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@ -25,9 +25,9 @@ class TestSamplingIdOp(OpTest):
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self.op_type = "sampling_id"
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self.op_type = "sampling_id"
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self.use_mkldnn = False
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self.use_mkldnn = False
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self.init_kernel_type()
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self.init_kernel_type()
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self.X = np.random.random((8, 4)).astype('float32')
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self.X = np.random.random((100, 10)).astype('float32')
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self.inputs = {"X": self.X}
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self.inputs = {"X": self.X}
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self.Y = np.random.random(8).astype('float32')
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self.Y = np.random.random(100).astype('int64')
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self.outputs = {'Out': self.Y}
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self.outputs = {'Out': self.Y}
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self.attrs = {'max': 1.0, 'min': 0.0, 'seed': 1}
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self.attrs = {'max': 1.0, 'min': 0.0, 'seed': 1}
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@ -36,6 +36,16 @@ class TestSamplingIdOp(OpTest):
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y1 = self.out
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y1 = self.out
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self.check_output_customized(self.verify_output)
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self.check_output_customized(self.verify_output)
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y2 = self.out
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y2 = self.out
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# check dtype
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assert y1.dtype == np.int64
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assert y2.dtype == np.int64
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# check output is index ids of inputs
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inputs_ids = np.arange(self.X.shape[1])
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assert np.isin(y1, inputs_ids).all()
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assert np.isin(y2, inputs_ids).all()
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self.assertTrue(np.array_equal(y1, y2))
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self.assertTrue(np.array_equal(y1, y2))
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self.assertEqual(len(y1), len(self.Y))
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self.assertEqual(len(y1), len(self.Y))
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