ImportError: cannot import name ‘_validate_lengths‘ from ‘numpy.lib.arraypad的解决方法（简单粗暴）

ImportError: cannot import name ‘_validate_lengths’ from 'numpy.lib.arraypad’解决方法

安装scikit-image库时，同时安装了numpy依赖库,运行某个程序时，出现上面的错误。

网上找了很多方法，有的说时版本太高了，但是安装了低版本也没有解决。直到在一篇博客找到了方法，虽然这个方法简单粗暴，但是好用的没得说。

cannot import name ‘_validate_lengths意思是不能导入这个函数，那就直接找到保存这个函数的所在文件把他给写进去就好了。

方法:

找到python环境下的这个路径的文件（arraypad.py）, ……/python3.7/site-packages/numpy/lib/arraypad.py，用记事本打开这个文件，复制拷贝下面函数，在文件末尾添加下面的函数保存即可，要重启环境，pycharm会自动更新。

--------------------------------------------------------------------------------------------------def _normalize_shape(ndarray, shape, cast_to_int=True):"""Private function which does some checks and normalizes the possiblymuch simpler representations of 'pad_width', 'stat_length','constant_values', 'end_values'.Parameters----------narray : ndarrayInput ndarrayshape : {sequence, array_like, float, int}, optionalThe width of padding (pad_width), the number of elements on theedge of the narray used for statistics (stat_length), the constantvalue(s) to use when filling padded regions (constant_values), or theendpoint target(s) for linear ramps (end_values).((before_1, after_1), ... (before_N, after_N)) unique number ofelements for each axis where `N` is rank of `narray`.((before, after),) yields same before and after constants for eachaxis.(constant,) or val is a shortcut for before = after = constant forall axes.cast_to_int : bool, optionalControls if values in ``shape`` will be rounded and cast to intbefore being returned.Returns-------normalized_shape : tuple of tuplesval => ((val, val), (val, val), ...)[[val1, val2], [val3, val4], ...] => ((val1, val2), (val3, val4), ...)((val1, val2), (val3, val4), ...) => no change[[val1, val2], ] => ((val1, val2), (val1, val2), ...)((val1, val2), ) => ((val1, val2), (val1, val2), ...)[[val ,], ] => ((val, val), (val, val), ...)((val ,), ) => ((val, val), (val, val), ...)"""ndims = ndarray.ndim# Shortcut shape=Noneif shape is None:return ((None, None), ) * ndims# Convert any input `info` to a NumPy arrayshape_arr = np.asarray(shape)try:shape_arr = np.broadcast_to(shape_arr, (ndims, 2))except ValueError:fmt = "Unable to create correctly shaped tuple from %s"raise ValueError(fmt % (shape,))# Cast if necessaryif cast_to_int is True:shape_arr = np.round(shape_arr).astype(int)# Convert list of lists to tuple of tuplesreturn tuple(tuple(axis) for axis in shape_arr.tolist())def _validate_lengths(narray, number_elements):"""Private function which does some checks and reformats pad_width andstat_length using _normalize_shape.Parameters----------narray : ndarrayInput ndarraynumber_elements : {sequence, int}, optionalThe width of padding (pad_width) or the number of elements on the edgeof the narray used for statistics (stat_length).((before_1, after_1), ... (before_N, after_N)) unique number ofelements for each axis.((before, after),) yields same before and after constants for eachaxis.(constant,) or int is a shortcut for before = after = constant for allaxes.Returns-------_validate_lengths : tuple of tuplesint => ((int, int), (int, int), ...)[[int1, int2], [int3, int4], ...] => ((int1, int2), (int3, int4), ...)((int1, int2), (int3, int4), ...) => no change[[int1, int2], ] => ((int1, int2), (int1, int2), ...)((int1, int2), ) => ((int1, int2), (int1, int2), ...)[[int ,], ] => ((int, int), (int, int), ...)((int ,), ) => ((int, int), (int, int), ...)"""normshp = _normalize_shape(narray, number_elements)for i in normshp:chk = [1 if x is None else x for x in i]chk = [1 if x >= 0 else -1 for x in chk]if (chk[0] < 0) or (chk[1] < 0):fmt = "%s cannot contain negative values."raise ValueError(fmt % (number_elements,))return normshp

思路来源于这篇博主，👍。

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