Bump torch from 1.12.1 to 1.13.0 in /requirements (#875)

* Bump torch from 1.12.1 to 1.13.0 in /requirements

Bumps [torch](https://github.com/pytorch/pytorch) from 1.12.1 to 1.13.0.
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/master/RELEASE.md)
- [Commits](pytorch/pytorch@v1.12.1...v1.13.0)

---
updated-dependencies:
- dependency-name: torch
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

* Bump torchvision from 1.13.1 to 1.14.0 in /requirements

* torch.load now has type hints

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

requirements/required.txt

@@ -18,6 +18,6 @@ scikit-learn==1.1.3;python_version>='3.8'
 segmentation-models-pytorch==0.3.0
 shapely==1.8.5.post1
 timm==0.4.12
-torch==1.12.1
+torch==1.13.0
 torchmetrics==0.10.0
-torchvision==0.13.1
+torchvision==0.14.0

setup.cfg

@@ -62,7 +62,7 @@ install_requires =
     # torchmetrics 0.10+ required for binary/multiclass/multilabel classification metrics
     torchmetrics>=0.10,<0.11
     # torchvision 0.10+ required for torchvision.utils.draw_segmentation_masks
-    torchvision>=0.10,<0.14
+    torchvision>=0.10,<0.15
 python_requires = ~= 3.7
 packages = find:
 

tests/models/test_resnet.py

@@ -24,7 +24,7 @@ def load_state_dict_from_file(
     file_name: Optional[str] = None,
 ) -> Any:
     """Mockup of ``torch.hub.load_state_dict_from_url``."""
-    return torch.load(file)  # type: ignore[no-untyped-call]
+    return torch.load(file)
 
 
 @pytest.mark.parametrize(

torchgeo/trainers/utils.py

@@ -31,9 +31,7 @@ def extract_encoder(path: str) -> Tuple[str, "OrderedDict[str, Tensor]"]:
         ValueError: if 'classification_model' or 'encoder' not in
             checkpoint['hyper_parameters']
     """
-    checkpoint = torch.load(  # type: ignore[no-untyped-call]
-        path, map_location=torch.device("cpu")
-    )
+    checkpoint = torch.load(path, map_location=torch.device("cpu"))
 
     if "classification_model" in checkpoint["hyper_parameters"]:
         name = checkpoint["hyper_parameters"]["classification_model"]