Update run-once lines (#2058)

detect.py

     colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
 
     # Run inference
+    if device.type != 'cpu':
+        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
     t0 = time.time()
-    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
-    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
     for path, img, im0s, vid_cap in dataset:
         img = torch.from_numpy(img).to(device)
         img = img.half() if half else img.float()  # uint8 to fp16/32

test.py

 
     # Dataloader
     if not training:
-        img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
-        _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
+        if device.type != 'cpu':
+            model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
         path = data['test'] if opt.task == 'test' else data['val']  # path to val/test images
         dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True,
                                        prefix=colorstr('test: ' if opt.task == 'test' else 'val: '))[0]