From ac4c90c817b87427c74e4d5bdbcf01964c62268f Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Tue, 14 Apr 2020 13:08:00 -0700
Subject: [PATCH] cleanup

---
 utils/utils.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/utils/utils.py b/utils/utils.py
index 7331d413..d45d14cd 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -440,21 +440,21 @@ def build_targets(p, targets, model):
 
     # m = list(model.modules())[-1]
     # for i in range(m.nl):
-    #     anchor_vec = m.anchor_vec[i]
+    #    anchors = m.anchors[i]
     multi_gpu = type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
     for i, j in enumerate(model.yolo_layers):
         # get number of grid points and anchor vec for this yolo layer
-        anchor_vec = model.module.module_list[j].anchor_vec if multi_gpu else model.module_list[j].anchor_vec
+        anchors = model.module.module_list[j].anchor_vec if multi_gpu else model.module_list[j].anchor_vec
 
         # iou of targets-anchors
         gain[2:] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
         t, a = targets * gain, []
         gwh = t[:, 4:6]
         if nt:
-            iou = wh_iou(anchor_vec, gwh)  # iou(3,n) = wh_iou(anchor_vec(3,2), gwh(n,2))
+            iou = wh_iou(anchors, gwh)  # iou(3,n) = wh_iou(anchors(3,2), gwh(n,2))
 
             if use_all_anchors:
-                na = anchor_vec.shape[0]  # number of anchors
+                na = anchors.shape[0]  # number of anchors
                 a = torch.arange(na).view(-1, 1).repeat(1, nt).view(-1)
                 t = t.repeat(na, 1)
             else:  # use best anchor only
@@ -475,7 +475,7 @@ def build_targets(p, targets, model):
         # Box
         gxy -= gxy.floor()  # xy
         tbox.append(torch.cat((gxy, gwh), 1))  # xywh (grids)
-        av.append(anchor_vec[a])  # anchor vec
+        av.append(anchors[a])  # anchor vec
 
         # Class
         tcls.append(c)
@@ -585,8 +585,8 @@ def print_model_biases(model):
             print(' ' * 20 + '%8g %18s%18s%18s' % (l, '%5.2f+/-%-5.2f' % (b[:, :4].mean(), b[:, :4].std()),
                                                    '%5.2f+/-%-5.2f' % (b[:, 4].mean(), b[:, 4].std()),
                                                    '%5.2f+/-%-5.2f' % (b[:, 5:].mean(), b[:, 5:].std())))
-        except:
-            pass
+    except:
+        pass
 
 
 def strip_optimizer(f='weights/last.pt'):  # from utils.utils import *; strip_optimizer()