Bump timm from 1.0.14 to 1.0.15 in /requirements

experiments/ssl4eo/flops.py

@@ -17,7 +17,7 @@
 for model in models:
     print(f'Model: {model}')
 
-    m = timm.create_model(model, num_classes=num_classes, in_chans=in_channels)  # type: ignore[attr-defined]
+    m = timm.create_model(model, num_classes=num_classes, in_chans=in_channels)
 
     # Calculate memory requirements of model
     mem_params = sum([p.nelement() * p.element_size() for p in m.parameters()])

requirements/required.txt

@@ -16,7 +16,7 @@ rasterio==1.4.3
 rtree==1.3.0
 segmentation-models-pytorch==0.4.0
 shapely==2.0.7
-timm==1.0.14
+timm==1.0.15
 torch==2.6.0
 torchmetrics==1.6.1
 torchvision==0.21.0

tests/models/test_resnet.py

@@ -34,7 +34,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model('resnet18', in_chans=weights.meta['in_chans'])  # type: ignore[attr-defined]
+        model = timm.create_model('resnet18', in_chans=weights.meta['in_chans'])
         torch.save(model.state_dict(), path)
         try:
             monkeypatch.setattr(weights.value, 'url', str(path))
@@ -78,7 +78,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model('resnet50', in_chans=weights.meta['in_chans'])  # type: ignore[attr-defined]
+        model = timm.create_model('resnet50', in_chans=weights.meta['in_chans'])
         torch.save(model.state_dict(), path)
         try:
             monkeypatch.setattr(weights.value, 'url', str(path))
@@ -122,7 +122,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model('resnet152', in_chans=weights.meta['in_chans'])  # type: ignore[attr-defined]
+        model = timm.create_model('resnet152', in_chans=weights.meta['in_chans'])
         torch.save(model.state_dict(), path)
         try:
             monkeypatch.setattr(weights.value, 'url', str(path))

tests/models/test_vit.py

@@ -27,7 +27,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model(  # type: ignore[attr-defined]
+        model = timm.create_model(
             weights.meta['model'], in_chans=weights.meta['in_chans']
         )
         torch.save(model.state_dict(), path)

tests/trainers/test_byol.py

@@ -89,7 +89,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model(  # type: ignore[attr-defined]
+        model = timm.create_model(
             weights.meta['model'], in_chans=weights.meta['in_chans']
         )
         torch.save(model.state_dict(), path)

tests/trainers/test_classification.py

@@ -126,7 +126,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model(  # type: ignore[attr-defined]
+        model = timm.create_model(
             weights.meta['model'], in_chans=weights.meta['in_chans']
         )
         torch.save(model.state_dict(), path)

tests/trainers/test_moco.py

@@ -91,7 +91,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model(  # type: ignore[attr-defined]
+        model = timm.create_model(
             weights.meta['model'], in_chans=weights.meta['in_chans']
         )
         torch.save(model.state_dict(), path)

tests/trainers/test_regression.py

@@ -115,7 +115,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model(  # type: ignore[attr-defined]
+        model = timm.create_model(
             weights.meta['model'], in_chans=weights.meta['in_chans']
         )
         torch.save(model.state_dict(), path)
@@ -273,7 +273,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model(  # type: ignore[attr-defined]
+        model = timm.create_model(
             weights.meta['model'], in_chans=weights.meta['in_chans']
         )
         torch.save(model.state_dict(), path)

tests/trainers/test_segmentation.py

@@ -139,7 +139,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model(  # type: ignore[attr-defined]
+        model = timm.create_model(
             weights.meta['model'], in_chans=weights.meta['in_chans']
         )
         torch.save(model.state_dict(), path)

tests/trainers/test_simclr.py

@@ -89,7 +89,7 @@ def mocked_weights(
         load_state_dict_from_url: None,
     ) -> WeightsEnum:
         path = tmp_path / f'{weights}.pth'
-        model = timm.create_model(  # type: ignore[attr-defined]
+        model = timm.create_model(
             weights.meta['model'], in_chans=weights.meta['in_chans']
         )
         torch.save(model.state_dict(), path)

tests/trainers/test_utils.py

@@ -34,7 +34,7 @@ def test_extract_backbone_unsupported_model(tmp_path: Path) -> None:
 
 
 def test_get_input_layer_name_and_module() -> None:
-    key, module = _get_input_layer_name_and_module(timm.create_model('resnet18'))  # type: ignore[attr-defined]
+    key, module = _get_input_layer_name_and_module(timm.create_model('resnet18'))
     assert key == 'conv1'
     assert isinstance(module, nn.Conv2d)
     assert module.in_channels == 3

torchgeo/models/resnet.py

@@ -768,7 +768,7 @@ def resnet18(
     if weights:
         kwargs['in_chans'] = weights.meta['in_chans']
 
-    model: ResNet = timm.create_model('resnet18', *args, **kwargs)  # type: ignore[attr-defined]
+    model: ResNet = timm.create_model('resnet18', *args, **kwargs)
 
     if weights:
         missing_keys, unexpected_keys = model.load_state_dict(
@@ -803,7 +803,7 @@ def resnet50(
     if weights:
         kwargs['in_chans'] = weights.meta['in_chans']
 
-    model: ResNet = timm.create_model('resnet50', *args, **kwargs)  # type: ignore[attr-defined]
+    model: ResNet = timm.create_model('resnet50', *args, **kwargs)
 
     if weights:
         missing_keys, unexpected_keys = model.load_state_dict(
@@ -837,7 +837,7 @@ def resnet152(
     if weights:
         kwargs['in_chans'] = weights.meta['in_chans']
 
-    model: ResNet = timm.create_model('resnet152', *args, **kwargs)  # type: ignore[attr-defined]
+    model: ResNet = timm.create_model('resnet152', *args, **kwargs)
 
     if weights:
         missing_keys, unexpected_keys = model.load_state_dict(

torchgeo/models/vit.py

@@ -243,7 +243,7 @@ def vit_small_patch16_224(
     if weights:
         kwargs['in_chans'] = weights.meta['in_chans']
 
-    model: VisionTransformer = timm.create_model(  # type: ignore[attr-defined]
+    model: VisionTransformer = timm.create_model(
         'vit_small_patch16_224', *args, **kwargs
     )
 

torchgeo/trainers/byol.py

@@ -332,7 +332,7 @@ def configure_models(self) -> None:
         in_channels: int = self.hparams['in_channels']
 
         # Create backbone
-        backbone = timm.create_model(  # type: ignore[attr-defined]
+        backbone = timm.create_model(
             self.hparams['model'], in_chans=in_channels, pretrained=weights is True
         )
 

torchgeo/trainers/classification.py

@@ -80,7 +80,7 @@ def configure_models(self) -> None:
         weights = self.weights
 
         # Create model
-        self.model = timm.create_model(  # type: ignore[attr-defined]
+        self.model = timm.create_model(
             self.hparams['model'],
             num_classes=self.hparams['num_classes'],
             in_chans=self.hparams['in_channels'],

torchgeo/trainers/moco.py

@@ -238,10 +238,10 @@ def configure_models(self) -> None:
         output_dim: int = self.hparams['output_dim']
 
         # Create backbone
-        self.backbone = timm.create_model(  # type: ignore[attr-defined]
+        self.backbone = timm.create_model(
             model, in_chans=in_channels, num_classes=0, pretrained=weights is True
         )
-        self.backbone_momentum = timm.create_model(  # type: ignore[attr-defined]
+        self.backbone_momentum = timm.create_model(
             model, in_chans=in_channels, num_classes=0, pretrained=weights is True
         )
         deactivate_requires_grad(self.backbone_momentum)

torchgeo/trainers/regression.py

@@ -83,7 +83,7 @@ def configure_models(self) -> None:
         """Initialize the model."""
         # Create model
         weights = self.weights
-        self.model = timm.create_model(  # type: ignore[attr-defined]
+        self.model = timm.create_model(
             self.hparams['model'],
             num_classes=self.hparams['num_outputs'],
             in_chans=self.hparams['in_channels'],

torchgeo/trainers/simclr.py

@@ -153,7 +153,7 @@ def configure_models(self) -> None:
         weights = self.weights
 
         # Create backbone
-        self.backbone = timm.create_model(  # type: ignore[attr-defined]
+        self.backbone = timm.create_model(
             self.hparams['model'],
             in_chans=self.hparams['in_channels'],
             num_classes=0,