♻️ Replace deprecated input pretrained with weights (#621)

- Replace `pretrained` with `weights` as this is replaced in torch by the new API.

---------

Co-authored-by: Mark Eastwood <[email protected]>

Author: shaneahmed

tiatoolbox/models/architecture/vanilla.py

@@ -9,7 +9,7 @@
 from tiatoolbox.utils.misc import select_device
 
 
-def _get_architecture(arch_name, pretrained=True, **kwargs):
+def _get_architecture(arch_name, weights="DEFAULT", **kwargs):
     """Get a model.
 
     Model architectures are either already defined within torchvision or
@@ -48,7 +48,7 @@ def _get_architecture(arch_name, pretrained=True, **kwargs):
         raise ValueError(f"Backbone `{arch_name}` is not supported.")
 
     creator = backbone_dict[arch_name]
-    model = creator(pretrained=pretrained, **kwargs)
+    model = creator(weights=weights, **kwargs)
 
     # Unroll all the definition and strip off the final GAP and FCN
     if "resnet" in arch_name or "resnext" in arch_name:

tiatoolbox/tools/registration/wsi_registration.py

@@ -10,6 +10,7 @@
 from skimage import exposure, filters
 from skimage.registration import phase_cross_correlation
 from skimage.util import img_as_float
+from torchvision.models import VGG16_Weights
 
 from tiatoolbox import logger
 from tiatoolbox.tools.patchextraction import PatchExtractor
@@ -306,7 +307,7 @@ def __init__(self):
         output_layers_key: list[str] = ["block3_pool", "block4_pool", "block5_pool"]
         self.features: dict = dict.fromkeys(output_layers_key, None)
         self.pretrained: torch.nn.Sequential = torchvision.models.vgg16(
-            pretrained=True
+            weights=VGG16_Weights.IMAGENET1K_V1
         ).features
         self.f_hooks = []