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93 lines
4.1 KiB
93 lines
4.1 KiB
# 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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import mindspore.dataset.text as text
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import mindspore.dataset as ds
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from mindspore.dataset.text import SentencePieceModel, to_str, SPieceTokenizerOutType
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VOCAB_FILE = "../data/dataset/test_sentencepiece/botchan.txt"
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DATA_FILE = "../data/dataset/testTokenizerData/sentencepiece_tokenizer.txt"
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def test_from_vocab_to_str():
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = ['▁I', '▁sa', 'w', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'co', 'pe', '.']
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for i in dataset.create_dict_iterator():
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_from_vocab_to_int():
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.INT)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = [6, 329, 183, 8, 945, 23, 8, 3783, 4382, 4641, 1405, 4]
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for i in dataset.create_dict_iterator():
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ret = i["text"]
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_from_file_to_str():
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
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text.SentencePieceVocab.save_model(vocab, "./", "m.model")
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tokenizer = text.SentencePieceTokenizer("./m.model", out_type=SPieceTokenizerOutType.STRING)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = ['▁I', '▁sa', 'w', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'co', 'pe', '.']
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for i in dataset.create_dict_iterator():
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_from_file_to_int():
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
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text.SentencePieceVocab.save_model(vocab, "./", "m.model")
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tokenizer = text.SentencePieceTokenizer("./m.model", out_type=SPieceTokenizerOutType.INT)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = [6, 329, 183, 8, 945, 23, 8, 3783, 4382, 4641, 1405, 4]
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for i in dataset.create_dict_iterator():
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ret = i["text"]
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_build_from_dataset():
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data = ds.TextFileDataset(VOCAB_FILE, shuffle=False)
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vocab = text.SentencePieceVocab.from_dataset(data, [""], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = ['▁I', '▁sa', 'w', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'co', 'pe', '.']
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for i in dataset.create_dict_iterator():
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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if __name__ == "__main__":
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test_from_vocab_to_str()
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test_from_vocab_to_int()
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test_from_file_to_str()
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test_from_file_to_int()
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test_build_from_dataset()
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