eric-haibin-lin commented on issue #18600:
URL:
https://github.com/apache/incubator-mxnet/issues/18600#issuecomment-647214447
np.delete also fails the compatibility test:
```
[2020-06-22T00:36:45.758Z] ================================== FAILURES
===================================
[2020-06-22T00:36:45.758Z] _______________________
test_np_array_function_protocol _______________________
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] args = (), kwargs = {}
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] @functools.wraps(func)
[2020-06-22T00:36:45.758Z] def _run_with_array_func_proto(*args,
**kwargs):
[2020-06-22T00:36:45.758Z] if cur_np_ver >= np_1_17_ver:
[2020-06-22T00:36:45.758Z] try:
[2020-06-22T00:36:45.758Z] > func(*args, **kwargs)
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z]
windows_package\python\mxnet\numpy_dispatch_protocol.py:55:
[2020-06-22T00:36:45.758Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] @with_seed()
[2020-06-22T00:36:45.758Z] @use_np
[2020-06-22T00:36:45.758Z] @with_array_function_protocol
[2020-06-22T00:36:45.758Z] @pytest.mark.serial
[2020-06-22T00:36:45.758Z] def test_np_array_function_protocol():
[2020-06-22T00:36:45.758Z] >
check_interoperability(_NUMPY_ARRAY_FUNCTION_LIST)
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z]
tests\python\unittest\test_numpy_interoperability.py:3262:
[2020-06-22T00:36:45.758Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] op_list = ['all', 'any', 'sometrue', 'argmin',
'argmax', 'around', ...]
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] def check_interoperability(op_list):
[2020-06-22T00:36:45.758Z] for name in op_list:
[2020-06-22T00:36:45.758Z] if name in _TVM_OPS and not
is_op_runnable():
[2020-06-22T00:36:45.758Z] continue
[2020-06-22T00:36:45.758Z] if name in ['shares_memory',
'may_share_memory', 'empty_like',
[2020-06-22T00:36:45.758Z] '__version__', 'dtype',
'_NoValue']: # skip list
[2020-06-22T00:36:45.758Z] continue
[2020-06-22T00:36:45.758Z] if name in ['full_like',
'zeros_like', 'ones_like'] and \
[2020-06-22T00:36:45.758Z]
StrictVersion(platform.python_version()) < StrictVersion('3.0.0'):
[2020-06-22T00:36:45.758Z] continue
[2020-06-22T00:36:45.758Z] print('Dispatch test:', name)
[2020-06-22T00:36:45.758Z] workloads =
OpArgMngr.get_workloads(name)
[2020-06-22T00:36:45.758Z] assert workloads is not None,
'Workloads for operator `{}` has not been ' \
[2020-06-22T00:36:45.758Z] 'added
for checking interoperability with ' \
[2020-06-22T00:36:45.758Z] 'the
official NumPy.'.format(name)
[2020-06-22T00:36:45.758Z] for workload in workloads:
[2020-06-22T00:36:45.758Z] >
_check_interoperability_helper(name, *workload['args'], **workload['kwargs'])
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z]
tests\python\unittest\test_numpy_interoperability.py:3240:
[2020-06-22T00:36:45.758Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] op_name = 'delete', args = (array([0., 1., 2.,
3., 4.]), array([]))
[2020-06-22T00:36:45.758Z] kwargs = {'axis': 0}, strs = ['delete']
[2020-06-22T00:36:45.758Z] onp_op = <function delete at 0x000002C47E8C7158>
[2020-06-22T00:36:45.758Z] out = array([0., 1., 2., 3., 4.])
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] def _check_interoperability_helper(op_name,
*args, **kwargs):
[2020-06-22T00:36:45.758Z] strs = op_name.split('.')
[2020-06-22T00:36:45.758Z] if len(strs) == 1:
[2020-06-22T00:36:45.758Z] onp_op = getattr(_np, op_name)
[2020-06-22T00:36:45.758Z] elif len(strs) == 2:
[2020-06-22T00:36:45.758Z] onp_op = getattr(getattr(_np,
strs[0]), strs[1])
[2020-06-22T00:36:45.758Z] else:
[2020-06-22T00:36:45.758Z] assert False
[2020-06-22T00:36:45.758Z] if not is_op_runnable():
[2020-06-22T00:36:45.758Z] return
[2020-06-22T00:36:45.758Z] out = onp_op(*args, **kwargs)
[2020-06-22T00:36:45.758Z] > expected_out =
_get_numpy_op_output(onp_op, *args, **kwargs)
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z]
tests\python\unittest\test_numpy_interoperability.py:3204:
[2020-06-22T00:36:45.758Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] onp_op = <function delete at 0x000002C47E8C7158>
[2020-06-22T00:36:45.758Z] args = (array([0., 1., 2., 3., 4.]), array([])),
kwargs = {'axis': 0}
[2020-06-22T00:36:45.758Z] onp_args = [array([0., 1., 2., 3., 4.],
dtype=float32), array([], dtype=float32)]
[2020-06-22T00:36:45.758Z] onp_kwargs = {'axis': 0}, i = 1, v = array([],
dtype=float32)
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] def _get_numpy_op_output(onp_op, *args,
**kwargs):
[2020-06-22T00:36:45.758Z] onp_args = [arg.asnumpy() if
isinstance(arg, np.ndarray) else arg for arg in args]
[2020-06-22T00:36:45.758Z] onp_kwargs = {k: v.asnumpy() if
isinstance(v, np.ndarray) else v for k, v in kwargs.items()}
[2020-06-22T00:36:45.758Z] for i, v in enumerate(onp_args):
[2020-06-22T00:36:45.758Z] if isinstance(v, (list, tuple)):
[2020-06-22T00:36:45.758Z] new_arrs = [a.asnumpy() if
isinstance(a, np.ndarray) else a for a in v]
[2020-06-22T00:36:45.758Z] onp_args[i] = new_arrs
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] > return onp_op(*onp_args, **onp_kwargs)
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z]
tests\python\unittest\test_numpy_interoperability.py:3190:
[2020-06-22T00:36:45.758Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] args = (array([0., 1., 2., 3., 4.],
dtype=float32), array([], dtype=float32))
[2020-06-22T00:36:45.758Z] kwargs = {'axis': 0}
[2020-06-22T00:36:45.758Z] relevant_args = (array([0., 1., 2., 3., 4.],
dtype=float32), array([], dtype=float32))
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] > ???
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] <__array_function__ internals>:6:
[2020-06-22T00:36:45.758Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] arr = array([0., 1., 2., 3., 4.], dtype=float32),
obj = array([], dtype=float32)
[2020-06-22T00:36:45.758Z] axis = 0
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] @array_function_dispatch(_delete_dispatcher)
[2020-06-22T00:36:45.758Z] def delete(arr, obj, axis=None):
[2020-06-22T00:36:45.758Z] """
[2020-06-22T00:36:45.758Z] Return a new array with sub-arrays along
an axis deleted. For a one
[2020-06-22T00:36:45.758Z] dimensional array, this returns those
entries not returned by
[2020-06-22T00:36:45.758Z] `arr[obj]`.
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] Parameters
[2020-06-22T00:36:45.758Z] ----------
[2020-06-22T00:36:45.758Z] arr : array_like
[2020-06-22T00:36:45.758Z] Input array.
[2020-06-22T00:36:45.758Z] obj : slice, int or array of ints
[2020-06-22T00:36:45.758Z] Indicate indices of sub-arrays to
remove along the specified axis.
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] .. versionchanged:: 1.19.0
[2020-06-22T00:36:45.758Z] Boolean indices are now treated
as a mask of elements to remove,
[2020-06-22T00:36:45.758Z] rather than being cast to the
integers 0 and 1.
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] axis : int, optional
[2020-06-22T00:36:45.758Z] The axis along which to delete the
subarray defined by `obj`.
[2020-06-22T00:36:45.758Z] If `axis` is None, `obj` is applied
to the flattened array.
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] Returns
[2020-06-22T00:36:45.758Z] -------
[2020-06-22T00:36:45.758Z] out : ndarray
[2020-06-22T00:36:45.758Z] A copy of `arr` with the elements
specified by `obj` removed. Note
[2020-06-22T00:36:45.758Z] that `delete` does not occur
in-place. If `axis` is None, `out` is
[2020-06-22T00:36:45.758Z] a flattened array.
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] See Also
[2020-06-22T00:36:45.758Z] --------
[2020-06-22T00:36:45.758Z] insert : Insert elements into an array.
[2020-06-22T00:36:45.758Z] append : Append elements at the end of an
array.
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] Notes
[2020-06-22T00:36:45.758Z] -----
[2020-06-22T00:36:45.758Z] Often it is preferable to use a boolean
mask. For example:
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] >>> arr = np.arange(12) + 1
[2020-06-22T00:36:45.758Z] >>> mask = np.ones(len(arr), dtype=bool)
[2020-06-22T00:36:45.758Z] >>> mask[[0,2,4]] = False
[2020-06-22T00:36:45.758Z] >>> result = arr[mask,...]
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] Is equivalent to `np.delete(arr, [0,2,4],
axis=0)`, but allows further
[2020-06-22T00:36:45.758Z] use of `mask`.
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] Examples
[2020-06-22T00:36:45.758Z] --------
[2020-06-22T00:36:45.758Z] >>> arr = np.array([[1,2,3,4], [5,6,7,8],
[9,10,11,12]])
[2020-06-22T00:36:45.758Z] >>> arr
[2020-06-22T00:36:45.758Z] array([[ 1, 2, 3, 4],
[2020-06-22T00:36:45.758Z] [ 5, 6, 7, 8],
[2020-06-22T00:36:45.758Z] [ 9, 10, 11, 12]])
[2020-06-22T00:36:45.758Z] >>> np.delete(arr, 1, 0)
[2020-06-22T00:36:45.758Z] array([[ 1, 2, 3, 4],
[2020-06-22T00:36:45.758Z] [ 9, 10, 11, 12]])
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] >>> np.delete(arr, np.s_[::2], 1)
[2020-06-22T00:36:45.758Z] array([[ 2, 4],
[2020-06-22T00:36:45.758Z] [ 6, 8],
[2020-06-22T00:36:45.758Z] [10, 12]])
[2020-06-22T00:36:45.758Z] >>> np.delete(arr, [1,3,5], None)
[2020-06-22T00:36:45.758Z] array([ 1, 3, 5, 7, 8, 9, 10, 11,
12])
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] """
[2020-06-22T00:36:45.758Z] wrap = None
[2020-06-22T00:36:45.758Z] if type(arr) is not ndarray:
[2020-06-22T00:36:45.758Z] try:
[2020-06-22T00:36:45.758Z] wrap = arr.__array_wrap__
[2020-06-22T00:36:45.758Z] except AttributeError:
[2020-06-22T00:36:45.758Z] pass
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] arr = asarray(arr)
[2020-06-22T00:36:45.758Z] ndim = arr.ndim
[2020-06-22T00:36:45.758Z] arrorder = 'F' if arr.flags.fnc else 'C'
[2020-06-22T00:36:45.758Z] if axis is None:
[2020-06-22T00:36:45.758Z] if ndim != 1:
[2020-06-22T00:36:45.758Z] arr = arr.ravel()
[2020-06-22T00:36:45.758Z] # needed for np.matrix, which is
still not 1d after being ravelled
[2020-06-22T00:36:45.758Z] ndim = arr.ndim
[2020-06-22T00:36:45.758Z] axis = ndim - 1
[2020-06-22T00:36:45.758Z] else:
[2020-06-22T00:36:45.758Z] axis = normalize_axis_index(axis,
ndim)
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] slobj = [slice(None)]*ndim
[2020-06-22T00:36:45.758Z] N = arr.shape[axis]
[2020-06-22T00:36:45.758Z] newshape = list(arr.shape)
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] if isinstance(obj, slice):
[2020-06-22T00:36:45.758Z] start, stop, step = obj.indices(N)
[2020-06-22T00:36:45.758Z] xr = range(start, stop, step)
[2020-06-22T00:36:45.758Z] numtodel = len(xr)
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] if numtodel <= 0:
[2020-06-22T00:36:45.758Z] if wrap:
[2020-06-22T00:36:45.758Z] return
wrap(arr.copy(order=arrorder))
[2020-06-22T00:36:45.758Z] else:
[2020-06-22T00:36:45.758Z] return
arr.copy(order=arrorder)
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] # Invert if step is negative:
[2020-06-22T00:36:45.758Z] if step < 0:
[2020-06-22T00:36:45.758Z] step = -step
[2020-06-22T00:36:45.758Z] start = xr[-1]
[2020-06-22T00:36:45.758Z] stop = xr[0] + 1
[2020-06-22T00:36:45.758Z]
[2020-06-22T00:36:45.758Z] newshape[axis] -= numtodel
[2020-06-22T00:36:45.758Z] new = empty(newshape, arr.dtype,
arrorder)
[2020-06-22T00:36:45.758Z] # copy initial chunk
[2020-06-22T00:36:45.758Z] if start == 0:
[2020-06-22T00:36:45.758Z] pass
[2020-06-22T00:36:45.758Z] else:
[2020-06-22T00:36:45.758Z] slobj[axis] = slice(None, start)
[2020-06-22T00:36:45.758Z] new[tuple(slobj)] =
arr[tuple(slobj)]
[2020-06-22T00:36:45.758Z] # copy end chunk
[2020-06-22T00:36:45.758Z] if stop == N:
[2020-06-22T00:36:45.758Z] pass
[2020-06-22T00:36:45.759Z] else:
[2020-06-22T00:36:45.759Z] slobj[axis] =
slice(stop-numtodel, None)
[2020-06-22T00:36:45.759Z] slobj2 = [slice(None)]*ndim
[2020-06-22T00:36:45.759Z] slobj2[axis] = slice(stop, None)
[2020-06-22T00:36:45.759Z] new[tuple(slobj)] =
arr[tuple(slobj2)]
[2020-06-22T00:36:45.759Z] # copy middle pieces
[2020-06-22T00:36:45.759Z] if step == 1:
[2020-06-22T00:36:45.759Z] pass
[2020-06-22T00:36:45.759Z] else: # use array indexing.
[2020-06-22T00:36:45.759Z] keep = ones(stop-start,
dtype=bool)
[2020-06-22T00:36:45.759Z] keep[:stop-start:step] = False
[2020-06-22T00:36:45.759Z] slobj[axis] = slice(start,
stop-numtodel)
[2020-06-22T00:36:45.759Z] slobj2 = [slice(None)]*ndim
[2020-06-22T00:36:45.759Z] slobj2[axis] = slice(start, stop)
[2020-06-22T00:36:45.759Z] arr = arr[tuple(slobj2)]
[2020-06-22T00:36:45.759Z] slobj2[axis] = keep
[2020-06-22T00:36:45.759Z] new[tuple(slobj)] =
arr[tuple(slobj2)]
[2020-06-22T00:36:45.759Z] if wrap:
[2020-06-22T00:36:45.759Z] return wrap(new)
[2020-06-22T00:36:45.759Z] else:
[2020-06-22T00:36:45.759Z] return new
[2020-06-22T00:36:45.759Z]
[2020-06-22T00:36:45.759Z] if isinstance(obj, (int, integer)) and
not isinstance(obj, bool):
[2020-06-22T00:36:45.759Z] # optimization for a single value
[2020-06-22T00:36:45.759Z] if (obj < -N or obj >= N):
[2020-06-22T00:36:45.759Z] raise IndexError(
[2020-06-22T00:36:45.759Z] "index %i is out of bounds
for axis %i with "
[2020-06-22T00:36:45.759Z] "size %i" % (obj, axis, N))
[2020-06-22T00:36:45.759Z] if (obj < 0):
[2020-06-22T00:36:45.759Z] obj += N
[2020-06-22T00:36:45.759Z] newshape[axis] -= 1
[2020-06-22T00:36:45.759Z] new = empty(newshape, arr.dtype,
arrorder)
[2020-06-22T00:36:45.759Z] slobj[axis] = slice(None, obj)
[2020-06-22T00:36:45.759Z] new[tuple(slobj)] = arr[tuple(slobj)]
[2020-06-22T00:36:45.759Z] slobj[axis] = slice(obj, None)
[2020-06-22T00:36:45.759Z] slobj2 = [slice(None)]*ndim
[2020-06-22T00:36:45.759Z] slobj2[axis] = slice(obj+1, None)
[2020-06-22T00:36:45.759Z] new[tuple(slobj)] = arr[tuple(slobj2)]
[2020-06-22T00:36:45.759Z] else:
[2020-06-22T00:36:45.759Z] _obj = obj
[2020-06-22T00:36:45.759Z] obj = np.asarray(obj)
[2020-06-22T00:36:45.759Z] if obj.size == 0 and not
isinstance(_obj, np.ndarray):
[2020-06-22T00:36:45.759Z] obj = obj.astype(intp)
[2020-06-22T00:36:45.759Z]
[2020-06-22T00:36:45.759Z] if obj.dtype == bool:
[2020-06-22T00:36:45.759Z] if obj.shape != (N,):
[2020-06-22T00:36:45.759Z] raise ValueError('boolean
array argument obj to delete '
[2020-06-22T00:36:45.759Z] 'must be one
dimensional and match the axis '
[2020-06-22T00:36:45.759Z] 'length of
{}'.format(N))
[2020-06-22T00:36:45.759Z]
[2020-06-22T00:36:45.759Z] # optimization, the other branch
is slower
[2020-06-22T00:36:45.759Z] keep = ~obj
[2020-06-22T00:36:45.759Z] else:
[2020-06-22T00:36:45.759Z] keep = ones(N, dtype=bool)
[2020-06-22T00:36:45.759Z] > keep[obj,] = False
[2020-06-22T00:36:45.759Z] E IndexError: arrays used as
indices must be of integer (or boolean) type
[2020-06-22T00:36:45.759Z]
[2020-06-22T00:36:45.759Z]
C:\Python37\lib\site-packages\numpy\lib\function_base.py:4406: IndexError
[2020-06-22T00:36:45.759Z]
[2020-06-22T00:36:45.759Z] During handling of the above exception, another
exception occurred:
[2020-06-22T00:36:45.759Z]
[2020-06-22T00:36:45.759Z] args = (), kwargs = {}, test_count = 1,
env_seed_str = None, i = 0
[2020-06-22T00:36:45.759Z] this_test_seed = 810322850, log_level = 10
[2020-06-22T00:36:45.759Z] post_test_state = ('MT19937', array([1938474601,
2158246084, 2260818444, 19860296, 1648467405,
[2020-06-22T00:36:45.759Z] 3604725506, 725127881, 21902135...,
1802122558, 653485402, 3067916522,
[2020-06-22T00:36:45.759Z] 1738520349, 675050936, 3660368646,
2725207562], dtype=uint32), 1, 0, 0.0)
[2020-06-22T00:36:45.759Z] logger = <Logger common (DEBUG)>, test_count_msg
= ''
[2020-06-22T00:36:45.759Z] test_msg = 'Setting test np/mx/python random
seeds, use MXNET_TEST_SEED=810322850 to reproduce.'
[2020-06-22T00:36:45.759Z]
[2020-06-22T00:36:45.759Z] @functools.wraps(orig_test)
[2020-06-22T00:36:45.759Z] def test_new(*args, **kwargs):
[2020-06-22T00:36:45.759Z] test_count =
int(os.getenv('MXNET_TEST_COUNT', '1'))
[2020-06-22T00:36:45.759Z] env_seed_str =
os.getenv('MXNET_TEST_SEED')
[2020-06-22T00:36:45.759Z] for i in range(test_count):
[2020-06-22T00:36:45.759Z] if seed is not None:
[2020-06-22T00:36:45.759Z] this_test_seed = seed
[2020-06-22T00:36:45.759Z] log_level = logging.INFO
[2020-06-22T00:36:45.759Z] elif env_seed_str is not None:
[2020-06-22T00:36:45.759Z] this_test_seed = int(env_seed_str)
[2020-06-22T00:36:45.759Z] log_level = logging.INFO
[2020-06-22T00:36:45.759Z] else:
[2020-06-22T00:36:45.759Z] this_test_seed =
np.random.randint(0, np.iinfo(np.int32).max)
[2020-06-22T00:36:45.759Z] log_level = logging.DEBUG
[2020-06-22T00:36:45.759Z] post_test_state =
np.random.get_state()
[2020-06-22T00:36:45.759Z] np.random.seed(this_test_seed)
[2020-06-22T00:36:45.759Z] mx.random.seed(this_test_seed)
[2020-06-22T00:36:45.759Z] random.seed(this_test_seed)
[2020-06-22T00:36:45.759Z] logger = default_logger()
[2020-06-22T00:36:45.759Z] # 'pytest --logging-level=DEBUG'
shows this msg even with an ensuing core dump.
[2020-06-22T00:36:45.759Z] test_count_msg = '{} of {}:
'.format(i+1,test_count) if test_count > 1 else ''
[2020-06-22T00:36:45.759Z] test_msg = ('{}Setting test
np/mx/python random seeds, use MXNET_TEST_SEED={}'
[2020-06-22T00:36:45.759Z] ' to
reproduce.').format(test_count_msg, this_test_seed)
[2020-06-22T00:36:45.759Z] logger.log(log_level, test_msg)
[2020-06-22T00:36:45.759Z] try:
[2020-06-22T00:36:45.759Z] > orig_test(*args, **kwargs)
[2020-06-22T00:36:45.759Z]
[2020-06-22T00:36:45.759Z] tests\python\unittest\common.py:223:
[2020-06-22T00:36:45.759Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-22T00:36:45.759Z] windows_package\python\mxnet\util.py:298: in
_with_np_shape
[2020-06-22T00:36:45.759Z] return func(*args, **kwargs)
[2020-06-22T00:36:45.759Z] windows_package\python\mxnet\util.py:482: in
_with_np_array
[2020-06-22T00:36:45.759Z] return func(*args, **kwargs)
[2020-06-22T00:36:45.759Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-22T00:36:45.759Z]
[2020-06-22T00:36:45.759Z] args = (), kwargs = {}
[2020-06-22T00:36:45.759Z]
[2020-06-22T00:36:45.759Z] @functools.wraps(func)
[2020-06-22T00:36:45.759Z] def _run_with_array_func_proto(*args,
**kwargs):
[2020-06-22T00:36:45.759Z] if cur_np_ver >= np_1_17_ver:
[2020-06-22T00:36:45.759Z] try:
[2020-06-22T00:36:45.759Z] func(*args, **kwargs)
[2020-06-22T00:36:45.759Z] except Exception as e:
[2020-06-22T00:36:45.759Z] raise RuntimeError('Running
function {} with NumPy array function protocol failed'
[2020-06-22T00:36:45.759Z] ' with
exception {}'
[2020-06-22T00:36:45.759Z] >
.format(func.__name__, str(e)))
[2020-06-22T00:36:45.759Z] E RuntimeError: Running function
test_np_array_function_protocol with NumPy array function protocol failed with
exception arrays used as indices must be of integer (or boolean) type
```
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