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)

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,
@@ -105,22 +106,21 @@ 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
-            t0 += time_synchronized() - t
-
-            # Compute loss
-            if compute_loss:
-                loss += compute_loss([x.float() for x in train_out], targets)[1][:3]  # box, obj, cls
-
-            # Run NMS
-            targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
-            lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
-            t = time_synchronized()
-            out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
-            t1 += time_synchronized() - t
+        # Run model
+        t = time_synchronized()
+        out, train_out = model(img, augment=augment)  # inference and training outputs
+        t0 += time_synchronized() - t
+
+        # Compute loss
+        if compute_loss:
+            loss += compute_loss([x.float() for x in train_out], targets)[1][:3]  # box, obj, cls
+
+        # Run NMS
+        targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
+        lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
+        t = time_synchronized()
+        out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
+        t1 += time_synchronized() - t
 
         # Statistics per image
         for si, pred in enumerate(out):