remove isfinite and has_nan (#48046)

python/paddle/fluid/layers/tensor.py

@@ -64,8 +64,6 @@ __all__ = [
     'zeros',
     'reverse',
     'has_inf',
-    'has_nan',
-    'isfinite',
     'linspace',
     'zeros_like',
     'ones_like',
@@ -1572,68 +1570,6 @@ def has_inf(x):
     return out
 
 
-def has_nan(x):
-    """
-    Test if any of x contains a NAN
-
-    Args:
-       x (Tensor): The Tensor to be checked.
-
-    Returns:
-       Tensor: The tensor variable storing the output, only a bool value, indicating that whether there is NAN in x or not.
-
-    Examples:
-        .. code-block:: python
-
-          import paddle
-          data = paddle.randn(shape=[2,3], dtype="float32")
-          res = paddle.fluid.layers.has_nan(data)
-          # [False]
-
-    """
-    if _non_static_mode():
-        return _legacy_C_ops.isnan(x)
-
-    check_type(x, 'x', (Variable), 'has_nan')
-    helper = LayerHelper("isnan", **locals())
-    out = helper.create_variable_for_type_inference(dtype=x.dtype)
-    helper.append_op(type="isnan", inputs={"X": x}, outputs={"Out": out})
-    return out
-
-
-def isfinite(x):
-    """
-
-    Test if any of x contains an infinity/NAN number. If all the elements are finite,
-    returns true, else false.
-
-    Args:
-        x(Tensor): The Tensor to be checked.
-
-    Returns:
-        Tensor: The tensor storing the output, contains a bool value.
-
-    Examples:
-
-        .. code-block:: python
-
-            import paddle
-
-            x = paddle.rand(shape=[4, 6], dtype='float32')
-            y = paddle.fluid.layers.isfinite(x)
-            print(y)
-
-    """
-    check_variable_and_dtype(
-        x, "x", ["float32", "float64", "int32", "int64"], "isfinite"
-    )
-    helper = LayerHelper("isfinite", **locals())
-
-    out = helper.create_variable_for_type_inference(dtype='bool')
-    helper.append_op(type="isfinite", inputs={"X": x}, outputs={"Out": out})
-    return out
-
-
 def linspace(start, stop, num, dtype=None, name=None):
     r"""
     This OP return fixed number of evenly spaced values within a given interval.

python/paddle/fluid/tests/unittests/test_isfinite_op.py

@@ -40,20 +40,6 @@ class TestInf(OpTest):
         self.check_output()
 
 
-class TestRaiseError(unittest.TestCase):
-    def test_errors(self):
-        def test_type():
-            fluid.layers.isfinite([10])
-
-        self.assertRaises(TypeError, test_type)
-
-        def test_dtype():
-            data = fluid.data(shape=[10], dtype="float16", name="input")
-            fluid.layers.isfinite(data)
-
-        self.assertRaises(TypeError, test_dtype)
-
-
 @unittest.skipIf(
     not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
 )
@@ -129,19 +115,11 @@ class BadInputTest(unittest.TestCase):
 
             self.assertRaises(TypeError, test_has_inf_bad_x)
 
-            def test_has_nan_bad_x():
-                data = [1, 2, 3]
-                result = fluid.layers.has_nan(data)
-
-            self.assertRaises(TypeError, test_has_nan_bad_x)
-
         with fluid.dygraph.guard():
             data = paddle.zeros([2, 3])
             result = paddle.fluid.layers.has_inf(data)
             expect_value = np.array([False])
             self.assertEqual((result.numpy() == expect_value).all(), True)
-            result = paddle.fluid.layers.has_nan(data)
-            self.assertEqual((result.numpy() == expect_value).all(), True)
 
 
 if __name__ == '__main__':