Bump torchvision from 0.14.1 to 0.15.1 in /requirements (#1177)

* Bump torchvision from 0.14.1 to 0.15.1 in /requirements

Bumps [torchvision](https://github.com/pytorch/vision) from 0.14.1 to 0.15.1.
- [Release notes](https://github.com/pytorch/vision/releases)
- [Commits](https://github.com/pytorch/vision/compare/v0.14.1...v0.15.1)

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

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

* bump pytorch too

* Fix tests

* bump precommit

* blacken

---------

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>

.pre-commit-config.yaml

@@ -34,5 +34,5 @@ repos:
         hooks:
         -   id: mypy
             args: [--strict, --ignore-missing-imports, --show-error-codes]
-            additional_dependencies: [torch>=1.13, torchmetrics>=0.10, lightning>=1.8, pytest>=6, pyvista>=0.20, omegaconf>=2.1, kornia>=0.6, numpy>=1.22.0]
+            additional_dependencies: [torch>=2, torchmetrics>=0.10, lightning>=1.8, pytest>=6, pyvista>=0.20, omegaconf>=2.1, kornia>=0.6, numpy>=1.22.0]
             exclude: (build|data|dist|logo|logs|output)/

evaluate.py

@@ -119,7 +119,7 @@ def run_eval_loop(
                 }
                 for i in range(len(batch["image"]))
             ]
-        with torch.inference_mode():  # type: ignore[no-untyped-call]
+        with torch.inference_mode():
             y_pred = model(x)
         metrics(y_pred, y)
     results = metrics.compute()

requirements/required.txt

@@ -17,6 +17,6 @@ scikit-learn==1.2.2
 segmentation-models-pytorch==0.3.2
 shapely==2.0.1
 timm==0.6.12
-torch==1.13.1
+torch==2.0.0
 torchmetrics==0.11.4
-torchvision==0.14.1
+torchvision==0.15.1

setup.cfg

@@ -55,11 +55,11 @@ install_requires =
     # timm 0.4.12 required by segmentation-models-pytorch
     timm>=0.4.12,<0.7
     # torch 1.12+ required by torchvision
-    torch>=1.12,<2
+    torch>=1.12,<3
     # torchmetrics 0.10+ required for binary/multiclass/multilabel classification metrics
     torchmetrics>=0.10,<0.12
     # torchvision 0.13+ required for torchvision.models._api.WeightsEnum
-    torchvision>=0.13,<0.15
+    torchvision>=0.13,<0.16
 python_requires = >=3.8,<4
 packages = find:
 

tests/models/test_resnet.py

@@ -32,7 +32,10 @@ class TestResNet18:
         path = tmp_path / f"{weights}.pth"
         model = timm.create_model("resnet18", in_chans=weights.meta["in_chans"])
         torch.save(model.state_dict(), path)
-        monkeypatch.setattr(weights, "url", str(path))
+        try:
+            monkeypatch.setattr(weights.value, "url", str(path))
+        except AttributeError:
+            monkeypatch.setattr(weights, "url", str(path))
         monkeypatch.setattr(torchvision.models._api, "load_state_dict_from_url", load)
         return weights
 
@@ -59,7 +62,10 @@ class TestResNet50:
         path = tmp_path / f"{weights}.pth"
         model = timm.create_model("resnet50", in_chans=weights.meta["in_chans"])
         torch.save(model.state_dict(), path)
-        monkeypatch.setattr(weights, "url", str(path))
+        try:
+            monkeypatch.setattr(weights.value, "url", str(path))
+        except AttributeError:
+            monkeypatch.setattr(weights, "url", str(path))
         monkeypatch.setattr(torchvision.models._api, "load_state_dict_from_url", load)
         return weights
 

tests/models/test_vit.py

@@ -34,7 +34,10 @@ class TestViTSmall16:
             weights.meta["model"], in_chans=weights.meta["in_chans"]
         )
         torch.save(model.state_dict(), path)
-        monkeypatch.setattr(weights, "url", str(path))
+        try:
+            monkeypatch.setattr(weights.value, "url", str(path))
+        except AttributeError:
+            monkeypatch.setattr(weights, "url", str(path))
         monkeypatch.setattr(torchvision.models._api, "load_state_dict_from_url", load)
         return weights
 

tests/trainers/test_byol.py

@@ -123,7 +123,10 @@ class TestBYOLTask:
             weights.meta["model"], in_chans=weights.meta["in_chans"]
         )
         torch.save(model.state_dict(), path)
-        monkeypatch.setattr(weights, "url", str(path))
+        try:
+            monkeypatch.setattr(weights.value, "url", str(path))
+        except AttributeError:
+            monkeypatch.setattr(weights, "url", str(path))
         monkeypatch.setattr(torchvision.models._api, "load_state_dict_from_url", load)
         return weights
 

tests/trainers/test_classification.py

@@ -146,7 +146,10 @@ class TestClassificationTask:
             weights.meta["model"], in_chans=weights.meta["in_chans"]
         )
         torch.save(model.state_dict(), path)
-        monkeypatch.setattr(weights, "url", str(path))
+        try:
+            monkeypatch.setattr(weights.value, "url", str(path))
+        except AttributeError:
+            monkeypatch.setattr(weights, "url", str(path))
         monkeypatch.setattr(torchvision.models._api, "load_state_dict_from_url", load)
         return weights
 

tests/trainers/test_regression.py

@@ -109,7 +109,10 @@ class TestRegressionTask:
             weights.meta["model"], in_chans=weights.meta["in_chans"]
         )
         torch.save(model.state_dict(), path)
-        monkeypatch.setattr(weights, "url", str(path))
+        try:
+            monkeypatch.setattr(weights.value, "url", str(path))
+        except AttributeError:
+            monkeypatch.setattr(weights, "url", str(path))
         monkeypatch.setattr(torchvision.models._api, "load_state_dict_from_url", load)
         return weights
 

torchgeo/losses/qr.py

@@ -56,9 +56,7 @@ class RQLoss(Module):
         q = probs
 
         # manually normalize due to https://github.com/pytorch/pytorch/issues/70100
-        z = q / q.norm(  # type: ignore[no-untyped-call]
-            p=1, dim=(0, 2, 3), keepdim=True
-        ).clamp_min(1e-12).expand_as(q)
+        z = q / q.norm(p=1, dim=(0, 2, 3), keepdim=True).clamp_min(1e-12).expand_as(q)
         r = F.normalize(z * target, p=1, dim=1)
 
         loss = torch.einsum("bcxy,bcxy->bxy", r, torch.log(r) - torch.log(q)).mean()