fix #5917

Signed-off-by: KumoLiu <yunl@nvidia.com>

monai/data/utils.py

@@ -28,7 +28,7 @@
 
 import numpy as np
 import torch
-from torch.utils.data._utils.collate import default_collate
+from torch.utils.data._utils.collate import collate_tensor_fn, default_collate, default_collate_fn_map
 
 from monai import config
 from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor, PathLike
@@ -444,29 +444,18 @@ def pickle_operations(data, key=PICKLE_KEY_SUFFIX, is_encode: bool = True):
     return data
 
 
-def collate_meta_tensor(batch):
+def collate_meta_tensor(batch, *, collate_fn_map=None):
     """collate a sequence of meta tensor sequences/dictionaries into
     a single batched metatensor or a dictionary of batched metatensor"""
-    if not isinstance(batch, Sequence):
-        raise NotImplementedError()
-    elem_0 = first(batch)
-    if isinstance(elem_0, MetaObj):
-        collated = default_collate(batch)
-        meta_dicts = [i.meta or TraceKeys.NONE for i in batch]
-        common_ = set.intersection(*[set(d.keys()) for d in meta_dicts if isinstance(d, dict)])
-        if common_:
-            meta_dicts = [{k: d[k] for k in common_} if isinstance(d, dict) else TraceKeys.NONE for d in meta_dicts]
-        collated.meta = default_collate(meta_dicts)
-        collated.applied_operations = [i.applied_operations or TraceKeys.NONE for i in batch]
-        collated.is_batch = True
-        return collated
-    if isinstance(elem_0, Mapping):
-        return {k: collate_meta_tensor([d[k] for d in batch]) for k in elem_0}
-    if isinstance(elem_0, (tuple, list)):
-        return [collate_meta_tensor([d[i] for d in batch]) for i in range(len(elem_0))]
-
-    # no more recursive search for MetaTensor
-    return default_collate(batch)
+    collated = collate_tensor_fn(batch)
+    meta_dicts = [i.meta or TraceKeys.NONE for i in batch]
+    common_ = set.intersection(*[set(d.keys()) for d in meta_dicts if isinstance(d, dict)])
+    if common_:
+        meta_dicts = [{k: d[k] for k in common_} if isinstance(d, dict) else TraceKeys.NONE for d in meta_dicts]
+    collated.meta = default_collate(meta_dicts)
+    collated.applied_operations = [i.applied_operations or TraceKeys.NONE for i in batch]
+    collated.is_batch = True
+    return collated
 
 
 def list_data_collate(batch: Sequence):
@@ -479,6 +468,9 @@ def list_data_collate(batch: Sequence):
         Need to use this collate if apply some transforms that can generate batch data.
 
     """
+    from monai.data.meta_tensor import MetaTensor
+
+    default_collate_fn_map.update({MetaTensor: collate_meta_tensor})
     elem = batch[0]
     data = [i for k in batch for i in k] if isinstance(elem, list) else batch
     key = None
@@ -490,9 +482,9 @@ def list_data_collate(batch: Sequence):
             for k in elem:
                 key = k
                 data_for_batch = [d[key] for d in data]
-                ret[key] = collate_meta_tensor(data_for_batch)
+                ret[key] = default_collate(data_for_batch)
         else:
-            ret = collate_meta_tensor(data)
+            ret = default_collate(data)
         return ret
     except RuntimeError as re:
         re_str = str(re)