- # 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.
- # ==============================================================================
-
- 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"
- SIMPLE_VOCAB_FILE = "../data/dataset/testVocab/simple_vocab_list.txt"
-
-
- def test_from_list_tutorial():
- vocab = text.Vocab.from_list("home IS behind the world ahead !".split(" "), ["<pad>", "<unk>"], True)
- lookup = text.Lookup(vocab, "<unk>")
- data = ds.TextFileDataset(DATA_FILE, shuffle=False)
- data = data.map(input_columns=["text"], operations=lookup)
- ind = 0
- res = [2, 1, 4, 5, 6, 7]
- for d in data.create_dict_iterator():
- assert d["text"] == res[ind], ind
- ind += 1
-
-
- def test_from_file_tutorial():
- vocab = text.Vocab.from_file(VOCAB_FILE, ",", None, ["<pad>", "<unk>"], True)
- lookup = text.Lookup(vocab)
- data = ds.TextFileDataset(DATA_FILE, shuffle=False)
- data = data.map(input_columns=["text"], operations=lookup)
- ind = 0
- res = [10, 11, 12, 15, 13, 14]
- for d in data.create_dict_iterator():
- assert d["text"] == res[ind], ind
- ind += 1
-
-
- def test_from_dict_tutorial():
- vocab = text.Vocab.from_dict({"home": 3, "behind": 2, "the": 4, "world": 5, "<unk>": 6})
- lookup = text.Lookup(vocab, "<unk>") # any unknown token will be mapped to the id of <unk>
- data = ds.TextFileDataset(DATA_FILE, shuffle=False)
- data = data.map(input_columns=["text"], operations=lookup)
- res = [3, 6, 2, 4, 5, 6]
- ind = 0
- for d in data.create_dict_iterator():
- assert d["text"] == res[ind], ind
- ind += 1
-
-
- def test_from_dict_exception():
- try:
- vocab = text.Vocab.from_dict({"home": -1, "behind": 0})
- if not vocab:
- raise ValueError("Vocab is None")
- except ValueError as e:
- assert "is not within the required interval" in str(e)
-
-
- def test_from_list():
- def gen(texts):
- for word in texts.split(" "):
- yield (np.array(word, dtype='S'),)
-
- def test_config(lookup_str, vocab_input, special_tokens, special_first, unknown_token):
- try:
- vocab = text.Vocab.from_list(vocab_input, special_tokens, special_first)
- data = ds.GeneratorDataset(gen(lookup_str), column_names=["text"])
- data = data.map(input_columns=["text"], operations=text.Lookup(vocab, unknown_token))
- res = []
- for d in data.create_dict_iterator():
- res.append(d["text"].item())
- return res
- except (ValueError, RuntimeError, TypeError) as e:
- return str(e)
-
- # test basic default config, special_token=None, unknown_token=None
- assert test_config("w1 w2 w3", ["w1", "w2", "w3"], None, True, None) == [0, 1, 2]
- # test normal operations
- assert test_config("w1 w2 w3 s1 s2 ephemeral", ["w1", "w2", "w3"], ["s1", "s2"], True, "s2") == [2, 3, 4, 0, 1, 1]
- assert test_config("w1 w2 w3 s1 s2", ["w1", "w2", "w3"], ["s1", "s2"], False, "s2") == [0, 1, 2, 3, 4]
- assert test_config("w3 w2 w1", ["w1", "w2", "w3"], None, True, "w1") == [2, 1, 0]
- assert test_config("w3 w2 w1", ["w1", "w2", "w3"], None, False, "w1") == [2, 1, 0]
- # test unknown token lookup
- assert test_config("w1 un1 w3 un2", ["w1", "w2", "w3"], ["<pad>", "<unk>"], True, "<unk>") == [2, 1, 4, 1]
- assert test_config("w1 un1 w3 un2", ["w1", "w2", "w3"], ["<pad>", "<unk>"], False, "<unk>") == [0, 4, 2, 4]
-
- # test exceptions
- assert "doesn't exist in vocab." in test_config("un1", ["w1"], [], False, "unk")
- assert "doesn't exist in vocab and no unknown token is specified." in test_config("un1", ["w1"], [], False, None)
- assert "doesn't exist in vocab" in test_config("un1", ["w1"], [], False, None)
- assert "word_list contains duplicate" in test_config("w1", ["w1", "w1"], [], True, "w1")
- assert "special_tokens contains duplicate" in test_config("w1", ["w1", "w2"], ["s1", "s1"], True, "w1")
- assert "special_tokens and word_list contain duplicate" in test_config("w1", ["w1", "w2"], ["s1", "w1"], True, "w1")
- assert "is not of type" in test_config("w1", ["w1", "w2"], ["s1"], True, 123)
-
-
- def test_from_file():
- def gen(texts):
- for word in texts.split(" "):
- yield (np.array(word, dtype='S'),)
-
- def test_config(lookup_str, vocab_size, special_tokens, special_first):
- try:
- vocab = text.Vocab.from_file(SIMPLE_VOCAB_FILE, vocab_size=vocab_size, special_tokens=special_tokens,
- special_first=special_first)
- data = ds.GeneratorDataset(gen(lookup_str), column_names=["text"])
- data = data.map(input_columns=["text"], operations=text.Lookup(vocab, "s2"))
- res = []
- for d in data.create_dict_iterator():
- res.append(d["text"].item())
- return res
- except ValueError as e:
- return str(e)
-
- # test special tokens are prepended
- assert test_config("w1 w2 w3 s1 s2 s3", None, ["s1", "s2", "s3"], True) == [3, 4, 5, 0, 1, 2]
- # test special tokens are appended
- assert test_config("w1 w2 w3 s1 s2 s3", None, ["s1", "s2", "s3"], False) == [0, 1, 2, 8, 9, 10]
- # test special tokens are prepended when not all words in file are used
- assert test_config("w1 w2 w3 s1 s2 s3", 3, ["s1", "s2", "s3"], False) == [0, 1, 2, 3, 4, 5]
- # text exception special_words contains duplicate words
- assert "special_tokens contains duplicate" in test_config("w1", None, ["s1", "s1"], True)
-
-
- if __name__ == '__main__':
- test_from_dict_exception()
- test_from_list_tutorial()
- test_from_file_tutorial()
- test_from_dict_tutorial()
- test_from_list()
- test_from_file()
|