Remove AugPipe (#1978)

* Initial commit

* Add issue link

* Remove explicit keepdim

* Fix coverage

* test transforms: Switch to kornia AugmentationSequential

* Switch boxes key to bbox_xyxy

* Switch class -> label

* Switch prediction_boxes -> prediction_bbox_xyxy

* Switch to relative imports

* Switch to kornia AugmentationSequential - for real

* Remove AugmentationSequential

* Exclude AugmentationSequential

Author: ashnair1

.pre-commit-config.yaml

@@ -29,6 +29,7 @@ repos:
           - pillow>=10.4.0
           - pytest>=6.1.2
           - scikit-image>=0.22.0
+          - timm>=1.0.14
           - torch>=2.6
           - torchmetrics>=0.10
           - torchvision>=0.18

docs/api/transforms.rst

@@ -2,3 +2,4 @@ torchgeo.transforms
 ===================
 
 .. automodule:: torchgeo.transforms
+    :exclude-members: AugmentationSequential

tests/datamodules/test_fair1m.py

@@ -35,7 +35,7 @@ def test_plot(self, datamodule: FAIR1MDataModule) -> None:
         batch = next(iter(datamodule.val_dataloader()))
         sample = {
             'image': batch['image'][0],
-            'boxes': batch['boxes'][0],
+            'bbox_xyxy': batch['bbox_xyxy'][0],
             'label': batch['label'][0],
         }
         datamodule.plot(sample)

tests/datasets/test_fair1m.py

@@ -70,9 +70,9 @@ def test_getitem(self, dataset: FAIR1M) -> None:
         assert x['image'].shape[0] == 3
 
         if dataset.split != 'test':
-            assert isinstance(x['boxes'], torch.Tensor)
+            assert isinstance(x['bbox_xyxy'], torch.Tensor)
             assert isinstance(x['label'], torch.Tensor)
-            assert x['boxes'].shape[-2:] == (5, 2)
+            assert x['bbox_xyxy'].shape[-2:] == (5, 2)
             assert x['label'].ndim == 1
 
     def test_len(self, dataset: FAIR1M) -> None:
@@ -124,6 +124,6 @@ def test_plot(self, dataset: FAIR1M) -> None:
         plt.close()
 
         if dataset.split != 'test':
-            x['prediction_boxes'] = x['boxes'].clone()
+            x['prediction_bbox_xyxy'] = x['bbox_xyxy'].clone()
             dataset.plot(x)
             plt.close()

tests/datasets/test_forestdamage.py

@@ -36,7 +36,7 @@ def test_getitem(self, dataset: ForestDamage) -> None:
         assert isinstance(x, dict)
         assert isinstance(x['image'], torch.Tensor)
         assert isinstance(x['label'], torch.Tensor)
-        assert isinstance(x['boxes'], torch.Tensor)
+        assert isinstance(x['bbox_xyxy'], torch.Tensor)
         assert x['image'].shape[0] == 3
         assert x['image'].ndim == 3
 
@@ -67,6 +67,6 @@ def test_plot(self, dataset: ForestDamage) -> None:
 
     def test_plot_prediction(self, dataset: ForestDamage) -> None:
         x = dataset[0].copy()
-        x['prediction_boxes'] = x['boxes'].clone()
+        x['prediction_bbox_xyxy'] = x['bbox_xyxy'].clone()
         dataset.plot(x, suptitle='Prediction')
         plt.close()

tests/datasets/test_idtrees.py

@@ -57,11 +57,11 @@ def test_getitem(self, dataset: IDTReeS) -> None:
 
         if 'label' in x:
             assert isinstance(x['label'], torch.Tensor)
-        if 'boxes' in x:
-            assert isinstance(x['boxes'], torch.Tensor)
-            if x['boxes'].ndim != 1:
-                assert x['boxes'].ndim == 2
-                assert x['boxes'].shape[-1] == 4
+        if 'bbox_xyxy' in x:
+            assert isinstance(x['bbox_xyxy'], torch.Tensor)
+            if x['bbox_xyxy'].ndim != 1:
+                assert x['bbox_xyxy'].ndim == 2
+                assert x['bbox_xyxy'].shape[-1] == 4
 
     def test_len(self, dataset: IDTReeS) -> None:
         assert len(dataset) == 3
@@ -87,8 +87,8 @@ def test_plot(self, dataset: IDTReeS) -> None:
         dataset.plot(x, show_titles=False)
         plt.close()
 
-        if 'boxes' in x:
-            x['prediction_boxes'] = x['boxes']
+        if 'bbox_xyxy' in x:
+            x['prediction_bbox_xyxy'] = x['bbox_xyxy']
             dataset.plot(x, show_titles=True)
             plt.close()
         if 'label' in x:

tests/datasets/test_nasa_marine_debris.py

@@ -28,9 +28,9 @@ def test_getitem(self, dataset: NASAMarineDebris) -> None:
         x = dataset[0]
         assert isinstance(x, dict)
         assert isinstance(x['image'], torch.Tensor)
-        assert isinstance(x['boxes'], torch.Tensor)
+        assert isinstance(x['bbox_xyxy'], torch.Tensor)
         assert x['image'].shape[0] == 3
-        assert x['boxes'].shape[-1] == 4
+        assert x['bbox_xyxy'].shape[-1] == 4
 
     def test_len(self, dataset: NASAMarineDebris) -> None:
         assert len(dataset) == 5
@@ -50,6 +50,6 @@ def test_plot(self, dataset: NASAMarineDebris) -> None:
         plt.close()
         dataset.plot(x, show_titles=False)
         plt.close()
-        x['prediction_boxes'] = x['boxes'].clone()
+        x['prediction_bbox_xyxy'] = x['bbox_xyxy'].clone()
         dataset.plot(x)
         plt.close()

tests/datasets/test_pastis.py

@@ -52,7 +52,7 @@ def test_getitem_instance(self, dataset: PASTIS) -> None:
         assert isinstance(x, dict)
         assert isinstance(x['image'], torch.Tensor)
         assert isinstance(x['mask'], torch.Tensor)
-        assert isinstance(x['boxes'], torch.Tensor)
+        assert isinstance(x['bbox_xyxy'], torch.Tensor)
         assert isinstance(x['label'], torch.Tensor)
 
     def test_len(self, dataset: PASTIS) -> None:

tests/datasets/test_reforestree.py

@@ -36,11 +36,11 @@ def test_getitem(self, dataset: ReforesTree) -> None:
         assert isinstance(x, dict)
         assert isinstance(x['image'], torch.Tensor)
         assert isinstance(x['label'], torch.Tensor)
-        assert isinstance(x['boxes'], torch.Tensor)
+        assert isinstance(x['bbox_xyxy'], torch.Tensor)
         assert isinstance(x['agb'], torch.Tensor)
         assert x['image'].shape[0] == 3
         assert x['image'].ndim == 3
-        assert len(x['boxes']) == 2
+        assert len(x['bbox_xyxy']) == 2
 
     def test_len(self, dataset: ReforesTree) -> None:
         assert len(dataset) == 2
@@ -67,6 +67,6 @@ def test_plot(self, dataset: ReforesTree) -> None:
 
     def test_plot_prediction(self, dataset: ReforesTree) -> None:
         x = dataset[0].copy()
-        x['prediction_boxes'] = x['boxes'].clone()
+        x['prediction_bbox_xyxy'] = x['bbox_xyxy'].clone()
         dataset.plot(x, suptitle='Prediction')
         plt.close()

tests/datasets/test_vhr10.py

@@ -41,10 +41,10 @@ def test_getitem(self, dataset: VHR10) -> None:
             assert isinstance(x, dict)
             assert isinstance(x['image'], torch.Tensor)
             if dataset.split == 'positive':
-                assert isinstance(x['labels'], torch.Tensor)
-                assert isinstance(x['boxes'], torch.Tensor)
-                if 'masks' in x:
-                    assert isinstance(x['masks'], torch.Tensor)
+                assert isinstance(x['label'], torch.Tensor)
+                assert isinstance(x['bbox_xyxy'], torch.Tensor)
+                if 'mask' in x:
+                    assert isinstance(x['mask'], torch.Tensor)
 
     def test_len(self, dataset: VHR10) -> None:
         if dataset.split == 'positive':
@@ -82,10 +82,10 @@ def test_plot(self, dataset: VHR10) -> None:
             scores = [0.7, 0.3, 0.7]
             for i in range(3):
                 x = dataset[i]
-                x['prediction_labels'] = x['labels']
-                x['prediction_boxes'] = x['boxes']
+                x['prediction_labels'] = x['label']
+                x['prediction_bbox_xyxy'] = x['bbox_xyxy']
                 x['prediction_scores'] = torch.Tensor([scores[i]])
-                if 'masks' in x:
-                    x['prediction_masks'] = x['masks']
+                if 'mask' in x:
+                    x['prediction_masks'] = x['mask']
                     dataset.plot(x, show_feats='masks')
                     plt.close()