修改 io 的测试文件; 删除了一些过时的测试

test/io/config

@@ -1,62 +0,0 @@
-[test]
-x = 1
-
-y = 2
-
-z = 3
-#this is an example
-input = [1,2,3]
-
-text = "this is text"
-
-doubles = 0.8
-
-tt = 0.5
-
-test = 105
-
-str = "this is a str"
-
-double = 0.5
-
-
-[t]
-x = "this is an test section"
-
-
-
-[test-case-2]
-double = 0.5
-
-doubles = 0.8
-
-tt = 0.5
-
-test = 105
-
-str = "this is a str"
-
-[another-test]
-doubles = 0.8
-
-tt = 0.5
-
-test = 105
-
-str = "this is a str"
-
-double = 0.5
-
-
-[one-another-test]
-doubles = 0.8
-
-tt = 0.5
-
-test = 105
-
-str = "this is a str"
-
-double = 0.5
-
-

test/io/test_config_saver.py

@@ -1,112 +0,0 @@
-import os
-import unittest
-
-# from fastNLP.io import ConfigSection, ConfigLoader, ConfigSaver
-
-
-class TestConfigSaver(unittest.TestCase):
-    def test_case_1(self):
-        config_file_dir = "."
-        config_file_name = "config"
-        config_file_path = os.path.join(config_file_dir, config_file_name)
-        
-        tmp_config_file_path = os.path.join(config_file_dir, "tmp_config")
-        
-        with open(config_file_path, "r") as f:
-            lines = f.readlines()
-        
-        standard_section = ConfigSection()
-        t_section = ConfigSection()
-        ConfigLoader().load_config(config_file_path, {"test": standard_section, "t": t_section})
-        
-        config_saver = ConfigSaver(config_file_path)
-        
-        section = ConfigSection()
-        section["doubles"] = 0.8
-        section["tt"] = 0.5
-        section["test"] = 105
-        section["str"] = "this is a str"
-        
-        test_case_2_section = section
-        test_case_2_section["double"] = 0.5
-        
-        for k in section.__dict__.keys():
-            standard_section[k] = section[k]
-        
-        config_saver.save_config_file("test", section)
-        config_saver.save_config_file("another-test", section)
-        config_saver.save_config_file("one-another-test", section)
-        config_saver.save_config_file("test-case-2", section)
-        
-        test_section = ConfigSection()
-        at_section = ConfigSection()
-        another_test_section = ConfigSection()
-        one_another_test_section = ConfigSection()
-        a_test_case_2_section = ConfigSection()
-        
-        ConfigLoader().load_config(config_file_path, {"test": test_section,
-                                                      "another-test": another_test_section,
-                                                      "t": at_section,
-                                                      "one-another-test": one_another_test_section,
-                                                      "test-case-2": a_test_case_2_section})
-        
-        assert test_section == standard_section
-        assert at_section == t_section
-        assert another_test_section == section
-        assert one_another_test_section == section
-        assert a_test_case_2_section == test_case_2_section
-        
-        config_saver.save_config_file("test", section)
-        
-        with open(config_file_path, "w") as f:
-            f.writelines(lines)
-        
-        with open(tmp_config_file_path, "w") as f:
-            f.write('[test]\n')
-            f.write('this is an fault example\n')
-        
-        tmp_config_saver = ConfigSaver(tmp_config_file_path)
-        try:
-            tmp_config_saver._read_section()
-        except Exception as e:
-            pass
-        os.remove(tmp_config_file_path)
-        
-        try:
-            tmp_config_saver = ConfigSaver("file-NOT-exist")
-        except Exception as e:
-            pass
-    
-    def test_case_2(self):
-        config = "[section_A]\n[section_B]\n"
-        
-        with open("./test.cfg", "w", encoding="utf-8") as f:
-            f.write(config)
-        saver = ConfigSaver("./test.cfg")
-        
-        section = ConfigSection()
-        section["doubles"] = 0.8
-        section["tt"] = [1, 2, 3]
-        section["test"] = 105
-        section["str"] = "this is a str"
-        
-        saver.save_config_file("section_A", section)
-        
-        os.system("rm ./test.cfg")
-    
-    def test_case_3(self):
-        config = "[section_A]\ndoubles = 0.9\ntt = [1, 2, 3]\n[section_B]\n"
-        
-        with open("./test.cfg", "w", encoding="utf-8") as f:
-            f.write(config)
-        saver = ConfigSaver("./test.cfg")
-        
-        section = ConfigSection()
-        section["doubles"] = 0.8
-        section["tt"] = [1, 2, 3]
-        section["test"] = 105
-        section["str"] = "this is a str"
-        
-        saver.save_config_file("section_A", section)
-        
-        os.system("rm ./test.cfg")

test/io/test_dataset_loader.py

@@ -9,22 +9,22 @@ class TestDatasetLoader(unittest.TestCase):
         """
             Test the the loader of Conll2003 dataset
         """
-        dataset_path = "../data_for_tests/conll_2003_example.txt"
+        dataset_path = "test/data_for_tests/conll_2003_example.txt"
         loader = Conll2003Loader()
         dataset_2003 = loader.load(dataset_path)
     
     def test_PeopleDailyCorpusLoader(self):
-        data_set = PeopleDailyCorpusLoader().load("../data_for_tests/people_daily_raw.txt")
+        data_set = PeopleDailyCorpusLoader().load("test/data_for_tests/people_daily_raw.txt")
     
     def test_CSVLoader(self):
         ds = CSVLoader(sep='\t', headers=['words', 'label']) \
-            .load('../data_for_tests/tutorial_sample_dataset.csv')
+            .load('test/data_for_tests/tutorial_sample_dataset.csv')
         assert len(ds) > 0
     
     def test_SNLILoader(self):
-        ds = SNLILoader().load('../data_for_tests/sample_snli.jsonl')
+        ds = SNLILoader().load('test/data_for_tests/sample_snli.jsonl')
         assert len(ds) == 3
     
     def test_JsonLoader(self):
-        ds = JsonLoader().load('../data_for_tests/sample_snli.jsonl')
+        ds = JsonLoader().load('test/data_for_tests/sample_snli.jsonl')
         assert len(ds) == 3

test/io/test_embed_loader.py

@@ -3,15 +3,13 @@ import numpy as np
 
 from fastNLP import Vocabulary
 from fastNLP.io import EmbedLoader
-import os
-from fastNLP.io.dataset_loader import SSTLoader
-from fastNLP.core.const import Const as C
+
 
 class TestEmbedLoader(unittest.TestCase):
     def test_load_with_vocab(self):
         vocab = Vocabulary()
-        glove = "../data_for_tests/glove.6B.50d_test.txt"
-        word2vec = "../data_for_tests/word2vec_test.txt"
+        glove = "test/data_for_tests/glove.6B.50d_test.txt"
+        word2vec = "test/data_for_tests/word2vec_test.txt"
         vocab.add_word('the')
         vocab.add_word('none')
         g_m = EmbedLoader.load_with_vocab(glove, vocab)
@@ -19,11 +17,11 @@ class TestEmbedLoader(unittest.TestCase):
         w_m = EmbedLoader.load_with_vocab(word2vec, vocab, normalize=True)
         self.assertEqual(w_m.shape, (4, 50))
         self.assertAlmostEqual(np.linalg.norm(w_m, axis=1).sum(), 4)
-
+    
     def test_load_without_vocab(self):
         words = ['the', 'of', 'in', 'a', 'to', 'and']
-        glove = "../data_for_tests/glove.6B.50d_test.txt"
-        word2vec = "../data_for_tests/word2vec_test.txt"
+        glove = "test/data_for_tests/glove.6B.50d_test.txt"
+        word2vec = "test/data_for_tests/word2vec_test.txt"
         g_m, vocab = EmbedLoader.load_without_vocab(glove)
         self.assertEqual(g_m.shape, (8, 50))
         for word in words:
@@ -39,9 +37,10 @@ class TestEmbedLoader(unittest.TestCase):
         self.assertAlmostEqual(np.linalg.norm(w_m, axis=1).sum(), 7)
         for word in words:
             self.assertIn(word, vocab)
-
+    
     def test_read_all_glove(self):
         pass
+        # TODO
         # 这是可以运行的，但是总数少于行数，应该是由于glove有重复的word
         # path = '/where/to/read/full/glove'
         # init_embed, vocab = EmbedLoader.load_without_vocab(path, error='strict')

test/modules/test_other_modules.py

@@ -2,55 +2,9 @@ import unittest
 
 import torch
 
-# from fastNLP.modules.other_modules import GroupNorm, LayerNormalization, BiLinear, BiAffine
 from fastNLP.modules.encoder.star_transformer import StarTransformer
 
 
-class TestGroupNorm(unittest.TestCase):
-    def test_case_1(self):
-        gn = GroupNorm(num_features=1, num_groups=10, eps=1.5e-5)
-        x = torch.randn((20, 50, 10))
-        y = gn(x)
-
-
-class TestLayerNormalization(unittest.TestCase):
-    def test_case_1(self):
-        ln = LayerNormalization(layer_size=5, eps=2e-3)
-        x = torch.randn((20, 50, 5))
-        y = ln(x)
-
-
-class TestBiLinear(unittest.TestCase):
-    def test_case_1(self):
-        bl = BiLinear(n_left=5, n_right=5, n_out=10, bias=True)
-        x_left = torch.randn((7, 10, 20, 5))
-        x_right = torch.randn((7, 10, 20, 5))
-        y = bl(x_left, x_right)
-        print(bl)
-        bl2 = BiLinear(n_left=15, n_right=15, n_out=10, bias=True)
-
-
-class TestBiAffine(unittest.TestCase):
-    def test_case_1(self):
-        batch_size = 16
-        encoder_length = 21
-        decoder_length = 32
-        layer = BiAffine(10, 10, 25, biaffine=True)
-        decoder_input = torch.randn((batch_size, encoder_length, 10))
-        encoder_input = torch.randn((batch_size, decoder_length, 10))
-        y = layer(decoder_input, encoder_input)
-        self.assertEqual(tuple(y.shape), (batch_size, 25, encoder_length, decoder_length))
-
-    def test_case_2(self):
-        batch_size = 16
-        encoder_length = 21
-        decoder_length = 32
-        layer = BiAffine(10, 10, 25, biaffine=False)
-        decoder_input = torch.randn((batch_size, encoder_length, 10))
-        encoder_input = torch.randn((batch_size, decoder_length, 10))
-        y = layer(decoder_input, encoder_input)
-        self.assertEqual(tuple(y.shape), (batch_size, 25, encoder_length, 1))
-
 class TestStarTransformer(unittest.TestCase):
     def test_1(self):
         model = StarTransformer(num_layers=6, hidden_size=100, num_head=8, head_dim=20, max_len=100)