check for degenerate boxes (fixes #2240) (#2258)

test/test_models.py

@@ -158,7 +158,7 @@ class ModelTester(TestCase):
     def _test_detection_model_validation(self, name):
         set_rng_seed(0)
         model = models.detection.__dict__[name](num_classes=50, pretrained_backbone=False)
-        input_shape = (1, 3, 300, 300)
+        input_shape = (3, 300, 300)
         x = [torch.rand(input_shape)]
 
         # validate that targets are present in training
@@ -173,6 +173,11 @@ class ModelTester(TestCase):
             targets = [{'boxes': boxes}]
             self.assertRaises(ValueError, model, x, targets=targets)
 
+        # validate that no degenerate boxes are present
+        boxes = torch.tensor([[1, 3, 1, 4], [2, 4, 3, 4]])
+        targets = [{'boxes': boxes}]
+        self.assertRaises(ValueError, model, x, targets=targets)
+
     def _test_video_model(self, name):
         # the default input shape is
         # bs * num_channels * clip_len * h *w

torchvision/models/detection/generalized_rcnn.py

@@ -77,6 +77,21 @@ class GeneralizedRCNN(nn.Module):
             original_image_sizes.append((val[0], val[1]))
 
         images, targets = self.transform(images, targets)
+
+        # Check for degenerate boxes
+        # TODO: Move this to a function
+        if targets is not None:
+            for target_idx, target in enumerate(targets):
+                boxes = target["boxes"]
+                degenerate_boxes = boxes[:, 2:] <= boxes[:, :2]
+                if degenerate_boxes.any():
+                    # print the first degenrate box
+                    bb_idx = degenerate_boxes.any(dim=1).nonzero().view(-1)[0]
+                    degen_bb: List[float] = boxes[bb_idx].tolist()
+                    raise ValueError("All bounding boxes should have positive height and width."
+                                     " Found invaid box {} for target at index {}."
+                                     .format(degen_bb, target_idx))
+
         features = self.backbone(images.tensors)
         if isinstance(features, torch.Tensor):
             features = OrderedDict([('0', features)])