Exposing LevelMapper params in MultiScaleRoIAlign (pytorch#3129) (#3151)

* Exposing LevelMapper params in MultiScaleRoIAlign

Ability to set the `canonical_scale` and `canonical_level` of LevelMapper from the init method of MultiScaleRoIAlign. 
Not sure if this is the right place to expose those parameters. 
Also, additional tests should be written.

Discussion in issue #3129

* MultiScaleRoIAlign keyword-only parameters + docs (pytorch#3129)

* Linter fix (trailing whitespace)
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

torchvision/ops/poolers.py

@@ -85,13 +85,20 @@ class MultiScaleRoIAlign(nn.Module):
     """
     Multi-scale RoIAlign pooling, which is useful for detection with or without FPN.
 
-    It infers the scale of the pooling via the heuristics present in the FPN paper.
+    It infers the scale of the pooling via the heuristics specified in eq. 1
+    of the `Feature Pyramid Network paper <https://arxiv.org/abs/1612.03144>`_.
+    They keyword-only parameters ``canonical_scale`` and ``canonical_level``
+    correspond respectively to ``224`` and ``k0=4`` in eq. 1, and
+    have the following meaning: ``canonical_level`` is the target level of the pyramid from
+    which to pool a region of interest with ``w x h = canonical_scale x canonical_scale``.
 
     Args:
         featmap_names (List[str]): the names of the feature maps that will be used
             for the pooling.
         output_size (List[Tuple[int, int]] or List[int]): output size for the pooled region
         sampling_ratio (int): sampling ratio for ROIAlign
+        canonical_scale (int, optional): canonical_scale for LevelMapper
+        canonical_level (int, optional): canonical_level for LevelMapper
 
     Examples::
 
@@ -120,6 +127,9 @@ class MultiScaleRoIAlign(nn.Module):
         featmap_names: List[str],
         output_size: Union[int, Tuple[int], List[int]],
         sampling_ratio: int,
+        *,
+        canonical_scale: int = 224,
+        canonical_level: int = 4,
     ):
         super(MultiScaleRoIAlign, self).__init__()
         if isinstance(output_size, int):
@@ -129,6 +139,8 @@ class MultiScaleRoIAlign(nn.Module):
         self.output_size = tuple(output_size)
         self.scales = None
         self.map_levels = None
+        self.canonical_scale = canonical_scale
+        self.canonical_level = canonical_level
 
     def convert_to_roi_format(self, boxes: List[Tensor]) -> Tensor:
         concat_boxes = torch.cat(boxes, dim=0)
@@ -173,7 +185,12 @@ class MultiScaleRoIAlign(nn.Module):
         lvl_min = -torch.log2(torch.tensor(scales[0], dtype=torch.float32)).item()
         lvl_max = -torch.log2(torch.tensor(scales[-1], dtype=torch.float32)).item()
         self.scales = scales
-        self.map_levels = initLevelMapper(int(lvl_min), int(lvl_max))
+        self.map_levels = initLevelMapper(
+            int(lvl_min),
+            int(lvl_max),
+            canonical_scale=self.canonical_scale,
+            canonical_level=self.canonical_level,
+        )
 
     def forward(
         self,