Refactor tests

datumaro/tests/test_project.py

@@ -134,18 +134,11 @@ def test_can_have_project_source(self):
 
     def test_can_batch_launch_custom_model(self):
         class TestExtractor(Extractor):
-            def __init__(self, url, n=0):
-                super().__init__(length=n)
-                self.n = n
-
             def __iter__(self):
-                for i in range(self.n):
+                for i in range(5):
                     yield DatasetItem(id=i, subset='train', image=i)
 
         class TestLauncher(Launcher):
-            def __init__(self, **kwargs):
-                pass
-
             def launch(self, inputs):
                 for i, inp in enumerate(inputs):
                     yield [ LabelObject(attributes={'idx': i, 'data': inp}) ]
@@ -157,7 +150,7 @@ def launch(self, inputs):
         project.env.launchers.register(launcher_name, TestLauncher)
         project.add_model(model_name, { 'launcher': launcher_name })
         model = project.make_executable_model(model_name)
-        extractor = TestExtractor('', n=5)
+        extractor = TestExtractor()
 
         batch_size = 3
         executor = InferenceWrapper(extractor, model, batch_size=batch_size)
@@ -171,27 +164,20 @@ def launch(self, inputs):
 
     def test_can_do_transform_with_custom_model(self):
         class TestExtractorSrc(Extractor):
-            def __init__(self, url, n=2):
-                super().__init__(length=n)
-                self.n = n
-
             def __iter__(self):
-                for i in range(self.n):
+                for i in range(2):
                     yield DatasetItem(id=i, subset='train', image=i,
                         annotations=[ LabelObject(i) ])
 
         class TestLauncher(Launcher):
-            def __init__(self, **kwargs):
-                pass
-
             def launch(self, inputs):
                 for inp in inputs:
                     yield [ LabelObject(inp) ]
 
         class TestConverter(Converter):
             def __call__(self, extractor, save_dir):
                 for item in extractor:
-                    with open(osp.join(save_dir, '%s.txt' % item.id), 'w+') as f:
+                    with open(osp.join(save_dir, '%s.txt' % item.id), 'w') as f:
                         f.write(str(item.subset) + '\n')
                         f.write(str(item.annotations[0].label) + '\n')
 
@@ -204,8 +190,8 @@ def __iter__(self):
                 for path in self.items:
                     with open(path, 'r') as f:
                         index = osp.splitext(osp.basename(path))[0]
-                        subset = f.readline()[:-1]
-                        label = int(f.readline()[:-1])
+                        subset = f.readline().strip()
+                        label = int(f.readline().strip())
                         assert subset == 'train'
                         yield DatasetItem(id=index, subset=subset,
                             annotations=[ LabelObject(label) ])
@@ -261,12 +247,8 @@ def __iter__(self):
 
     def test_project_filter_can_be_applied(self):
         class TestExtractor(Extractor):
-            def __init__(self, url, n=10):
-                super().__init__(length=n)
-                self.n = n
-
             def __iter__(self):
-                for i in range(self.n):
+                for i in range(10):
                     yield DatasetItem(id=i, subset='train')
 
         e_type = 'type'
@@ -331,30 +313,23 @@ def test_project_compound_child_can_be_modified_recursively(self):
             self.assertEqual(1, len(dataset.sources['child2']))
 
     def test_project_can_merge_item_annotations(self):
-        class TestExtractor(Extractor):
-            def __init__(self, url, v=None):
-                super().__init__()
-                self.v = v
-
+        class TestExtractor1(Extractor):
             def __iter__(self):
-                v1_item = DatasetItem(id=1, subset='train', annotations=[
+                yield DatasetItem(id=1, subset='train', annotations=[
                     LabelObject(2, id=3),
                     LabelObject(3, attributes={ 'x': 1 }),
                 ])
 
-                v2_item = DatasetItem(id=1, subset='train', annotations=[
+        class TestExtractor2(Extractor):
+            def __iter__(self):
+                yield DatasetItem(id=1, subset='train', annotations=[
                     LabelObject(3, attributes={ 'x': 1 }),
                     LabelObject(4, id=4),
                 ])
 
-                if self.v == 1:
-                    yield v1_item
-                else:
-                    yield v2_item
-
         project = Project()
-        project.env.extractors.register('t1', lambda p: TestExtractor(p, v=1))
-        project.env.extractors.register('t2', lambda p: TestExtractor(p, v=2))
+        project.env.extractors.register('t1', TestExtractor1)
+        project.env.extractors.register('t2', TestExtractor2)
         project.add_source('source1', { 'format': 't1' })
         project.add_source('source2', { 'format': 't2' })
 
@@ -494,9 +469,6 @@ def test_can_produce_multilayer_config_from_dict(self):
 class ExtractorTest(TestCase):
     def test_custom_extractor_can_be_created(self):
         class CustomExtractor(Extractor):
-            def __init__(self, url):
-                super().__init__()
-
             def __iter__(self):
                 return iter([
                     DatasetItem(id=0, subset='train'),