Question

我正在实现一个函数，该函数涉及对numpy数组的操作，并且我得到了Memory Error。我明确指出了造成问题的numpy数组的尺寸。

a = np.random.rand(15239,1)
b = np.random.rand(1,329960)
c  = np.subtract(a,b)**2
d = np.random.rand(15239,1)
e = np.random.rand(1,329960)
del a
gc.collect()
f = np.subtract(d,e)**2
del d
gc.collect()
g = np.sqrt(c + f).min(axis=0)
del c,f
gc.collect()

我在运行它们时得到Memory Error。

尽管如此，使用它们的功能在下面给出-

def make_weight_map(masks):
    """
    Generate the weight maps as specified in the UNet paper
    for a set of binary masks.

    Parameters
    ----------
    masks: array-like
        A 3D array of shape (n_masks, image_height, image_width),
        where each slice of the matrix along the 0th axis represents one binary mask.

    Returns
    -------
    array-like
        A 2D array of shape (image_height, image_width)

    """
    masks = masks.numpy()
    nrows, ncols = masks.shape[1:]
    masks = (masks > 0).astype(int)
    distMap = np.zeros((nrows * ncols, masks.shape[0]))
    X1, Y1 = np.meshgrid(np.arange(nrows), np.arange(ncols))
    X1, Y1 = np.c_[X1.ravel(), Y1.ravel()].T
    for i, mask in enumerate(masks):
        # find the boundary of each mask,
        # compute the distance of each pixel from this boundary
        bounds = find_boundaries(mask, mode='inner')
        X2, Y2 = np.nonzero(bounds)
        xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2
        ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2
        distMap[:, i] = np.sqrt(xSum + ySum).min(axis=0)
    ix = np.arange(distMap.shape[0])
    if distMap.shape[1] == 1:
        d1 = distMap.ravel()
        border_loss_map = w0 * np.exp((-1 * (d1) ** 2) / (2 * (sigma ** 2)))
    else:
        if distMap.shape[1] == 2:
            d1_ix, d2_ix = np.argpartition(distMap, 1, axis=1)[:, :2].T
        else:
            d1_ix, d2_ix = np.argpartition(distMap, 2, axis=1)[:, :2].T
        d1 = distMap[ix, d1_ix]
        d2 = distMap[ix, d2_ix]
        border_loss_map = w0 * np.exp((-1 * (d1 + d2) ** 2) / (2 * (sigma ** 2)))
    xBLoss = np.zeros((nrows, ncols))
    xBLoss[X1, Y1] = border_loss_map
    # class weight map
    loss = np.zeros((nrows, ncols))
    w_1 = 1 - masks.sum() / loss.size
    w_0 = 1 - w_1
    loss[masks.sum(0) == 1] = w_1
    loss[masks.sum(0) == 0] = w_0
    ZZ = xBLoss + loss
    return ZZ

在函数中使用时错误的跟踪低于- 我正在使用32 GB RAM的系统，我也在61 GB RAM上测试了代码-

---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
<ipython-input-32-0f30ef7dc24d> in <module>
----> 1 img = make_weight_map(img)

<ipython-input-31-e75a6281476f> in make_weight_map(masks)
     34         xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2
     35         ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2
---> 36         distMap[:, i] = np.sqrt(xSum + ySum).min(axis=0)
     37     ix = np.arange(distMap.shape[0])
     38     if distMap.shape[1] == 1:

MemoryError:

我检查了以下问题，但找不到解决问题的方法-
Python/Numpy Memory Error
Memory growth with broadcast operations in NumPy

这是Memmap方法的另一个问题，但我不知道如何在用例中应用。

Answer 1

这并不神秘，它们确实很大。以64位精度，需要形状为(15239,329960)的数组...

>>> np.product((15239,329960)) * 8 / 2**30
37.46345967054367

...大约37GiB！可以尝试的事情：

减少位深度，例如使用np.float16，需要25％的内存。
数据实际上是密集的还是可以使用scipy.sparse？
也许是时候dask了？
获取更多RAM！

有关对numpy数组进行操作的内存错误

1 个答案: