Question

我的函数必须进行jit编译，但是我收到以下弃用警告：

我该如何解决，因此问题已解决？（这样我就不必担心该功能将来无法正常工作）

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它将创建第5个随机值数组e_labeling.py:418: NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "get_result" failed type inference due to: non-precise type array(pyobject, 1d, C) During: typing of argument at D:/Arc/Arc_Project\Architecture\_3_Labeling\CRV_Weighted_Score_labeling.py (422) File "..\_3_Labeling\CRV_Weighted_Score_labeling.py", line 422: def get_result(RatiosUp, RatiosDown, UpPointsSlices, DownPointsSlices, shapes, result, len_result): <source elided> for i in prange(len_result): ^ @nb.jit D:/Arc/Arc_Project\Architecture\_3_Labeling\CRV_Weighted_Score_labeling.py:418: NumbaWarning: Compilation is falling back to object mode WITHOUT looplifting enabled because Function "get_result" failed type inference due to: cannot determine Numba type of <class 'numba.core.dispatcher.LiftedLoop'> File "..\_3_Labeling\CRV_Weighted_Score_labeling.py", line 422: def get_result(RatiosUp, RatiosDown, UpPointsSlices, DownPointsSlices, shapes, result, len_result): <source elided> for i in prange(len_result): ^ @nb.jit c:\users\ben\appdata\local\programs\python\python38\lib\site-packages\numba\core\object_mode_passes.py:177: NumbaWarning: Function "get_result" was compiled in object mode without forceobj=True, but has lifted loops. File "..\_3_Labeling\CRV_Weighted_Score_labeling.py", line 422: def get_result(RatiosUp, RatiosDown, UpPointsSlices, DownPointsSlices, shapes, result, len_result): <source elided> for i in prange(len_result): ^ warnings.warn(errors.NumbaWarning(warn_msg, c:\users\ben\appdata\local\programs\python\python38\lib\site-packages\numba\core\object_mode_passes.py:187: NumbaDeprecationWarning: Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit File "..\_3_Labeling\CRV_Weighted_Score_labeling.py", line 422: def get_result(RatiosUp, RatiosDown, UpPointsSlices, DownPointsSlices, shapes, result, len_result): <source elided> for i in prange(len_result): ^ warnings.warn(errors.NumbaDeprecationWarning(msg, D:/Arc/Arc_Project\Architecture\_3_Labeling\CRV_Weighted_Score_labeling.py:418: NumbaWarning: Compilation is falling back to object mode WITHOUT looplifting enabled because Function "get_result" failed type inference due to: non-precise type pyobject During: typing of argument at D:/Arc/Arc_Project\Architecture\_3_Labeling\CRV_Weighted_Score_labeling.py (422) File "..\_3_Labeling\CRV_Weighted_Score_labeling.py", line 422: def get_result(RatiosUp, RatiosDown, UpPointsSlices, DownPointsSlices, shapes, result, len_result): <source elided> for i in prange(len_result): ^ @nb.jit c:\users\ben\appdata\local\programs\python\python38\lib\site-packages\numba\core\object_mode_passes.py:177: NumbaWarning: Function "get_result" was compiled in object mode without forceobj=True. File "..\_3_Labeling\CRV_Weighted_Score_labeling.py", line 422: def get_result(RatiosUp, RatiosDown, UpPointsSlices, DownPointsSlices, shapes, result, len_result): <source elided> for i in prange(len_result): ^ warnings.warn(errors.NumbaWarning(warn_msg, c:\users\ben\appdata\local\programs\python\python38\lib\site-packages\numba\core\object_mode_passes.py:187: NumbaDeprecationWarning: Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit File "..\_3_Labeling\CRV_Weighted_Score_labeling.py", line 422: def get_result(RatiosUp, RatiosDown, UpPointsSlices, DownPointsSlices, shapes, result, len_result): <source elided> for i in prange(len_result): ^ warnings.warn(errors.NumbaDeprecationWarning(msg,，并根据条件设置result或1 s ：（是的，每个条件都有2个条件，但这是因为它们必须以其他顺序检查。

0是对象的numpy数组（因为它的numpy数组是不同大小的numpy数组。（在同一索引下，这4个np.array的子数组长度相同）

这里是函数：（底部有一个可重现的样本）

RatiosUp, RatiosDown, UpPointsSlices, DownPointsSlices

可重现的样本：（具有正确的结果列表理解，以确保更改后的结果仍然正确）

from numba import prange
@nb.njit 
def compare_size_filter(a,b): 
    return a > b 
@nb.njit(parallel=True)
def loop_func(sub_RatiosUp, sub_RatiosDown, sub_UpPointsSlices, sub_DownPointsSlices, sub_result, len_shape):
     for j in prange(len_shape): 
        if compare_size_filter(sub_RatiosUp[j],sub_RatiosDown[j]):
            sub_result[j] = 1
        elif compare_size_filter(sub_RatiosDown[j],sub_RatiosUp[j]):
            sub_result[j] = 0
        elif compare_size_filter(sub_DownPointsSlices[j], sub_UpPointsSlices[j]): 
            sub_result[j] = 0
        else:                       
            sub_result[j] = 1  
@nb.jit
def get_result(RatiosUp, RatiosDown, UpPointsSlices, DownPointsSlices, shapes, result, len_result): 
    for i in prange(len_result):
       loop_func(RatiosUp[i], RatiosDown[i], UpPointsSlices[i], DownPointsSlices[i], result[i], shapes[i]) 
    return result

Answer 1

您可以禁止弃用警告。从warnings filter

from numba.core.errors import NumbaDeprecationWarning, NumbaPendingDeprecationWarning
import warnings

warnings.simplefilter('ignore', category=NumbaDeprecationWarning)
warnings.simplefilter('ignore', category=NumbaPendingDeprecationWarning)

编辑：从下面的评论中，我看不到这对您有用。因此，我查看了您提供的内容，发现您没有发送Numba所需的数据类型。

您正在向其发送NumPy数组。

RatiosUp = np.array([np.random.uniform(size=rand) for rand in temp], dtype=object)

当数组需要更多类似的东西

>>> numba.float32[:]
array(float32, 1d, A)

同样，来自文档：

As an optimizing compiler, Numba needs to decide on the type of each variable to generate efficient machine code. Python’s standard types are not precise enough for that, so we had to develop our own fine-grained type system。

如何停止弃用警告？

1 个答案: