Numba 函数使用相同的给定输入随机工作，这是一个错误吗？

Question

我在 Numba 中编写了一个名为 not_test 的函数来获取作为排水网络的二维数组列表，然后我得到一个假想的水滴路由从下图可以看出。代码的要点是获取每个可能的排水流的下降路径。

结果

这个区域是我得到的结果，这是水滴落在流的开头时所采用的路由流，例如。然后落在点 1，路由流 [16, 15, 2, 1]。

[[16, 3], 
[16, 15, 2, 0], 
[16, 15, 2, 1], 
[16, 15, 14, 13], 
[16, 15, 14, 12, 4], 
[16, 15, 14, 12, 11, 6], 
[16, 15, 14, 12, 11, 10, 9], 
[16, 15, 14, 12, 11, 10, 8, 5], 
[16, 15, 14, 12, 11, 10, 8, 7]]

问题

该代码可以在普通 python 上运行，并且在使用 Numba 编译时也可以运行。如果您多次运行使用 Numba 编译的代码，就会出现问题，有时会出现错误，有时会起作用。

我无法在 Numba 中调试代码，并且在 python 模式下没有给出错误。它不会在 python 控制台或 pycharm 运行中显示任何特定错误，它只是停止。

注释的代码肯定不是我遇到的问题的一部分。

我真的很希望能够在这个函数上使用 Numba ，因为它的速度提高了 653 倍，并且这个函数将运行大约 5k 次，这意味着：

with Numba:  0.0015003681182861328s per run -> 7.5s total time
with Python: 0.9321613311767578s per run -> 1.3 hours total time

使用 Numba对于这个特定问题来说是一个很大帮助，所以我将不胜感激任何帮助，因为普通的 python 不适用于应用程序的使用。

'错误示例'

in Pycharm error:

        Now
        0.0
        0.2295396327972412
        [16]
        [ 3 15]
        [ 3 15]
        [ 2 14]
        [0 1]
        [12 13]
        
        Process finished with exit code -1073740940 (0xC0000374)
    
    
    in Pycharm no error:

    Now
    0.0
    0.2430422306060791
    [16]
    [ 3 15]
    [ 3 15]
    [ 2 14]
    [0 1]
    [12 13]
    [ 4 11]
    [ 4 11]
    [ 4 11]
    [ 6 10]
    [ 6 10]
    [8 9]
    [5 7]
    [[16, 3], [16, 15, 2, 0], [16, 15, 2, 1], [16, 15, 14, 13], [16, 15, 14, 12, 4], [16, 15, 14, 12, 11, 6], [16, 15, 14, 12, 11, 10, 9], [16, 15, 14, 12, 11, 10, 8, 5], [16, 15, 14, 12, 11, 10, 8, 7]]
    0.0016527080535889
    
    Process finished with exit code 0

代码

文件链接：https://drive.google.com/file/d/1guAe1C2sKZyy2U2_qXAhMA1v46PfeKnN/view

import numpy as np
#from pypiper import RUT_5
import numba   

def convert2(x, dtype=np.float64):
    try:
        # Try and convert x to a Numpy array. If this succeeds
        # then we have reached the end of the nesting-depth.
        y = np.asarray(x, dtype=dtype)
    except:
        # If the conversion to a Numpy array fails, then it can
        # be because not all elements of x can be converted to
        # the given dtype. There is currently no way to distinguish
        # if this is because x is a nested list, or just a list
        # of simple elements with incompatible types.

        # Recursively call this function on all elements of x.
        y = [convert2(x_, dtype=dtype) for x_ in x]

        # Convert Python list to Numba list.
        y = numba.typed.List(y)

    return y
  

@numba.njit('(ListType(float64[:, ::1]), float64[:])')
def not_test(branches, outlet):
    # get len of branches
    _len_branches = len(branches)
    # # empty array
    # d_array = np.empty(shape=_len_branches, dtype=np.float64)
    # # set outlet coordinates as arrays
    # x_outlet, y_outlet = outlet
    # x_outlet, y_outlet = np.array([x_outlet]), np.array([y_outlet])
    #
    # # get min distance from branches
    # for pos in numba.prange(_len_branches):
    #     # get current branch
    #     branch = branches[pos]
    #     # get min distance from outlet point
    #     d_min = RUT_5.nb_cdist(branch, x_outlet, y_outlet).min()
    #     # add to array
    #     d_array[pos] = d_min
    #
    # #get index for minimun distance
    # index_branch = np.argmin(d_array)
    index_branch = 16

    #remove initial branch
    update_branches = branches.copy()
    del update_branches[index_branch]

    #define arrays
    not_read = np.empty(shape=0, dtype=np.int64)
    paths_update = [[np.int(x)] for x in range(0)]
    points = np.empty(shape=(2, 2))
    a_list = [np.int(x) for x in range(0)]

    # avoid from loop
    not_read = np.append(index_branch, not_read)
    # iterable in loop
    iterable = not_read.copy()

    # conditions
    cond = 0
    cont = 0

    while cond == 0:
        for pos_idx in iterable:
            print(iterable)
            if cont > 0:
                paths = paths_update.copy()

            branch = branches[pos_idx]
            points[0] = branch[0]
            points[1] = branch[-1]

            for point in points:
                for pos_j in range(_len_branches):
                    if pos_j not in not_read:
                        diff = np.sum(point - branches[pos_j], axis=1)
                        if 0 in diff:
                            a_list.append(pos_j)

            if cont == 0:
                paths = [[pos_idx] + [i] for i in a_list]
                paths_update = paths.copy()
                cont = cont + 1

                not_read = np.append(not_read, a_list)
                iterable = np.array(a_list)
                a_list = [np.int(x) for x in range(0)]

            else:
                if len(a_list):
                    path_arr = [_i for _i in paths if pos_idx in _i]
                    for path in path_arr:
                        for conexion in a_list:
                            temp_list = path.copy()
                            temp_list.append(conexion)
                            paths_update.append(temp_list)
                        paths_update.remove(path)

                    not_read = np.append(not_read, a_list)
                    iterable = np.array(a_list)
                    a_list = [np.int(x) for x in range(0)]
                else:
                    continue

            if len(branches) == len(np.unique(not_read)):
                cond = 1
    return paths




if __name__ == '__main__':

    print('Now')
    branches = np.load('test.npy', allow_pickle=True).item()
    x_snap, y_snap = 717110.7843995667, 9669749.115011858

    import time
    t0 = time.time()
    arr = []
    for pos, branch in enumerate(branches.features):
        arr.append(list(branch.geometry.coordinates))
    print(time.time() - t0)

    t0 = time.time()
    arr = convert2(arr)
    print(time.time() - t0)

    t0 = time.time()
    outlet = np.array([x_snap, y_snap])
    print(not_test(branches=arr, outlet=outlet))
    print(time.time() - t0)

Answer 1

这不是一个真正的答案，因为它没有解决 numba 代码中潜在错误的实际问题，但它完成了工作。

使用 @numba.njit 装饰器时在代码中使用 pop 或 remove list 方法似乎是一个问题，该问题已报告，开发人员正在调试它。

我最终避免了这些方法，当然它并不理想，因为它迭代了一些不应该的路径，但它比普通的 python 快得多。

代码

@numba.njit('(ListType(float64[:, ::1]), float64[:])')
def not_test(branches, outlet):
    # get len of branches
    _len_branches = len(branches)
    # # empty array
    # d_array = np.empty(shape=_len_branches, dtype=np.float64)
    # # set outlet coordinates as arrays
    # x_outlet, y_outlet = outlet
    # x_outlet, y_outlet = np.array([x_outlet]), np.array([y_outlet])
    #
    # # get min distance from branches
    # for pos in numba.prange(_len_branches):
    #     # get current branch
    #     branch = branches[pos]
    #     # get min distance from outlet point
    #     d_min = RUT_5.nb_cdist(branch, x_outlet, y_outlet).min()
    #     # add to array
    #     d_array[pos] = d_min
    #
    # #get index for minimun distance
    # index_branch = np.argmin(d_array)
    index_branch = 16

    #remove initial branch
    update_branches = branches.copy()
    del update_branches[index_branch]

    #define arrays
    not_read = np.empty(shape=0, dtype=np.int64)
    paths_update = [[np.int(_)] for _ in range(0)]
    paths_remove = [np.int(_) for _ in range(0)]
    points = np.empty(shape=(2, 2))
    a_list = [np.int(x) for x in range(0)]
    diff = np.empty(shape=0, dtype=np.int64)

    # avoid from loop
    not_read = np.append(index_branch, not_read)
    # iterable in loop
    iterable = not_read.copy()

    # conditions
    cond = 0
    cont = 0

    while cond == 0:
        for pos_idx in iterable:
            if cont > 0:
                paths = paths_update.copy()

            branch = branches[pos_idx]
            points[0] = branch[0]
            points[1] = branch[-1]

            for point in points:
                for pos_j in numba.prange(_len_branches):
                    if pos_j not in not_read:
                        diff = np.sum(point - branches[pos_j], axis=1)
                        if len(diff[diff == 0]) > 0:
                            a_list.append(pos_j)

            if cont == 0:
                paths = [[pos_idx] + [i] for i in a_list]
                paths_update = paths.copy()
                cont = 1

                not_read = np.append(not_read, a_list)
                iterable = np.array(a_list)
                a_list = [np.int(x) for x in range(0)]

            else:
                if len(a_list):
                    for pos, path in enumerate(paths):
                        if pos_idx in path:
                            for conexion in a_list:
                                temp_list = path.copy()
                                temp_list.append(conexion)
                                paths_update.append(temp_list)
                            paths_remove.append(pos)

                    not_read = np.append(not_read, a_list)
                    iterable = np.array(a_list)
                    a_list = [np.int(x) for x in range(0)]

            if len(branches) == len(np.unique(not_read)):
                cond = 1

    paths = [_ for _i, _ in enumerate(paths) if _i not in paths_remove]

    return paths