Refactor new model.warmup() method (ultralytics#5810)

* Refactor new `model.warmup()` method

* Add half

detect.py

@@ -97,8 +97,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     vid_path, vid_writer = [None] * bs, [None] * bs
 
     # Run inference
-    if pt and device.type != 'cpu':
-        model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmup
+    model.warmup(imgsz=(1, 3, *imgsz), half=half)  # warmup
     dt, seen = [0.0, 0.0, 0.0], 0
     for path, im, im0s, vid_cap, s in dataset:
         t1 = time_sync()

models/common.py

@@ -421,6 +421,13 @@ def forward(self, im, augment=False, visualize=False, val=False):
         y = torch.tensor(y) if isinstance(y, np.ndarray) else y
         return (y, []) if val else y
 
+    def warmup(self, imgsz=(1, 3, 640, 640), half=False):
+        # Warmup model by running inference once
+        if self.pt or self.engine or self.onnx:  # warmup types
+            if isinstance(self.device, torch.device) and self.device.type != 'cpu':  # only warmup GPU models
+                im = torch.zeros(*imgsz).to(self.device).type(torch.half if half else torch.float)  # input image
+                self.forward(im)  # warmup
+
 
 class AutoShape(nn.Module):
     # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS

val.py

@@ -149,8 +149,7 @@ def run(data,
 
     # Dataloader
     if not training:
-        if pt and device.type != 'cpu':
-            model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.model.parameters())))  # warmup
+        model.warmup(imgsz=(1, 3, imgsz, imgsz), half=half)  # warmup
         pad = 0.0 if task == 'speed' else 0.5
         task = task if task in ('train', 'val', 'test') else 'val'  # path to train/val/test images
         dataloader = create_dataloader(data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=pt,