diff --git a/torchvision/models/detection/generalized_rcnn.py b/torchvision/models/detection/generalized_rcnn.py
index 1ee0542c9c6..4a8fadfe345 100644
--- a/torchvision/models/detection/generalized_rcnn.py
+++ b/torchvision/models/detection/generalized_rcnn.py
@@ -4,6 +4,7 @@
 """
 
 from collections import OrderedDict
+from typing import Union
 import torch
 from torch import nn
 import warnings
@@ -35,7 +36,7 @@ def __init__(self, backbone, rpn, roi_heads, transform):
 
     @torch.jit.unused
     def eager_outputs(self, losses, detections):
-        # type: (Dict[str, Tensor], List[Dict[str, Tensor]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]
+        # type: (Dict[str, Tensor], List[Dict[str, Tensor]]) -> Union[Dict[str, Tensor], List[Dict[str, Tensor]]]
         if self.training:
             return losses
 
@@ -85,11 +86,11 @@ def forward(self, images, targets=None):
                 boxes = target["boxes"]
                 degenerate_boxes = boxes[:, 2:] <= boxes[:, :2]
                 if degenerate_boxes.any():
-                    # print the first degenrate box
+                    # print the first degenerate 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 {}."
+                                     " Found invalid box {} for target at index {}."
                                      .format(degen_bb, target_idx))
 
         features = self.backbone(images.tensors)
diff --git a/torchvision/ops/poolers.py b/torchvision/ops/poolers.py
index 32734cff86a..bf9412af056 100644
--- a/torchvision/ops/poolers.py
+++ b/torchvision/ops/poolers.py
@@ -1,4 +1,6 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+from typing import Union
+
 import torch
 import torch.nn.functional as F
 from torch import nn, Tensor
@@ -119,7 +121,7 @@ class MultiScaleRoIAlign(nn.Module):
     def __init__(
         self,
         featmap_names: List[str],
-        output_size: List[int],
+        output_size: Union[int, Tuple[int], List[int]],
         sampling_ratio: int,
     ):
         super(MultiScaleRoIAlign, self).__init__()