Merge branch 'main' into update-metrics

README.md

@@ -239,7 +239,7 @@ torchgeo fit --config config.yaml
 # Validate-only
 torchgeo validate --config config.yaml
 # Calculate and report test accuracy
-torchgeo test --config config.yaml ckpt_path=...
+torchgeo test --config config.yaml --ckpt_path=...
 ```
 
 It can also be imported and used in a Python script if you need to extend it to add new features:

docs/conf.py

@@ -144,22 +144,6 @@
    :class: colabbadge
    :alt: Open in Colab
    :target: {{ host }}/github/{{ repo }}/blob/{{ branch }}/{{ urlpath }}
-
-{% set host = "https://pccompute.westeurope.cloudapp.azure.com" %}
-{% set host = host ~ "/compute/hub/user-redirect/git-pull" %}
-{% set repo = "https%3A%2F%2Fgithub.com%2Fmicrosoft%2Ftorchgeo" %}
-{% set urlpath = "tree%2Ftorchgeo%2Fdocs%2F" %}
-{% set urlpath = urlpath ~ env.docname | replace("/", "%2F") ~ ".ipynb" %}
-{% if "dev" in env.config.release %}
-    {% set branch = "main" %}
-{% else %}
-    {% set branch = "releases%2Fv" ~ env.config.version %}
-{% endif %}
-
-.. image:: https://img.shields.io/badge/-Open%20on%20Planetary%20Computer-blue
-   :class: colabbadge
-   :alt: Open on Planetary Computer
-   :target: {{ host }}?repo={{ repo }}&urlpath={{ urlpath }}&branch={{ branch }}
 """
 
 # Disables requirejs in nbsphinx to enable compatibility with the pytorch_sphinx_theme

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 0.7.2+ required for dict support in AugmentationSequential
-    "kornia>=0.7.2",
+    # kornia 0.7.3+ required for instance segmentation support in AugmentationSequential
+    "kornia>=0.7.3",
     # 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

requirements/datasets.txt

@@ -7,5 +7,5 @@ pyvista==0.43.10
 radiant-mlhub==0.4.1
 rarfile==4.2
 scikit-image==0.24.0
-scipy==1.13.1
+scipy==1.14.0
 zipfile-deflate64==0.2.0

requirements/min-reqs.old

@@ -4,7 +4,7 @@ setuptools==61.0.0
 # install
 einops==0.3.0
 fiona==1.8.21
-kornia==0.7.2
+kornia==0.7.3
 lightly==1.4.4
 lightning[pytorch-extra]==2.0.0
 matplotlib==3.5.0

requirements/package.json

@@ -2,6 +2,6 @@
   "name": "torchgeo",
   "private": "true",
   "dependencies": {
-    "prettier": ">=3.0.0"
+    "prettier": ">=3.3.2"
   }
 }

requirements/required.txt

@@ -1,11 +1,11 @@
 # setup
-setuptools==70.1.0
+setuptools==70.2.0
 
 # install
 einops==0.8.0
 fiona==1.9.6
-kornia==0.7.2
-lightly==1.5.7
+kornia==0.7.3
+lightly==1.5.8
 lightning[pytorch-extra]==2.2.5
 matplotlib==3.9.0
 numpy==1.26.4

requirements/style.txt

@@ -1,3 +1,3 @@
 # style
-mypy==1.10.0
-ruff==0.4.10
+mypy==1.10.1
+ruff==0.5.0

tests/transforms/test_color.py

@@ -1,11 +1,12 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
+import kornia.augmentation as K
 import pytest
 import torch
 from torch import Tensor
 
-from torchgeo.transforms import AugmentationSequential, RandomGrayscale
+from torchgeo.transforms import RandomGrayscale
 
 
 @pytest.fixture
@@ -33,12 +34,15 @@ def batch() -> dict[str, Tensor]:
     ],
 )
 def test_random_grayscale_sample(weights: Tensor, sample: dict[str, Tensor]) -> None:
-    aug = AugmentationSequential(RandomGrayscale(weights, p=1), data_keys=['image'])
+    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
-    assert output['image'].sum() == sample['image'].sum()
     for i in range(1, 3):
-        assert torch.allclose(output['image'][0, 0], output['image'][0, i])
+        assert torch.allclose(output['image'][0], output['image'][i])
 
 
 @pytest.mark.parametrize(
@@ -50,9 +54,8 @@ def test_random_grayscale_sample(weights: Tensor, sample: dict[str, Tensor]) ->
     ],
 )
 def test_random_grayscale_batch(weights: Tensor, batch: dict[str, Tensor]) -> None:
-    aug = AugmentationSequential(RandomGrayscale(weights, p=1), data_keys=['image'])
+    aug = K.AugmentationSequential(RandomGrayscale(weights, p=1), data_keys=None)
     output = aug(batch)
     assert output['image'].shape == batch['image'].shape
-    assert output['image'].sum() == batch['image'].sum()
     for i in range(1, 3):
         assert torch.allclose(output['image'][0, 0], output['image'][0, i])

tests/transforms/test_indices.py

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
+import kornia.augmentation as K
 import pytest
 import torch
 from torch import Tensor
@@ -20,7 +21,6 @@
     AppendRBNDVI,
     AppendSWI,
     AppendTriBandNormalizedDifferenceIndex,
-    AugmentationSequential,
 )
 
 
@@ -42,29 +42,27 @@ def batch() -> dict[str, Tensor]:
 
 def test_append_index_sample(sample: dict[str, Tensor]) -> None:
     c, h, w = sample['image'].shape
-    aug = AugmentationSequential(
-        AppendNormalizedDifferenceIndex(index_a=0, index_b=1),
-        data_keys=['image', 'mask'],
+    aug = K.AugmentationSequential(
+        AppendNormalizedDifferenceIndex(index_a=0, index_b=1), data_keys=None
     )
     output = aug(sample)
     assert output['image'].shape == (1, c + 1, h, w)
 
 
 def test_append_index_batch(batch: dict[str, Tensor]) -> None:
     b, c, h, w = batch['image'].shape
-    aug = AugmentationSequential(
-        AppendNormalizedDifferenceIndex(index_a=0, index_b=1),
-        data_keys=['image', 'mask'],
+    aug = K.AugmentationSequential(
+        AppendNormalizedDifferenceIndex(index_a=0, index_b=1), data_keys=None
     )
     output = aug(batch)
     assert output['image'].shape == (b, c + 1, h, w)
 
 
 def test_append_triband_index_batch(batch: dict[str, Tensor]) -> None:
     b, c, h, w = batch['image'].shape
-    aug = AugmentationSequential(
+    aug = K.AugmentationSequential(
         AppendTriBandNormalizedDifferenceIndex(index_a=0, index_b=1, index_c=2),
-        data_keys=['image', 'mask'],
+        data_keys=None,
     )
     output = aug(batch)
     assert output['image'].shape == (b, c + 1, h, w)
@@ -88,7 +86,7 @@ def test_append_normalized_difference_indices(
     sample: dict[str, Tensor], index: AppendNormalizedDifferenceIndex
 ) -> None:
     c, h, w = sample['image'].shape
-    aug = AugmentationSequential(index(0, 1), data_keys=['image', 'mask'])
+    aug = K.AugmentationSequential(index(0, 1), data_keys=None)
     output = aug(sample)
     assert output['image'].shape == (1, c + 1, h, w)
 
@@ -98,6 +96,6 @@ def test_append_tri_band_normalized_difference_indices(
     sample: dict[str, Tensor], index: AppendTriBandNormalizedDifferenceIndex
 ) -> None:
     c, h, w = sample['image'].shape
-    aug = AugmentationSequential(index(0, 1, 2), data_keys=['image', 'mask'])
+    aug = K.AugmentationSequential(index(0, 1, 2), data_keys=None)
     output = aug(sample)
     assert output['image'].shape == (1, c + 1, h, w)

torchgeo/datasets/agrifieldnet.py

@@ -51,20 +51,20 @@ class AgriFieldNet(RasterDataset):
 
     Dataset classes:
 
-    0 - No-Data
-    1 - Wheat
-    2 - Mustard
-    3 - Lentil
-    4 - No Crop/Fallow
-    5 - Green pea
-    6 - Sugarcane
-    8 - Garlic
-    9 - Maize
-    13 - Gram
-    14 - Coriander
-    15 - Potato
-    16 - Berseem
-    36 - Rice
+    * 0. No-Data
+    * 1. Wheat
+    * 2. Mustard
+    * 3. Lentil
+    * 4. No Crop/Fallow
+    * 5. Green pea
+    * 6. Sugarcane
+    * 8. Garlic
+    * 9. Maize
+    * 13. Gram
+    * 14. Coriander
+    * 15. Potato
+    * 16. Berseem
+    * 36. Rice
 
     If you use this dataset in your research, please cite the following dataset: