forked from mindspore-Ecosystem/mindspore
140 lines
6.9 KiB
Python
140 lines
6.9 KiB
Python
# 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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"""
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Testing BasicTokenizer op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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from mindspore import log as logger
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import mindspore.dataset.text as text
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BASIC_TOKENIZER_FILE = "../data/dataset/testTokenizerData/basic_tokenizer.txt"
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test_paras = [
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dict(
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first=1,
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last=6,
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expected_tokens=
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[['Welcome', 'to', 'Beijing', '北', '京', '欢', '迎', '您'],
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['長', '風', '破', '浪', '會', '有', '時', '，', '直', '掛', '雲', '帆', '濟', '滄', '海'],
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['😀', '嘿', '嘿', '😃', '哈', '哈', '😄', '大', '笑', '😁', '嘻', '嘻'],
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['明', '朝', '（', '1368', '—', '1644', '年', '）', '和', '清', '朝',
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'（', '1644', '—', '1911', '年', '）', '，', '是', '中', '国', '封',
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'建', '王', '朝', '史', '上', '最', '后', '两', '个', '朝', '代'],
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['明', '代', '（', '1368', '-', '1644', '）', 'と', '清', '代',
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'（', '1644', '-', '1911', '）', 'は', '、', '中', '国', 'の', '封',
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'建', '王', '朝', 'の', '歴', '史', 'における', '最', '後', 'の2つの', '王', '朝', 'でした'],
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['명나라', '(', '1368', '-', '1644', ')', '와', '청나라', '(', '1644', '-', '1911', ')', '는',
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'중국', '봉건', '왕조의', '역사에서', '마지막', '두', '왕조였다']],
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expected_offsets_start=[[0, 8, 11, 18, 21, 24, 27, 30],
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[0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42],
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[0, 4, 7, 10, 14, 17, 20, 24, 27, 30, 34, 37],
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[0, 3, 6, 9, 13, 16, 20, 23, 26, 29, 32, 35, 38, 42, 45, 49,
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52, 55, 58, 61, 64, 67, 70, 73, 76, 79, 82, 85, 88, 91, 94, 97, 100],
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[0, 3, 6, 9, 13, 14, 18, 21, 24, 27, 30, 33, 37, 38, 42, 45, 48, 51,
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54, 57, 60, 63, 66, 69, 72, 75, 78, 81, 93, 96, 99, 109, 112, 115],
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[0, 10, 11, 15, 16, 20, 21, 25, 35, 36, 40, 41, 45, 46, 50, 57, 64, 74, 87, 97, 101]],
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expected_offsets_limit=[[7, 10, 18, 21, 24, 27, 30, 33],
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[3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45],
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[4, 7, 10, 14, 17, 20, 24, 27, 30, 34, 37, 40],
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[3, 6, 9, 13, 16, 20, 23, 26, 29, 32, 35, 38, 42, 45, 49, 52, 55, 58,
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61, 64, 67, 70, 73, 76, 79, 82, 85, 88, 91, 94, 97, 100, 103],
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[3, 6, 9, 13, 14, 18, 21, 24, 27, 30, 33, 37, 38, 42, 45, 48, 51, 54,
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57, 60, 63, 66, 69, 72, 75, 78, 81, 93, 96, 99, 109, 112, 115, 124],
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[9, 11, 15, 16, 20, 21, 24, 34, 36, 40, 41, 45, 46, 49, 56, 63, 73, 86, 96, 100, 113]]
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),
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dict(
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first=7,
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last=7,
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expected_tokens=[['this', 'is', 'a', 'funky', 'string']],
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expected_offsets_start=[[0, 5, 8, 10, 16]],
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expected_offsets_limit=[[4, 7, 9, 15, 22]],
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lower_case=True
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),
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]
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def check_basic_tokenizer_default(first, last, expected_tokens, expected_offsets_start, expected_offsets_limit,
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lower_case=False, keep_whitespace=False,
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normalization_form=text.utils.NormalizeForm.NONE, preserve_unused_token=False):
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dataset = ds.TextFileDataset(BASIC_TOKENIZER_FILE, shuffle=False)
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if first > 1:
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dataset = dataset.skip(first - 1)
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if last >= first:
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dataset = dataset.take(last - first + 1)
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basic_tokenizer = text.BasicTokenizer(lower_case=lower_case,
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keep_whitespace=keep_whitespace,
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normalization_form=normalization_form,
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preserve_unused_token=preserve_unused_token)
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dataset = dataset.map(operations=basic_tokenizer)
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count = 0
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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token = text.to_str(i['text'])
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logger.info("Out:", token)
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logger.info("Exp:", expected_tokens[count])
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np.testing.assert_array_equal(token, expected_tokens[count])
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count = count + 1
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def check_basic_tokenizer_with_offsets(first, last, expected_tokens, expected_offsets_start, expected_offsets_limit,
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lower_case=False, keep_whitespace=False,
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normalization_form=text.utils.NormalizeForm.NONE, preserve_unused_token=False):
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dataset = ds.TextFileDataset(BASIC_TOKENIZER_FILE, shuffle=False)
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if first > 1:
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dataset = dataset.skip(first - 1)
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if last >= first:
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dataset = dataset.take(last - first + 1)
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basic_tokenizer = text.BasicTokenizer(lower_case=lower_case,
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keep_whitespace=keep_whitespace,
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normalization_form=normalization_form,
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preserve_unused_token=preserve_unused_token,
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with_offsets=True)
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dataset = dataset.map(operations=basic_tokenizer, input_columns=['text'],
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output_columns=['token', 'offsets_start', 'offsets_limit'],
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column_order=['token', 'offsets_start', 'offsets_limit'])
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count = 0
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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token = text.to_str(i['token'])
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logger.info("Out:", token)
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logger.info("Exp:", expected_tokens[count])
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np.testing.assert_array_equal(token, expected_tokens[count])
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np.testing.assert_array_equal(i['offsets_start'], expected_offsets_start[count])
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np.testing.assert_array_equal(i['offsets_limit'], expected_offsets_limit[count])
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count = count + 1
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def test_basic_tokenizer_with_offsets():
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"""
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Test BasicTokenizer
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"""
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for paras in test_paras:
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check_basic_tokenizer_with_offsets(**paras)
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def test_basic_tokenizer_default():
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"""
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Test BasicTokenizer
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"""
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for paras in test_paras:
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check_basic_tokenizer_default(**paras)
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if __name__ == '__main__':
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test_basic_tokenizer_default()
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test_basic_tokenizer_with_offsets()
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