haojin2 commented on a change in pull request #17084: [numpy] add op median
URL: https://github.com/apache/incubator-mxnet/pull/17084#discussion_r380002724
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -7083,6 +7083,47 @@ def test_np_share_memory():
assert not op(np.ones((5, 0), dtype=dt), np.ones((0, 3, 0),
dtype=adt))
+def test_np_median():
+ class TestMedian(HybridBlock):
+ def __init__(self, axis=None, keepdims=False):
+ super(TestMedian, self).__init__()
+ self._axis = axis
+ self._keepdims = keepdims
+
+ def hybrid_forward(self, F, a):
+ return F.np.median(a, axis=self._axis, keepdims=self._keepdims)
+
+ flags = [True, False]
+ dtypes = ['float16', 'float32', 'float64']
+ qtypes = ['float32', 'float64']
+ tensor_shapes = [
+ ((2, 3), None),
+ ((2, 3, 4, 5), 3),
+ ((2, 3, 4), (0, 2)),
+ ((2, 3, 4), 1)
+ ]
+
+ for hybridize, keepdims, (a_shape, axis), dtype in \
+ itertools.product(flags, flags, tensor_shapes, dtypes):
+ atol = 3e-4 if dtype == 'float16' else 1e-4
+ rtol = 3e-2 if dtype == 'float16' else 1e-2
+ test_median = TestMedian(axis=axis, keepdims=keepdims)
+ if hybridize:
+ test_median.hybridize()
+ a = np.random.uniform(-1.0, 1.0, size=a_shape)
+ np_out = _np.median(a.asnumpy(), axis=axis, keepdims=keepdims)
+ mx_out = test_median(a)
+
+ assert mx_out.shape == np_out.shape
+ assert_almost_equal(mx_out.asnumpy(), np_out, atol=atol, rtol=rtol)
+
+ mx_out = np.median(a, axis=axis, keepdims=keepdims)
+ np_out = _np.median(a.asnumpy(), axis=axis, keepdims=keepdims)
+
+ assert_almost_equal(mx_out.asnumpy(), np_out, atol=atol, rtol=rtol)
+
Review comment:
2 blank lines between functions.
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