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- import unittest
-
- from fastNLP.embeddings import StaticEmbedding
- from fastNLP import Vocabulary
- import torch
- import os
-
-
- class TestLoad(unittest.TestCase):
- def test_norm1(self):
- # 测试只对可以找到的norm
- vocab = Vocabulary().add_word_lst(['the', 'a', 'notinfile'])
- embed = StaticEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_static_embedding/'
- 'glove.6B.50d_test.txt',
- only_norm_found_vector=True)
- self.assertEqual(round(torch.norm(embed(torch.LongTensor([[2]]))).item(), 4), 1)
- self.assertNotEqual(torch.norm(embed(torch.LongTensor([[4]]))).item(), 1)
-
- def test_norm2(self):
- # 测试对所有都norm
- vocab = Vocabulary().add_word_lst(['the', 'a', 'notinfile'])
- embed = StaticEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_static_embedding/'
- 'glove.6B.50d_test.txt',
- normalize=True)
- self.assertEqual(round(torch.norm(embed(torch.LongTensor([[2]]))).item(), 4), 1)
- self.assertEqual(round(torch.norm(embed(torch.LongTensor([[4]]))).item(), 4), 1)
-
- def test_dropword(self):
- # 测试是否可以通过drop word
- vocab = Vocabulary().add_word_lst([chr(i) for i in range(1, 200)])
- embed = StaticEmbedding(vocab, model_dir_or_name=None, embedding_dim=10, dropout=0.1, word_dropout=0.4)
- for i in range(10):
- length = torch.randint(1, 50, (1,)).item()
- batch = torch.randint(1, 4, (1,)).item()
- words = torch.randint(1, 200, (batch, length)).long()
- embed(words)
-
- class TestRandomSameEntry(unittest.TestCase):
- def test_same_vector(self):
- vocab = Vocabulary().add_word_lst(["The", "the", "THE", 'a', "A"])
- embed = StaticEmbedding(vocab, model_dir_or_name=None, embedding_dim=5, lower=True)
- words = torch.LongTensor([[vocab.to_index(word) for word in ["The", "the", "THE", 'a', 'A']]])
- words = embed(words)
- embed_0 = words[0, 0]
- for i in range(1, 3):
- assert torch.sum(embed_0==words[0, i]).eq(len(embed_0))
- embed_0 = words[0, 3]
- for i in range(3, 5):
- assert torch.sum(embed_0 == words[0, i]).eq(len(embed_0))
-
- @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
- def test_same_vector2(self):
- vocab = Vocabulary().add_word_lst(["The", 'a', 'b', "the", "THE", "B", 'a', "A"])
- embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-6B-100d',
- lower=True)
- words = torch.LongTensor([[vocab.to_index(word) for word in ["The", "the", "THE", 'b', "B", 'a', 'A']]])
- words = embed(words)
- embed_0 = words[0, 0]
- for i in range(1, 3):
- assert torch.sum(embed_0==words[0, i]).eq(len(embed_0))
- embed_0 = words[0, 3]
- for i in range(3, 5):
- assert torch.sum(embed_0 == words[0, i]).eq(len(embed_0))
-
- @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
- def test_same_vector3(self):
- # 验证lower
- word_lst = ["The", "the"]
- no_create_word_lst = ['of', 'Of', 'With', 'with']
- vocab = Vocabulary().add_word_lst(word_lst)
- vocab.add_word_lst(no_create_word_lst, no_create_entry=True)
- embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-6B-100d',
- lower=True)
- words = torch.LongTensor([[vocab.to_index(word) for word in word_lst+no_create_word_lst]])
- words = embed(words)
-
- lowered_word_lst = [word.lower() for word in word_lst]
- lowered_no_create_word_lst = [word.lower() for word in no_create_word_lst]
- lowered_vocab = Vocabulary().add_word_lst(lowered_word_lst)
- lowered_vocab.add_word_lst(lowered_no_create_word_lst, no_create_entry=True)
- lowered_embed = StaticEmbedding(lowered_vocab, model_dir_or_name='en-glove-6B-100d',
- lower=False)
- lowered_words = torch.LongTensor([[lowered_vocab.to_index(word) for word in lowered_word_lst+lowered_no_create_word_lst]])
- lowered_words = lowered_embed(lowered_words)
-
- all_words = word_lst + no_create_word_lst
-
- for idx, (word_i, word_j) in enumerate(zip(words[0], lowered_words[0])):
- with self.subTest(idx=idx, word=all_words[idx]):
- assert torch.sum(word_i == word_j).eq(lowered_embed.embed_size)
-
- @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
- def test_same_vector4(self):
- # 验证在有min_freq下的lower
- word_lst = ["The", "the", "the", "The", "a", "A"]
- no_create_word_lst = ['of', 'Of', "Of", "of", 'With', 'with']
- all_words = word_lst[:-2] + no_create_word_lst[:-2]
- vocab = Vocabulary(min_freq=2).add_word_lst(word_lst)
- vocab.add_word_lst(no_create_word_lst, no_create_entry=True)
- embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-6B-100d',
- lower=True)
- words = torch.LongTensor([[vocab.to_index(word) for word in all_words]])
- words = embed(words)
-
- lowered_word_lst = [word.lower() for word in word_lst]
- lowered_no_create_word_lst = [word.lower() for word in no_create_word_lst]
- lowered_vocab = Vocabulary().add_word_lst(lowered_word_lst)
- lowered_vocab.add_word_lst(lowered_no_create_word_lst, no_create_entry=True)
- lowered_embed = StaticEmbedding(lowered_vocab, model_dir_or_name='en-glove-6B-100d',
- lower=False)
- lowered_words = torch.LongTensor([[lowered_vocab.to_index(word.lower()) for word in all_words]])
- lowered_words = lowered_embed(lowered_words)
-
- for idx in range(len(all_words)):
- word_i, word_j = words[0, idx], lowered_words[0, idx]
- with self.subTest(idx=idx, word=all_words[idx]):
- assert torch.sum(word_i == word_j).eq(lowered_embed.embed_size)
-
- @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
- def test_same_vector5(self):
- # 检查通过使用min_freq后的word是否内容一致
- word_lst = ["they", "the", "they", "the", 'he', 'he', "a", "A"]
- no_create_word_lst = ['of', "of", "she", "she", 'With', 'with']
- all_words = word_lst[:-2] + no_create_word_lst[:-2]
- vocab = Vocabulary().add_word_lst(word_lst)
- vocab.add_word_lst(no_create_word_lst, no_create_entry=True)
- embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-6B-100d',
- lower=False, min_freq=2)
- words = torch.LongTensor([[vocab.to_index(word) for word in all_words]])
- words = embed(words)
-
- min_freq_vocab = Vocabulary(min_freq=2).add_word_lst(word_lst)
- min_freq_vocab.add_word_lst(no_create_word_lst, no_create_entry=True)
- min_freq_embed = StaticEmbedding(min_freq_vocab, model_dir_or_name='en-glove-6B-100d',
- lower=False)
- min_freq_words = torch.LongTensor([[min_freq_vocab.to_index(word.lower()) for word in all_words]])
- min_freq_words = min_freq_embed(min_freq_words)
-
- for idx in range(len(all_words)):
- word_i, word_j = words[0, idx], min_freq_words[0, idx]
- with self.subTest(idx=idx, word=all_words[idx]):
- assert torch.sum(word_i == word_j).eq(min_freq_embed.embed_size)
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