Bump the torch group in /requirements with 2 updates (#2192)

* Bump the torch group in /requirements with 2 updates

Bumps the torch group in /requirements with 2 updates: [torch](https://github.com/pytorch/pytorch) and [torchvision](https://github.com/pytorch/vision).


Updates `torch` from 2.3.1 to 2.4.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](https://github.com/pytorch/pytorch/compare/v2.3.1...v2.4.0)

Updates `torchvision` from 0.18.1 to 0.19.0
- [Release notes](https://github.com/pytorch/vision/releases)
- [Commits](https://github.com/pytorch/vision/compare/v0.18.1...0.19.0)

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

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

* Fix or silence mypy warnings

---------

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>

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)
+    optimizer = optim.SGD(params, lr=0.0001)  # type: ignore[attr-defined]
 
     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==0.3.3
 shapely==2.0.5
 timm==0.9.2
-torch==2.3.1
+torch==2.4.0
 torchmetrics==1.4.0.post0
-torchvision==0.18.1
+torchvision==0.19.0

torchgeo/datasets/dfc2022.py

@@ -306,7 +306,7 @@ class DFC2022(NonGeoDataset):
         ncols = 2
         image = sample['image'][:3]
         image = image.to(torch.uint8)
-        image = image.permute(1, 2, 0).numpy()
+        image_arr = image.permute(1, 2, 0).numpy()
 
         dem = sample['image'][-1].numpy()
         dem = percentile_normalization(dem, lower=0, upper=100, axis=(0, 1))
@@ -325,7 +325,7 @@ class DFC2022(NonGeoDataset):
 
         fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(10, ncols * 10))
 
-        axs[0].imshow(image)
+        axs[0].imshow(image_arr)
         axs[0].axis('off')
         axs[1].imshow(dem)
         axs[1].axis('off')

torchgeo/datasets/nasa_marine_debris.py

@@ -243,25 +243,25 @@ class NASAMarineDebris(NonGeoDataset):
         image = sample['image']
         if 'boxes' in sample and len(sample['boxes']):
             image = draw_bounding_boxes(image=sample['image'], boxes=sample['boxes'])
-        image = image.permute((1, 2, 0)).numpy()
+        image_arr = image.permute((1, 2, 0)).numpy()
 
         if 'prediction_boxes' in sample and len(sample['prediction_boxes']):
             ncols += 1
             preds = draw_bounding_boxes(
                 image=sample['image'], boxes=sample['prediction_boxes']
             )
-            preds = preds.permute((1, 2, 0)).numpy()
+            preds_arr = preds.permute((1, 2, 0)).numpy()
 
         fig, axs = plt.subplots(ncols=ncols, figsize=(ncols * 10, 10))
         if ncols < 2:
-            axs.imshow(image)
+            axs.imshow(image_arr)
             axs.axis('off')
             if show_titles:
                 axs.set_title('Ground Truth')
         else:
-            axs[0].imshow(image)
+            axs[0].imshow(image_arr)
             axs[0].axis('off')
-            axs[1].imshow(preds)
+            axs[1].imshow(preds_arr)
             axs[1].axis('off')
 
             if show_titles:

torchgeo/trainers/base.py

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

torchgeo/trainers/iobench.py

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

torchgeo/trainers/moco.py

@@ -19,7 +19,7 @@ from lightly.models.modules import MoCoProjectionHead
 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
+from torch.optim import SGD, AdamW, Optimizer  # type: ignore[attr-defined]
 from torch.optim.lr_scheduler import (
     CosineAnnealingLR,
     LinearLR,

torchgeo/trainers/simclr.py

@@ -16,7 +16,7 @@ import torch.nn.functional as F
 from lightly.loss import NTXentLoss
 from lightly.models.modules import SimCLRProjectionHead
 from torch import Tensor
-from torch.optim import Adam
+from torch.optim import Adam  # type: ignore[attr-defined]
 from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR, SequentialLR
 from torchvision.models._api import WeightsEnum