[BUGFIX] Remove dangerous mutable default arguments

See
https://stackoverflow.com/questions/1132941/least-astonishment-and-the-mutable-default-argument
for more information.

prototorch/core/initializers.py

@@ -179,7 +179,7 @@ class AbstractClassAwareCompInitializer(AbstractDataAwareCompInitializer):
         self.num_classes = len(self.clabels)
 
     @abstractmethod
-    def generate(self, distribution: Union[dict, list, tuple] = []):
+    def generate(self, distribution: Union[dict, list, tuple]):
         ...
         return self.generate_end_hook(...)
 
@@ -190,7 +190,7 @@ class AbstractClassAwareCompInitializer(AbstractDataAwareCompInitializer):
 
 class ClassAwareCompInitializer(AbstractClassAwareCompInitializer):
     """'Generate' components from provided data and requested distribution."""
-    def generate(self, distribution: Union[dict, list, tuple] = []):
+    def generate(self, distribution: Union[dict, list, tuple]):
         """Ignore `distribution` and simply return transformed `self.data`."""
         components = self.generate_end_hook(self.data)
         return components
@@ -249,7 +249,7 @@ class LiteralLabelsInitializer(AbstractLabelsInitializer):
     def __init__(self, labels):
         self.labels = labels
 
-    def generate(self, distribution: Union[dict, list, tuple] = []):
+    def generate(self, distribution: Union[dict, list, tuple]):
         """Ignore `distribution` and simply return `self.labels`.
 
         Convert to long tensor, if necessary.
@@ -267,7 +267,7 @@ class DataAwareLabelsInitializer(AbstractLabelsInitializer):
     def __init__(self, data):
         self.data, self.targets = parse_data_arg(data)
 
-    def generate(self, distribution: Union[dict, list, tuple] = []):
+    def generate(self, distribution: Union[dict, list, tuple]):
         """Ignore `num_components` and simply return `self.targets`."""
         return self.targets
 
@@ -326,7 +326,7 @@ class LiteralReasoningsInitializer(AbstractReasoningsInitializer):
         super().__init__(**kwargs)
         self.reasonings = reasonings
 
-    def generate(self, distribution: Union[dict, list, tuple] = []):
+    def generate(self, distribution: Union[dict, list, tuple]):
         """Ignore `distributuion` and simply return self.reasonings."""
         reasonings = self.reasonings
         if not isinstance(reasonings, torch.Tensor):

tests/test_core.py

@@ -52,7 +52,7 @@ def test_parse_distribution_custom_labels():
 def test_literal_comp_generate():
     protos = torch.rand(4, 3, 5, 5)
     c = pt.initializers.LiteralCompInitializer(protos)
-    components = c.generate()
+    components = c.generate([])
     assert torch.allclose(components, protos)
 
 
@@ -60,7 +60,7 @@ def test_literal_comp_generate_from_list():
     protos = [[0, 1], [2, 3], [4, 5]]
     c = pt.initializers.LiteralCompInitializer(protos)
     with pytest.warns(UserWarning):
-        components = c.generate()
+        components = c.generate([])
     assert torch.allclose(components, torch.Tensor(protos))
 
 
@@ -162,7 +162,7 @@ def test_stratified_selection_comp_generate():
 def test_literal_labels_init():
     l = pt.initializers.LiteralLabelsInitializer([0, 0, 1, 2])
     with pytest.warns(UserWarning):
-        labels = l.generate()
+        labels = l.generate([])
     assert torch.allclose(labels, torch.LongTensor([0, 0, 1, 2]))
 
 
@@ -188,7 +188,7 @@ def test_data_aware_labels_init():
     data, targets = [0, 1, 2, 3], [0, 0, 1, 1]
     ds = pt.datasets.NumpyDataset(data, targets)
     l = pt.initializers.DataAwareLabelsInitializer(ds)
-    labels = l.generate()
+    labels = l.generate([])
     assert torch.allclose(labels, torch.LongTensor(targets))
 
 
@@ -196,7 +196,7 @@ def test_data_aware_labels_init():
 def test_literal_reasonings_init():
     r = pt.initializers.LiteralReasoningsInitializer([0, 0, 1, 2])
     with pytest.warns(UserWarning):
-        reasonings = r.generate()
+        reasonings = r.generate([])
     assert torch.allclose(reasonings, torch.Tensor([0, 0, 1, 2]))