def test_static(self):
for use_cuda in ([False, True] if
paddle.device.is_compiled_with_cuda() else [False]):
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
paddle.enable_static()
prog = paddle.static.Program()
with paddle.static.program_guard(prog):
x = paddle.static.data(name=f"x_{self.dtype}",
shape=[13, 17],
dtype=self.dtype)
y = paddle.static.data(name=f"y_{self.dtype}",
shape=[13, 17],
dtype=self.dtype)
out = paddle.heaviside(x, y)
exe = paddle.static.Executor(place=place)
res = exe.run(prog,
feed={
f"x_{self.dtype}": self.x_np,
f"y_{self.dtype}": self.y_np
},
fetch_list=out,
use_prune=True)
self.assertTrue(np.allclose(res, self.out_np))
def test_dygraph(self):
for use_cuda in ([False, True] if
paddle.device.is_compiled_with_cuda() else [False]):
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
paddle.disable_static(place=place)
result = paddle.heaviside(paddle.to_tensor(self.x_np),
paddle.to_tensor(self.y_np))
self.assertTrue(np.allclose(result.numpy(), self.out_np))
def test_broadcast(self):
paddle.disable_static()
self.tensor_1 = paddle.to_tensor(self.input_1)
self.tensor_2 = paddle.to_tensor(self.input_2)
self.tensor_3 = paddle.to_tensor(self.input_3)
self.tensor_4 = paddle.to_tensor(self.input_4)
self.tensor_5 = paddle.to_tensor(self.input_5)
res = paddle.heaviside(self.tensor_1, self.tensor_2)
res = res.numpy()
self.assertTrue(np.allclose(res, self.np_expected1))
res = paddle.heaviside(self.tensor_2, self.tensor_3)
res = res.numpy()
self.assertTrue(np.allclose(res, self.np_expected2))
res = paddle.heaviside(self.tensor_2, self.tensor_4)
res = res.numpy()
self.assertTrue(np.allclose(res, self.np_expected3))
res = paddle.heaviside(self.tensor_4, self.tensor_5)
res = res.numpy()
self.assertTrue(np.allclose(res, self.np_expected4))
def test_input_xy():
paddle.heaviside(paddle.randn([100], 'float32'),
paddle.randn([100], 'float64'))
def test_input_y():
paddle.heaviside(paddle.randn([100]), 1)
def test_input_x():
paddle.heaviside(1, paddle.randn([100]))