fix(nyz): fix to_item compatibility bug (#646)

ding/torch_utils/data_helper.py

@@ -280,12 +280,14 @@ def tensor_to_list(item):
         raise TypeError("not support item type: {}".format(type(item)))
 
 
-def to_item(data):
+def to_item(data: Any, ignore_error: bool = True) -> Any:
     """
     Overview:
         Transform data into python native scalar (i.e. data item), keep other data types unchanged.
     Arguments:
         - data (:obj:`Any`): The data that needs to be transformed.
+        - ignore_error (:obj:`bool`): Whether to ignore the error when the data type is not supported. That is to \
+            say, only the data can be transformed into a python native scalar will be returned.
     Returns:
         - data (:obj:`Any`): Transformed data.
     """
@@ -300,7 +302,16 @@ def to_item(data):
     elif isinstance(data, list) or isinstance(data, tuple):
         return [to_item(d) for d in data]
     elif isinstance(data, dict):
-        return {k: to_item(v) for k, v in data.items()}
+        new_data = {}
+        for k, v in data.items():
+            if ignore_error:
+                try:
+                    new_data[k] = to_item(v)
+                except ValueError:
+                    pass
+            else:
+                new_data[k] = to_item(v)
+        return new_data
     else:
         raise TypeError("not support data type: {}".format(data))
 

ding/torch_utils/tests/test_data_helper.py

@@ -139,7 +139,10 @@ def test_to_item(self):
         assert np.isscalar(new_data.a)
 
         with pytest.raises(ValueError):
-            to_item(torch.randn(4))
+            to_item({'a': torch.randn(4), 'b': torch.rand(1)}, ignore_error=False)
+        output = to_item({'a': torch.randn(4), 'b': torch.rand(1)}, ignore_error=True)
+        assert 'a' not in output
+        assert 'b' in output
 
     def test_same_shape(self):
         tlist = [torch.randn(3, 5) for i in range(5)]