eric-haibin-lin commented on issue #18600:
URL:
https://github.com/apache/incubator-mxnet/issues/18600#issuecomment-647192100
Error Log
```
[2020-06-20T23:26:16.963Z] _______________________________ test_np_delete
________________________________
[2020-06-20T23:26:16.963Z] [gw3] win32 -- Python 3.7.3 C:\Python37\python.exe
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] @with_seed()
[2020-06-20T23:26:16.963Z] @use_np
[2020-06-20T23:26:16.963Z] def test_np_delete():
[2020-06-20T23:26:16.963Z] class TestDelete(HybridBlock):
[2020-06-20T23:26:16.963Z] def __init__(self, obj, axis=None):
[2020-06-20T23:26:16.963Z] super(TestDelete, self).__init__()
[2020-06-20T23:26:16.963Z] self._obj = obj
[2020-06-20T23:26:16.963Z] self._axis = axis
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] def hybrid_forward(self, F, a):
[2020-06-20T23:26:16.963Z] return F.np.delete(a, self._obj,
axis=self._axis)
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] def GetSize(shp):
[2020-06-20T23:26:16.963Z] if len(shp) == 0:
[2020-06-20T23:26:16.963Z] return 0
[2020-06-20T23:26:16.963Z] else:
[2020-06-20T23:26:16.963Z] res = 1
[2020-06-20T23:26:16.963Z] shp_list = list(shp)
[2020-06-20T23:26:16.963Z] for x in shp:
[2020-06-20T23:26:16.963Z] res *= x
[2020-06-20T23:26:16.963Z] return res
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] def GetDimSize(shp, axis):
[2020-06-20T23:26:16.963Z] if axis is None:
[2020-06-20T23:26:16.963Z] return GetSize(shp)
[2020-06-20T23:26:16.963Z] shp_list = list(shp)
[2020-06-20T23:26:16.963Z] return shp_list[axis]
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] shape = [(), (0, ), (1, ), (2, 3), (2, 1,
4, 5)]
[2020-06-20T23:26:16.963Z] config = []
[2020-06-20T23:26:16.963Z] for shp in shape:
[2020-06-20T23:26:16.963Z] for ax in range(-1 * len(shp),
len(shp), 2):
[2020-06-20T23:26:16.963Z] #test slice
[2020-06-20T23:26:16.963Z] for st in [-5, -2, 0, 2, 5, None]:
[2020-06-20T23:26:16.963Z] for ed in [-5, -2, 0, 2, 5,
None]:
[2020-06-20T23:26:16.963Z] for stp in [-5, -2, 2, 5,
None]:
[2020-06-20T23:26:16.963Z]
config.append(tuple([shp, slice(st, ed, stp), None]))
[2020-06-20T23:26:16.963Z]
config.append(tuple([shp, slice(st, ed, stp), ax]))
[2020-06-20T23:26:16.963Z] #test iteger
[2020-06-20T23:26:16.963Z] for idx in range(-1 *
GetDimSize(shp, ax), GetDimSize(shp, ax)):
[2020-06-20T23:26:16.963Z] config.append(tuple([shp,
idx, ax]))
[2020-06-20T23:26:16.963Z] #test ndarray indices
[2020-06-20T23:26:16.963Z] idx = _np.random.randint(-1 *
shp[ax], shp[ax] + 1, size = (4)).tolist()
[2020-06-20T23:26:16.963Z] config.append(tuple([shp, idx,
ax]))
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] for arr_shape, obj, axis in config:
[2020-06-20T23:26:16.963Z] for objtype in ['int32', 'int64']:
[2020-06-20T23:26:16.963Z] if type(obj) == list:
[2020-06-20T23:26:16.963Z] obj_mxnp = np.array(obj,
dtype=objtype)
[2020-06-20T23:26:16.963Z] obj_onp = _np.array(obj,
dtype=objtype)
[2020-06-20T23:26:16.963Z] elif type(obj) == slice:
[2020-06-20T23:26:16.963Z] obj_mxnp = obj
[2020-06-20T23:26:16.963Z] obj_onp = obj
[2020-06-20T23:26:16.963Z] else:
[2020-06-20T23:26:16.963Z] obj_mxnp = (_np.int32(obj) if
objtype == 'int32' else _np.int64(obj))
[2020-06-20T23:26:16.963Z] obj_onp = (_np.int32(obj) if
objtype == 'int32' else _np.int64(obj))
[2020-06-20T23:26:16.963Z] test_delete =
TestDelete(obj=obj_mxnp, axis=axis)
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] a = mx.nd.random.uniform(-1.0,
1.0, shape=arr_shape).as_np_ndarray()
[2020-06-20T23:26:16.963Z] a.attach_grad()
[2020-06-20T23:26:16.963Z] > expected_ret =
_np.delete(a.asnumpy(), obj_onp, axis=axis)
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] tests\python\unittest\test_numpy_op.py:4255:
[2020-06-20T23:26:16.963Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-20T23:26:16.963Z] <__array_function__ internals>:6: in delete
[2020-06-20T23:26:16.963Z] ???
[2020-06-20T23:26:16.963Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] arr = array([], dtype=float32), obj = array([0,
0, 0, 0]), axis = 0
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] @array_function_dispatch(_delete_dispatcher)
[2020-06-20T23:26:16.963Z] def delete(arr, obj, axis=None):
[2020-06-20T23:26:16.963Z] """
[2020-06-20T23:26:16.963Z] Return a new array with sub-arrays along
an axis deleted. For a one
[2020-06-20T23:26:16.963Z] dimensional array, this returns those
entries not returned by
[2020-06-20T23:26:16.963Z] `arr[obj]`.
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] Parameters
[2020-06-20T23:26:16.963Z] ----------
[2020-06-20T23:26:16.963Z] arr : array_like
[2020-06-20T23:26:16.963Z] Input array.
[2020-06-20T23:26:16.963Z] obj : slice, int or array of ints
[2020-06-20T23:26:16.963Z] Indicate indices of sub-arrays to
remove along the specified axis.
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] .. versionchanged:: 1.19.0
[2020-06-20T23:26:16.963Z] Boolean indices are now treated
as a mask of elements to remove,
[2020-06-20T23:26:16.963Z] rather than being cast to the
integers 0 and 1.
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] axis : int, optional
[2020-06-20T23:26:16.963Z] The axis along which to delete the
subarray defined by `obj`.
[2020-06-20T23:26:16.963Z] If `axis` is None, `obj` is applied
to the flattened array.
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] Returns
[2020-06-20T23:26:16.963Z] -------
[2020-06-20T23:26:16.963Z] out : ndarray
[2020-06-20T23:26:16.963Z] A copy of `arr` with the elements
specified by `obj` removed. Note
[2020-06-20T23:26:16.963Z] that `delete` does not occur
in-place. If `axis` is None, `out` is
[2020-06-20T23:26:16.963Z] a flattened array.
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] See Also
[2020-06-20T23:26:16.963Z] --------
[2020-06-20T23:26:16.963Z] insert : Insert elements into an array.
[2020-06-20T23:26:16.963Z] append : Append elements at the end of an
array.
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] Notes
[2020-06-20T23:26:16.963Z] -----
[2020-06-20T23:26:16.963Z] Often it is preferable to use a boolean
mask. For example:
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] >>> arr = np.arange(12) + 1
[2020-06-20T23:26:16.963Z] >>> mask = np.ones(len(arr), dtype=bool)
[2020-06-20T23:26:16.963Z] >>> mask[[0,2,4]] = False
[2020-06-20T23:26:16.963Z] >>> result = arr[mask,...]
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] Is equivalent to `np.delete(arr, [0,2,4],
axis=0)`, but allows further
[2020-06-20T23:26:16.963Z] use of `mask`.
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] Examples
[2020-06-20T23:26:16.963Z] --------
[2020-06-20T23:26:16.963Z] >>> arr = np.array([[1,2,3,4], [5,6,7,8],
[9,10,11,12]])
[2020-06-20T23:26:16.963Z] >>> arr
[2020-06-20T23:26:16.963Z] array([[ 1, 2, 3, 4],
[2020-06-20T23:26:16.963Z] [ 5, 6, 7, 8],
[2020-06-20T23:26:16.963Z] [ 9, 10, 11, 12]])
[2020-06-20T23:26:16.963Z] >>> np.delete(arr, 1, 0)
[2020-06-20T23:26:16.963Z] array([[ 1, 2, 3, 4],
[2020-06-20T23:26:16.963Z] [ 9, 10, 11, 12]])
[2020-06-20T23:26:16.963Z]
[2020-06-20T23:26:16.963Z] >>> np.delete(arr, np.s_[::2], 1)
[2020-06-20T23:26:16.963Z] array([[ 2, 4],
[2020-06-20T23:26:16.964Z] [ 6, 8],
[2020-06-20T23:26:16.964Z] [10, 12]])
[2020-06-20T23:26:16.964Z] >>> np.delete(arr, [1,3,5], None)
[2020-06-20T23:26:16.964Z] array([ 1, 3, 5, 7, 8, 9, 10, 11,
12])
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] """
[2020-06-20T23:26:16.964Z] wrap = None
[2020-06-20T23:26:16.964Z] if type(arr) is not ndarray:
[2020-06-20T23:26:16.964Z] try:
[2020-06-20T23:26:16.964Z] wrap = arr.__array_wrap__
[2020-06-20T23:26:16.964Z] except AttributeError:
[2020-06-20T23:26:16.964Z] pass
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] arr = asarray(arr)
[2020-06-20T23:26:16.964Z] ndim = arr.ndim
[2020-06-20T23:26:16.964Z] arrorder = 'F' if arr.flags.fnc else 'C'
[2020-06-20T23:26:16.964Z] if axis is None:
[2020-06-20T23:26:16.964Z] if ndim != 1:
[2020-06-20T23:26:16.964Z] arr = arr.ravel()
[2020-06-20T23:26:16.964Z] # needed for np.matrix, which is
still not 1d after being ravelled
[2020-06-20T23:26:16.964Z] ndim = arr.ndim
[2020-06-20T23:26:16.964Z] axis = ndim - 1
[2020-06-20T23:26:16.964Z] else:
[2020-06-20T23:26:16.964Z] axis = normalize_axis_index(axis,
ndim)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] slobj = [slice(None)]*ndim
[2020-06-20T23:26:16.964Z] N = arr.shape[axis]
[2020-06-20T23:26:16.964Z] newshape = list(arr.shape)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] if isinstance(obj, slice):
[2020-06-20T23:26:16.964Z] start, stop, step = obj.indices(N)
[2020-06-20T23:26:16.964Z] xr = range(start, stop, step)
[2020-06-20T23:26:16.964Z] numtodel = len(xr)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] if numtodel <= 0:
[2020-06-20T23:26:16.964Z] if wrap:
[2020-06-20T23:26:16.964Z] return
wrap(arr.copy(order=arrorder))
[2020-06-20T23:26:16.964Z] else:
[2020-06-20T23:26:16.964Z] return
arr.copy(order=arrorder)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] # Invert if step is negative:
[2020-06-20T23:26:16.964Z] if step < 0:
[2020-06-20T23:26:16.964Z] step = -step
[2020-06-20T23:26:16.964Z] start = xr[-1]
[2020-06-20T23:26:16.964Z] stop = xr[0] + 1
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] newshape[axis] -= numtodel
[2020-06-20T23:26:16.964Z] new = empty(newshape, arr.dtype,
arrorder)
[2020-06-20T23:26:16.964Z] # copy initial chunk
[2020-06-20T23:26:16.964Z] if start == 0:
[2020-06-20T23:26:16.964Z] pass
[2020-06-20T23:26:16.964Z] else:
[2020-06-20T23:26:16.964Z] slobj[axis] = slice(None, start)
[2020-06-20T23:26:16.964Z] new[tuple(slobj)] =
arr[tuple(slobj)]
[2020-06-20T23:26:16.964Z] # copy end chunk
[2020-06-20T23:26:16.964Z] if stop == N:
[2020-06-20T23:26:16.964Z] pass
[2020-06-20T23:26:16.964Z] else:
[2020-06-20T23:26:16.964Z] slobj[axis] =
slice(stop-numtodel, None)
[2020-06-20T23:26:16.964Z] slobj2 = [slice(None)]*ndim
[2020-06-20T23:26:16.964Z] slobj2[axis] = slice(stop, None)
[2020-06-20T23:26:16.964Z] new[tuple(slobj)] =
arr[tuple(slobj2)]
[2020-06-20T23:26:16.964Z] # copy middle pieces
[2020-06-20T23:26:16.964Z] if step == 1:
[2020-06-20T23:26:16.964Z] pass
[2020-06-20T23:26:16.964Z] else: # use array indexing.
[2020-06-20T23:26:16.964Z] keep = ones(stop-start,
dtype=bool)
[2020-06-20T23:26:16.964Z] keep[:stop-start:step] = False
[2020-06-20T23:26:16.964Z] slobj[axis] = slice(start,
stop-numtodel)
[2020-06-20T23:26:16.964Z] slobj2 = [slice(None)]*ndim
[2020-06-20T23:26:16.964Z] slobj2[axis] = slice(start, stop)
[2020-06-20T23:26:16.964Z] arr = arr[tuple(slobj2)]
[2020-06-20T23:26:16.964Z] slobj2[axis] = keep
[2020-06-20T23:26:16.964Z] new[tuple(slobj)] =
arr[tuple(slobj2)]
[2020-06-20T23:26:16.964Z] if wrap:
[2020-06-20T23:26:16.964Z] return wrap(new)
[2020-06-20T23:26:16.964Z] else:
[2020-06-20T23:26:16.964Z] return new
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] if isinstance(obj, (int, integer)) and
not isinstance(obj, bool):
[2020-06-20T23:26:16.964Z] # optimization for a single value
[2020-06-20T23:26:16.964Z] if (obj < -N or obj >= N):
[2020-06-20T23:26:16.964Z] raise IndexError(
[2020-06-20T23:26:16.964Z] "index %i is out of bounds
for axis %i with "
[2020-06-20T23:26:16.964Z] "size %i" % (obj, axis, N))
[2020-06-20T23:26:16.964Z] if (obj < 0):
[2020-06-20T23:26:16.964Z] obj += N
[2020-06-20T23:26:16.964Z] newshape[axis] -= 1
[2020-06-20T23:26:16.964Z] new = empty(newshape, arr.dtype,
arrorder)
[2020-06-20T23:26:16.964Z] slobj[axis] = slice(None, obj)
[2020-06-20T23:26:16.964Z] new[tuple(slobj)] = arr[tuple(slobj)]
[2020-06-20T23:26:16.964Z] slobj[axis] = slice(obj, None)
[2020-06-20T23:26:16.964Z] slobj2 = [slice(None)]*ndim
[2020-06-20T23:26:16.964Z] slobj2[axis] = slice(obj+1, None)
[2020-06-20T23:26:16.964Z] new[tuple(slobj)] = arr[tuple(slobj2)]
[2020-06-20T23:26:16.964Z] else:
[2020-06-20T23:26:16.964Z] _obj = obj
[2020-06-20T23:26:16.964Z] obj = np.asarray(obj)
[2020-06-20T23:26:16.964Z] if obj.size == 0 and not
isinstance(_obj, np.ndarray):
[2020-06-20T23:26:16.964Z] obj = obj.astype(intp)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] if obj.dtype == bool:
[2020-06-20T23:26:16.964Z] if obj.shape != (N,):
[2020-06-20T23:26:16.964Z] raise ValueError('boolean
array argument obj to delete '
[2020-06-20T23:26:16.964Z] 'must be one
dimensional and match the axis '
[2020-06-20T23:26:16.964Z] 'length of
{}'.format(N))
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] # optimization, the other branch
is slower
[2020-06-20T23:26:16.964Z] keep = ~obj
[2020-06-20T23:26:16.964Z] else:
[2020-06-20T23:26:16.964Z] keep = ones(N, dtype=bool)
[2020-06-20T23:26:16.964Z] > keep[obj,] = False
[2020-06-20T23:26:16.964Z] E IndexError: index 0 is out of
bounds for axis 0 with size 0
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z]
C:\Python37\lib\site-packages\numpy\lib\function_base.py:4406: IndexError
[2020-06-20T23:26:16.964Z] ----------------------------- Captured log setup
------------------------------
[2020-06-20T23:26:16.964Z] [35mDEBUG [0m root:conftest.py:193
np/mx/python random seeds are set to 1481650552, use MXNET_TEST_SEED=1481650552
to reproduce.
[2020-06-20T23:26:16.964Z] ---------------------------- Captured stderr call
-----------------------------
[2020-06-20T23:26:16.964Z] [DEBUG] Setting test np/mx/python random seeds,
use MXNET_TEST_SEED=117564977 to reproduce.
[2020-06-20T23:26:16.964Z] [INFO] Setting test np/mx/python random seeds,
use MXNET_TEST_SEED=117564977 to reproduce.
[2020-06-20T23:26:16.964Z] ------------------------------ Captured log call
------------------------------
[2020-06-20T23:26:16.964Z] [35mDEBUG [0m common:common.py:221 Setting
test np/mx/python random seeds, use MXNET_TEST_SEED=117564977 to reproduce.
[2020-06-20T23:26:16.964Z] [32mINFO [0m common:common.py:227 Setting
test np/mx/python random seeds, use MXNET_TEST_SEED=117564977 to reproduce.
[2020-06-20T23:26:16.964Z] ---------------------------- Captured log
teardown ----------------------------
[2020-06-20T23:26:16.964Z] [32mINFO [0m root:conftest.py:210
np/mx/python random seeds are set to 1481650552, use MXNET_TEST_SEED=1481650552
to reproduce.
[2020-06-20T23:26:16.964Z] _____________________________ test_np_random_beta
_____________________________
[2020-06-20T23:26:16.964Z] [gw3] win32 -- Python 3.7.3 C:\Python37\python.exe
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] @with_seed()
[2020-06-20T23:26:16.964Z] @use_np
[2020-06-20T23:26:16.964Z] def test_np_random_beta():
[2020-06-20T23:26:16.964Z] class TestRandomBeta(HybridBlock):
[2020-06-20T23:26:16.964Z] def __init__(self, size=None,
dtype=None, ctx=None):
[2020-06-20T23:26:16.964Z] super(TestRandomBeta,
self).__init__()
[2020-06-20T23:26:16.964Z] self._size = size
[2020-06-20T23:26:16.964Z] self._dtype = dtype
[2020-06-20T23:26:16.964Z] self._ctx = ctx
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] def hybrid_forward(self, F, a, b):
[2020-06-20T23:26:16.964Z] return F.np.random.beta(a, b,
size=self._size, dtype=self._dtype, ctx=self._ctx)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] def _test_random_beta_range(output):
[2020-06-20T23:26:16.964Z] bigger_than_zero = _np.all(output > 0)
[2020-06-20T23:26:16.964Z] smaller_than_one = _np.all(output < 1)
[2020-06-20T23:26:16.964Z] return bigger_than_zero and
smaller_than_one
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] shape_list = [(), (1,), (2, 3), (4, 0,
5), 6, (7, 8), None]
[2020-06-20T23:26:16.964Z] # since fp16 might incur precision issue,
the corresponding test is skipped
[2020-06-20T23:26:16.964Z] dtype_list = [np.float32, np.float64]
[2020-06-20T23:26:16.964Z] hybridize_list = [False, True]
[2020-06-20T23:26:16.964Z] data = np.array([1])
[2020-06-20T23:26:16.964Z] for [param_shape, in_dtype, out_dtype,
hybridize] in itertools.product(shape_list,
[2020-06-20T23:26:16.964Z] dtype_list, dtype_list,
hybridize_list):
[2020-06-20T23:26:16.964Z] mx_data = data.astype(in_dtype)
[2020-06-20T23:26:16.964Z] np_data = mx_data.asnumpy()
[2020-06-20T23:26:16.964Z] test_random_beta =
TestRandomBeta(size=param_shape, dtype=out_dtype)
[2020-06-20T23:26:16.964Z] if hybridize:
[2020-06-20T23:26:16.964Z] test_random_beta.hybridize()
[2020-06-20T23:26:16.964Z] > np_out = _np.random.beta(np_data,
np_data, size=param_shape)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] tests\python\unittest\test_numpy_op.py:4786:
[2020-06-20T23:26:16.964Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-20T23:26:16.964Z] mtrand.pyx:478: in
numpy.random.mtrand.RandomState.beta
[2020-06-20T23:26:16.964Z] ???
[2020-06-20T23:26:16.964Z] _common.pyx:541: in numpy.random._common.cont
[2020-06-20T23:26:16.964Z] ???
[2020-06-20T23:26:16.964Z] _common.pyx:459: in
numpy.random._common.cont_broadcast_2
[2020-06-20T23:26:16.964Z] ???
[2020-06-20T23:26:16.964Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] > ???
[2020-06-20T23:26:16.964Z] E ValueError: Output size () is not compatible
with broadcast dimensions of inputs (1,).
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] _common.pyx:229: ValueError
[2020-06-20T23:26:16.964Z] ----------------------------- Captured log setup
------------------------------
[2020-06-20T23:26:16.964Z] [35mDEBUG [0m root:conftest.py:193
np/mx/python random seeds are set to 404557395, use MXNET_TEST_SEED=404557395
to reproduce.
[2020-06-20T23:26:16.964Z] ---------------------------- Captured stderr call
-----------------------------
[2020-06-20T23:26:16.964Z] [DEBUG] Setting test np/mx/python random seeds,
use MXNET_TEST_SEED=1213699728 to reproduce.
[2020-06-20T23:26:16.964Z] [INFO] Setting test np/mx/python random seeds,
use MXNET_TEST_SEED=1213699728 to reproduce.
[2020-06-20T23:26:16.964Z] ------------------------------ Captured log call
------------------------------
[2020-06-20T23:26:16.964Z] [35mDEBUG [0m common:common.py:221 Setting
test np/mx/python random seeds, use MXNET_TEST_SEED=1213699728 to reproduce.
[2020-06-20T23:26:16.964Z] [32mINFO [0m common:common.py:227 Setting
test np/mx/python random seeds, use MXNET_TEST_SEED=1213699728 to reproduce.
[2020-06-20T23:26:16.964Z] ---------------------------- Captured log
teardown ----------------------------
[2020-06-20T23:26:16.964Z] [32mINFO [0m root:conftest.py:210
np/mx/python random seeds are set to 404557395, use MXNET_TEST_SEED=404557395
to reproduce.
[2020-06-20T23:26:16.964Z] ______________________________ test_np_random_f
_______________________________
[2020-06-20T23:26:16.964Z] [gw3] win32 -- Python 3.7.3 C:\Python37\python.exe
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] @with_seed()
[2020-06-20T23:26:16.964Z] @use_np
[2020-06-20T23:26:16.964Z] def test_np_random_f():
[2020-06-20T23:26:16.964Z] class TestRandomF(HybridBlock):
[2020-06-20T23:26:16.964Z] def __init__(self, size=None):
[2020-06-20T23:26:16.964Z] super(TestRandomF,
self).__init__()
[2020-06-20T23:26:16.964Z] self._size = size
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] def hybrid_forward(self, F, dfnum,
dfden):
[2020-06-20T23:26:16.964Z] return F.np.random.f(dfnum,
dfden, size=self._size)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] shape_list = [(), (1,), (2, 3), (4, 0,
5), 6, (7, 8), None]
[2020-06-20T23:26:16.964Z] hybridize_list = [False, True]
[2020-06-20T23:26:16.964Z] df = np.array([1])
[2020-06-20T23:26:16.964Z] for [param_shape, hybridize] in
itertools.product(shape_list,
[2020-06-20T23:26:16.964Z] hybridize_list):
[2020-06-20T23:26:16.964Z] if sys.version_info.major < 3 and
param_shape == ():
[2020-06-20T23:26:16.964Z] continue
[2020-06-20T23:26:16.964Z] mx_df = df
[2020-06-20T23:26:16.964Z] np_df = mx_df.asnumpy()
[2020-06-20T23:26:16.964Z] test_random_f =
TestRandomF(size=param_shape)
[2020-06-20T23:26:16.964Z] if hybridize:
[2020-06-20T23:26:16.964Z] test_random_f.hybridize()
[2020-06-20T23:26:16.964Z] > np_out = _np.random.f(np_df, np_df,
size=param_shape)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] tests\python\unittest\test_numpy_op.py:4823:
[2020-06-20T23:26:16.964Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-20T23:26:16.964Z] mtrand.pyx:1753: in
numpy.random.mtrand.RandomState.f
[2020-06-20T23:26:16.964Z] ???
[2020-06-20T23:26:16.964Z] _common.pyx:541: in numpy.random._common.cont
[2020-06-20T23:26:16.964Z] ???
[2020-06-20T23:26:16.964Z] _common.pyx:459: in
numpy.random._common.cont_broadcast_2
[2020-06-20T23:26:16.964Z] ???
[2020-06-20T23:26:16.964Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] > ???
[2020-06-20T23:26:16.964Z] E ValueError: Output size () is not compatible
with broadcast dimensions of inputs (1,).
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] _common.pyx:229: ValueError
[2020-06-20T23:26:16.964Z] ----------------------------- Captured log setup
------------------------------
[2020-06-20T23:26:16.964Z] [35mDEBUG [0m root:conftest.py:193
np/mx/python random seeds are set to 364715442, use MXNET_TEST_SEED=364715442
to reproduce.
[2020-06-20T23:26:16.964Z] ---------------------------- Captured stderr call
-----------------------------
[2020-06-20T23:26:16.964Z] [DEBUG] Setting test np/mx/python random seeds,
use MXNET_TEST_SEED=867177126 to reproduce.
[2020-06-20T23:26:16.964Z] [INFO] Setting test np/mx/python random seeds,
use MXNET_TEST_SEED=867177126 to reproduce.
[2020-06-20T23:26:16.964Z] ------------------------------ Captured log call
------------------------------
[2020-06-20T23:26:16.964Z] [35mDEBUG [0m common:common.py:221 Setting
test np/mx/python random seeds, use MXNET_TEST_SEED=867177126 to reproduce.
[2020-06-20T23:26:16.964Z] [32mINFO [0m common:common.py:227 Setting
test np/mx/python random seeds, use MXNET_TEST_SEED=867177126 to reproduce.
[2020-06-20T23:26:16.964Z] ---------------------------- Captured log
teardown ----------------------------
[2020-06-20T23:26:16.964Z] [32mINFO [0m root:conftest.py:210
np/mx/python random seeds are set to 364715442, use MXNET_TEST_SEED=364715442
to reproduce.
[2020-06-20T23:26:16.964Z] __________________________
test_np_random_chisquare ___________________________
[2020-06-20T23:26:16.964Z] [gw3] win32 -- Python 3.7.3 C:\Python37\python.exe
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] @with_seed()
[2020-06-20T23:26:16.964Z] @use_np
[2020-06-20T23:26:16.964Z] def test_np_random_chisquare():
[2020-06-20T23:26:16.964Z] class TestRandomChisquare(HybridBlock):
[2020-06-20T23:26:16.964Z] def __init__(self, size=None,
dtype=None, ctx=None):
[2020-06-20T23:26:16.964Z] super(TestRandomChisquare,
self).__init__()
[2020-06-20T23:26:16.964Z] self._size = size
[2020-06-20T23:26:16.964Z] self._dtype = dtype
[2020-06-20T23:26:16.964Z] self._ctx = ctx
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] def hybrid_forward(self, F, df):
[2020-06-20T23:26:16.964Z] return F.np.random.chisquare(df,
size=self._size, dtype=self._dtype, ctx=self._ctx)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] shape_list = [(), (1,), (2, 3), (4, 0,
5), 6, (7, 8), None]
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] dtype_list = [np.float16, np.float32,
np.float64]
[2020-06-20T23:26:16.964Z] hybridize_list = [False, True]
[2020-06-20T23:26:16.964Z] df = np.array([1])
[2020-06-20T23:26:16.964Z] for [param_shape, in_dtype, out_dtype,
hybridize] in itertools.product(shape_list,
[2020-06-20T23:26:16.964Z] dtype_list, dtype_list,
hybridize_list):
[2020-06-20T23:26:16.964Z] if sys.version_info.major < 3 and
param_shape == ():
[2020-06-20T23:26:16.964Z] continue
[2020-06-20T23:26:16.964Z] mx_df = df.astype(in_dtype)
[2020-06-20T23:26:16.964Z] np_df = mx_df.asnumpy()
[2020-06-20T23:26:16.964Z] test_random_chisquare =
TestRandomChisquare(size=param_shape, dtype=out_dtype)
[2020-06-20T23:26:16.964Z] if hybridize:
[2020-06-20T23:26:16.964Z] test_random_chisquare.hybridize()
[2020-06-20T23:26:16.964Z] > np_out = _np.random.chisquare(np_df,
size=param_shape)
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] tests\python\unittest\test_numpy_op.py:4858:
[2020-06-20T23:26:16.964Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-20T23:26:16.964Z] mtrand.pyx:1913: in
numpy.random.mtrand.RandomState.chisquare
[2020-06-20T23:26:16.964Z] ???
[2020-06-20T23:26:16.964Z] _common.pyx:537: in numpy.random._common.cont
[2020-06-20T23:26:16.964Z] ???
[2020-06-20T23:26:16.964Z] _common.pyx:421: in
numpy.random._common.cont_broadcast_1
[2020-06-20T23:26:16.964Z] ???
[2020-06-20T23:26:16.964Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] > ???
[2020-06-20T23:26:16.964Z] E ValueError: Output size () is not compatible
with broadcast dimensions of inputs (1,).
[2020-06-20T23:26:16.964Z]
[2020-06-20T23:26:16.964Z] _common.pyx:229: ValueError
[2020-06-20T23:26:16.964Z]
```
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