main code

@albertfaromatics WongKinYiu/yolov7#35 (comment)

utils/loss.py

@@ -501,7 +501,7 @@ def build_targets(self, p, targets):
         # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
         na, nt = self.na, targets.shape[0]  # number of anchors, targets
         tcls, tbox, indices, anch = [], [], [], []
-        gain = torch.ones(7, device=targets.device)  # normalized to gridspace gain
+        gain = torch.ones(7, device=targets.device).long()  # normalized to gridspace gain
         ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
         targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices
 
@@ -775,7 +775,7 @@ def build_targets(self, p, targets, imgs):
                 matching_anchs[i].append(all_anch[layer_idx])
 
         for i in range(nl):
-            if matching_gis[i] != []:
+            if matching_targets[i] != []:
                 matching_bs[i] = torch.cat(matching_bs[i], dim=0)
                 matching_as[i] = torch.cat(matching_as[i], dim=0)
                 matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
@@ -796,7 +796,7 @@ def find_3_positive(self, p, targets):
         # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
         na, nt = self.na, targets.shape[0]  # number of anchors, targets
         indices, anch = [], []
-        gain = torch.ones(7, device=targets.device)  # normalized to gridspace gain
+        gain = torch.ones(7, device=targets.device).long()  # normalized to gridspace gain
         ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
         targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices