Implement @torch.no_grad() decorator (#3312)

* `@torch.no_grad()` decorator

* Update detect.py

detect.py

@@ -14,6 +14,7 @@ from utils.plots import colors, plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
+@torch.no_grad()
 def detect(opt):
     source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
     save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
@@ -175,10 +176,9 @@ if __name__ == '__main__':
     print(opt)
     check_requirements(exclude=('tensorboard', 'pycocotools', 'thop'))
 
-    with torch.no_grad():
-        if opt.update:  # update all models (to fix SourceChangeWarning)
-            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
-                detect(opt=opt)
-                strip_optimizer(opt.weights)
-        else:
+    if opt.update:  # update all models (to fix SourceChangeWarning)
+        for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
             detect(opt=opt)
+            strip_optimizer(opt.weights)
+    else:
+        detect(opt=opt)