Bump the torch group in /requirements with 2 updates

experiments/torchgeo/benchmark.py

@@ -217,7 +217,7 @@ def main(args: argparse.Namespace) -> None:
 
     criterion = nn.CrossEntropyLoss()
     params = model.parameters()
-    optimizer = optim.SGD(params, lr=0.0001)  # type: ignore[attr-defined]
+    optimizer = optim.SGD(params, lr=0.0001)
 
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu', args.device)
     model = model.to(device)

requirements/required.txt

@@ -17,6 +17,6 @@ rtree==1.3.0
 segmentation-models-pytorch==0.3.4
 shapely==2.0.6
 timm==0.9.7
-torch==2.4.1
+torch==2.5.0
 torchmetrics==1.4.3
-torchvision==0.19.1
+torchvision==0.20.0

torchgeo/trainers/base.py

@@ -9,7 +9,7 @@
 
 import lightning
 from lightning.pytorch import LightningModule
-from torch.optim import AdamW  # type: ignore[attr-defined]
+from torch.optim import AdamW
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 
 

torchgeo/trainers/iobench.py

@@ -8,7 +8,7 @@
 import lightning
 import torch
 from torch import Tensor
-from torch.optim import SGD  # type: ignore[attr-defined]
+from torch.optim import SGD
 
 from .base import BaseTask
 

torchgeo/trainers/moco.py

@@ -19,7 +19,7 @@
 from lightly.models.utils import deactivate_requires_grad, update_momentum
 from lightly.utils.scheduler import cosine_schedule
 from torch import Tensor
-from torch.optim import SGD, AdamW, Optimizer  # type: ignore[attr-defined]
+from torch.optim import SGD, AdamW, Optimizer
 from torch.optim.lr_scheduler import (
     CosineAnnealingLR,
     LinearLR,