PyTorch Hub amp.autocast() inference (ultralytics#2641)

I think this should help speed up CUDA inference, as currently models may be running in FP32 inference mode on CUDA devices unnecesarily.

(cherry picked from commit 2bf34f5)

models/common.py

@@ -8,6 +8,7 @@
 import torch
 import torch.nn as nn
 from PIL import Image
+from torch.cuda import amp
 
 from utils.datasets import letterbox
 from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh
@@ -219,17 +220,17 @@ def forward(self, imgs, size=640, augment=False, profile=False):
         x = torch.from_numpy(x).to(p.device).type_as(p) / 255.  # uint8 to fp16/32
         t.append(time_synchronized())
 
-        # Inference
-        with torch.no_grad():
+        with torch.no_grad(), amp.autocast(enabled=p.device.type != 'cpu'):
+            # Inference
             y = self.model(x, augment, profile)[0]  # forward
-        t.append(time_synchronized())
+            t.append(time_synchronized())
 
-        # Post-process
-        y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
-        for i in range(n):
-            scale_coords(shape1, y[i][:, :4], shape0[i])
-        t.append(time_synchronized())
+            # Post-process
+            y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
+            for i in range(n):
+                scale_coords(shape1, y[i][:, :4], shape0[i])
 
+        t.append(time_synchronized())
         return Detections(imgs, y, files, t, self.names, x.shape)