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new ced029a Mark test_npx_batch_norm as flaky (#19508)
ced029a is described below
commit ced029a299a09e1822e49434239307de8d37632b
Author: Leonard Lausen <[email protected]>
AuthorDate: Tue Nov 10 21:48:04 2020 +0000
Mark test_npx_batch_norm as flaky (#19508)
[2020-11-09T19:36:41.967Z] _________________
test_npx_batch_norm[True-False-False-shape0] _________________
[...]
[2020-11-09T19:36:41.968Z] grad_reqs = ['write'] if len(shape) != 4
else ['null', 'write', 'add']
[2020-11-09T19:36:41.968Z] for data_grad_req in grad_reqs:
[2020-11-09T19:36:41.968Z] for gamma_grad_req in grad_reqs:
[2020-11-09T19:36:41.968Z] if fix_gamma and gamma_grad_req
!= 'null':
[2020-11-09T19:36:41.968Z] continue
[2020-11-09T19:36:41.968Z] for beta_grad_req in grad_reqs:
[2020-11-09T19:36:41.968Z] for axis in
range(len(shape)):
[2020-11-09T19:36:41.968Z]
_test_batchnorm_impl(axis,
[2020-11-09T19:36:41.968Z] > data_grad_req,
gamma_grad_req, beta_grad_req)
[...]
[2020-11-09T19:36:41.968Z] E AssertionError:
[2020-11-09T19:36:41.968Z] E Items are not equal:
[2020-11-09T19:36:41.968Z] E Error 1.007330 exceeds tolerance
rtol=5.000000e-02, atol=5.000000e-02 (mismatch 25.000000%).
[2020-11-09T19:36:41.968Z] E Location of maximum error: (1,),
a=0.46512768, b=0.54283488
[2020-11-09T19:36:41.968Z] E ACTUAL: array([0.4038983 , 0.46512768,
0.40274522, 0.4092987 ], dtype=float32)
[2020-11-09T19:36:41.968Z] E DESIRED: array([0.42037624, 0.5428349 ,
0.41807014, 0.4311771 ], dtype=float32)
[...]
[2020-11-09T19:36:41.968Z] DEBUG root:conftest.py:213 Setting
np/mx/python random seeds to 1000326137. Use MXNET_TEST_SEED=1000326137 to
reproduce.
---
tests/python/unittest/test_numpy_op.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/tests/python/unittest/test_numpy_op.py
b/tests/python/unittest/test_numpy_op.py
index d2bfda4..dab8545 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1780,6 +1780,7 @@ def test_npx_batch_dot():
@pytest.mark.parametrize('fix_gamma', [False, True])
@pytest.mark.parametrize('cudnn_off', [False, True])
@pytest.mark.parametrize('output_mean_var', [False, True])
[email protected]
def test_npx_batch_norm(shape, fix_gamma, cudnn_off, output_mean_var):
momentum = 0.9
epsilon = 1e-5
