fix circular import problems (#2059)

Co-authored-by: Adeel Hassan <ahassan@element84.com>
azavea · Feb 12, 2024 · e6510d7 · e6510d7
1 parent cf95e94
commit e6510d7
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Showing 4 changed files with 16 additions and 12 deletions.
diff --git a/rastervision_pytorch_backend/rastervision/pytorch_backend/pytorch_chip_classification.py b/rastervision_pytorch_backend/rastervision/pytorch_backend/pytorch_chip_classification.py
@@ -7,14 +7,15 @@
 from rastervision.pytorch_backend.pytorch_learner_backend import (
     PyTorchLearnerSampleWriter, PyTorchLearnerBackend)
 from rastervision.pytorch_backend.utils import chip_collate_fn_cc
-from rastervision.pytorch_learner import (
-    ClassificationGeoDataConfig, ClassificationSlidingWindowGeoDataset)
+from rastervision.pytorch_learner.dataset import (
+    ClassificationSlidingWindowGeoDataset)
 from rastervision.core.data import ChipClassificationLabels
 
 if TYPE_CHECKING:
     import numpy as np
     from rastervision.core.data import DatasetConfig, Scene
     from rastervision.core.rv_pipeline import ChipOptions, PredictOptions
+    from rastervision.pytorch_learner import ClassificationGeoDataConfig
 
 
 class PyTorchChipClassificationSampleWriter(PyTorchLearnerSampleWriter):
@@ -89,7 +90,8 @@ def predict_scene(self, scene: 'Scene', predict_options: 'PredictOptions'
 
     def _make_chip_data_config(
             self, dataset: 'DatasetConfig',
-            chip_options: 'ChipOptions') -> ClassificationGeoDataConfig:
+            chip_options: 'ChipOptions') -> 'ClassificationGeoDataConfig':
+        from rastervision.pytorch_learner import (ClassificationGeoDataConfig)
         data_config = ClassificationGeoDataConfig(
             scene_dataset=dataset, sampling=chip_options.sampling)
         return data_config
diff --git a/rastervision_pytorch_backend/rastervision/pytorch_backend/pytorch_learner_backend.py b/rastervision_pytorch_backend/rastervision/pytorch_backend/pytorch_learner_backend.py
@@ -12,13 +12,12 @@
 from rastervision.core.data.utils.misc import save_img
 from rastervision.core.data_sample import DataSample
 from rastervision.pytorch_learner.learner import Learner
-from rastervision.pytorch_learner.learner_config import DataConfig
 
 if TYPE_CHECKING:
     from torch.utils.data import Dataset
     from rastervision.core.data import ClassConfig, DatasetConfig, Scene
     from rastervision.core.rv_pipeline import RVPipelineConfig, ChipOptions
-    from rastervision.pytorch_learner.learner_config import LearnerConfig
+    from rastervision.pytorch_learner import DataConfig, LearnerConfig
 
 SPLITS = ['train', 'valid', 'test']
 

diff --git a/rastervision_pytorch_backend/rastervision/pytorch_backend/pytorch_object_detection.py b/rastervision_pytorch_backend/rastervision/pytorch_backend/pytorch_object_detection.py
@@ -12,14 +12,14 @@
 from rastervision.pytorch_backend.utils import chip_collate_fn_od
 from rastervision.pytorch_learner.dataset import (
     ObjectDetectionSlidingWindowGeoDataset)
-from rastervision.pytorch_learner.object_detection_learner_config import (
-    ObjectDetectionGeoDataConfig)
 
 if TYPE_CHECKING:
     from rastervision.core.data import DatasetConfig, Scene
     from rastervision.core.rv_pipeline import (ChipOptions,
                                                ObjectDetectionPredictOptions)
     from rastervision.pytorch_learner.object_detection_utils import BoxList
+    from rastervision.pytorch_learner.object_detection_learner_config import (
+        ObjectDetectionGeoDataConfig)
 
 
 class PyTorchObjectDetectionSampleWriter(PyTorchLearnerSampleWriter):
@@ -154,7 +154,8 @@ def predict_scene(self, scene: 'Scene',
 
     def _make_chip_data_config(
             self, dataset: 'DatasetConfig',
-            chip_options: 'ChipOptions') -> ObjectDetectionGeoDataConfig:
+            chip_options: 'ChipOptions') -> 'ObjectDetectionGeoDataConfig':
+        from rastervision.pytorch_learner import (ObjectDetectionGeoDataConfig)
         data_config = ObjectDetectionGeoDataConfig(
             scene_dataset=dataset, sampling=chip_options.sampling)
         return data_config
diff --git a/rastervision_pytorch_backend/rastervision/pytorch_backend/pytorch_semantic_segmentation.py b/rastervision_pytorch_backend/rastervision/pytorch_backend/pytorch_semantic_segmentation.py
@@ -12,13 +12,13 @@
 from rastervision.pytorch_backend.utils import chip_collate_fn_ss
 from rastervision.pytorch_learner.dataset import (
     SemanticSegmentationSlidingWindowGeoDataset)
-from rastervision.pytorch_learner import SemanticSegmentationGeoDataConfig
 
 if TYPE_CHECKING:
     from rastervision.core.data import (DatasetConfig, Scene,
                                         SemanticSegmentationLabelStore)
     from rastervision.core.rv_pipeline import (
         ChipOptions, SemanticSegmentationPredictOptions)
+    from rastervision.pytorch_learner import SemanticSegmentationGeoDataConfig
 
 
 class PyTorchSemanticSegmentationSampleWriter(PyTorchLearnerSampleWriter):
@@ -118,9 +118,11 @@ def predict_scene(self, scene: 'Scene',
 
         return labels
 
-    def _make_chip_data_config(
-            self, dataset: 'DatasetConfig',
-            chip_options: 'ChipOptions') -> SemanticSegmentationGeoDataConfig:
+    def _make_chip_data_config(self, dataset: 'DatasetConfig',
+                               chip_options: 'ChipOptions'
+                               ) -> 'SemanticSegmentationGeoDataConfig':
+        from rastervision.pytorch_learner import (
+            SemanticSegmentationGeoDataConfig)
         data_config = SemanticSegmentationGeoDataConfig(
             scene_dataset=dataset, sampling=chip_options.sampling)
         return data_config