hesham
5 years ago
parent
df361d1d26
commit
6c21e556c4
@ -1,7 +0,0 @@
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add_subdirectory(kernels)
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add_library(nlp OBJECT
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vocab.cc
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)
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add_dependencies(nlp nlp-kernels)
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@ -1,3 +0,0 @@
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add_library(nlp-kernels OBJECT
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lookup_op.cc
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)
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@ -0,0 +1,7 @@
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add_subdirectory(kernels)
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add_library(text OBJECT
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vocab.cc
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)
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add_dependencies(text text-kernels)
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@ -1,6 +1,7 @@
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file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc")
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set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
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add_library(kernels-text OBJECT
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add_library(text-kernels OBJECT
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lookup_op.cc
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jieba_tokenizer_op.cc
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unicode_char_tokenizer_op.cc
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)
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@ -1,21 +0,0 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This module is to support nlp augmentations. It includes two parts:
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c_transforms and py_transforms. C_transforms is a high performance
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image augmentation module which is developed with c++ opencv. Py_transforms
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provide more kinds of image augmentations which is developed with python PIL.
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"""
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from .utils import as_text, JiebaMode
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from . import c_transforms
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@ -1,43 +0,0 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Some basic function for nlp
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"""
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from enum import IntEnum
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import numpy as np
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def as_text(array, encoding='utf8'):
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"""
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Convert data of array to unicode.
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Args:
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array (numpy array): Data of array should be ASCII values of each character after converted.
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encoding (string): Indicating the charset for decoding.
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Returns:
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A 'str' object.
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"""
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if not isinstance(array, np.ndarray):
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raise ValueError('input should be a numpy array')
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decode = np.vectorize(lambda x: x.decode(encoding))
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return decode(array)
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class JiebaMode(IntEnum):
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MIX = 0
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MP = 1
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HMM = 2
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@ -1,79 +0,0 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Validators for TensorOps.
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"""
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from functools import wraps
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from ...transforms.validators import check_uint32
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def check_jieba_init(method):
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"""Wrapper method to check the parameters of jieba add word."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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hmm_path, mp_path, model = (list(args) + 3 * [None])[:3]
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if "hmm_path" in kwargs:
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hmm_path = kwargs.get("hmm_path")
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if "mp_path" in kwargs:
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mp_path = kwargs.get("mp_path")
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if hmm_path is None:
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raise ValueError(
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"the dict of HMMSegment in cppjieba is not provided")
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kwargs["hmm_path"] = hmm_path
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if mp_path is None:
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raise ValueError(
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"the dict of MPSegment in cppjieba is not provided")
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kwargs["mp_path"] = mp_path
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if model is not None:
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kwargs["model"] = model
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return method(self, **kwargs)
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return new_method
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def check_jieba_add_word(method):
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"""Wrapper method to check the parameters of jieba add word."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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word, freq = (list(args) + 2 * [None])[:2]
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if "word" in kwargs:
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word = kwargs.get("word")
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if "freq" in kwargs:
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freq = kwargs.get("freq")
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if word is None:
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raise ValueError("word is not provided")
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kwargs["word"] = word
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if freq is not None:
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check_uint32(freq)
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kwargs["freq"] = freq
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return method(self, **kwargs)
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return new_method
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def check_jieba_add_dict(method):
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"""Wrapper method to check the parameters of add dict"""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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user_dict = (list(args) + [None])[0]
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if "user_dict" in kwargs:
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user_dict = kwargs.get("user_dict")
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if user_dict is None:
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raise ValueError("user_dict is not provided")
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kwargs["user_dict"] = user_dict
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return method(self, **kwargs)
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return new_method
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