Update DetectMultiBackend for tuple outputs (#9274)

models/common.py

@@ -465,17 +465,15 @@ class DetectMultiBackend(nn.Module):
 
         if self.pt:  # PyTorch
             y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
-            if isinstance(y, tuple):
-                y = y[0]
         elif self.jit:  # TorchScript
-            y = self.model(im)[0]
+            y = self.model(im)
         elif self.dnn:  # ONNX OpenCV DNN
             im = im.cpu().numpy()  # torch to numpy
             self.net.setInput(im)
             y = self.net.forward()
         elif self.onnx:  # ONNX Runtime
             im = im.cpu().numpy()  # torch to numpy
-            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})[0]
+            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
         elif self.xml:  # OpenVINO
             im = im.cpu().numpy()  # FP32
             y = self.executable_network([im])[self.output_layer]
@@ -522,6 +520,8 @@ class DetectMultiBackend(nn.Module):
                     y = (y.astype(np.float32) - zero_point) * scale  # re-scale
             y[..., :4] *= [w, h, w, h]  # xywh normalized to pixels
 
+        if isinstance(y, (list, tuple)):
+            y = y[0]
         if isinstance(y, np.ndarray):
             y = torch.from_numpy(y).to(self.device)
         return (y, []) if val else y