Simplify autoshape() post-process (#1603)

* Simplify autoshape() post-process

* cleanup

models/common.py

@@ -167,8 +167,7 @@ class autoShape(nn.Module):
 
         # Post-process
         for i in batch:
-            if y[i] is not None:
-                y[i][:, :4] = scale_coords(shape1, y[i][:, :4], shape0[i])
+            scale_coords(shape1, y[i][:, :4], shape0[i])
 
         return Detections(imgs, y, self.names)
 
@@ -177,13 +176,13 @@ class Detections:
     # detections class for YOLOv5 inference results
     def __init__(self, imgs, pred, names=None):
         super(Detections, self).__init__()
+        d = pred[0].device  # device
+        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
         self.imgs = imgs  # list of images as numpy arrays
         self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
         self.names = names  # class names
         self.xyxy = pred  # xyxy pixels
         self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
-        d = pred[0].device  # device
-        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
         self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
         self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
         self.n = len(self.pred)