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
@@ -175,7 +176,6 @@ 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)

test.py

@@ -18,6 +18,7 @@ from utils.plots import plot_images, output_to_target, plot_study_txt
 from utils.torch_utils import select_device, time_synchronized
 
 
+@torch.no_grad()
 def test(data,
          weights=None,
          batch_size=32,
@@ -105,7 +106,6 @@ def test(data,
         targets = targets.to(device)
         nb, _, height, width = img.shape  # batch size, channels, height, width
 
-        with torch.no_grad():
         # Run model
         t = time_synchronized()
         out, train_out = model(img, augment=augment)  # inference and training outputs