models: Switch to kornia AugmentationSequential

pyproject.toml

@@ -40,8 +40,8 @@ dependencies = [
     "einops>=0.3",
     # fiona 1.8.21+ required for Python 3.10 wheels
     "fiona>=1.8.21",
-    # kornia 0.6.9+ required for kornia.augmentation.RandomBrightness
-    "kornia>=0.6.9",
+    # kornia 0.7.2+ required for dict support in AugmentationSequential
+    "kornia>=0.7.2",
     # lightly 1.4.4+ required for MoCo v3 support
     # lightly 1.4.26 is incompatible with the version of timm required by smp
     # https://github.com/microsoft/torchgeo/issues/1824
@@ -228,8 +228,6 @@ filterwarnings = [
     "ignore:Call to deprecated create function:DeprecationWarning:tensorboard.compat.proto",
     # https://github.com/treebeardtech/nbmake/issues/68
     'ignore:The \(fspath. py.path.local\) argument to NotebookFile is deprecated:pytest.PytestDeprecationWarning:nbmake.pytest_plugin',
-    # https://github.com/kornia/kornia/issues/777
-    "ignore:Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0:UserWarning:torch.nn.functional",
     # https://github.com/pytorch/pytorch/pull/24929
     "ignore:Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0:UserWarning:torch.nn.functional",
     # https://github.com/scikit-image/scikit-image/issues/6663
@@ -259,8 +257,6 @@ filterwarnings = [
     "ignore:Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package:UserWarning",
     # https://github.com/Lightning-AI/lightning/issues/18545
     "ignore:LightningCLI's args parameter is intended to run from within Python like if it were from the command line.:UserWarning",
-    # https://github.com/kornia/kornia/pull/1611
-    "ignore:`ColorJitter` is now following Torchvision implementation.:DeprecationWarning:kornia.augmentation._2d.intensity.color_jitter",
     # https://github.com/kornia/kornia/pull/1663
     "ignore:`RandomGaussianBlur` has changed its behavior and now randomly sample sigma for both axes.:DeprecationWarning",
     # https://github.com/pytorch/pytorch/pull/111576

requirements/min-reqs.old

@@ -4,7 +4,7 @@ setuptools==61.0.0
 # install
 einops==0.3.0
 fiona==1.8.21
-kornia==0.6.9
+kornia==0.7.2
 lightly==1.4.4
 lightning[pytorch-extra]==2.0.0
 matplotlib==3.5.0

torchgeo/models/dofa.py

@@ -15,8 +15,6 @@
 from torch import Tensor
 from torchvision.models._api import Weights, WeightsEnum
 
-from ..transforms import AugmentationSequential
-
 __all__ = ["DOFABase16_Weights", "DOFALarge16_Weights"]
 
 
@@ -375,7 +373,7 @@ def forward(self, x: Tensor, wavelengths: list[float]) -> Tensor:
 
 # https://github.com/zhu-xlab/DOFA/blob/master/normalize_dataset.py
 # Normalization is sensor-dependent and is therefore left out
-_dofa_transforms = AugmentationSequential(K.CenterCrop((224, 224)), data_keys=["image"])
+_dofa_transforms = K.AugmentationSequential(K.CenterCrop((224, 224)), data_keys=None)
 
 # https://github.com/pytorch/vision/pull/6883
 # https://github.com/pytorch/vision/pull/7107

torchgeo/models/resnet.py

@@ -11,19 +11,17 @@
 from timm.models import ResNet
 from torchvision.models._api import Weights, WeightsEnum
 
-from ..transforms import AugmentationSequential
-
 __all__ = ["ResNet50_Weights", "ResNet18_Weights"]
 
 
 # https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/linear_BE_moco.py#L167  # noqa: E501
 # https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py#L97  # noqa: E501
 # Normalization either by 10K or channel-wise with band statistics
-_zhu_xlab_transforms = AugmentationSequential(
+_zhu_xlab_transforms = K.AugmentationSequential(
     K.Resize(256),
     K.CenterCrop(224),
     K.Normalize(mean=torch.tensor(0), std=torch.tensor(10000)),
-    data_keys=["image"],
+    data_keys=None,
 )
 
 # Normalization only available for RGB dataset, defined here:
@@ -32,31 +30,31 @@
 _max = torch.tensor([88, 103, 129])
 _mean = torch.tensor([0.485, 0.456, 0.406])
 _std = torch.tensor([0.229, 0.224, 0.225])
-_seco_transforms = AugmentationSequential(
+_seco_transforms = K.AugmentationSequential(
     K.Resize(256),
     K.CenterCrop(224),
     K.Normalize(mean=_min, std=_max - _min),
     K.Normalize(mean=torch.tensor(0), std=1 / torch.tensor(255)),
     K.Normalize(mean=_mean, std=_std),
-    data_keys=["image"],
+    data_keys=None,
 )
 
 # Normalization only available for RGB dataset, defined here:
 # https://github.com/sustainlab-group/geography-aware-ssl/blob/main/moco_fmow/main_moco_geo%2Btp.py#L287  # noqa: E501
 _mean = torch.tensor([0.485, 0.456, 0.406])
 _std = torch.tensor([0.229, 0.224, 0.225])
-_gassl_transforms = AugmentationSequential(
+_gassl_transforms = K.AugmentationSequential(
     K.Resize(224),
     K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)),
     K.Normalize(mean=_mean, std=_std),
-    data_keys=["image"],
+    data_keys=None,
 )
 
 # https://github.com/microsoft/torchgeo/blob/8b53304d42c269f9001cb4e861a126dc4b462606/torchgeo/datamodules/ssl4eo_benchmark.py#L43  # noqa: E501
-_ssl4eo_l_transforms = AugmentationSequential(
+_ssl4eo_l_transforms = K.AugmentationSequential(
     K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)),
     K.CenterCrop((224, 224)),
-    data_keys=["image"],
+    data_keys=None,
 )
 
 # https://github.com/pytorch/vision/pull/6883

torchgeo/models/swin.py

@@ -12,16 +12,14 @@
 from torchvision.models import SwinTransformer
 from torchvision.models._api import Weights, WeightsEnum
 
-from ..transforms import AugmentationSequential
-
 __all__ = ["Swin_V2_B_Weights"]
 
 # https://github.com/allenai/satlas/blob/bcaa968da5395f675d067613e02613a344e81415/satlas/cmd/model/train.py#L42 # noqa: E501
 # Satlas uses the TCI product for Sentinel-2 RGB, which is in the range (0, 255).
 # See details:  https://github.com/allenai/satlas/blob/main/Normalization.md#sentinel-2-images.  # noqa: E501
 # Satlas Sentinel-1 and RGB Sentinel-2 and NAIP imagery is uint8 and is normalized to (0, 1) by dividing by 255. # noqa: E501
-_satlas_transforms = AugmentationSequential(
-    K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)), data_keys=["image"]
+_satlas_transforms = K.AugmentationSequential(
+    K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)), data_keys=None
 )
 
 # Satlas uses the TCI product for Sentinel-2 RGB, which is in the range (0, 255).
@@ -31,17 +29,17 @@
     [255.0, 255.0, 255.0, 8160.0, 8160.0, 8160.0, 8160.0, 8160.0, 8160.0]
 )  # noqa: E501
 _mean = torch.zeros_like(_std)
-_sentinel2_ms_satlas_transforms = AugmentationSequential(
+_sentinel2_ms_satlas_transforms = K.AugmentationSequential(
     K.Normalize(mean=_mean, std=_std),
-    Lambda(lambda x: torch.clamp(x, min=0.0, max=1.0)),
-    data_keys=["image"],
+    K.ImageSequential(Lambda(lambda x: torch.clamp(x, min=0.0, max=1.0))),
+    data_keys=None,
 )
 
 # Satlas Landsat imagery is 16-bit, normalized by clipping some pixel N with (N-4000)/16320 to (0, 1). # noqa: E501
-_landsat_satlas_transforms = AugmentationSequential(
+_landsat_satlas_transforms = K.AugmentationSequential(
     K.Normalize(mean=torch.tensor(4000), std=torch.tensor(16320)),
-    Lambda(lambda x: torch.clamp(x, min=0.0, max=1.0)),
-    data_keys=["image"],
+    K.ImageSequential(Lambda(lambda x: torch.clamp(x, min=0.0, max=1.0))),
+    data_keys=None,
 )
 
 # https://github.com/pytorch/vision/pull/6883

torchgeo/models/vit.py

@@ -11,25 +11,23 @@
 from timm.models.vision_transformer import VisionTransformer
 from torchvision.models._api import Weights, WeightsEnum
 
-from ..transforms import AugmentationSequential
-
 __all__ = ["ViTSmall16_Weights"]
 
 # https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/linear_BE_moco.py#L167 # noqa: E501
 # https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py#L97 # noqa: E501
 # Normalization either by 10K or channel-wise with band statistics
-_zhu_xlab_transforms = AugmentationSequential(
+_zhu_xlab_transforms = K.AugmentationSequential(
     K.Resize(256),
     K.CenterCrop(224),
     K.Normalize(mean=torch.tensor(0), std=torch.tensor(10000)),
-    data_keys=["image"],
+    data_keys=None,
 )
 
 # https://github.com/microsoft/torchgeo/blob/8b53304d42c269f9001cb4e861a126dc4b462606/torchgeo/datamodules/ssl4eo_benchmark.py#L43 # noqa: E501
-_ssl4eo_l_transforms = AugmentationSequential(
+_ssl4eo_l_transforms = K.AugmentationSequential(
     K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)),
     K.CenterCrop((224, 224)),
-    data_keys=["image"],
+    data_keys=None,
 )
 
 # https://github.com/pytorch/vision/pull/6883