diff --git a/tests/test_models.py b/tests/test_models.py
index a1b5f2c6..206635e2 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -23,19 +23,21 @@ def get_encoders():
 
 def get_sample(model_class):
     if model_class in [
-        smp.Unet,
-        smp.Linknet,
         smp.FPN,
-        smp.PSPNet,
+        smp.Linknet,
+        smp.Unet,
         smp.UnetPlusPlus,
         smp.MAnet,
-        smp.UPerNet,
     ]:
         sample = torch.ones([1, 3, 64, 64])
     elif model_class == smp.PAN:
         sample = torch.ones([2, 3, 256, 256])
-    elif model_class == smp.DeepLabV3:
+    elif model_class in [smp.DeepLabV3, smp.DeepLabV3Plus]:
         sample = torch.ones([2, 3, 128, 128])
+    elif model_class in [smp.PSPNet, smp.UPerNet]:
+        # Batch size 2 needed due to nn.BatchNorm2d not supporting (1, C, 1, 1) input
+        # from PSPModule pooling in PSPNet/UPerNet.
+        sample = torch.ones([2, 3, 64, 64])
     else:
         raise ValueError("Not supported model class {}".format(model_class))
     return sample
@@ -102,6 +104,8 @@ def test_forward(model_class, encoder_name, encoder_depth, **kwargs):
         smp.UnetPlusPlus,
         smp.MAnet,
         smp.DeepLabV3,
+        smp.DeepLabV3Plus,
+        smp.UPerNet,
     ],
 )
 def test_forward_backward(model_class):
@@ -112,7 +116,18 @@ def test_forward_backward(model_class):
 
 @pytest.mark.parametrize(
     "model_class",
-    [smp.PAN, smp.FPN, smp.PSPNet, smp.Linknet, smp.Unet, smp.UnetPlusPlus, smp.MAnet],
+    [
+        smp.PAN,
+        smp.FPN,
+        smp.PSPNet,
+        smp.Linknet,
+        smp.Unet,
+        smp.UnetPlusPlus,
+        smp.MAnet,
+        smp.DeepLabV3,
+        smp.DeepLabV3Plus,
+        smp.UPerNet,
+    ],
 )
 def test_aux_output(model_class):
     model = model_class(