i], as_str(d["line"]))
-        np.testing.assert_array_equal(words[i], as_str(d["words"]))
-        np.testing.assert_array_equal(chinese[i], as_str(d["chinese"]))
+        np.testing.assert_array_equal(line[i], to_str(d["line"]))
+        np.testing.assert_array_equal(words[i], to_str(d["words"]))
+        np.testing.assert_array_equal(chinese[i], to_str(d["chinese"]))
 
 
 def test_tfrecord3():
@@ -134,9 +146,9 @@ def test_tfrecord3():
         assert d["line"].shape == line[i].shape
         assert d["words"].shape == words[i].reshape([2, 2]).shape
         assert d["chinese"].shape == chinese[i].shape
-        np.testing.assert_array_equal(line[i], as_str(d["line"]))
-        np.testing.assert_array_equal(words[i].reshape([2, 2]), as_str(d["words"]))
-        np.testing.assert_array_equal(chinese[i], as_str(d["chinese"]))
+        np.testing.assert_array_equal(line[i], to_str(d["line"]))
+        np.testing.assert_array_equal(words[i].reshape([2, 2]), to_str(d["words"]))
+        np.testing.assert_array_equal(chinese[i], to_str(d["chinese"]))
 
 
 def create_text_mindrecord():
@@ -166,16 +178,17 @@ def test_mindrecord():
     for i, d in enumerate(data.create_dict_iterator()):
         assert d["english"].shape == line[i].shape
         assert d["chinese"].shape == chinese[i].shape
-        np.testing.assert_array_equal(line[i], as_str(d["english"]))
-        np.testing.assert_array_equal(chinese[i], as_str(d["chinese"]))
+        np.testing.assert_array_equal(line[i], to_str(d["english"]))
+        np.testing.assert_array_equal(chinese[i], to_str(d["chinese"]))
 
 
 if __name__ == '__main__':
-    # test_generator()
-    # test_basic()
-    # test_batching_strings()
+    test_generator()
+    test_basic()
+    test_batching_strings()
     test_map()
-    # test_tfrecord1()
-    # test_tfrecord2()
-    # test_tfrecord3()
-    # test_mindrecord()
+    test_map2()
+    test_tfrecord1()
+    test_tfrecord2()
+    test_tfrecord3()
+    test_mindrecord()

tests/ut/python/dataset/test_tokenizer.py

@@ -17,8 +17,7 @@ Testing UnicodeCharTokenizer op in DE
 """
 import mindspore.dataset as ds
 from mindspore import log as logger
-import mindspore.dataset.transforms.text.c_transforms as nlp
-import mindspore.dataset.transforms.text.utils as nlp_util
+import mindspore.dataset.text as nlp
 
 DATA_FILE = "../data/dataset/testTokenizerData/1.txt"
 
@@ -43,7 +42,7 @@ def test_unicode_char_tokenizer():
     dataset = dataset.map(operations=tokenizer)
     tokens = []
     for i in dataset.create_dict_iterator():
-        text = nlp_util.as_text(i['text']).tolist()
+        text = nlp.to_str(i['text']).tolist()
         tokens.append(text)
     logger.info("The out tokens is : {}".format(tokens))
     assert split_by_unicode_char(input_strs) == tokens