修改读取测试数据的位置

tests/core/test_dataset.py

@@ -228,7 +228,7 @@ class TestDataSetMethods(unittest.TestCase):
         def split_sent(ins):
             return ins['raw_sentence'].split()
         csv_loader = CSVLoader(headers=['raw_sentence', 'label'], sep='\t')
-        data_bundle = csv_loader.load('test/data_for_tests/tutorial_sample_dataset.csv')
+        data_bundle = csv_loader.load('tests/data_for_tests/tutorial_sample_dataset.csv')
         dataset = data_bundle.datasets['train']
         dataset.drop(lambda x: len(x['raw_sentence'].split()) == 0, inplace=True)
         dataset.apply(split_sent, new_field_name='words', is_input=True)

tests/core/test_utils.py

@@ -120,8 +120,8 @@ class TestCache(unittest.TestCase):
     def test_cache_save(self):
         try:
             start_time = time.time()
-            embed, vocab, d = process_data_1('test/data_for_tests/embedding/small_static_embedding/word2vec_test.txt',
-                                             'test/data_for_tests/cws_train')
+            embed, vocab, d = process_data_1('tests/data_for_tests/embedding/small_static_embedding/word2vec_test.txt',
+                                             'tests/data_for_tests/cws_train')
             end_time = time.time()
             pre_time = end_time - start_time
             with open('test/demo1.pkl', 'rb') as f:
@@ -130,8 +130,8 @@ class TestCache(unittest.TestCase):
             for i in range(embed.shape[0]):
                 self.assertListEqual(embed[i].tolist(), _embed[i].tolist())
             start_time = time.time()
-            embed, vocab, d = process_data_1('test/data_for_tests/embedding/small_static_embedding/word2vec_test.txt',
-                                             'test/data_for_tests/cws_train')
+            embed, vocab, d = process_data_1('tests/data_for_tests/embedding/small_static_embedding/word2vec_test.txt',
+                                             'tests/data_for_tests/cws_train')
             end_time = time.time()
             read_time = end_time - start_time
             print("Read using {:.3f}, while prepare using:{:.3f}".format(read_time, pre_time))
@@ -142,7 +142,7 @@ class TestCache(unittest.TestCase):
     def test_cache_save_overwrite_path(self):
         try:
             start_time = time.time()
-            embed, vocab, d = process_data_1('test/data_for_tests/embedding/small_static_embedding/word2vec_test.txt', 'test/data_for_tests/cws_train',
+            embed, vocab, d = process_data_1('tests/data_for_tests/embedding/small_static_embedding/word2vec_test.txt', 'tests/data_for_tests/cws_train',
                                              _cache_fp='test/demo_overwrite.pkl')
             end_time = time.time()
             pre_time = end_time - start_time
@@ -152,8 +152,8 @@ class TestCache(unittest.TestCase):
             for i in range(embed.shape[0]):
                 self.assertListEqual(embed[i].tolist(), _embed[i].tolist())
             start_time = time.time()
-            embed, vocab, d = process_data_1('test/data_for_tests/embedding/small_static_embedding/word2vec_test.txt',
-                                             'test/data_for_tests/cws_train',
+            embed, vocab, d = process_data_1('tests/data_for_tests/embedding/small_static_embedding/word2vec_test.txt',
+                                             'tests/data_for_tests/cws_train',
                                              _cache_fp='test/demo_overwrite.pkl')
             end_time = time.time()
             read_time = end_time - start_time
@@ -165,8 +165,8 @@ class TestCache(unittest.TestCase):
     def test_cache_refresh(self):
         try:
             start_time = time.time()
-            embed, vocab, d = process_data_1('test/data_for_tests/embedding/small_static_embedding/word2vec_test.txt',
-                                             'test/data_for_tests/cws_train',
+            embed, vocab, d = process_data_1('tests/data_for_tests/embedding/small_static_embedding/word2vec_test.txt',
+                                             'tests/data_for_tests/cws_train',
                                              _refresh=True)
             end_time = time.time()
             pre_time = end_time - start_time
@@ -176,8 +176,8 @@ class TestCache(unittest.TestCase):
             for i in range(embed.shape[0]):
                 self.assertListEqual(embed[i].tolist(), _embed[i].tolist())
             start_time = time.time()
-            embed, vocab, d = process_data_1('test/data_for_tests/embedding/small_static_embedding/word2vec_test.txt',
-                                             'test/data_for_tests/cws_train',
+            embed, vocab, d = process_data_1('tests/data_for_tests/embedding/small_static_embedding/word2vec_test.txt',
+                                             'tests/data_for_tests/cws_train',
                                              _refresh=True)
             end_time = time.time()
             read_time = end_time - start_time

tests/embeddings/test_bert_embedding.py

@@ -32,7 +32,7 @@ class TestDownload(unittest.TestCase):
 class TestBertEmbedding(unittest.TestCase):
     def test_bert_embedding_1(self):
         vocab = Vocabulary().add_word_lst("this is a test . [SEP] NotInBERT".split())
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert', word_dropout=0.1)
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert', word_dropout=0.1)
         requires_grad = embed.requires_grad
         embed.requires_grad = not requires_grad
         embed.train()
@@ -40,14 +40,14 @@ class TestBertEmbedding(unittest.TestCase):
         result = embed(words)
         self.assertEqual(result.size(), (1, 4, 16))
 
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert', word_dropout=0.1)
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert', word_dropout=0.1)
         embed.eval()
         words = torch.LongTensor([[2, 3, 4, 0]])
         result = embed(words)
         self.assertEqual(result.size(), (1, 4, 16))
 
         # 自动截断而不报错
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert', word_dropout=0.1,
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert', word_dropout=0.1,
                               auto_truncate=True)
 
         words = torch.LongTensor([[2, 3, 4, 1]*10,
@@ -60,7 +60,7 @@ class TestBertEmbedding(unittest.TestCase):
         try:
             os.makedirs(bert_save_test, exist_ok=True)
             vocab = Vocabulary().add_word_lst("this is a test . [SEP] NotInBERT".split())
-            embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert', word_dropout=0.1,
+            embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert', word_dropout=0.1,
                                   auto_truncate=True)
 
             embed.save(bert_save_test)
@@ -76,7 +76,7 @@ class TestBertEmbedding(unittest.TestCase):
 
 class TestBertWordPieceEncoder(unittest.TestCase):
     def test_bert_word_piece_encoder(self):
-        embed = BertWordPieceEncoder(model_dir_or_name='test/data_for_tests/embedding/small_bert', word_dropout=0.1)
+        embed = BertWordPieceEncoder(model_dir_or_name='tests/data_for_tests/embedding/small_bert', word_dropout=0.1)
         ds = DataSet({'words': ["this is a test . [SEP]".split()]})
         embed.index_datasets(ds, field_name='words')
         self.assertTrue(ds.has_field('word_pieces'))
@@ -84,7 +84,7 @@ class TestBertWordPieceEncoder(unittest.TestCase):
 
     def test_bert_embed_eq_bert_piece_encoder(self):
         ds = DataSet({'words': ["this is a texta model vocab".split(), 'this is'.split()]})
-        encoder = BertWordPieceEncoder(model_dir_or_name='test/data_for_tests/embedding/small_bert')
+        encoder = BertWordPieceEncoder(model_dir_or_name='tests/data_for_tests/embedding/small_bert')
         encoder.eval()
         encoder.index_datasets(ds, field_name='words')
         word_pieces = torch.LongTensor(ds['word_pieces'].get([0, 1]))
@@ -95,7 +95,7 @@ class TestBertWordPieceEncoder(unittest.TestCase):
         vocab.index_dataset(ds, field_name='words', new_field_name='words')
         ds.set_input('words')
         words = torch.LongTensor(ds['words'].get([0, 1]))
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               pool_method='first', include_cls_sep=True, pooled_cls=False, min_freq=1)
         embed.eval()
         words_res = embed(words)
@@ -109,7 +109,7 @@ class TestBertWordPieceEncoder(unittest.TestCase):
         bert_save_test = 'bert_save_test'
         try:
             os.makedirs(bert_save_test, exist_ok=True)
-            embed = BertWordPieceEncoder(model_dir_or_name='test/data_for_tests/embedding/small_bert', word_dropout=0.0,
+            embed = BertWordPieceEncoder(model_dir_or_name='tests/data_for_tests/embedding/small_bert', word_dropout=0.0,
                                          layers='-2')
             ds = DataSet({'words': ["this is a test . [SEP]".split()]})
             embed.index_datasets(ds, field_name='words')

tests/embeddings/test_elmo_embedding.py

@@ -21,7 +21,7 @@ class TestDownload(unittest.TestCase):
 class TestRunElmo(unittest.TestCase):
     def test_elmo_embedding(self):
         vocab = Vocabulary().add_word_lst("This is a test .".split())
-        elmo_embed = ElmoEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_elmo', layers='0,1')
+        elmo_embed = ElmoEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_elmo', layers='0,1')
         words = torch.LongTensor([[0, 1, 2]])
         hidden = elmo_embed(words)
         print(hidden.size())
@@ -30,7 +30,7 @@ class TestRunElmo(unittest.TestCase):
     def test_elmo_embedding_layer_assertion(self):
         vocab = Vocabulary().add_word_lst("This is a test .".split())
         try:
-            elmo_embed = ElmoEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_elmo',
+            elmo_embed = ElmoEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_elmo',
                                        layers='0,1,2')
         except AssertionError as e:
             print(e)

tests/embeddings/test_gpt2_embedding.py

@@ -21,7 +21,7 @@ class TestGPT2Embedding(unittest.TestCase):
             print(embed(words).size())
 
     def test_gpt2_embedding(self):
-        weight_path = 'test/data_for_tests/embedding/small_gpt2'
+        weight_path = 'tests/data_for_tests/embedding/small_gpt2'
         vocab = Vocabulary().add_word_lst("this is a texta sentence".split())
         embed = GPT2Embedding(vocab, model_dir_or_name=weight_path, word_dropout=0.1)
         requires_grad = embed.requires_grad
@@ -49,7 +49,7 @@ class TestGPT2Embedding(unittest.TestCase):
     def test_gpt2_ebembedding_2(self):
         # 测试only_use_pretrain_vocab与truncate_embed是否正常工作
         Embedding = GPT2Embedding
-        weight_path = 'test/data_for_tests/embedding/small_gpt2'
+        weight_path = 'tests/data_for_tests/embedding/small_gpt2'
         vocab = Vocabulary().add_word_lst("this is a texta and".split())
         embed1 = Embedding(vocab, model_dir_or_name=weight_path,layers=list(range(3)),
                               only_use_pretrain_bpe=True, truncate_embed=True, min_freq=1)
@@ -89,13 +89,13 @@ class TestGPT2Embedding(unittest.TestCase):
     def test_gpt2_tokenizer(self):
         from fastNLP.modules.tokenizer import GPT2Tokenizer
 
-        tokenizer = GPT2Tokenizer.from_pretrained('test/data_for_tests/embedding/small_gpt2')
+        tokenizer = GPT2Tokenizer.from_pretrained('tests/data_for_tests/embedding/small_gpt2')
         print(tokenizer.encode("this is a texta a sentence"))
         print(tokenizer.encode('this is'))
 
     def test_gpt2_embed_eq_gpt2_piece_encoder(self):
         # 主要检查一下embedding的结果与wordpieceencoder的结果是否一致
-        weight_path = 'test/data_for_tests/embedding/small_gpt2'
+        weight_path = 'tests/data_for_tests/embedding/small_gpt2'
         ds = DataSet({'words': ["this is a texta a sentence".split(), 'this is'.split()]})
         encoder = GPT2WordPieceEncoder(model_dir_or_name=weight_path)
         encoder.eval()
@@ -187,7 +187,7 @@ class TestGPT2WordPieceEncoder(unittest.TestCase):
 
         print(used_pairs)
         import json
-        with open('test/data_for_tests/embedding/small_gpt2/vocab.json', 'w') as f:
+        with open('tests/data_for_tests/embedding/small_gpt2/vocab.json', 'w') as f:
             new_used_vocab = {}
             for idx, key in enumerate(used_vocab.keys()):
                 new_used_vocab[key] = len(new_used_vocab)
@@ -201,12 +201,12 @@ class TestGPT2WordPieceEncoder(unittest.TestCase):
 
             json.dump(new_used_vocab, f)
 
-        with open('test/data_for_tests/embedding/small_gpt2/merges.txt', 'w') as f:
+        with open('tests/data_for_tests/embedding/small_gpt2/merges.txt', 'w') as f:
             f.write('#version: small\n')
             for k,v in sorted(sorted(used_pairs.items(), key=lambda kv:kv[1])):
                 f.write('{} {}\n'.format(k[0], k[1]))
 
-        new_tokenizer = GPT2Tokenizer.from_pretrained('test/data_for_tests/embedding/small_gpt2')
+        new_tokenizer = GPT2Tokenizer.from_pretrained('tests/data_for_tests/embedding/small_gpt2')
         new_all_tokens = []
         for sent in [sent1, sent2, sent3]:
             tokens = new_tokenizer.tokenize(sent, add_prefix_space=True)
@@ -227,21 +227,21 @@ class TestGPT2WordPieceEncoder(unittest.TestCase):
                       "n_positions": 20,
                       "vocab_size": len(new_used_vocab)
                     }
-        with open('test/data_for_tests/embedding/small_gpt2/config.json', 'w') as f:
+        with open('tests/data_for_tests/embedding/small_gpt2/config.json', 'w') as f:
             json.dump(config, f)
 
         # 生成更小的merges.txt与vocab.json, 方法是通过记录tokenizer中的值实现
         from fastNLP.modules.encoder.gpt2 import GPT2LMHeadModel, GPT2Config
 
-        config = GPT2Config.from_pretrained('test/data_for_tests/embedding/small_gpt2')
+        config = GPT2Config.from_pretrained('tests/data_for_tests/embedding/small_gpt2')
 
         model = GPT2LMHeadModel(config)
-        torch.save(model.state_dict(), 'test/data_for_tests/embedding/small_gpt2/small_pytorch_model.bin')
+        torch.save(model.state_dict(), 'tests/data_for_tests/embedding/small_gpt2/small_pytorch_model.bin')
         print(model(torch.LongTensor([[0,1,2,3]])))
 
     def test_gpt2_word_piece_encoder(self):
         # 主要检查可以运行
-        weight_path = 'test/data_for_tests/embedding/small_gpt2'
+        weight_path = 'tests/data_for_tests/embedding/small_gpt2'
         ds = DataSet({'words': ["this is a test sentence".split()]})
         embed = GPT2WordPieceEncoder(model_dir_or_name=weight_path, word_dropout=0.1)
         embed.index_datasets(ds, field_name='words')
@@ -256,7 +256,7 @@ class TestGPT2WordPieceEncoder(unittest.TestCase):
 
     @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
     def test_generate(self):
-        # weight_path = 'test/data_for_tests/embedding/small_gpt2'
+        # weight_path = 'tests/data_for_tests/embedding/small_gpt2'
         weight_path = 'en'
 
         encoder = GPT2WordPieceEncoder(model_dir_or_name=weight_path, language_model=True)

tests/embeddings/test_roberta_embedding.py

@@ -24,7 +24,7 @@ class TestRobertWordPieceEncoder(unittest.TestCase):
 
     def test_robert_word_piece_encoder(self):
         # 可正常运行即可
-        weight_path = 'test/data_for_tests/embedding/small_roberta'
+        weight_path = 'tests/data_for_tests/embedding/small_roberta'
         encoder = RobertaWordPieceEncoder(model_dir_or_name=weight_path, word_dropout=0.1)
         ds = DataSet({'words': ["this is a test . [SEP]".split()]})
         encoder.index_datasets(ds, field_name='words')
@@ -33,7 +33,7 @@ class TestRobertWordPieceEncoder(unittest.TestCase):
 
     def test_roberta_embed_eq_roberta_piece_encoder(self):
         # 主要检查一下embedding的结果与wordpieceencoder的结果是否一致
-        weight_path = 'test/data_for_tests/embedding/small_roberta'
+        weight_path = 'tests/data_for_tests/embedding/small_roberta'
         ds = DataSet({'words': ["this is a texta a sentence".split(), 'this is'.split()]})
         encoder = RobertaWordPieceEncoder(model_dir_or_name=weight_path)
         encoder.eval()
@@ -120,7 +120,7 @@ class TestRobertWordPieceEncoder(unittest.TestCase):
             used_vocab.update({t:i for t,i in zip(tokens, token_ids)})
 
         import json
-        with open('test/data_for_tests/embedding/small_roberta/vocab.json', 'w') as f:
+        with open('tests/data_for_tests/embedding/small_roberta/vocab.json', 'w') as f:
             new_used_vocab = {}
             for token in ['<s>', '<pad>', '</s>', '<unk>', '<mask>']:  # <pad>必须为1
                 new_used_vocab[token] = len(new_used_vocab)
@@ -135,7 +135,7 @@ class TestRobertWordPieceEncoder(unittest.TestCase):
                  new_used_vocab[key] = len(new_used_vocab)
             json.dump(new_used_vocab, f)
 
-        with open('test/data_for_tests/embedding/small_roberta/merges.txt', 'w') as f:
+        with open('tests/data_for_tests/embedding/small_roberta/merges.txt', 'w') as f:
             f.write('#version: tiny\n')
             for k,v in sorted(sorted(used_pairs.items(), key=lambda kv:kv[1])):
                 f.write('{} {}\n'.format(k[0], k[1]))
@@ -162,10 +162,10 @@ class TestRobertWordPieceEncoder(unittest.TestCase):
               "type_vocab_size": 1,
               "vocab_size": len(new_used_vocab)
             }
-        with open('test/data_for_tests/embedding/small_roberta/config.json', 'w') as f:
+        with open('tests/data_for_tests/embedding/small_roberta/config.json', 'w') as f:
             json.dump(config, f)
 
-        new_tokenizer = RobertaTokenizer.from_pretrained('test/data_for_tests/embedding/small_roberta')
+        new_tokenizer = RobertaTokenizer.from_pretrained('tests/data_for_tests/embedding/small_roberta')
         new_all_tokens = []
         for sent in [sent1, sent2, sent3]:
             tokens = new_tokenizer.tokenize(sent, add_prefix_space=True)
@@ -177,17 +177,17 @@ class TestRobertWordPieceEncoder(unittest.TestCase):
         # 生成更小的merges.txt与vocab.json, 方法是通过记录tokenizer中的值实现
         from fastNLP.modules.encoder.roberta import RobertaModel, BertConfig
 
-        config = BertConfig.from_json_file('test/data_for_tests/embedding/small_roberta/config.json')
+        config = BertConfig.from_json_file('tests/data_for_tests/embedding/small_roberta/config.json')
 
         model = RobertaModel(config)
-        torch.save(model.state_dict(), 'test/data_for_tests/embedding/small_roberta/small_pytorch_model.bin')
+        torch.save(model.state_dict(), 'tests/data_for_tests/embedding/small_roberta/small_pytorch_model.bin')
         print(model(torch.LongTensor([[0,1,2,3]])))
 
     def test_save_load(self):
         bert_save_test = 'roberta_save_test'
         try:
             os.makedirs(bert_save_test, exist_ok=True)
-            embed = RobertaWordPieceEncoder(model_dir_or_name='test/data_for_tests/embedding/small_roberta', word_dropout=0.0,
+            embed = RobertaWordPieceEncoder(model_dir_or_name='tests/data_for_tests/embedding/small_roberta', word_dropout=0.0,
                                          layers='-2')
             ds = DataSet({'words': ["this is a test . [SEP]".split()]})
             embed.index_datasets(ds, field_name='words')
@@ -204,7 +204,7 @@ class TestRobertWordPieceEncoder(unittest.TestCase):
 
 class TestRobertaEmbedding(unittest.TestCase):
     def test_roberta_embedding_1(self):
-        weight_path = 'test/data_for_tests/embedding/small_roberta'
+        weight_path = 'tests/data_for_tests/embedding/small_roberta'
         vocab = Vocabulary().add_word_lst("this is a test . [SEP] NotInRoberta".split())
         embed = RobertaEmbedding(vocab, model_dir_or_name=weight_path, word_dropout=0.1)
         requires_grad = embed.requires_grad
@@ -224,7 +224,7 @@ class TestRobertaEmbedding(unittest.TestCase):
     def test_roberta_ebembedding_2(self):
         # 测试only_use_pretrain_vocab与truncate_embed是否正常工作
         Embedding = RobertaEmbedding
-        weight_path = 'test/data_for_tests/embedding/small_roberta'
+        weight_path = 'tests/data_for_tests/embedding/small_roberta'
         vocab = Vocabulary().add_word_lst("this is a texta and".split())
         embed1 = Embedding(vocab, model_dir_or_name=weight_path, layers=list(range(3)),
                               only_use_pretrain_bpe=True, truncate_embed=True, min_freq=1)
@@ -266,7 +266,7 @@ class TestRobertaEmbedding(unittest.TestCase):
         try:
             os.makedirs(bert_save_test, exist_ok=True)
             vocab = Vocabulary().add_word_lst("this is a test . [SEP] NotInBERT".split())
-            embed = RobertaEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_roberta',
+            embed = RobertaEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_roberta',
                                      word_dropout=0.1,
                                      auto_truncate=True)
             embed.save(bert_save_test)

tests/embeddings/test_static_embedding.py

@@ -10,7 +10,7 @@ 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/'
+        embed = StaticEmbedding(vocab, model_dir_or_name='tests/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)
@@ -19,7 +19,7 @@ class TestLoad(unittest.TestCase):
     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/'
+        embed = StaticEmbedding(vocab, model_dir_or_name='tests/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)
@@ -50,13 +50,13 @@ class TestLoad(unittest.TestCase):
                 v2 = embed_dict[word]
                 for v1i, v2i in zip(v1, v2):
                     self.assertAlmostEqual(v1i, v2i, places=4)
-        embed_dict = read_static_embed('test/data_for_tests/embedding/small_static_embedding/'
+        embed_dict = read_static_embed('tests/data_for_tests/embedding/small_static_embedding/'
                                                          'glove.6B.50d_test.txt')
 
         # 测试是否只使用pretrain的word
         vocab = Vocabulary().add_word_lst(['the', 'a', 'notinfile'])
         vocab.add_word('of', no_create_entry=True)
-        embed = StaticEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_static_embedding/'
+        embed = StaticEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_static_embedding/'
                                                          'glove.6B.50d_test.txt',
                                 only_use_pretrain_word=True)
         #   notinfile应该被置为unk
@@ -66,13 +66,13 @@ class TestLoad(unittest.TestCase):
         # 测试在大小写情况下的使用
         vocab = Vocabulary().add_word_lst(['The', 'a', 'notinfile'])
         vocab.add_word('Of', no_create_entry=True)
-        embed = StaticEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_static_embedding/'
+        embed = StaticEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_static_embedding/'
                                                          'glove.6B.50d_test.txt',
                                 only_use_pretrain_word=True)
         check_word_unk(['The', 'Of', 'notinfile'], vocab, embed)  # 这些词应该找不到
         check_vector_equal(['a'], vocab, embed, embed_dict)
 
-        embed = StaticEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_static_embedding/'
+        embed = StaticEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_static_embedding/'
                                                          'glove.6B.50d_test.txt',
                                 only_use_pretrain_word=True, lower=True)
         check_vector_equal(['The', 'Of', 'a'], vocab, embed, embed_dict, lower=True)
@@ -82,7 +82,7 @@ class TestLoad(unittest.TestCase):
         vocab = Vocabulary().add_word_lst(['The', 'a', 'notinfile1', 'A', 'notinfile2', 'notinfile2'])
         vocab.add_word('Of', no_create_entry=True)
 
-        embed = StaticEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_static_embedding/'
+        embed = StaticEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_static_embedding/'
                                                          'glove.6B.50d_test.txt',
                                 only_use_pretrain_word=True, lower=True, min_freq=2, only_train_min_freq=True)
 
@@ -92,12 +92,12 @@ class TestLoad(unittest.TestCase):
     def test_sequential_index(self):
         # 当不存在no_create_entry时，words_to_words应该是顺序的
         vocab = Vocabulary().add_word_lst(['The', 'a', 'notinfile1', 'A', 'notinfile2', 'notinfile2'])
-        embed = StaticEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_static_embedding/'
+        embed = StaticEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_static_embedding/'
                                                          'glove.6B.50d_test.txt')
         for index,i in enumerate(embed.words_to_words):
             assert index==i
 
-        embed_dict = read_static_embed('test/data_for_tests/embedding/small_static_embedding/'
+        embed_dict = read_static_embed('tests/data_for_tests/embedding/small_static_embedding/'
                                                          'glove.6B.50d_test.txt')
 
         for word, index in vocab:
@@ -116,7 +116,7 @@ class TestLoad(unittest.TestCase):
 
             vocab = Vocabulary().add_word_lst(['The', 'a', 'notinfile1', 'A'])
             vocab.add_word_lst(['notinfile2', 'notinfile2'], no_create_entry=True)
-            embed = StaticEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_static_embedding/'
+            embed = StaticEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_static_embedding/'
                                                              'glove.6B.50d_test.txt')
             embed.save(static_test_folder)
             load_embed = StaticEmbedding.load(static_test_folder)
@@ -125,7 +125,7 @@ class TestLoad(unittest.TestCase):
 
             # 测试不包含no_create_entry
             vocab = Vocabulary().add_word_lst(['The', 'a', 'notinfile1', 'A'])
-            embed = StaticEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_static_embedding/'
+            embed = StaticEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_static_embedding/'
                                                              'glove.6B.50d_test.txt')
             embed.save(static_test_folder)
             load_embed = StaticEmbedding.load(static_test_folder)
@@ -134,7 +134,7 @@ class TestLoad(unittest.TestCase):
 
             # 测试lower, min_freq
             vocab = Vocabulary().add_word_lst(['The', 'the', 'the', 'A', 'a', 'B'])
-            embed = StaticEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_static_embedding/'
+            embed = StaticEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_static_embedding/'
                                                              'glove.6B.50d_test.txt', min_freq=2, lower=True)
             embed.save(static_test_folder)
             load_embed = StaticEmbedding.load(static_test_folder)

tests/io/loader/test_classification_loader.py

@@ -23,14 +23,14 @@ class TestDownload(unittest.TestCase):
 class TestLoad(unittest.TestCase):
     def test_process_from_file(self):
         data_set_dict = {
-            'yelp.p': ('test/data_for_tests/io/yelp_review_polarity', YelpPolarityLoader, (6, 6, 6), False),
-            'yelp.f': ('test/data_for_tests/io/yelp_review_full', YelpFullLoader, (6, 6, 6), False),
-            'sst-2': ('test/data_for_tests/io/SST-2', SST2Loader, (5, 5, 5), True),
-            'sst': ('test/data_for_tests/io/SST', SSTLoader, (6, 6, 6), False),
-            'imdb': ('test/data_for_tests/io/imdb', IMDBLoader, (6, 6, 6), False),
-            'ChnSentiCorp': ('test/data_for_tests/io/ChnSentiCorp', ChnSentiCorpLoader, (6, 6, 6), False),
-            'THUCNews': ('test/data_for_tests/io/THUCNews', THUCNewsLoader, (9, 9, 9), False),
-            'WeiboSenti100k': ('test/data_for_tests/io/WeiboSenti100k', WeiboSenti100kLoader, (6, 7, 6), False),
+            'yelp.p': ('tests/data_for_tests/io/yelp_review_polarity', YelpPolarityLoader, (6, 6, 6), False),
+            'yelp.f': ('tests/data_for_tests/io/yelp_review_full', YelpFullLoader, (6, 6, 6), False),
+            'sst-2': ('tests/data_for_tests/io/SST-2', SST2Loader, (5, 5, 5), True),
+            'sst': ('tests/data_for_tests/io/SST', SSTLoader, (6, 6, 6), False),
+            'imdb': ('tests/data_for_tests/io/imdb', IMDBLoader, (6, 6, 6), False),
+            'ChnSentiCorp': ('tests/data_for_tests/io/ChnSentiCorp', ChnSentiCorpLoader, (6, 6, 6), False),
+            'THUCNews': ('tests/data_for_tests/io/THUCNews', THUCNewsLoader, (9, 9, 9), False),
+            'WeiboSenti100k': ('tests/data_for_tests/io/WeiboSenti100k', WeiboSenti100kLoader, (6, 7, 6), False),
         }
         for k, v in data_set_dict.items():
             path, loader, data_set, warns = v

tests/io/loader/test_conll_loader.py

@@ -27,12 +27,12 @@ class TestWeiboNER(unittest.TestCase):
 
 class TestConll2003Loader(unittest.TestCase):
     def test_load(self):
-        Conll2003Loader()._load('test/data_for_tests/conll_2003_example.txt')
+        Conll2003Loader()._load('tests/data_for_tests/conll_2003_example.txt')
 
 
 class TestConllLoader(unittest.TestCase):
     def test_conll(self):
-        db = Conll2003Loader().load('test/data_for_tests/io/conll2003')
+        db = Conll2003Loader().load('tests/data_for_tests/io/conll2003')
         print(db)
 
 class TestConllLoader(unittest.TestCase):
@@ -40,5 +40,5 @@ class TestConllLoader(unittest.TestCase):
         headers = [
             'raw_words',  'ner',
         ]
-        db = ConllLoader(headers = headers,sep="\n").load('test/data_for_tests/io/MSRA_NER')
+        db = ConllLoader(headers = headers,sep="\n").load('tests/data_for_tests/io/MSRA_NER')
         print(db)

tests/io/loader/test_coreference_loader.py

@@ -5,7 +5,7 @@ import unittest
 class TestCR(unittest.TestCase):
     def test_load(self):
 
-        test_root = "test/data_for_tests/io/coreference/"
+        test_root = "tests/data_for_tests/io/coreference/"
         train_path = test_root+"coreference_train.json"
         dev_path = test_root+"coreference_dev.json"
         test_path = test_root+"coreference_test.json"

tests/io/loader/test_cws_loader.py

@@ -19,6 +19,6 @@ class TestRunCWSLoader(unittest.TestCase):
         for dataset_name in dataset_names:
             with self.subTest(dataset_name=dataset_name):
                 data_bundle = CWSLoader(dataset_name=dataset_name).load(
-                    f'test/data_for_tests/io/cws_{dataset_name}'
+                    f'tests/data_for_tests/io/cws_{dataset_name}'
                 )
                 print(data_bundle)

tests/io/loader/test_matching_loader.py

@@ -25,14 +25,14 @@ class TestMatchingDownload(unittest.TestCase):
 class TestMatchingLoad(unittest.TestCase):
     def test_load(self):
         data_set_dict = {
-            'RTE': ('test/data_for_tests/io/RTE', RTELoader, (5, 5, 5), True),
-            'SNLI': ('test/data_for_tests/io/SNLI', SNLILoader, (5, 5, 5), False),
-            'QNLI': ('test/data_for_tests/io/QNLI', QNLILoader, (5, 5, 5), True),
-            'MNLI': ('test/data_for_tests/io/MNLI', MNLILoader, (5, 5, 5, 5, 6), True),
-            'Quora': ('test/data_for_tests/io/Quora', QuoraLoader, (2, 2, 2), False),
-            'BQCorpus': ('test/data_for_tests/io/BQCorpus', BQCorpusLoader, (5, 5, 5), False),
-            'XNLI': ('test/data_for_tests/io/XNLI', CNXNLILoader, (6, 6, 8), False),
-            'LCQMC': ('test/data_for_tests/io/LCQMC', LCQMCLoader, (6, 5, 6), False),
+            'RTE': ('tests/data_for_tests/io/RTE', RTELoader, (5, 5, 5), True),
+            'SNLI': ('tests/data_for_tests/io/SNLI', SNLILoader, (5, 5, 5), False),
+            'QNLI': ('tests/data_for_tests/io/QNLI', QNLILoader, (5, 5, 5), True),
+            'MNLI': ('tests/data_for_tests/io/MNLI', MNLILoader, (5, 5, 5, 5, 6), True),
+            'Quora': ('tests/data_for_tests/io/Quora', QuoraLoader, (2, 2, 2), False),
+            'BQCorpus': ('tests/data_for_tests/io/BQCorpus', BQCorpusLoader, (5, 5, 5), False),
+            'XNLI': ('tests/data_for_tests/io/XNLI', CNXNLILoader, (6, 6, 8), False),
+            'LCQMC': ('tests/data_for_tests/io/LCQMC', LCQMCLoader, (6, 5, 6), False),
         }
         for k, v in data_set_dict.items():
             path, loader, instance, warns = v

tests/io/loader/test_qa_loader.py

@@ -5,10 +5,10 @@ from fastNLP.io.loader.qa import CMRC2018Loader
 class TestCMRC2018Loader(unittest.TestCase):
     def test__load(self):
         loader = CMRC2018Loader()
-        dataset = loader._load('test/data_for_tests/io/cmrc/train.json')
+        dataset = loader._load('tests/data_for_tests/io/cmrc/train.json')
         print(dataset)
 
     def test_load(self):
         loader = CMRC2018Loader()
-        data_bundle = loader.load('test/data_for_tests/io/cmrc/')
+        data_bundle = loader.load('tests/data_for_tests/io/cmrc/')
         print(data_bundle)

tests/io/pipe/test_classification.py

@@ -20,7 +20,7 @@ class TestClassificationPipe(unittest.TestCase):
 class TestRunPipe(unittest.TestCase):
     def test_load(self):
         for pipe in [IMDBPipe]:
-            data_bundle = pipe(tokenizer='raw').process_from_file('test/data_for_tests/io/imdb')
+            data_bundle = pipe(tokenizer='raw').process_from_file('tests/data_for_tests/io/imdb')
             print(data_bundle)
 
 
@@ -37,35 +37,35 @@ class TestCNClassificationPipe(unittest.TestCase):
 class TestRunClassificationPipe(unittest.TestCase):
     def test_process_from_file(self):
         data_set_dict = {
-            'yelp.p': ('test/data_for_tests/io/yelp_review_polarity', YelpPolarityPipe,
+            'yelp.p': ('tests/data_for_tests/io/yelp_review_polarity', YelpPolarityPipe,
                        {'train': 6, 'dev': 6, 'test': 6}, {'words': 1176, 'target': 2},
                        False),
-            'yelp.f': ('test/data_for_tests/io/yelp_review_full', YelpFullPipe,
+            'yelp.f': ('tests/data_for_tests/io/yelp_review_full', YelpFullPipe,
                        {'train': 6, 'dev': 6, 'test': 6}, {'words': 1166, 'target': 5},
                        False),
-            'sst-2': ('test/data_for_tests/io/SST-2', SST2Pipe,
+            'sst-2': ('tests/data_for_tests/io/SST-2', SST2Pipe,
                       {'train': 5, 'dev': 5, 'test': 5}, {'words': 139, 'target': 2},
                       True),
-            'sst': ('test/data_for_tests/io/SST', SSTPipe,
+            'sst': ('tests/data_for_tests/io/SST', SSTPipe,
                     {'train': 354, 'dev': 6, 'test': 6}, {'words': 232, 'target': 5},
                     False),
-            'imdb': ('test/data_for_tests/io/imdb', IMDBPipe,
+            'imdb': ('tests/data_for_tests/io/imdb', IMDBPipe,
                      {'train': 6, 'dev': 6, 'test': 6}, {'words': 1670, 'target': 2},
                      False),
-            'ag': ('test/data_for_tests/io/ag', AGsNewsPipe,
+            'ag': ('tests/data_for_tests/io/ag', AGsNewsPipe,
                    {'train': 4, 'test': 5}, {'words': 257, 'target': 4},
                    False),
-            'dbpedia': ('test/data_for_tests/io/dbpedia', DBPediaPipe,
+            'dbpedia': ('tests/data_for_tests/io/dbpedia', DBPediaPipe,
                         {'train': 14, 'test': 5}, {'words': 496, 'target': 14},
                         False),
-            'ChnSentiCorp': ('test/data_for_tests/io/ChnSentiCorp', ChnSentiCorpPipe,
+            'ChnSentiCorp': ('tests/data_for_tests/io/ChnSentiCorp', ChnSentiCorpPipe,
                              {'train': 6, 'dev': 6, 'test': 6},
                              {'chars': 529, 'bigrams': 1296, 'trigrams': 1483, 'target': 2},
                              False),
-            'Chn-THUCNews': ('test/data_for_tests/io/THUCNews', THUCNewsPipe,
+            'Chn-THUCNews': ('tests/data_for_tests/io/THUCNews', THUCNewsPipe,
                              {'train': 9, 'dev': 9, 'test': 9}, {'chars': 1864, 'target': 9},
                              False),
-            'Chn-WeiboSenti100k': ('test/data_for_tests/io/WeiboSenti100k', WeiboSenti100kPipe,
+            'Chn-WeiboSenti100k': ('tests/data_for_tests/io/WeiboSenti100k', WeiboSenti100kPipe,
                                    {'train': 6, 'dev': 6, 'test': 7}, {'chars': 452, 'target': 2},
                                    False),
         }

tests/io/pipe/test_conll.py

@@ -21,7 +21,7 @@ class TestRunPipe(unittest.TestCase):
         for pipe in [Conll2003Pipe, Conll2003NERPipe]:
             with self.subTest(pipe=pipe):
                 print(pipe)
-                data_bundle = pipe().process_from_file('test/data_for_tests/conll_2003_example.txt')
+                data_bundle = pipe().process_from_file('tests/data_for_tests/conll_2003_example.txt')
                 print(data_bundle)
 
 
@@ -35,18 +35,18 @@ class TestNERPipe(unittest.TestCase):
         for k, v in data_dict.items():
             pipe = v
             with self.subTest(pipe=pipe):
-                data_bundle = pipe(bigrams=True, trigrams=True).process_from_file(f'test/data_for_tests/io/{k}')
+                data_bundle = pipe(bigrams=True, trigrams=True).process_from_file(f'tests/data_for_tests/io/{k}')
                 print(data_bundle)
-                data_bundle = pipe(encoding_type='bioes').process_from_file(f'test/data_for_tests/io/{k}')
+                data_bundle = pipe(encoding_type='bioes').process_from_file(f'tests/data_for_tests/io/{k}')
                 print(data_bundle)
 
 
 class TestConll2003Pipe(unittest.TestCase):
     def test_conll(self):
         with self.assertWarns(Warning):
-            data_bundle = Conll2003Pipe().process_from_file('test/data_for_tests/io/conll2003')
+            data_bundle = Conll2003Pipe().process_from_file('tests/data_for_tests/io/conll2003')
         print(data_bundle)
 
     def test_OntoNotes(self):
-        data_bundle = OntoNotesNERPipe().process_from_file('test/data_for_tests/io/OntoNotes')
+        data_bundle = OntoNotesNERPipe().process_from_file('tests/data_for_tests/io/OntoNotes')
         print(data_bundle)

tests/io/pipe/test_coreference.py

@@ -11,7 +11,7 @@ class TestCR(unittest.TestCase):
             char_path = None
         config = Config()
 
-        file_root_path = "test/data_for_tests/io/coreference/"
+        file_root_path = "tests/data_for_tests/io/coreference/"
         train_path = file_root_path + "coreference_train.json"
         dev_path = file_root_path + "coreference_dev.json"
         test_path = file_root_path + "coreference_test.json"

tests/io/pipe/test_cws.py

@@ -31,11 +31,11 @@ class TestRunCWSPipe(unittest.TestCase):
         for dataset_name in dataset_names:
             with self.subTest(dataset_name=dataset_name):
                 data_bundle = CWSPipe(bigrams=True, trigrams=True).\
-                    process_from_file(f'test/data_for_tests/io/cws_{dataset_name}')
+                    process_from_file(f'tests/data_for_tests/io/cws_{dataset_name}')
                 print(data_bundle)
 
     def test_replace_number(self):
         data_bundle = CWSPipe(bigrams=True, replace_num_alpha=True).\
-                    process_from_file(f'test/data_for_tests/io/cws_pku')
+                    process_from_file(f'tests/data_for_tests/io/cws_pku')
         for word in ['<', '>', '<NUM>']:
             self.assertNotEqual(data_bundle.get_vocab('chars').to_index(word), 1)

tests/io/pipe/test_matching.py

@@ -33,13 +33,13 @@ class TestRunMatchingPipe(unittest.TestCase):
 
     def test_load(self):
         data_set_dict = {
-            'RTE': ('test/data_for_tests/io/RTE', RTEPipe, RTEBertPipe, (5, 5, 5), (449, 2), True),
-            'SNLI': ('test/data_for_tests/io/SNLI', SNLIPipe, SNLIBertPipe, (5, 5, 5), (110, 3), False),
-            'QNLI': ('test/data_for_tests/io/QNLI', QNLIPipe, QNLIBertPipe, (5, 5, 5), (372, 2), True),
-            'MNLI': ('test/data_for_tests/io/MNLI', MNLIPipe, MNLIBertPipe, (5, 5, 5, 5, 6), (459, 3), True),
-            'BQCorpus': ('test/data_for_tests/io/BQCorpus', BQCorpusPipe, BQCorpusBertPipe, (5, 5, 5), (32, 2), False),
-            'XNLI': ('test/data_for_tests/io/XNLI', CNXNLIPipe, CNXNLIBertPipe, (6, 6, 8), (39, 3), False),
-            'LCQMC': ('test/data_for_tests/io/LCQMC', LCQMCPipe, LCQMCBertPipe, (6, 5, 6), (36, 2), False),
+            'RTE': ('tests/data_for_tests/io/RTE', RTEPipe, RTEBertPipe, (5, 5, 5), (449, 2), True),
+            'SNLI': ('tests/data_for_tests/io/SNLI', SNLIPipe, SNLIBertPipe, (5, 5, 5), (110, 3), False),
+            'QNLI': ('tests/data_for_tests/io/QNLI', QNLIPipe, QNLIBertPipe, (5, 5, 5), (372, 2), True),
+            'MNLI': ('tests/data_for_tests/io/MNLI', MNLIPipe, MNLIBertPipe, (5, 5, 5, 5, 6), (459, 3), True),
+            'BQCorpus': ('tests/data_for_tests/io/BQCorpus', BQCorpusPipe, BQCorpusBertPipe, (5, 5, 5), (32, 2), False),
+            'XNLI': ('tests/data_for_tests/io/XNLI', CNXNLIPipe, CNXNLIBertPipe, (6, 6, 8), (39, 3), False),
+            'LCQMC': ('tests/data_for_tests/io/LCQMC', LCQMCPipe, LCQMCBertPipe, (6, 5, 6), (36, 2), False),
         }
         for k, v in data_set_dict.items():
             path, pipe1, pipe2, data_set, vocab, warns = v
@@ -76,7 +76,7 @@ class TestRunMatchingPipe(unittest.TestCase):
 
     def test_spacy(self):
         data_set_dict = {
-            'Quora': ('test/data_for_tests/io/Quora', QuoraPipe, QuoraBertPipe, (2, 2, 2), (93, 2)),
+            'Quora': ('tests/data_for_tests/io/Quora', QuoraPipe, QuoraBertPipe, (2, 2, 2), (93, 2)),
             }
         for k, v in data_set_dict.items():
             path, pipe1, pipe2, data_set, vocab = v

tests/io/pipe/test_qa.py

@@ -6,7 +6,7 @@ from fastNLP.io.loader.qa import CMRC2018Loader
 
 class CMRC2018PipeTest(unittest.TestCase):
     def test_process(self):
-        data_bundle = CMRC2018Loader().load('test/data_for_tests/io/cmrc/')
+        data_bundle = CMRC2018Loader().load('tests/data_for_tests/io/cmrc/')
         pipe = CMRC2018BertPipe()
         data_bundle = pipe.process(data_bundle)
 

tests/io/pipe/test_summary.py

@@ -27,9 +27,9 @@ from fastNLP.io.pipe.summarization import ExtCNNDMPipe
 class TestRunExtCNNDMPipe(unittest.TestCase):
 
     def test_load(self):
-        data_dir = 'test/data_for_tests/io/cnndm'
+        data_dir = 'tests/data_for_tests/io/cnndm'
         vocab_size = 100000
-        VOCAL_FILE = 'test/data_for_tests/io/cnndm/vocab'
+        VOCAL_FILE = 'tests/data_for_tests/io/cnndm/vocab'
         sent_max_len = 100
         doc_max_timesteps = 50
         dbPipe = ExtCNNDMPipe(vocab_size=vocab_size,

tests/io/test_embed_loader.py

@@ -8,8 +8,8 @@ from fastNLP.io import EmbedLoader
 class TestEmbedLoader(unittest.TestCase):
     def test_load_with_vocab(self):
         vocab = Vocabulary()
-        glove = "test/data_for_tests/embedding/small_static_embedding/glove.6B.50d_test.txt"
-        word2vec = "test/data_for_tests/embedding/small_static_embedding/word2vec_test.txt"
+        glove = "tests/data_for_tests/embedding/small_static_embedding/glove.6B.50d_test.txt"
+        word2vec = "tests/data_for_tests/embedding/small_static_embedding/word2vec_test.txt"
         vocab.add_word('the')
         vocab.add_word('none')
         g_m = EmbedLoader.load_with_vocab(glove, vocab)
@@ -20,8 +20,8 @@ class TestEmbedLoader(unittest.TestCase):
     
     def test_load_without_vocab(self):
         words = ['the', 'of', 'in', 'a', 'to', 'and']
-        glove = "test/data_for_tests/embedding/small_static_embedding/glove.6B.50d_test.txt"
-        word2vec = "test/data_for_tests/embedding/small_static_embedding/word2vec_test.txt"
+        glove = "tests/data_for_tests/embedding/small_static_embedding/glove.6B.50d_test.txt"
+        word2vec = "tests/data_for_tests/embedding/small_static_embedding/word2vec_test.txt"
         g_m, vocab = EmbedLoader.load_without_vocab(glove)
         self.assertEqual(g_m.shape, (8, 50))
         for word in words:

tests/models/test_bert.py

@@ -11,7 +11,7 @@ from fastNLP.embeddings.bert_embedding import BertEmbedding
 class TestBert(unittest.TestCase):
     def test_bert_1(self):
         vocab = Vocabulary().add_word_lst("this is a test .".split())
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               include_cls_sep=True)
 
         model = BertForSequenceClassification(embed, 2)
@@ -30,7 +30,7 @@ class TestBert(unittest.TestCase):
 
     def test_bert_1_w(self):
         vocab = Vocabulary().add_word_lst("this is a test .".split())
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               include_cls_sep=False)
 
         with self.assertWarns(Warning):
@@ -46,7 +46,7 @@ class TestBert(unittest.TestCase):
     def test_bert_2(self):
 
         vocab = Vocabulary().add_word_lst("this is a test [SEP] .".split())
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               include_cls_sep=True)
 
         model = BertForMultipleChoice(embed, 2)
@@ -62,7 +62,7 @@ class TestBert(unittest.TestCase):
     def test_bert_2_w(self):
 
         vocab = Vocabulary().add_word_lst("this is a test [SEP] .".split())
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               include_cls_sep=False)
 
         with self.assertWarns(Warning):
@@ -79,7 +79,7 @@ class TestBert(unittest.TestCase):
     def test_bert_3(self):
 
         vocab = Vocabulary().add_word_lst("this is a test [SEP] .".split())
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               include_cls_sep=False)
         model = BertForTokenClassification(embed, 7)
 
@@ -93,7 +93,7 @@ class TestBert(unittest.TestCase):
     def test_bert_3_w(self):
 
         vocab = Vocabulary().add_word_lst("this is a test [SEP] .".split())
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               include_cls_sep=True)
 
         with self.assertWarns(Warning):
@@ -108,7 +108,7 @@ class TestBert(unittest.TestCase):
 
     def test_bert_4(self):
         vocab = Vocabulary().add_word_lst("this is a test [SEP] .".split())
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               include_cls_sep=False)
         model = BertForQuestionAnswering(embed)
 
@@ -126,12 +126,12 @@ class TestBert(unittest.TestCase):
         from fastNLP.io import CMRC2018BertPipe
         from fastNLP import Trainer
 
-        data_bundle = CMRC2018BertPipe().process_from_file('test/data_for_tests/io/cmrc')
+        data_bundle = CMRC2018BertPipe().process_from_file('tests/data_for_tests/io/cmrc')
         data_bundle.rename_field('chars', 'words')
         train_data = data_bundle.get_dataset('train')
         vocab = data_bundle.get_vocab('words')
 
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               include_cls_sep=False, auto_truncate=True)
         model = BertForQuestionAnswering(embed)
         loss = CMRC2018Loss()
@@ -142,7 +142,7 @@ class TestBert(unittest.TestCase):
     def test_bert_5(self):
 
         vocab = Vocabulary().add_word_lst("this is a test [SEP] .".split())
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               include_cls_sep=True)
         model = BertForSentenceMatching(embed)
 
@@ -156,7 +156,7 @@ class TestBert(unittest.TestCase):
     def test_bert_5_w(self):
 
         vocab = Vocabulary().add_word_lst("this is a test [SEP] .".split())
-        embed = BertEmbedding(vocab, model_dir_or_name='test/data_for_tests/embedding/small_bert',
+        embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
                               include_cls_sep=False)
 
         with self.assertWarns(Warning):

tests/modules/decoder/test_CRF.py

@@ -223,7 +223,7 @@ class TestCRF(unittest.TestCase):
         import torch
         from fastNLP import seq_len_to_mask
 
-        with open('test/data_for_tests/modules/decoder/crf.json', 'r') as f:
+        with open('tests/data_for_tests/modules/decoder/crf.json', 'r') as f:
             data = json.load(f)
 
         bio_logits = torch.FloatTensor(data['bio_logits'])

tests/modules/tokenizer/test_bert_tokenizer.py

@@ -5,7 +5,7 @@ from fastNLP.modules.tokenizer import BertTokenizer
 class TestBertTokenizer(unittest.TestCase):
     def test_run(self):
         # 测试支持的两种encode方式
-        tokenizer = BertTokenizer.from_pretrained('test/data_for_tests/embedding/small_bert')
+        tokenizer = BertTokenizer.from_pretrained('tests/data_for_tests/embedding/small_bert')
 
         tokens1 = tokenizer.encode("This is a demo")
         tokens2 = tokenizer.encode("This is a demo", add_special_tokens=False)

tests/test_tutorials.py

@@ -85,7 +85,7 @@ class TestTutorial(unittest.TestCase):
 class TestOldTutorial(unittest.TestCase):
     def test_fastnlp_10min_tutorial(self):
         # 从csv读取数据到DataSet
-        sample_path = "test/data_for_tests/tutorial_sample_dataset.csv"
+        sample_path = "tests/data_for_tests/tutorial_sample_dataset.csv"
         dataset = CSVLoader(headers=['raw_sentence', 'label'], sep='	')._load(sample_path)
         print(len(dataset))
         print(dataset[0])
@@ -183,7 +183,7 @@ class TestOldTutorial(unittest.TestCase):
 
     def test_fastnlp_1min_tutorial(self):
         # tutorials/fastnlp_1min_tutorial.ipynb
-        data_path = "test/data_for_tests/tutorial_sample_dataset.csv"
+        data_path = "tests/data_for_tests/tutorial_sample_dataset.csv"
         ds = CSVLoader(headers=['raw_sentence', 'label'], sep='	')._load(data_path)
         print(ds[1])