python内存占用过多问题以及其解决方案

import numpy as np

def heavies(row_sums, col_sums, col_index, mat_h):
    if col_index == len(col_sums) - 1:
        for stuff in heavy_col_permutations(row_sums, col_sums, col_index):
            mat_h[:, col_index] = stuff[0]
            yield mat_h.copy()
        return

    for stuff in heavy_col_permutations(row_sums, col_sums, col_index):
        mat_h[:, col_index] = stuff[0]
        row_sums = stuff[1]
        yield from heavies(row_sums, col_sums, col_index+1, mat_h)

def heavy_col_permutations(row_sums, col_sums, col_index):
    # 返回所需特征的矩阵的一列
    pass

if __name__ == "__main__":
    r = int(argv[1])
    n = int(argv[2])
    m = np.zeros((r, r), np.dtype=int32)
    for row, col in heavy_listing(r, n):
        for matrix in heavies(row, col, 0, m):
            # 对矩阵执行其他操作

import gc

# 设置内存回收阈值（单位：字节）
# http://jshk.com.cn/mb/reg.asp?kefu=zhangyajie
gc.set_threshold(100 * 1024 * 1024)

# 调用垃圾回收器，释放内存
gc.collect()

def heavies_iterative(row_sums, col_sums):
    stack = [(row_sums, col_sums, 0, np.zeros((len(row_sums), len(col_sums)), np.dtype=int32))]

    while stack:
        row_sums, col_sums, col_index, mat_h = stack.pop()

        if col_index == len(col_sums) - 1:
            for stuff in heavy_col_permutations(row_sums, col_sums, col_index):
                mat_h[:, col_index] = stuff[0]
                yield mat_h.copy()
            continue

        for stuff in heavy_col_permutations(row_sums, col_sums, col_index):
            mat_h[:, col_index] = stuff[0]
            new_row_sums = stuff[1]
            stack.append((new_row_sums, col_sums, col_index+1, mat_h))

if __name__ == "__main__":
    r = int(argv[1])
    n = int(argv[2])
    for matrix in heavies_iterative([r] * r, [n] * r):
        # 对矩阵执行其他操作