🐛 Fix in test_arch_mapde and test_arch_sccnn (#911)

- If cuda is available model should be moved to cuda otherwise tests will fail as test data is moved to cuda.

tests/models/test_arch_mapde.py

@@ -14,14 +14,14 @@
 ON_GPU = toolbox_env.has_gpu()
 
 
-def _load_mapde(name: str) -> torch.nn.Module:
+def _load_mapde(name: str) -> MapDe:
     """Loads MapDe model with specified weights."""
     model = MapDe()
     weights_path = fetch_pretrained_weights(name)
     map_location = select_device(on_gpu=ON_GPU)
     pretrained = torch.load(weights_path, map_location=map_location)
     model.load_state_dict(pretrained)
-
+    model.to(map_location)
     return model
 
 
@@ -45,7 +45,6 @@ def test_functionality(remote_sample: Callable) -> None:
     model = _load_mapde(name="mapde-conic")
     patch = model.preproc(patch)
     batch = torch.from_numpy(patch)[None]
-    model = model.to()
     output = model.infer_batch(model, batch, device=select_device(on_gpu=ON_GPU))
     output = model.postproc(output[0])
     assert np.all(output[0:2] == [[19, 171], [53, 89]])

tests/models/test_arch_sccnn.py

@@ -12,14 +12,14 @@
 from tiatoolbox.wsicore.wsireader import WSIReader
 
 
-def _load_sccnn(name: str) -> torch.nn.Module:
+def _load_sccnn(name: str) -> SCCNN:
     """Loads SCCNN model with specified weights."""
     model = SCCNN()
     weights_path = fetch_pretrained_weights(name)
     map_location = select_device(on_gpu=env_detection.has_gpu())
     pretrained = torch.load(weights_path, map_location=map_location)
     model.load_state_dict(pretrained)
-
+    model.to(map_location)
     return model
 
 
@@ -48,7 +48,6 @@ def test_functionality(remote_sample: Callable) -> None:
     )
     output = model.postproc(output[0])
     assert np.all(output == [[8, 7]])
-
     model = _load_sccnn(name="sccnn-conic")
     output = model.infer_batch(
         model,