[fbsync] Add missing type hints to anchor_utils (#6735)

Summary:
* Use the variable name sizes instead of scales for consistency

* Add the missing type hints

* Restore the naming back to scales instead of sizes to avoid backwards incompatibility

Reviewed By: NicolasHug

Differential Revision: D40427481

fbshipit-source-id: bf653023c41f2d93597928e4ea0e4461e8b8afc6

Author: Yosua Michael Maranatha

torchvision/models/detection/anchor_utils.py

@@ -61,7 +61,7 @@ def generate_anchors(
         aspect_ratios: List[float],
         dtype: torch.dtype = torch.float32,
         device: torch.device = torch.device("cpu"),
-    ):
+    ) -> Tensor:
         scales = torch.as_tensor(scales, dtype=dtype, device=device)
         aspect_ratios = torch.as_tensor(aspect_ratios, dtype=dtype, device=device)
         h_ratios = torch.sqrt(aspect_ratios)
@@ -76,7 +76,7 @@ def generate_anchors(
     def set_cell_anchors(self, dtype: torch.dtype, device: torch.device):
         self.cell_anchors = [cell_anchor.to(dtype=dtype, device=device) for cell_anchor in self.cell_anchors]
 
-    def num_anchors_per_location(self):
+    def num_anchors_per_location(self) -> List[int]:
         return [len(s) * len(a) for s, a in zip(self.sizes, self.aspect_ratios)]
 
     # For every combination of (a, (g, s), i) in (self.cell_anchors, zip(grid_sizes, strides), 0:2),
@@ -201,7 +201,7 @@ def _generate_wh_pairs(
             _wh_pairs.append(torch.as_tensor(wh_pairs, dtype=dtype, device=device))
         return _wh_pairs
 
-    def num_anchors_per_location(self):
+    def num_anchors_per_location(self) -> List[int]:
         # Estimate num of anchors based on aspect ratios: 2 default boxes + 2 * ratios of feaure map.
         return [2 + 2 * len(r) for r in self.aspect_ratios]