rename pos_mask to pos_inds

mmdet/models/bbox_heads/bbox_head.py

 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+
 from mmdet.core import (delta2bbox, multiclass_nms, bbox_target,
                         weighted_cross_entropy, weighted_smoothl1, accuracy)
-
 from ..registry import HEADS
 
 
@@ -94,16 +94,16 @@ class BBoxHead(nn.Module):
                 cls_score, labels, label_weights, reduce=reduce)
             losses['acc'] = accuracy(cls_score, labels)
         if bbox_pred is not None:
-            pos_mask = labels > 0
+            pos_inds = labels > 0
             if self.reg_class_agnostic:
-                pos_bbox_pred = bbox_pred.view(bbox_pred.size(0), 4)[pos_mask]
+                pos_bbox_pred = bbox_pred.view(bbox_pred.size(0), 4)[pos_inds]
             else:
                 pos_bbox_pred = bbox_pred.view(bbox_pred.size(0), -1,
-                                               4)[pos_mask, labels[pos_mask]]
+                                               4)[pos_inds, labels[pos_inds]]
             losses['loss_reg'] = weighted_smoothl1(
                 pos_bbox_pred,
-                bbox_targets[pos_mask],
-                bbox_weights[pos_mask],
+                bbox_targets[pos_inds],
+                bbox_weights[pos_inds],
                 avg_factor=bbox_targets.size(0))
         return losses