test_np_round

[leezu]

[2020-09-02T00:02:22.521Z] ================================== FAILURES ===================================
[2020-09-02T00:02:22.521Z] ________________________________ test_np_round ________________________________
[2020-09-02T00:02:22.521Z] [gw3] win32 -- Python 3.7.3 C:\Python37\python.exe
[2020-09-02T00:02:22.521Z] 
[2020-09-02T00:02:22.521Z]     @with_seed()
[2020-09-02T00:02:22.521Z]     @use_np
[2020-09-02T00:02:22.521Z]     def test_np_round():
[2020-09-02T00:02:22.521Z]         class TestRound(HybridBlock):
[2020-09-02T00:02:22.521Z]             def __init__(self, func, decimals):
[2020-09-02T00:02:22.521Z]                 super(TestRound, self).__init__()
[2020-09-02T00:02:22.521Z]                 self.func = func
[2020-09-02T00:02:22.521Z]                 self.decimals = decimals
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]             def hybrid_forward(self, F, x):
[2020-09-02T00:02:22.521Z]                 return getattr(F.np, self.func)(x, self.decimals)
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]         shapes = [(), (1, 2, 3), (1, 0)]
[2020-09-02T00:02:22.521Z]         types = ['int32', 'int64', 'float32', 'float64']
[2020-09-02T00:02:22.521Z]         funcs = ['round', 'round_']
[2020-09-02T00:02:22.521Z]         for hybridize, oneType, func in itertools.product([True, False], types, funcs):
[2020-09-02T00:02:22.521Z]             rtol, atol = 1e-3, 1e-5
[2020-09-02T00:02:22.521Z]             for shape in shapes:
[2020-09-02T00:02:22.521Z]                 for d in range(-5, 6):
[2020-09-02T00:02:22.521Z]                     test_round = TestRound(func, d)
[2020-09-02T00:02:22.521Z]                     if hybridize:
[2020-09-02T00:02:22.521Z]                         test_round.hybridize()
[2020-09-02T00:02:22.521Z]                     x = rand_ndarray(shape, dtype=oneType).as_np_ndarray()
[2020-09-02T00:02:22.521Z]                     np_out = getattr(_np, func)(x.asnumpy(), d)
[2020-09-02T00:02:22.521Z]                     mx_out = test_round(x)
[2020-09-02T00:02:22.521Z]                     assert mx_out.shape == np_out.shape
[2020-09-02T00:02:22.521Z] >                   assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
[2020-09-02T00:02:22.521Z] 
[2020-09-02T00:02:22.521Z] tests\python\unittest\test_numpy_op.py:7691: 
[2020-09-02T00:02:22.521Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[2020-09-02T00:02:22.521Z] 
[2020-09-02T00:02:22.521Z] a = array([[[ 1.029    , -0.371    ,  1.11     ],
[2020-09-02T00:02:22.521Z]         [ 1.4289999, -0.636    , -0.306    ]]], dtype=float32)
[2020-09-02T00:02:22.521Z] b = array([[[ 1.029, -0.371,  1.11 ],
[2020-09-02T00:02:22.521Z]         [ 1.429, -0.636, -0.307]]], dtype=float32)
[2020-09-02T00:02:22.521Z] rtol = 0.001, atol = 1e-05, names = ('a', 'b'), equal_nan = False
[2020-09-02T00:02:22.521Z] use_broadcast = True, mismatches = (10, 10)
[2020-09-02T00:02:22.521Z] 
[2020-09-02T00:02:22.521Z]     def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=False,
[2020-09-02T00:02:22.521Z]                             use_broadcast=True, mismatches=(10, 10)):
[2020-09-02T00:02:22.521Z]         """Test that two numpy arrays are almost equal. Raise exception message if not.
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]         Parameters
[2020-09-02T00:02:22.521Z]         ----------
[2020-09-02T00:02:22.521Z]         a : np.ndarray or mx.nd.array
[2020-09-02T00:02:22.521Z]         b : np.ndarray or mx.nd.array
[2020-09-02T00:02:22.521Z]         rtol : None or float or dict of dtype -> float
[2020-09-02T00:02:22.521Z]             The relative threshold. Default threshold will be used if set to ``None``.
[2020-09-02T00:02:22.521Z]         atol : None or float or dict of dtype -> float
[2020-09-02T00:02:22.521Z]             The absolute threshold. Default threshold will be used if set to ``None``.
[2020-09-02T00:02:22.521Z]         names : tuple of names, optional
[2020-09-02T00:02:22.521Z]             The names used in error message when an exception occurs
[2020-09-02T00:02:22.521Z]         equal_nan : boolean, optional
[2020-09-02T00:02:22.521Z]             The flag determining how to treat NAN values in comparison
[2020-09-02T00:02:22.521Z]         mismatches : tuple of mismatches
[2020-09-02T00:02:22.521Z]             Maximum number of mismatches to be printed (mismatches[0]) and determine (mismatches[1])
[2020-09-02T00:02:22.521Z]         """
[2020-09-02T00:02:22.521Z]         if not use_broadcast:
[2020-09-02T00:02:22.521Z]             checkShapes(a, b)
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]         rtol, atol = get_tols(a, b, rtol, atol)
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]         if isinstance(a, mx.numpy.ndarray):
[2020-09-02T00:02:22.521Z]             a = a.asnumpy()
[2020-09-02T00:02:22.521Z]         if isinstance(b, mx.numpy.ndarray):
[2020-09-02T00:02:22.521Z]             b = b.asnumpy()
[2020-09-02T00:02:22.521Z]         use_np_allclose = isinstance(a, np.ndarray) and isinstance(b, np.ndarray)
[2020-09-02T00:02:22.521Z]         if not use_np_allclose:
[2020-09-02T00:02:22.521Z]             if not (hasattr(a, 'ctx') and hasattr(b, 'ctx') and a.ctx == b.ctx and a.dtype == b.dtype):
[2020-09-02T00:02:22.521Z]                 use_np_allclose = True
[2020-09-02T00:02:22.521Z]                 if isinstance(a, mx.nd.NDArray):
[2020-09-02T00:02:22.521Z]                     a = a.asnumpy()
[2020-09-02T00:02:22.521Z]                 if isinstance(b, mx.nd.NDArray):
[2020-09-02T00:02:22.521Z]                     b = b.asnumpy()
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]         if use_np_allclose:
[2020-09-02T00:02:22.521Z]             if hasattr(a, 'dtype') and a.dtype == np.bool_ and hasattr(b, 'dtype') and b.dtype == np.bool_:
[2020-09-02T00:02:22.521Z]                 np.testing.assert_equal(a, b)
[2020-09-02T00:02:22.521Z]                 return
[2020-09-02T00:02:22.521Z]             if almost_equal(a, b, rtol, atol, equal_nan=equal_nan):
[2020-09-02T00:02:22.521Z]                 return
[2020-09-02T00:02:22.521Z]         else:
[2020-09-02T00:02:22.521Z]             output = mx.nd.contrib.allclose(a, b, rtol, atol, equal_nan)
[2020-09-02T00:02:22.521Z]             if output.asnumpy() == 1:
[2020-09-02T00:02:22.521Z]                 return
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]             a = a.asnumpy()
[2020-09-02T00:02:22.521Z]             b = b.asnumpy()
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]         index, rel = _find_max_violation(a, b, rtol, atol)
[2020-09-02T00:02:22.521Z]         if index != ():
[2020-09-02T00:02:22.521Z]             # a, b are the numpy arrays
[2020-09-02T00:02:22.521Z]             indexErr = index
[2020-09-02T00:02:22.521Z]             relErr = rel
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]             print('\n*** Maximum errors for vector of size {}:  rtol={}, atol={}\n'.format(a.size, rtol, atol))
[2020-09-02T00:02:22.521Z]             aTmp = a.copy()
[2020-09-02T00:02:22.521Z]             bTmp = b.copy()
[2020-09-02T00:02:22.521Z]             i = 1
[2020-09-02T00:02:22.521Z]             while i <= a.size:
[2020-09-02T00:02:22.521Z]                 if i <= mismatches[0]:
[2020-09-02T00:02:22.521Z]                     print("%3d: Error %f  %s" %(i, rel, locationError(a, b, index, names)))
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]                 aTmp[index] = bTmp[index] = 0
[2020-09-02T00:02:22.521Z]                 if almost_equal(aTmp, bTmp, rtol, atol, equal_nan=equal_nan):
[2020-09-02T00:02:22.521Z]                     break
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]                 i += 1
[2020-09-02T00:02:22.521Z]                 if i <= mismatches[1] or mismatches[1] <= 0:
[2020-09-02T00:02:22.521Z]                     index, rel = _find_max_violation(aTmp, bTmp, rtol, atol)
[2020-09-02T00:02:22.521Z]                 else:
[2020-09-02T00:02:22.521Z]                     break
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]             mismatchDegree = "at least " if mismatches[1] > 0 and i > mismatches[1] else ""
[2020-09-02T00:02:22.521Z]             errMsg = "Error %f exceeds tolerance rtol=%e, atol=%e (mismatch %s%f%%).\n%s" % \
[2020-09-02T00:02:22.521Z]                      (relErr, rtol, atol, mismatchDegree, 100*i/a.size, \
[2020-09-02T00:02:22.521Z]                       locationError(a, b, indexErr, names, maxError=True))
[2020-09-02T00:02:22.521Z]         else:
[2020-09-02T00:02:22.521Z]             errMsg = "Error %f exceeds tolerance rtol=%e, atol=%e.\n" % (rel, rtol, atol)
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z]         np.set_printoptions(threshold=4, suppress=True)
[2020-09-02T00:02:22.521Z]         msg = npt.build_err_msg([a, b], err_msg=errMsg)
[2020-09-02T00:02:22.521Z]     
[2020-09-02T00:02:22.521Z] >       raise AssertionError(msg)
[2020-09-02T00:02:22.521Z] E       AssertionError: 
[2020-09-02T00:02:22.521Z] E       Items are not equal:
[2020-09-02T00:02:22.521Z] E       Error 3.154627 exceeds tolerance rtol=1.000000e-03, atol=1.000000e-05 (mismatch 16.666667%).
[2020-09-02T00:02:22.521Z] E       Location of maximum error: (0, 1, 2), a=-0.30599999, b=-0.30700001
[2020-09-02T00:02:22.521Z] E        ACTUAL: array([[[ 1.029    , -0.371    ,  1.11     ],
[2020-09-02T00:02:22.521Z] E               [ 1.4289999, -0.636    , -0.306    ]]], dtype=float32)
[2020-09-02T00:02:22.521Z] E        DESIRED: array([[[ 1.029, -0.371,  1.11 ],
[2020-09-02T00:02:22.521Z] E               [ 1.429, -0.636, -0.307]]], dtype=float32)
[2020-09-02T00:02:22.521Z] 
[2020-09-02T00:02:22.521Z] windows_package\python\mxnet\test_utils.py:735: AssertionError

https://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fwindows-gpu/detail/PR-19034/5/pipeline/

[kpuatamazon]

nosetests test_numpy_op.py:test_np_round
[INFO] Setting test np/mx/python random seeds, use MXNET_TEST_SEED=675318784 to reproduce.
[WARNING] Error seen with seeded test, use MXNET_TEST_SEED=675318784 to reproduce.
F
======================================================================
FAIL: test_numpy_op.test_np_round
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/ubuntu/mxnet-fallback-venv/lib/python3.6/site-packages/nose/case.py", line 198, in runTest
    self.test(*self.arg)
  File "/home/ubuntu/mxnet-fallback/tests/python/unittest/common.py", line 218, in test_new
    orig_test(*args, **kwargs)
  File "/home/ubuntu/mxnet-fallback/python/mxnet/util.py", line 297, in _with_np_shape
    return func(*args, **kwargs)
  File "/home/ubuntu/mxnet-fallback/python/mxnet/util.py", line 481, in _with_np_array
    return func(*args, **kwargs)
  File "/home/ubuntu/mxnet-fallback/tests/python/unittest/test_numpy_op.py", line 6585, in test_np_round
    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
  File "/home/ubuntu/mxnet-fallback/python/mxnet/test_utils.py", line 749, in assert_almost_equal
    raise AssertionError(msg)
AssertionError: 
Items are not equal:
Error 3.662956 exceeds tolerance rtol=1.000000e-03, atol=1.000000e-05 (mismatch 16.666667%).
Location of maximum error: (0, 0, 1), a=-0.26400000, b=-0.26300001
 ACTUAL: array([[[-0.32400003, -0.264     ,  1.54      ],
        [-0.9969999 , -0.317     , -0.249     ]]], dtype=float32)
 DESIRED: array([[[-0.324, -0.263,  1.54 ],
        [-0.997, -0.317, -0.249]]], dtype=float32)
-------------------- >> begin captured stdout << ---------------------

*** Maximum errors for vector of size 6:  rtol=0.001, atol=1e-05

  1: Error 3.662956  Location of error: (0, 0, 1), a=-0.26400000, b=-0.26300001

--------------------- >> end captured stdout << ----------------------
-------------------- >> begin captured logging << --------------------
common: INFO: Setting test np/mx/python random seeds, use MXNET_TEST_SEED=675318784 to reproduce.
root: INFO: NumPy-shape semantics has been activated in your code. This is required for creating and manipulating scalar and zero-size tensors, which were not supported in MXNet before, as in the official NumPy library. Please DO NOT manually deactivate this semantics while using `mxnet.numpy` and `mxnet.numpy_extension` modules.
common: WARNING: Error seen with seeded test, use MXNET_TEST_SEED=675318784 to reproduce.
--------------------- >> end captured logging << ---------------------

----------------------------------------------------------------------
Ran 1 test in 0.142s

FAILED (failures=1)

Seen in https://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Funix-cpu/detail/PR-19601/4/pipeline based on v1.x