release(hansbug): version to 0.1.0

treetensor/common/wrappers.py

-from collections import namedtuple
 from functools import wraps
-from itertools import chain
+from operator import itemgetter
 
-from treevalue import TreeValue
-from treevalue import reduce_ as treevalue_reduce
+from treevalue import TreeValue, walk
 
 __all__ = [
-    'kwreduce', 'ireduce', 'vreduce',
+    'ireduce',
     'return_self',
 ]
 
 
-def kwreduce(rfunc):
-    def _decorator(func):
-        @wraps(func)
-        def _new_func(*args, **kwargs):
-            _result = func(*args, **kwargs)
-            if isinstance(_result, TreeValue):
-                return treevalue_reduce(_result, rfunc)
-            else:
-                return _result
-
-        return _new_func
-
-    return _decorator
-
-
-def vreduce(rfunc):
-    return kwreduce(lambda **kws: rfunc(kws.values()))
-
-
 def ireduce(rfunc, piter=None):
-    _IterReduceWrapper = namedtuple("_IterReduceWrapper", ['v'])
     piter = piter or (lambda x: x)
 
-    def _reduce_func(values):
-        _list = []
-        for item in values:
-            if isinstance(item, _IterReduceWrapper):
-                _list.append(item.v)
-            else:
-                _list.append([item])
-        return _IterReduceWrapper(chain(*_list))
-
     def _decorator(func):
-        rifunc = vreduce(_reduce_func)(func)
-
         @wraps(func)
         def _new_func(*args, **kwargs):
-            _iw = rifunc(*args, **kwargs)
-            if isinstance(_iw, _IterReduceWrapper):
-                return rfunc(piter(_iw.v))
+            result = func(*args, **kwargs)
+            if isinstance(result, TreeValue):
+                it = map(itemgetter(1), walk(result, include_nodes=False))
+                return rfunc(piter(it))
             else:
-                return _iw
+                return result
 
         return _new_func
 

treetensor/config/meta.py

@@ -7,7 +7,7 @@ Overview:
 __TITLE__ = "DI-treetensor"
 
 #: Version of this project.
-__VERSION__ = "0.0.1"
+__VERSION__ = "0.1.0"
 
 #: Short description of the project, will be included in ``setup.py``.
 __DESCRIPTION__ = 'A flexible, generalized tree-based tensor structure.'

treetensor/torch/tensor.py

 import numpy as np
 import torch as pytorch
 from hbutils.reflection import post_process
-from treevalue import method_treelize, TreeValue
+from treevalue import method_treelize, TreeValue, typetrans
 
 from .base import Torch, rmreduce, post_reduce, auto_reduce
 from .size import Size
@@ -15,7 +15,18 @@ __all__ = [
 ]
 
 doc_from_base = replaceable_partial(original_doc_from_base, base=pytorch.Tensor)
-_TorchProxy, _InstanceTorchProxy = get_tree_proxy(pytorch.Tensor)
+
+
+def _auto_tensor(t):
+    if isinstance(t, TreeValue):
+        t = typetrans(t, Object)
+        if t.map(lambda x, _: pytorch.is_tensor(x)).all():
+            return typetrans(t, Tensor)
+
+    return t
+
+
+_TorchProxy, _InstanceTorchProxy = get_tree_proxy(pytorch.Tensor, _auto_tensor)
 
 
 def _to_tensor(data, *args, **kwargs):