python - 计算从 hdf5 文件进行内存映射的大型 numpy 数组的平均值

Question

我在计算 numpy 中的数组平均值时遇到问题，该数组对于 RAM(~100G)来说太大了。

<小时/>

我研究过使用np.memmap，但不幸的是我的数组作为数据集存储在 hdf5 文件中。根据我的尝试，np.memmap 不接受 hdf5 数据集作为输入。
类型错误:强制转换为 Unicode:需要字符串或缓冲区，找到数据集


那么如何才能有效地从磁盘调用np.mean 到内存映射数组呢？当然，我可以循环访问数据集的各个部分，其中每个部分都适合内存。
然而，这感觉太像是一种解决方法，而且我也不确定它是否能实现最佳性能。

<小时/>

这是一些示例代码:

data = np.randint(0, 255, 100000*10*10*10, dtype=np.uint8)
data.reshape((100000,10,10,10)) # typically lot larger, ~100G

hdf5_file = h5py.File('data.h5', 'w')
hdf5_file.create_dataset('x', data=data, dtype='uint8')

def get_mean_image(filepath):
    """
    Returns the mean_array of a dataset.
    """
    f = h5py.File(filepath, "r")
    xs_mean = np.mean(f['x'], axis=0) # memory error with large enough array

    return xs_mean

xs_mean = get_mean_image('./data.h5')

Answer 1

正如 hpaulj 在评论中建议的那样，我只是将平均值计算分为多个步骤。
这是我的(简化的)代码，如果它可能对某人有用:

def get_mean_image(filepath):
    """
    Returns the mean_image of a xs dataset.
    :param str filepath: Filepath of the data upon which the mean_image should be calculated.
    :return: ndarray xs_mean: mean_image of the x dataset. 
    """
    f = h5py.File(filepath, "r")

    # check available memory and divide the mean calculation in steps
    total_memory = 0.5 * psutil.virtual_memory() # In bytes. Take 1/2 of what is available, just to make sure.
    filesize = os.path.getsize(filepath)
    steps = int(np.ceil(filesize/total_memory))
    n_rows = f['x'].shape[0]
    stepsize = int(n_rows / float(steps))

    xs_mean_arr = None
    for i in xrange(steps):
        if xs_mean_arr is None: # create xs_mean_arr that stores intermediate mean_temp results
            xs_mean_arr = np.zeros((steps, ) + f['x'].shape[1:], dtype=np.float64)

        if i == steps-1: # for the last step, calculate mean till the end of the file
            xs_mean_temp = np.mean(f['x'][i * stepsize: n_rows], axis=0, dtype=np.float64)
        else:
            xs_mean_temp = np.mean(f['x'][i*stepsize : (i+1) * stepsize], axis=0, dtype=np.float64)
        xs_mean_arr[i] = xs_mean_temp

    xs_mean = np.mean(xs_mean_arr, axis=0, dtype=np.float64).astype(np.float32)

    return xs_mean