Minor torchscript fixes for Mask R-CNN (#1639)

torchvision/models/detection/generalized_rcnn.py

@@ -64,7 +64,7 @@ class GeneralizedRCNN(nn.Module):
         images, targets = self.transform(images, targets)
         features = self.backbone(images.tensors)
         if isinstance(features, torch.Tensor):
-            features = OrderedDict([(0, features)])
+            features = OrderedDict([('0', features)])
         proposals, proposal_losses = self.rpn(images, features, targets)
         detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
         detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes)

torchvision/ops/poolers.py

@@ -139,7 +139,7 @@ class MultiScaleRoIAlign(nn.Module):
         size = feature.shape[-2:]
         possible_scales = torch.jit.annotate(List[float], [])
         for s1, s2 in zip(size, original_size):
-            approx_scale = float(s1) / s2
+            approx_scale = float(s1) / float(s2)
             scale = 2 ** float(torch.tensor(approx_scale).log2().round())
             possible_scales.append(scale)
         assert possible_scales[0] == possible_scales[1]