forked from mindspore-Ecosystem/mindspore
88 lines
3.1 KiB
Python
88 lines
3.1 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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import mindspore.dataset as ds
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from mindspore import log as logger
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import mindspore.dataset.transforms.nlp.utils as nlp
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DATA_FILE = "../data/dataset/testTextFileDataset/1.txt"
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DATA_ALL_FILE = "../data/dataset/testTextFileDataset/*"
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def test_textline_dataset_one_file():
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data = ds.TextFileDataset(DATA_FILE)
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count = 0
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for i in data.create_dict_iterator():
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logger.info("{}".format(i["text"]))
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count += 1
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assert(count == 3)
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def test_textline_dataset_all_file():
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data = ds.TextFileDataset(DATA_ALL_FILE)
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count = 0
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for i in data.create_dict_iterator():
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logger.info("{}".format(i["text"]))
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count += 1
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assert(count == 5)
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def test_textline_dataset_totext():
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data = ds.TextFileDataset(DATA_ALL_FILE, shuffle=False)
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count = 0
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line = ["This is a text file.", "Another file.", "Be happy every day.", "End of file.", "Good luck to everyone."]
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for i in data.create_dict_iterator():
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str = nlp.as_text(i["text"])
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assert(str == line[count])
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count += 1
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assert(count == 5)
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def test_textline_dataset_num_samples():
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data = ds.TextFileDataset(DATA_FILE, num_samples=2)
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count = 0
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for i in data.create_dict_iterator():
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count += 1
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assert(count == 2)
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def test_textline_dataset_distribution():
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data = ds.TextFileDataset(DATA_ALL_FILE, num_shards=2, shard_id=1)
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count = 0
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for i in data.create_dict_iterator():
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count += 1
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assert(count == 3)
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def test_textline_dataset_repeat():
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data = ds.TextFileDataset(DATA_FILE, shuffle=False)
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data = data.repeat(3)
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count = 0
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line = ["This is a text file.", "Be happy every day.", "Good luck to everyone.",
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"This is a text file.", "Be happy every day.", "Good luck to everyone.",
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"This is a text file.", "Be happy every day.", "Good luck to everyone."]
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for i in data.create_dict_iterator():
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str = nlp.as_text(i["text"])
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assert(str == line[count])
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count += 1
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assert(count == 9)
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def test_textline_dataset_get_datasetsize():
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data = ds.TextFileDataset(DATA_FILE)
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size = data.get_dataset_size()
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assert(size == 3)
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if __name__ == "__main__":
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test_textline_dataset_one_file()
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test_textline_dataset_all_file()
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test_textline_dataset_totext()
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test_textline_dataset_num_samples()
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test_textline_dataset_distribution()
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test_textline_dataset_repeat()
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test_textline_dataset_get_datasetsize()
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