Use torch.amax instead of torch.max

detectron2/evaluation/coco_evaluation.py

@@ -187,7 +187,7 @@ def evaluate(self, img_ids=None):
             if not comm.is_main_process():
                 return {}
         else:
-            predictions = self._predictions
+            predictions = copy.deepcopy(self._predictions)
 
         if len(predictions) == 0:
             self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")

detectron2/layers/wrappers.py

@@ -41,9 +41,7 @@ def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> tor
 
 def check_if_dynamo_compiling():
     if TORCH_VERSION >= (2, 1):
-        from torch._dynamo import is_compiling
-
-        return is_compiling()
+        return torch.compiler.is_compiling()
     else:
         return False
 

detectron2/structures/image_list.py

@@ -86,7 +86,7 @@ def from_tensors(
 
         image_sizes = [(im.shape[-2], im.shape[-1]) for im in tensors]
         image_sizes_tensor = [shapes_to_tensor(x) for x in image_sizes]
-        max_size = torch.stack(image_sizes_tensor).max(0).values
+        max_size = torch.stack(image_sizes_tensor).amax(0)
 
         if padding_constraints is not None:
             square_size = padding_constraints.get("square_size", 0)