✅ Improve coverage

tests/layers/test_bayesian_layers.py

@@ -147,7 +147,7 @@ def test_conv3_even(self, cube_input_even: torch.Tensor) -> None:
 class TestTrainableDistribution:
     """Testing the TrainableDistribution class."""
 
-    def test_error(self) -> None:
+    def test_log_posterior(self) -> None:
         sampler = TrainableDistribution(torch.ones(1), torch.ones(1))
         with pytest.raises(ValueError):
             sampler.log_posterior()

tests/models/test_deep_ensembles.py

@@ -25,6 +25,11 @@ def test_list_and_num_estimators(self):
         with pytest.raises(ValueError):
             deep_ensembles([model_1, model_2], num_estimators=2)
 
+    def test_list_singleton(self):
+        model_1 = dummy_model(1, 10, 1)
+        with pytest.raises(ValueError):
+            deep_ensembles([model_1], num_estimators=2)
+
     def test_model_and_no_num_estimator(self):
         model_1 = dummy_model(1, 10, 1)
         with pytest.raises(ValueError):

torch_uncertainty/models/vgg/base.py

@@ -111,14 +111,16 @@ def _init_weights(self):
                 nn.init.kaiming_normal_(
                     m.weight, mode="fan_out", nonlinearity="relu"
                 )
-                if m.bias is not None:
+                if m.bias is not None: # coverage: ignore
                     nn.init.constant_(m.bias, 0)
             elif isinstance(m, nn.BatchNorm2d):
                 nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
+                if m.bias is not None: # coverage: ignore
+                    nn.init.constant_(m.bias, 0)
             elif isinstance(m, nn.Linear) or isinstance(m, PackedLinear):
                 nn.init.normal_(m.weight, 0, 0.01)
-                nn.init.constant_(m.bias, 0)
+                if m.bias is not None: # coverage: ignore
+                    nn.init.constant_(m.bias, 0)
 
 
 def _vgg(