完美解决cannot import name ‘_validate_lengths‘ from ‘numpy.lib.arraypad‘错误

运行以下语句：from skimage import transform
出现如题所示错误，出现错误是因为
C:\Users\Anaconda3\Lib\site-packages\skimage\util
这个文件中的第八行代码要从arraypad.py中导入一个名为_validate_lengths的函数，而系统在该文件中找不到该函数，出现这个错误可能是因为numpy版本升级导致的，但不建议降低numpy版本。解决办法如下：

第一步：

在C:\Users\Anaconda3\Lib\site-packages\skimage\util目录中找到arraycrop.py，并打开，该文件中已经给出了因为numpy版本变化导致arraycrop不可用的方法，即将以注释方式给出的两个函数复制到
C:\Users\Anaconda3\Lib\site-packages\numpy\lib目录中找到arraypad.py文件中。相关注释如下所示：

# The below functions are retained in comments in case the NumPy architecture
# changes and we need copies of these helper functions for `crop`.
# These are identical to functions in numpy.lib.arraypad.py as of NumPy v1.11# def _normalize_shape(ndarray, shape, cast_to_int=True):
#     """
#     Private function which does some checks and normalizes the possibly
#     much simpler representations of 'pad_width', 'stat_length',
#     'constant_values', 'end_values'.
#
#     Parameters
#     ----------
#     narray : ndarray
#         Input ndarray
#     shape : {sequence, array_like, float, int}, optional
#         The width of padding (pad_width), the number of elements on the
#         edge of the narray used for statistics (stat_length), the constant
#         value(s) to use when filling padded regions (constant_values), or the
#         endpoint target(s) for linear ramps (end_values).
#         ((before_1, after_1), ... (before_N, after_N)) unique number of
#         elements for each axis where `N` is rank of `narray`.
#         ((before, after),) yields same before and after constants for each
#         axis.
#         (constant,) or val is a shortcut for before = after = constant for
#         all axes.
#     cast_to_int : bool, optional
#         Controls if values in ``shape`` will be rounded and cast to int
#         before being returned.
#
#     Returns
#     -------
#     normalized_shape : tuple of tuples
#         val                               => ((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=None
#     if shape is None:
#         return ((None, None), ) * ndims
#
#     # Convert any input `info` to a NumPy array
#     arr = np.asarray(shape)
#
#     # Switch based on what input looks like
#     if arr.ndim <= 1:
#         if arr.shape == () or arr.shape == (1,):
#             # Single scalar input
#             #   Create new array of ones, multiply by the scalar
#             arr = np.ones((ndims, 2), dtype=ndarray.dtype) * arr
#         elif arr.shape == (2,):
#             # Apply padding (before, after) each axis
#             #   Create new axis 0, repeat along it for every axis
#             arr = arr[np.newaxis, :].repeat(ndims, axis=0)
#         else:
#             fmt = "Unable to create correctly shaped tuple from %s"
#             raise ValueError(fmt % (shape,))
#
#     elif arr.ndim == 2:
#         if arr.shape[1] == 1 and arr.shape[0] == ndims:
#             # Padded before and after by the same amount
#             arr = arr.repeat(2, axis=1)
#         elif arr.shape[0] == ndims:
#             # Input correctly formatted, pass it on as `arr`
#             arr = shape
#         else:
#             fmt = "Unable to create correctly shaped tuple from %s"
#             raise ValueError(fmt % (shape,))
#
#     else:
#         fmt = "Unable to create correctly shaped tuple from %s"
#         raise ValueError(fmt % (shape,))
#
#     # Cast if necessary
#     if cast_to_int is True:
#         arr = np.round(arr).astype(int)
#
#     # Convert list of lists to tuple of tuples
#     return tuple(tuple(axis) for axis in arr.tolist())# def _validate_lengths(narray, number_elements):
#     """
#     Private function which does some checks and reformats pad_width and
#     stat_length using _normalize_shape.
#
#     Parameters
#     ----------
#     narray : ndarray
#         Input ndarray
#     number_elements : {sequence, int}, optional
#         The width of padding (pad_width) or the number of elements on the edge
#         of the narray used for statistics (stat_length).
#         ((before_1, after_1), ... (before_N, after_N)) unique number of
#         elements for each axis.
#         ((before, after),) yields same before and after constants for each
#         axis.
#         (constant,) or int is a shortcut for before = after = constant for all
#         axes.
#
#     Returns
#     -------
#     _validate_lengths : tuple of tuples
#         int                               => ((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

第二步：

在C:\Users\Anaconda3\Lib\site-packages\numpy\lib目录中找到arraypad.py并打开，在954行之后，插入去掉注释的两个函数：

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

保存后，重启该环境对应的kernel，注意一定不要忘了重启！！！