`LinAlgError: SVD did not converge` when attempting to rescale a 4D array using `skimage.transform.rescale`

`LinAlgError: SVD did not converge` when attempting to rescale a 4D array using `skimage.transform.rescale`

我想将 MNIST 数据的 4D 数组重新缩放 0.5 倍。我在使用 skimage.transform.rescale:

时遇到错误
LinAlgError: SVD did not converge

我感觉这可能与图像尺寸有关,但文档中没有提及图像尺寸。

from skimage import transform
...
...
data = load_mnist() #Contains mnist data in format (50000, 1, 28, 28)
data_rescaled = transform.rescale(data, 0.5)

来自 the documentation:

skimage.transform.rescale(image, scale, order=1, mode='constant', cval=0, clip=True, preserve_range=False)[source]

Scale image by a certain factor.

Performs interpolation to upscale or down-scale images. For down-sampling N-dimensional images with integer factors by applying the arithmetic sum or mean, see skimage.measure.local_sum and skimage.transform.downscale_local_mean, respectively. ...

scale : {float, tuple of floats}

Scale factors. Separate scale factors can be defined as (row_scale, col_scale).

我的解释是skimage.measure.rescale只支持二维图像。快速尝试为每个维度传递单独的比例因子似乎证实了这一点:

In [1]: data = np.random.randn(500, 1, 28, 28)

In [2]: rescaled = transform.rescale(data, (0.5, 0.5, 0.5, 0.5))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-638fc58c2154> in <module>()
----> 1 rescaled = transform.rescale(data, (0.5, 0.5, 0.5, 0.5))

/home/alistair/.venvs/rfmap/lib/python2.7/site-packages/skimage/transform/_warps.pyc in rescale(image, scale, order, mode, cval, clip, preserve_range)
    164 
    165     try:
--> 166         row_scale, col_scale = scale
    167     except TypeError:
    168         row_scale = col_scale = scale

ValueError: too many values to unpack

如文档所述,您可以改用 skimage.transform.local_sumskimage.downscale_local_mean,前提是您只需要按整数因子(在您的情况下为 2)进行下采样。

另一个支持对非整数缩放因子使用插值的替代方法是 scipy.ndimage.zoom:

In [3]: from scipy import ndimage

In [4]: rescaled = ndimage.zoom(data, 0.5)

In [5]: rescaled.shape
Out[5]: (250, 1, 14, 14)