Fix float zeros format (#5491)

* Fix float zeros format

* 255 to integer

utils/loss.py

@@ -108,7 +108,7 @@ class ComputeLoss:
             BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
 
         det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
-        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
+        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, 0.02])  # P3-P7
         self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
         self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
         for k in 'na', 'nc', 'nl', 'anchors':
@@ -129,7 +129,7 @@ class ComputeLoss:
                 ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
 
                 # Regression
-                pxy = ps[:, :2].sigmoid() * 2. - 0.5
+                pxy = ps[:, :2].sigmoid() * 2 - 0.5
                 pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
                 pbox = torch.cat((pxy, pwh), 1)  # predicted box
                 iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
@@ -189,15 +189,15 @@ class ComputeLoss:
             if nt:
                 # Matches
                 r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
+                j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t']  # compare
                 # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
                 t = t[j]  # filter
 
                 # Offsets
                 gxy = t[:, 2:4]  # grid xy
                 gxi = gain[[2, 3]] - gxy  # inverse
-                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
-                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
+                j, k = ((gxy % 1 < g) & (gxy > 1)).T
+                l, m = ((gxi % 1 < g) & (gxi > 1)).T
                 j = torch.stack((torch.ones_like(j), j, k, l, m))
                 t = t.repeat((5, 1, 1))[j]
                 offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]