Bump lightning[pytorch-extra] from 2.0.9.post0 to 2.1.0 in /requireme…

…nts (#1662)

* Bump lightning[pytorch-extra] from 2.0.9.post0 to 2.1.0 in /requirements

Bumps [lightning[pytorch-extra]](https://github.com/Lightning-AI/lightning) from 2.0.9.post0 to 2.1.0.
- [Release notes](https://github.com/Lightning-AI/lightning/releases)
- [Commits](Lightning-AI/pytorch-lightning@2.0.9.post0...2.1.0)

---
updated-dependencies:
- dependency-name: lightning[pytorch-extra]
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <[email protected]>

* Remove type ignores

* Capture more warnings

---------

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Caleb Robinson <[email protected]>
Co-authored-by: Adam J. Stewart <[email protected]>

pyproject.toml

@@ -246,7 +246,8 @@ filterwarnings = [
 
     # Expected warnings
     # Lightning warns us about using num_workers=0, but it's faster on macOS
-    "ignore:The dataloader, .*, does not have many workers which may be a bottleneck:UserWarning",
+    "ignore:The .*dataloader.* does not have many workers which may be a bottleneck:UserWarning:lightning",
+    "ignore:The .*dataloader.* does not have many workers which may be a bottleneck:lightning.fabric.utilities.warnings.PossibleUserWarning:lightning",
     # Lightning warns us about using the CPU when GPU/MPS is available
     "ignore:GPU available but not used.:UserWarning",
     "ignore:MPS available but not used.:UserWarning",

torchgeo/trainers/classification.py

@@ -164,7 +164,7 @@ def training_step(
         loss: Tensor = self.criterion(y_hat, y)
         self.log("train_loss", loss)
         self.train_metrics(y_hat_hard, y)
-        self.log_dict(self.train_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.train_metrics)
 
         return loss
 
@@ -185,7 +185,7 @@ def validation_step(
         loss = self.criterion(y_hat, y)
         self.log("val_loss", loss)
         self.val_metrics(y_hat_hard, y)
-        self.log_dict(self.val_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.val_metrics)
 
         if (
             batch_idx < 10
@@ -226,7 +226,7 @@ def test_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None
         loss = self.criterion(y_hat, y)
         self.log("test_loss", loss)
         self.test_metrics(y_hat_hard, y)
-        self.log_dict(self.test_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.test_metrics)
 
     def predict_step(
         self, batch: Any, batch_idx: int, dataloader_idx: int = 0
@@ -288,7 +288,7 @@ def training_step(
         loss: Tensor = self.criterion(y_hat, y.to(torch.float))
         self.log("train_loss", loss)
         self.train_metrics(y_hat_hard, y)
-        self.log_dict(self.train_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.train_metrics)
 
         return loss
 
@@ -309,7 +309,7 @@ def validation_step(
         loss = self.criterion(y_hat, y.to(torch.float))
         self.log("val_loss", loss)
         self.val_metrics(y_hat_hard, y)
-        self.log_dict(self.val_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.val_metrics)
 
         if (
             batch_idx < 10
@@ -349,7 +349,7 @@ def test_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None
         loss = self.criterion(y_hat, y.to(torch.float))
         self.log("test_loss", loss)
         self.test_metrics(y_hat_hard, y)
-        self.log_dict(self.test_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.test_metrics)
 
     def predict_step(
         self, batch: Any, batch_idx: int, dataloader_idx: int = 0

torchgeo/trainers/regression.py

@@ -157,7 +157,7 @@ def training_step(
         loss: Tensor = self.criterion(y_hat, y)
         self.log("train_loss", loss)
         self.train_metrics(y_hat, y)
-        self.log_dict(self.train_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.train_metrics)
 
         return loss
 
@@ -180,7 +180,7 @@ def validation_step(
         loss = self.criterion(y_hat, y)
         self.log("val_loss", loss)
         self.val_metrics(y_hat, y)
-        self.log_dict(self.val_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.val_metrics)
 
         if (
             batch_idx < 10
@@ -226,7 +226,7 @@ def test_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None
         loss = self.criterion(y_hat, y)
         self.log("test_loss", loss)
         self.test_metrics(y_hat, y)
-        self.log_dict(self.test_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.test_metrics)
 
     def predict_step(
         self, batch: Any, batch_idx: int, dataloader_idx: int = 0

torchgeo/trainers/segmentation.py

@@ -220,7 +220,7 @@ def training_step(
         loss: Tensor = self.criterion(y_hat, y)
         self.log("train_loss", loss)
         self.train_metrics(y_hat_hard, y)
-        self.log_dict(self.train_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.train_metrics)
         return loss
 
     def validation_step(
@@ -240,7 +240,7 @@ def validation_step(
         loss = self.criterion(y_hat, y)
         self.log("val_loss", loss)
         self.val_metrics(y_hat_hard, y)
-        self.log_dict(self.val_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.val_metrics)
 
         if (
             batch_idx < 10
@@ -281,7 +281,7 @@ def test_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None
         loss = self.criterion(y_hat, y)
         self.log("test_loss", loss)
         self.test_metrics(y_hat_hard, y)
-        self.log_dict(self.test_metrics)  # type: ignore[arg-type]
+        self.log_dict(self.test_metrics)
 
     def predict_step(
         self, batch: Any, batch_idx: int, dataloader_idx: int = 0