Switch prediction_boxes -> prediction_bbox_xyxy

tests/datasets/test_fair1m.py

@@ -124,6 +124,6 @@ def test_plot(self, dataset: FAIR1M) -> None:
         plt.close()
 
         if dataset.split != 'test':
-            x['prediction_boxes'] = x['bbox_xyxy'].clone()
+            x['prediction_bbox_xyxy'] = x['bbox_xyxy'].clone()
             dataset.plot(x)
             plt.close()

tests/datasets/test_forestdamage.py

@@ -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['bbox_xyxy'].clone()
+        x['prediction_bbox_xyxy'] = x['bbox_xyxy'].clone()
         dataset.plot(x, suptitle='Prediction')
         plt.close()

tests/datasets/test_idtrees.py

@@ -88,7 +88,7 @@ def test_plot(self, dataset: IDTReeS) -> None:
         plt.close()
 
         if 'bbox_xyxy' in x:
-            x['prediction_boxes'] = x['bbox_xyxy']
+            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

@@ -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['bbox_xyxy'].clone()
+        x['prediction_bbox_xyxy'] = x['bbox_xyxy'].clone()
         dataset.plot(x)
         plt.close()

tests/datasets/test_reforestree.py

@@ -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['bbox_xyxy'].clone()
+        x['prediction_bbox_xyxy'] = x['bbox_xyxy'].clone()
         dataset.plot(x, suptitle='Prediction')
         plt.close()

tests/datasets/test_vhr10.py

@@ -83,7 +83,7 @@ def test_plot(self, dataset: VHR10) -> None:
             for i in range(3):
                 x = dataset[i]
                 x['prediction_labels'] = x['label']
-                x['prediction_boxes'] = x['bbox_xyxy']
+                x['prediction_bbox_xyxy'] = x['bbox_xyxy']
                 x['prediction_scores'] = torch.Tensor([scores[i]])
                 if 'mask' in x:
                     x['prediction_masks'] = x['mask']

torchgeo/datasets/fair1m.py

@@ -401,7 +401,7 @@ def plot(
         image = sample['image'].permute((1, 2, 0)).numpy()
 
         ncols = 1
-        if 'prediction_boxes' in sample:
+        if 'prediction_bbox_xyxy' in sample:
             ncols += 1
 
         fig, axs = plt.subplots(ncols=ncols, figsize=(ncols * 10, 10))
@@ -427,7 +427,7 @@ def plot(
             axs[1].axis('off')
             polygons = [
                 patches.Polygon(points, color='r', fill=False)
-                for points in sample['prediction_boxes'].numpy()
+                for points in sample['prediction_bbox_xyxy'].numpy()
             ]
             for polygon in polygons:
                 axs[0].add_patch(polygon)

torchgeo/datasets/forestdamage.py

@@ -264,7 +264,7 @@ def plot(
         image = sample['image'].permute((1, 2, 0)).numpy()
 
         ncols = 1
-        showing_predictions = 'prediction_boxes' in sample
+        showing_predictions = 'prediction_bbox_xyxy' in sample
         if showing_predictions:
             ncols += 1
 
@@ -305,7 +305,7 @@ def plot(
                     edgecolor='r',
                     facecolor='none',
                 )
-                for bbox in sample['prediction_boxes'].numpy()
+                for bbox in sample['prediction_bbox_xyxy'].numpy()
             ]
             for bbox in pred_bboxes:
                 axs[1].add_patch(bbox)

torchgeo/datasets/idtrees.py

@@ -521,15 +521,15 @@ def normalize(x: Tensor) -> Tensor:
         else:
             image = sample['image'].permute((1, 2, 0)).numpy()
 
-        if 'prediction_boxes' in sample and len(sample['prediction_boxes']):
+        if 'prediction_bbox_xyxy' in sample and len(sample['prediction_bbox_xyxy']):
             ncols += 1
             labels = (
                 [self.idx2class[int(i)] for i in sample['prediction_label']]
                 if 'prediction_label' in sample
                 else None
             )
             preds = draw_bounding_boxes(
-                image=sample['image'], boxes=sample['prediction_boxes'], labels=labels
+                image=sample['image'], boxes=sample['prediction_bbox_xyxy'], labels=labels
             )
             preds = preds.permute((1, 2, 0)).numpy()
 

torchgeo/datasets/nasa_marine_debris.py

@@ -167,10 +167,10 @@ def plot(
             )
         image_arr = image.permute((1, 2, 0)).numpy()
 
-        if 'prediction_boxes' in sample and len(sample['prediction_boxes']):
+        if 'prediction_bbox_xyxy' in sample and len(sample['prediction_bbox_xyxy']):
             ncols += 1
             preds = draw_bounding_boxes(
-                image=sample['image'], boxes=sample['prediction_boxes']
+                image=sample['image'], boxes=sample['prediction_bbox_xyxy']
             )
             preds_arr = preds.permute((1, 2, 0)).numpy()
 

torchgeo/datasets/reforestree.py

@@ -219,7 +219,7 @@ def plot(
         """
         image = sample['image'].permute((1, 2, 0)).numpy()
         ncols = 1
-        showing_predictions = 'prediction_boxes' in sample
+        showing_predictions = 'prediction_bbox_xyxy' in sample
         if showing_predictions:
             ncols += 1
 
@@ -260,7 +260,7 @@ def plot(
                     edgecolor='r',
                     facecolor='none',
                 )
-                for bbox in sample['prediction_boxes'].numpy()
+                for bbox in sample['prediction_bbox_xyxy'].numpy()
             ]
             for bbox in pred_bboxes:
                 axs[1].add_patch(bbox)

torchgeo/datasets/vhr10.py

@@ -416,8 +416,8 @@ def plot(
             show_pred_masks = False
             prediction_labels = sample['prediction_labels'].numpy()
             prediction_scores = sample['prediction_scores'].numpy()
-            if 'prediction_boxes' in sample:
-                prediction_boxes = sample['prediction_boxes'].numpy()
+            if 'prediction_bbox_xyxy' in sample:
+                prediction_bbox_xyxy = sample['prediction_bbox_xyxy'].numpy()
                 show_pred_boxes = True
             if 'prediction_masks' in sample:
                 prediction_masks = sample['prediction_masks'].numpy()
@@ -485,7 +485,7 @@ def plot(
 
                 if show_pred_boxes:
                     # Add bounding boxes
-                    x1, y1, x2, y2 = prediction_boxes[i]
+                    x1, y1, x2, y2 = prediction_bbox_xyxy[i]
                     r = patches.Rectangle(
                         (x1, y1),
                         x2 - x1,

torchgeo/trainers/detection.py

@@ -282,7 +282,7 @@ def validation_step(
             and hasattr(self.logger.experiment, 'add_figure')
         ):
             datamodule = self.trainer.datamodule
-            batch['prediction_boxes'] = [b['boxes'].cpu() for b in y_hat]
+            batch['prediction_bbox_xyxy'] = [b['boxes'].cpu() for b in y_hat]
             batch['prediction_labels'] = [b['labels'].cpu() for b in y_hat]
             batch['prediction_scores'] = [b['scores'].cpu() for b in y_hat]
             batch['image'] = batch['image'].cpu()