python – Numpy内存映射数组赋值的内存问题

@profile
def do():

    import random
    import numpy as np

    image = np.memmap('image.np',mode='w+',dtype=np.float32,shape=(10000,10000))

    print("Before assignment")

    x = random.uniform(1000,9000)
    y = random.uniform(1000,9000)
    imin = int(x) - 128
    imax = int(x) + 128
    jmin = int(y) - 128
    jmax = int(y) + 128
    data = np.random.random((256,256))
    image[imin:imax,jmin:jmax] = image[imin:imax,jmin:jmax] + data

    del x,y,imin,imax,jmin,jmax,data

    print("After assignment")

do()

与第一个print语句结束时相比,第二个print语句中使用的内存增加了 – 这是memory_profiler输出：

Line #    Mem usage    Increment   Line Contents
================================================
     1                             @profile
     2                             def do():
     3    10.207 MB     0.000 MB   
     4    10.734 MB     0.527 MB       import random
     5    21.066 MB    10.332 MB       import numpy as np
     6                             
     7    21.105 MB     0.039 MB       image = np.memmap('image.np',10000))
     8                             
     9    21.109 MB     0.004 MB       print("Before assignment")
    10                             
    11    21.109 MB     0.000 MB       x = random.uniform(1000,9000)
    12    21.109 MB     0.000 MB       y = random.uniform(1000,9000)
    13    21.109 MB     0.000 MB       imin = int(x) - 128
    14    21.109 MB     0.000 MB       imax = int(x) + 128
    15    21.113 MB     0.004 MB       jmin = int(y) - 128
    16    21.113 MB     0.000 MB       jmax = int(y) + 128
    17    21.625 MB     0.512 MB       data = np.random.random((256,256))
    18    23.574 MB     1.949 MB       image[imin:imax,jmin:jmax] + data
    19                             
    20    23.574 MB     0.000 MB       del x,data
    21                             
    22    23.574 MB     0.000 MB       print("After assigment")

RAM从21.109Mb增加到23.574Mb.如果我将该代码块放在循环中,这会导致问题：

Line #    Mem usage    Increment   Line Contents
================================================
     1                             @profile
     2                             def do():
     3    10.207 MB     0.000 MB   
     4    10.734 MB     0.527 MB       import random
     5    21.066 MB    10.332 MB       import numpy as np
     6                             
     7    21.105 MB     0.039 MB       image = np.memmap('image.np',10000))
     8                             
     9    21.109 MB     0.004 MB       print("Before assignment")
    10                             
    11   292.879 MB   271.770 MB       for i in range(1000):
    12                             
    13   292.879 MB     0.000 MB           x = random.uniform(1000,9000)
    14   292.879 MB     0.000 MB           y = random.uniform(1000,9000)
    15   292.879 MB     0.000 MB           imin = int(x) - 128
    16   292.879 MB     0.000 MB           imax = int(x) + 128
    17   292.879 MB     0.000 MB           jmin = int(y) - 128
    18   292.879 MB     0.000 MB           jmax = int(y) + 128
    19   292.879 MB     0.000 MB           data = np.random.random((256,256))
    20   292.879 MB     0.000 MB           image[imin:imax,jmin:jmax] + data
    21                             
    22   292.879 MB     0.000 MB           del x,data
    23                             
    24   292.879 MB     0.000 MB       print("After assignment")

并且使用的RAM将在每次迭代时增加.有没有办法避免这个问题？这是一个Numpy bug还是我做错了什么？

编辑：这是在MacOS X上,我看到Python 2.7和3.2的问题,Numpy 1.6.2及更高版本(包括开发版).

编辑2：我也在Linux上看到了这个问题.