Clean up no longer needed workarorudns (#2261)

Co-authored-by: eellison <eellison@fb.com>

torchvision/models/detection/_utils.py

@@ -6,13 +6,6 @@ from torch import Tensor
 import torchvision
 
 
-# TODO: https://github.com/pytorch/pytorch/issues/26727
-def zeros_like(tensor, dtype):
-    # type: (Tensor, int) -> Tensor
-    return torch.zeros_like(tensor, dtype=dtype, layout=tensor.layout,
-                            device=tensor.device, pin_memory=tensor.is_pinned())
-
-
 class BalancedPositiveNegativeSampler(object):
     """
     This class samples batches, ensuring that they contain a fixed proportion of positives
@@ -66,15 +59,15 @@ class BalancedPositiveNegativeSampler(object):
             neg_idx_per_image = negative[perm2]
 
             # create binary mask from indices
-            pos_idx_per_image_mask = zeros_like(
+            pos_idx_per_image_mask = torch.zeros_like(
                 matched_idxs_per_image, dtype=torch.uint8
             )
-            neg_idx_per_image_mask = zeros_like(
+            neg_idx_per_image_mask = torch.zeros_like(
                 matched_idxs_per_image, dtype=torch.uint8
             )
 
-            pos_idx_per_image_mask[pos_idx_per_image] = torch.tensor(1, dtype=torch.uint8)
-            neg_idx_per_image_mask[neg_idx_per_image] = torch.tensor(1, dtype=torch.uint8)
+            pos_idx_per_image_mask[pos_idx_per_image] = 1
+            neg_idx_per_image_mask[neg_idx_per_image] = 1
 
             pos_idx.append(pos_idx_per_image_mask)
             neg_idx.append(neg_idx_per_image_mask)
@@ -304,8 +297,8 @@ class Matcher(object):
         between_thresholds = (matched_vals >= self.low_threshold) & (
             matched_vals < self.high_threshold
         )
-        matches[below_low_threshold] = torch.tensor(self.BELOW_LOW_THRESHOLD)
-        matches[between_thresholds] = torch.tensor(self.BETWEEN_THRESHOLDS)
+        matches[below_low_threshold] = self.BELOW_LOW_THRESHOLD
+        matches[between_thresholds] = self.BETWEEN_THRESHOLDS
 
         if self.allow_low_quality_matches:
             assert all_matches is not None

torchvision/models/detection/roi_heads.py

@@ -155,8 +155,8 @@ def keypoints_to_heatmap(keypoints, rois, heatmap_size):
     y = (y - offset_y) * scale_y
     y = y.floor().long()
 
-    x[x_boundary_inds] = torch.tensor(heatmap_size - 1)
-    y[y_boundary_inds] = torch.tensor(heatmap_size - 1)
+    x[x_boundary_inds] = heatmap_size - 1
+    y[y_boundary_inds] = heatmap_size - 1
 
     valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size)
     vis = keypoints[..., 2] > 0
@@ -584,11 +584,11 @@ class RoIHeads(torch.nn.Module):
 
                 # Label background (below the low threshold)
                 bg_inds = matched_idxs_in_image == self.proposal_matcher.BELOW_LOW_THRESHOLD
-                labels_in_image[bg_inds] = torch.tensor(0)
+                labels_in_image[bg_inds] = 0
 
                 # Label ignore proposals (between low and high thresholds)
                 ignore_inds = matched_idxs_in_image == self.proposal_matcher.BETWEEN_THRESHOLDS
-                labels_in_image[ignore_inds] = torch.tensor(-1)  # -1 is ignored by sampler
+                labels_in_image[ignore_inds] = -1  # -1 is ignored by sampler
 
             matched_idxs.append(clamped_matched_idxs_in_image)
             labels.append(labels_in_image)

torchvision/models/detection/rpn.py

@@ -350,11 +350,11 @@ class RegionProposalNetwork(torch.nn.Module):
 
                 # Background (negative examples)
                 bg_indices = matched_idxs == self.proposal_matcher.BELOW_LOW_THRESHOLD
-                labels_per_image[bg_indices] = torch.tensor(0.0)
+                labels_per_image[bg_indices] = 0.0
 
                 # discard indices that are between thresholds
                 inds_to_discard = matched_idxs == self.proposal_matcher.BETWEEN_THRESHOLDS
-                labels_per_image[inds_to_discard] = torch.tensor(-1.0)
+                labels_per_image[inds_to_discard] = -1.0
 
             labels.append(labels_per_image)
             matched_gt_boxes.append(matched_gt_boxes_per_image)