我正在使用python 3.6和{}。在

这个问题有一个sister，在这里我从另一个视角来处理这个问题。但它们不是同一个问题。

我实现了一个r-tree结构，并尝试使用Numba来提高性能，但是当我在搜索项目时不得不对必须访问的节点进行排队时，我遇到了一些问题。在

下面我提供了一个基本元素(节点)和get_all函数的示例，在这里我模拟了树中的搜索(这里我只获取其中的所有元素)。在

这个例子有点长，但希望足够清楚。在import numba as nb

import numpy as np

INF = np.iinfo(np.int64).max

node_type = nb.deferred_type()

stack_type = nb.deferred_type()

node_spec = [

('bbox', nb.int64[:]),

('data', nb.optional(nb.int32)),

('leaf', nb.optional(nb.boolean)),

('height', nb.optional(nb.int16)),

('children', nb.optional(stack_type))

]

@nb.jitclass(node_spec)

class Node(object):

def __init__(self, bbox, data, leaf, height, children):

self.bbox = bbox

self.data = data

self.leaf = leaf

self.height = height

self.children = children

node_type.define(Node.class_type.instance_type)

stack_spec = [

('node', node_type),

('next', nb.optional(stack_type)),

('valid', nb.boolean)

]

@nb.jitclass(stack_spec)

class Stack(object):

def __init__(self, node, next):

self.node = node

self.next = next

self.valid = True

stack_type.define(Stack.class_type.instance_type)

@nb.jit

def push(node, stack=None):

return Stack(node, stack)

@nb.jit

def get(stack, i):

ind = 0

while stack is not None:

if ind == i:

return stack

stack = stack.next

ind+=1

return stack

@nb.jit

def length(stack):

cnt = 0

while stack is not None and stack.valid == True:

cnt+=1

stack = stack.next

return cnt

def create_node(bbox, data=None, leaf=None, height=None, children=None):

node = Node(bbox, data, leaf, height, children)

return node

def create_root(children=None, height=1, leaf=True):

bbox = np.array([INF, INF, -INF, -INF], dtype=int)

return create_node(bbox, leaf=leaf, height=height, children=children)

def create_tree():

data = np.asarray([[1,2,1,2],

[2,3,2,3],

[3,5,3,5],

[5,7,6,8],

[5,6,5,6],

[6,8,6,8]], dtype=int)

bbox1 = np.array([1,3,1,3], dtype=int)

node1 = create_node(bbox1, leaf=True)

item0 = create_node(data[0], data=0)

node1.children = push(item0, node1.children)

item1 = create_node(data[1], data=1)

node1.children = push(item1, node1.children)

bbox2 = np.array([3,5,3,5], dtype=int)

node2 = create_node(bbox2, leaf=True)

item2 = create_node(data[2], data=2)

node2.children = push(item2, node2.children)

bbox3 = np.array([5,9,5,9], dtype=int)

node3 = create_node(bbox3, leaf=True)

item3 = create_node(data[3], data=3)

node3.children = push(item3, node3.children)

item4 = create_node(data[4], data=4)

node3.children = push(item4, node3.children)

item5 = create_node(data[5], data=5)

node3.children = push(item5, node3.children)

root = create_root(leaf=False)

root.children = push(node1, root.children)

root.children = push(node2, root.children)

root.children = push(node3, root.children)

return root

@nb.njit

def get_all(root_node):

nodes_to_search = [root_node]

items = []

while len(nodes_to_search) > 0:

print(len(nodes_to_search))

node = nodes_to_search.pop()

if not node.leaf:

for i in range(length(node.children)):

child = get(node.children, i)

nodes_to_search.append(child.node)

else:

for i in range(length(node.children)):

item = get(node.children, i)

items.append(item.node.data)

return items

def runme():

root = create_tree()

data = get_all(root)

print(data)

if __name__ == '__main__':

runme()

当我们使用njit修饰符运行此代码时，numba会引发以下错误：numba.errors.LoweringError: Failed at nopython (nopython mode backend)

list(instance.jitclass.Node#7fe171d3a5a8):

unsupported nested memory-managed object

如果我使用nopython=False(即jit)修饰符，代码运行良好，尽管numba warnings会抱怨lowering问题以及{}的问题。在

我的问题是：我做错了什么？如何避免上述列表问题？在

谢谢。在