xidulu commented on a change in pull request #17390: [Numpy]add op random.beta
URL: https://github.com/apache/incubator-mxnet/pull/17390#discussion_r368904233
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File path: tests/python/unittest/test_numpy_op.py
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@@ -3431,6 +3431,52 @@ def hybrid_forward(self, F, x):
assert out.shape == expected_shape
+@with_seed()
+@use_np
+def test_np_random_beta():
+ class TestRandomBeta(HybridBlock):
+ def __init__(self, size=None, dtype=None, ctx=None):
+ super(TestRandomBeta, self).__init__()
+ self._size = size
+ self._dtype = dtype
+ self._ctx = ctx
+
+ def hybrid_forward(self, F, a, b):
+ return F.np.random.beta(a, b, size=self._size, dtype=self._dtype,
ctx=self._ctx)
+
+ def _test_random_beta_range(output):
+ bigger_than_zero = mx.np.all(output > 0)
+ smaller_than_one = mx.np.all(output < 1)
+ return (output.size == 0) or (bigger_than_zero and smaller_than_one)
+
+ shape_list = [(), (1,), (2, 3), (4, 0, 5), 6, (7, 8), None]
+ # since fp16 might incur precision issue, the corresponding test is skipped
+ dtype_list = [np.float32, np.float64]
+ hybridize_list = [False, True]
+ data = np.array([1])
+ for [param_shape, in_dtype, out_dtype, hybridize] in
itertools.product(shape_list,
+ dtype_list, dtype_list, hybridize_list):
+ mx_data = data.astype(in_dtype)
+ np_data = mx_data.asnumpy()
+ test_random_beta = TestRandomBeta(size=param_shape, dtype=out_dtype)
+ if hybridize:
+ test_random_beta.hybridize()
+ np_out = _np.random.beta(np_data, np_data, size=param_shape)
+ mx_out = test_random_beta(mx_data, mx_data)
+ # print("param_shape, in_dtype, out_dtype, hybridize", param_shape,
in_dtype, out_dtype, hybridize)
Review comment:
remove unused code.
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