datamodules: Switch to kornia AugmentationSequential

.pre-commit-config.yaml

@@ -13,7 +13,7 @@ repos:
           - python
           - jupyter
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.9.0
+    rev: v1.13.0
     hooks:
       - id: mypy
         args:

docs/tutorials/transforms.ipynb

@@ -93,7 +93,7 @@
     "from torch.utils.data import DataLoader\n",
     "\n",
     "from torchgeo.datasets import EuroSAT100\n",
-    "from torchgeo.transforms import AugmentationSequential, indices"
+    "from torchgeo.transforms import indices"
    ]
   },
   {
@@ -515,15 +515,15 @@
    },
    "outputs": [],
    "source": [
-    "transforms = AugmentationSequential(\n",
+    "transforms = K.AugmentationSequential(\n",
     "    MinMaxNormalize(mins, maxs),\n",
     "    indices.AppendNDBI(index_swir=11, index_nir=7),\n",
     "    indices.AppendNDSI(index_green=3, index_swir=11),\n",
     "    indices.AppendNDVI(index_nir=7, index_red=3),\n",
     "    indices.AppendNDWI(index_green=2, index_nir=7),\n",
     "    K.RandomHorizontalFlip(p=0.5),\n",
     "    K.RandomVerticalFlip(p=0.5),\n",
-    "    data_keys=['image'],\n",
+    "    data_keys=None,\n",
     ")\n",
     "\n",
     "batch = next(dataloader)\n",
@@ -569,7 +569,7 @@
    "source": [
     "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
     "\n",
-    "transforms = AugmentationSequential(\n",
+    "transforms = K.AugmentationSequential(\n",
     "    MinMaxNormalize(mins, maxs),\n",
     "    indices.AppendNDBI(index_swir=11, index_nir=7),\n",
     "    indices.AppendNDSI(index_green=3, index_swir=11),\n",
@@ -580,10 +580,10 @@
     "    K.RandomAffine(degrees=(0, 90), p=0.25),\n",
     "    K.RandomGaussianBlur(kernel_size=(3, 3), sigma=(0.1, 2.0), p=0.25),\n",
     "    K.RandomResizedCrop(size=(512, 512), scale=(0.8, 1.0), p=0.25),\n",
-    "    data_keys=['image'],\n",
+    "    data_keys=None,\n",
     ")\n",
     "\n",
-    "transforms_gpu = AugmentationSequential(\n",
+    "transforms_gpu = K.AugmentationSequential(\n",
     "    MinMaxNormalize(mins.to(device), maxs.to(device)),\n",
     "    indices.AppendNDBI(index_swir=11, index_nir=7),\n",
     "    indices.AppendNDSI(index_green=3, index_swir=11),\n",
@@ -594,7 +594,7 @@
     "    K.RandomAffine(degrees=(0, 90), p=0.25),\n",
     "    K.RandomGaussianBlur(kernel_size=(3, 3), sigma=(0.1, 2.0), p=0.25),\n",
     "    K.RandomResizedCrop(size=(512, 512), scale=(0.8, 1.0), p=0.25),\n",
-    "    data_keys=['image'],\n",
+    "    data_keys=None,\n",
     ").to(device)\n",
     "\n",
     "\n",
@@ -664,7 +664,7 @@
    },
    "outputs": [],
    "source": [
-    "transforms = AugmentationSequential(MinMaxNormalize(mins, maxs), data_keys=['image'])\n",
+    "transforms = K.AugmentationSequential(MinMaxNormalize(mins, maxs), data_keys=None)\n",
     "dataset = EuroSAT100(root, transforms=transforms)"
    ]
   },

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 0.7.3+ required for instance segmentation support in AugmentationSequential
-    "kornia>=0.7.3",
+    # kornia 0.7.4+ required for AugmentationSequential support for unknown keys
+    "kornia>=0.7.4",
     # lightly 1.4.5+ required for LARS optimizer
     # lightly 1.4.26 is incompatible with the version of timm required by smp
     # https://github.com/microsoft/torchgeo/issues/1824

requirements/min-reqs.old

@@ -4,7 +4,7 @@ setuptools==61.0.0
 # install
 einops==0.3.0
 fiona==1.8.21
-kornia==0.7.3
+kornia==0.7.4
 lightly==1.4.5
 lightning[pytorch-extra]==2.0.0
 matplotlib==3.5.0

tests/datamodules/test_geo.py

@@ -31,7 +31,7 @@ def __init__(
         self.res = 1
 
     def __getitem__(self, query: BoundingBox) -> dict[str, Any]:
-        image = torch.arange(3 * 2 * 2).view(3, 2, 2)
+        image = torch.arange(3 * 2 * 2, dtype=torch.float).view(3, 2, 2)
         return {'image': image, 'crs': CRS.from_epsg(4326), 'bounds': query}
 
     def plot(self, *args: Any, **kwargs: Any) -> Figure:
@@ -68,7 +68,7 @@ def __init__(
         self.length = length
 
     def __getitem__(self, index: int) -> dict[str, Tensor]:
-        return {'image': torch.arange(3 * 2 * 2).view(3, 2, 2)}
+        return {'image': torch.arange(3 * 2 * 2, dtype=torch.float).view(3, 2, 2)}
 
     def __len__(self) -> int:
         return self.length

tests/datasets/test_chesapeake.py

@@ -212,6 +212,9 @@ def test_plot(self, dataset: ChesapeakeCVPR) -> None:
         plt.close()
         dataset.plot(x, show_titles=False)
         plt.close()
-        x['prediction'] = x['mask'][:, :, 0].clone().unsqueeze(2)
+        if x['mask'].ndim == 2:
+            x['prediction'] = x['mask'].clone()
+        else:
+            x['prediction'] = x['mask'][0, :, :].clone()
         dataset.plot(x)
         plt.close()

tests/transforms/test_color.py

@@ -37,8 +37,6 @@ def test_random_grayscale_sample(weights: Tensor, sample: dict[str, Tensor]) ->
     aug = K.AugmentationSequential(
         RandomGrayscale(weights, p=1), keepdim=True, data_keys=None
     )
-    # https://github.com/kornia/kornia/issues/2848
-    aug.keepdim = True
     output = aug(sample)
     assert output['image'].shape == sample['image'].shape
     for i in range(1, 3):

torchgeo/datamodules/agrifieldnet.py

@@ -12,7 +12,6 @@
 from ..datasets import AgriFieldNet, random_bbox_assignment
 from ..samplers import GridGeoSampler, RandomBatchGeoSampler
 from ..samplers.utils import _to_tuple
-from ..transforms import AugmentationSequential
 from .geo import GeoDataModule
 
 
@@ -49,12 +48,13 @@ def __init__(
             **kwargs,
         )
 
-        self.train_aug = AugmentationSequential(
+        self.train_aug = K.AugmentationSequential(
             K.Normalize(mean=self.mean, std=self.std),
             K.RandomResizedCrop(_to_tuple(self.patch_size), scale=(0.6, 1.0)),
             K.RandomVerticalFlip(p=0.5),
             K.RandomHorizontalFlip(p=0.5),
-            data_keys=['image', 'mask'],
+            data_keys=None,
+            keepdim=True,
             extra_args={
                 DataKey.MASK: {'resample': Resample.NEAREST, 'align_corners': None}
             },

torchgeo/datamodules/caffe.py

@@ -9,7 +9,6 @@
 import torch
 
 from ..datasets import CaFFe
-from ..transforms import AugmentationSequential
 from .geo import NonGeoDataModule
 
 
@@ -40,16 +39,18 @@ def __init__(
 
         self.size = size
 
-        self.train_aug = AugmentationSequential(
+        self.train_aug = K.AugmentationSequential(
             K.Normalize(mean=self.mean, std=self.std),
             K.Resize(size),
             K.RandomHorizontalFlip(p=0.5),
             K.RandomVerticalFlip(p=0.5),
-            data_keys=['image', 'mask'],
+            data_keys=None,
+            keepdim=True,
         )
 
-        self.aug = AugmentationSequential(
+        self.aug = K.AugmentationSequential(
             K.Normalize(mean=self.mean, std=self.std),
             K.Resize(size),
-            data_keys=['image', 'mask'],
+            data_keys=None,
+            keepdim=True,
         )

torchgeo/datamodules/chesapeake.py

@@ -6,49 +6,14 @@
 from typing import Any
 
 import kornia.augmentation as K
-import torch.nn as nn
 import torch.nn.functional as F
-from einops import rearrange
 from torch import Tensor
 
 from ..datasets import ChesapeakeCVPR
 from ..samplers import GridGeoSampler, RandomBatchGeoSampler
-from ..transforms import AugmentationSequential
 from .geo import GeoDataModule
 
 
-class _Transform(nn.Module):
-    """Version of AugmentationSequential designed for samples, not batches."""
-
-    def __init__(self, aug: nn.Module) -> None:
-        """Initialize a new _Transform instance.
-
-        Args:
-            aug: Augmentation to apply.
-        """
-        super().__init__()
-        self.aug = aug
-
-    def forward(self, sample: dict[str, Any]) -> dict[str, Any]:
-        """Apply the augmentation.
-
-        Args:
-            sample: Input sample.
-
-        Returns:
-            Augmented sample.
-        """
-        for key in ['image', 'mask']:
-            dtype = sample[key].dtype
-            # All inputs must be float
-            sample[key] = sample[key].float()
-            sample[key] = self.aug(sample[key])
-            sample[key] = sample[key].to(dtype)
-            # Kornia adds batch dimension
-            sample[key] = rearrange(sample[key], '() c h w -> c h w')
-        return sample
-
-
 class ChesapeakeCVPRDataModule(GeoDataModule):
     """LightningDataModule implementation for the Chesapeake CVPR Land Cover dataset.
 
@@ -94,7 +59,9 @@ def __init__(
         # This is a rough estimate of how large of a patch we will need to sample in
         # EPSG:3857 in order to guarantee a large enough patch in the local CRS.
         self.original_patch_size = patch_size * 3
-        kwargs['transforms'] = _Transform(K.CenterCrop(patch_size))
+        kwargs['transforms'] = K.AugmentationSequential(
+            K.CenterCrop(patch_size), data_keys=None, keepdim=True
+        )
 
         super().__init__(
             ChesapeakeCVPR, batch_size, patch_size, length, num_workers, **kwargs
@@ -122,8 +89,8 @@ def __init__(
         else:
             self.layers = ['naip-new', 'lc']
 
-        self.aug = AugmentationSequential(
-            K.Normalize(mean=self.mean, std=self.std), data_keys=['image', 'mask']
+        self.aug = K.AugmentationSequential(
+            K.Normalize(mean=self.mean, std=self.std), data_keys=None, keepdim=True
         )
 
     def setup(self, stage: str) -> None:

torchgeo/datamodules/deepglobelandcover.py

@@ -11,7 +11,6 @@
 
 from ..datasets import DeepGlobeLandCover
 from ..samplers.utils import _to_tuple
-from ..transforms import AugmentationSequential
 from ..transforms.transforms import _RandomNCrop
 from .geo import NonGeoDataModule
 
@@ -46,10 +45,11 @@ def __init__(
         self.patch_size = _to_tuple(patch_size)
         self.val_split_pct = val_split_pct
 
-        self.aug = AugmentationSequential(
+        self.aug = K.AugmentationSequential(
             K.Normalize(mean=self.mean, std=self.std),
             _RandomNCrop(self.patch_size, batch_size),
-            data_keys=['image', 'mask'],
+            data_keys=None,
+            keepdim=True,
         )
 
     def setup(self, stage: str) -> None:

torchgeo/datamodules/fire_risk.py

@@ -8,7 +8,6 @@
 import kornia.augmentation as K
 
 from ..datasets import FireRisk
-from ..transforms import AugmentationSequential
 from .geo import NonGeoDataModule
 
 
@@ -30,15 +29,16 @@ def __init__(
                 :class:`~torchgeo.datasets.FireRisk`.
         """
         super().__init__(FireRisk, batch_size, num_workers, **kwargs)
-        self.train_aug = AugmentationSequential(
+        self.train_aug = K.AugmentationSequential(
             K.Normalize(mean=self.mean, std=self.std),
             K.RandomRotation(p=0.5, degrees=90),
             K.RandomHorizontalFlip(p=0.5),
             K.RandomVerticalFlip(p=0.5),
             K.RandomSharpness(p=0.5),
             K.RandomErasing(p=0.1),
             K.ColorJitter(p=0.5, brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
-            data_keys=['image'],
+            data_keys=None,
+            keepdim=True,
         )
 
     def setup(self, stage: str) -> None:

torchgeo/datamodules/ftw.py

@@ -9,7 +9,6 @@
 import torch
 
 from ..datasets import FieldsOfTheWorld
-from ..transforms import AugmentationSequential
 from .geo import NonGeoDataModule
 
 
@@ -55,16 +54,17 @@ def __init__(
         self.val_countries = val_countries
         self.test_countries = test_countries
 
-        self.train_aug = AugmentationSequential(
+        self.train_aug = K.AugmentationSequential(
             K.Normalize(mean=self.mean, std=self.std),
             K.RandomRotation(p=0.5, degrees=90),
             K.RandomHorizontalFlip(p=0.5),
             K.RandomVerticalFlip(p=0.5),
             K.RandomSharpness(p=0.5),
-            data_keys=['image', 'mask'],
+            data_keys=None,
+            keepdim=True,
         )
-        self.aug = AugmentationSequential(
-            K.Normalize(mean=self.mean, std=self.std), data_keys=['image', 'mask']
+        self.aug = K.AugmentationSequential(
+            K.Normalize(mean=self.mean, std=self.std), data_keys=None, keepdim=True
         )
 
     def setup(self, stage: str) -> None:

torchgeo/datamodules/geo.py

@@ -20,7 +20,6 @@
     GridGeoSampler,
     RandomBatchGeoSampler,
 )
-from ..transforms import AugmentationSequential
 from .utils import MisconfigurationException
 
 
@@ -70,9 +69,10 @@ def __init__(
 
         # Data augmentation
         Transform = Callable[[dict[str, Tensor]], dict[str, Tensor]]
-        self.aug: Transform = AugmentationSequential(
-            K.Normalize(mean=self.mean, std=self.std), data_keys=['image']
+        self.aug: Transform = K.AugmentationSequential(
+            K.Normalize(mean=self.mean, std=self.std), data_keys=None, keepdim=True
         )
+
         self.train_aug: Transform | None = None
         self.val_aug: Transform | None = None
         self.test_aug: Transform | None = None

torchgeo/datamodules/geonrw.py

@@ -10,7 +10,6 @@
 from torch.utils.data import Subset
 
 from ..datasets import GeoNRW
-from ..transforms import AugmentationSequential
 from .geo import NonGeoDataModule
 from .utils import group_shuffle_split
 
@@ -38,14 +37,17 @@ def __init__(
         """
         super().__init__(GeoNRW, batch_size, num_workers, **kwargs)
 
-        self.train_aug = AugmentationSequential(
+        self.train_aug = K.AugmentationSequential(
             K.Resize(size),
             K.RandomHorizontalFlip(p=0.5),
             K.RandomVerticalFlip(p=0.5),
-            data_keys=['image', 'mask'],
+            data_keys=None,
+            keepdim=True,
         )
 
-        self.aug = AugmentationSequential(K.Resize(size), data_keys=['image', 'mask'])
+        self.aug = K.AugmentationSequential(
+            K.Resize(size), data_keys=None, keepdim=True
+        )
 
         self.size = size