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未验证 提交 6363a336 编写于 作者: S Shang Zhizhou 提交者： GitHub
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add paddle.enable_static() && random.seed(xx) to test_activation_op (#27585) 

* add paddle.enable_static() to some testcase

* add seed to random in test_activation_op

* update a randomseed

* update a randomseed
上级 6871e57b
隐藏空白更改
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Showing with 89 addition and 1 deletion
python/paddle/fluid/tests/unittests/test_activation_op.py
浏览文件 @ 6363a336
......@@ -50,6 +50,7 @@ class TestActivation(OpTest):
self.init_dtype()
self.init_kernel_type()
np.random.seed(2049)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.exp(x)
......@@ -99,6 +100,7 @@ class TestSigmoid(TestActivation):
self.op_type = "sigmoid"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
out = 1 / (1 + np.exp(-x))
......@@ -120,6 +122,7 @@ class TestLogSigmoid(TestActivation):
self.op_type = "logsigmoid"
self.init_dtype()
np.random.seed(2048)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
out = np.log(1 / (1 + np.exp(-x)))
......@@ -135,11 +138,13 @@ class TestLogSigmoid(TestActivation):
class TestLogSigmoidAPI(unittest.TestCase):
# test paddle.nn.LogSigmoid, paddle.nn.functional.log_sigmoid
def setUp(self):
np.random.seed(1024)
self.x_np = np.random.uniform(-1, 1, [11, 17]).astype('float32')
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', [11, 17])
out1 = F.log_sigmoid(x)
......@@ -163,6 +168,7 @@ class TestLogSigmoidAPI(unittest.TestCase):
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', [11, 17])
out = paddle.fluid.layers.logsigmoid(x)
......@@ -172,6 +178,7 @@ class TestLogSigmoidAPI(unittest.TestCase):
self.assertTrue(np.allclose(out_ref, res[0]))
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.log_sigmoid, 1)
......@@ -188,6 +195,7 @@ class TestTanh(TestActivation, TestParameter):
paddle.enable_static()
self.op_type = "tanh"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.tanh(x)
......@@ -210,11 +218,13 @@ class TestTanhAPI(unittest.TestCase):
# test paddle.tanh, paddle.nn.tanh, paddle.nn.functional.tanh
def setUp(self):
self.dtype = 'float32'
np.random.seed(1024)
self.x_np = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', [10, 12], self.dtype)
out1 = F.tanh(x)
......@@ -239,6 +249,7 @@ class TestTanhAPI(unittest.TestCase):
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', [10, 12], self.dtype)
out = fluid.layers.tanh(x)
......@@ -248,6 +259,7 @@ class TestTanhAPI(unittest.TestCase):
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.tanh, 1)
......@@ -265,6 +277,7 @@ class TestAtan(TestActivation, TestParameter):
self.op_type = "atan"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.arctan(x)
......@@ -302,6 +315,7 @@ class TestSinh(TestActivation):
self.op_type = "sinh"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.sinh(x)
......@@ -375,6 +389,7 @@ class TestCosh(TestActivation):
self.op_type = "cosh"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.cosh(x)
......@@ -453,6 +468,7 @@ class TestTanhshrink(TestActivation):
self.op_type = "tanh_shrink"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(10, 20, [10, 17]).astype(self.dtype)
out = ref_tanhshrink(x)
......@@ -468,11 +484,13 @@ class TestTanhshrink(TestActivation):
class TestTanhshrinkAPI(unittest.TestCase):
# test paddle.nn.Tanhshrink, paddle.nn.functional.tanhshrink
def setUp(self):
np.random.seed(1024)
self.x_np = np.random.uniform(10, 20, [10, 17]).astype(np.float64)
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.tanhshrink(x)
......@@ -496,6 +514,7 @@ class TestTanhshrinkAPI(unittest.TestCase):
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.tanh_shrink(x)
......@@ -505,6 +524,7 @@ class TestTanhshrinkAPI(unittest.TestCase):
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.tanhshrink, 1)
......@@ -530,6 +550,7 @@ class TestHardShrink(TestActivation):
self.threshold = 0.5
self.set_attrs()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype) * 10
out = ref_hardshrink(x, self.threshold)
......@@ -555,11 +576,13 @@ class TestHardShrinkAPI(unittest.TestCase):
# test paddle.nn.Hardshrink, paddle.nn.functional.hardshrink
def setUp(self):
paddle.enable_static()
np.random.seed(1024)
self.x_np = np.random.uniform(-1, 1, [10, 12]).astype('float32')
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', [10, 12])
out1 = F.hardshrink(x)
......@@ -590,6 +613,7 @@ class TestHardShrinkAPI(unittest.TestCase):
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', [10, 12])
out = fluid.layers.hard_shrink(x)
......@@ -599,6 +623,7 @@ class TestHardShrinkAPI(unittest.TestCase):
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.hardshrink, 1)
......@@ -621,11 +646,13 @@ def ref_hardtanh(x, min=-1.0, max=1.0):
class TestHardtanhAPI(unittest.TestCase):
# test paddle.nn.Hardtanh, paddle.nn.functional.hardtanh
def setUp(self):
np.random.seed(1024)
self.x_np = np.random.uniform(-3, 3, [10, 12]).astype('float32')
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', [10, 12])
out1 = F.hardtanh(x)
......@@ -656,6 +683,7 @@ class TestHardtanhAPI(unittest.TestCase):
paddle.enable_static()
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.hardtanh, 1)
......@@ -682,6 +710,7 @@ class TestSoftshrink(TestActivation):
threshold = 0.8
np.random.seed(1023)
x = np.random.uniform(0.25, 10, [10, 12]).astype(self.dtype)
out = ref_softshrink(x, threshold)
self.inputs = {'X': x}
......@@ -698,11 +727,13 @@ class TestSoftshrinkAPI(unittest.TestCase):
# test paddle.nn.Softshrink, paddle.nn.functional.softshrink
def setUp(self):
self.threshold = 0.8
np.random.seed(1024)
self.x_np = np.random.uniform(0.25, 10, [10, 12]).astype(np.float64)
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.softshrink(x, self.threshold)
......@@ -726,6 +757,7 @@ class TestSoftshrinkAPI(unittest.TestCase):
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.softshrink(x, self.threshold)
......@@ -735,6 +767,7 @@ class TestSoftshrinkAPI(unittest.TestCase):
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.softshrink, 1)
......@@ -755,6 +788,7 @@ class TestSqrt(TestActivation, TestParameter):
self.op_type = "sqrt"
self.init_dtype()
np.random.seed(1023)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.sqrt(x)
......@@ -773,6 +807,7 @@ class TestRsqrt(TestActivation):
self.op_type = "rsqrt"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [10, 12]).astype(self.dtype) * 10
out = 1.0 / np.sqrt(x)
......@@ -791,6 +826,7 @@ class TestAbs(TestActivation):
self.op_type = "abs"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [4, 25]).astype(self.dtype)
# Because we set delta = 0.005 in calculating numeric gradient,
# if x is too small, such as 0.002, x_neg will be -0.003
......@@ -814,6 +850,7 @@ class TestCeil(TestActivation):
self.op_type = "ceil"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
out = np.ceil(x)
......@@ -831,6 +868,7 @@ class TestFloor(TestActivation):
self.op_type = "floor"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
out = np.floor(x)
......@@ -850,6 +888,7 @@ class TestCos(TestActivation):
self.op_type = "cos"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
out = np.cos(x)
......@@ -868,6 +907,7 @@ class TestAcos(TestActivation):
self.op_type = "acos"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-0.95, 0.95, [10, 12]).astype(self.dtype)
out = np.arccos(x)
......@@ -886,6 +926,7 @@ class TestSin(TestActivation, TestParameter):
self.op_type = "sin"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
out = np.sin(x)
......@@ -904,6 +945,7 @@ class TestAsin(TestActivation):
self.op_type = "asin"
self.init_dtype()
np.random.seed(2048)
x = np.random.uniform(-0.95, 0.95, [10, 12]).astype(self.dtype)
out = np.arcsin(x)
......@@ -922,6 +964,7 @@ class TestRound(TestActivation):
self.op_type = "round"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
out = np.round(x)
......@@ -938,6 +981,7 @@ class TestRelu(TestActivation):
self.op_type = "relu"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02
......@@ -955,11 +999,13 @@ class TestRelu(TestActivation):
class TestReluAPI(unittest.TestCase):
# test paddle.nn.ReLU, paddle.nn.functional.relu
def setUp(self):
np.random.seed(1024)
self.x_np = np.random.uniform(-1, 1, [10, 12]).astype('float32')
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', [10, 12])
out1 = F.relu(x)
......@@ -983,6 +1029,7 @@ class TestReluAPI(unittest.TestCase):
paddle.enable_static()
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.relu, 1)
......@@ -1010,7 +1057,7 @@ class TestLeakyRelu(TestActivation):
self.init_dtype()
alpha = self.get_alpha()
np.random.seed(10)
np.random.seed(1024)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.05
......@@ -1045,11 +1092,13 @@ class TestLeakyReluAPI(unittest.TestCase):
# test paddle.nn.LeakyReLU, paddle.nn.functional.leaky_relu,
# fluid.layers.leaky_relu
def setUp(self):
np.random.seed(1024)
self.x_np = np.random.uniform(-1, 1, [10, 12]).astype('float32')
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', [10, 12])
out1 = F.leaky_relu(x)
......@@ -1080,6 +1129,7 @@ class TestLeakyReluAPI(unittest.TestCase):
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', [10, 12])
out = fluid.layers.leaky_relu(x, 0.01)
......@@ -1089,6 +1139,7 @@ class TestLeakyReluAPI(unittest.TestCase):
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.leaky_relu, 1)
......@@ -1115,6 +1166,7 @@ class TestGeluApproximate(TestActivation):
self.op_type = "gelu"
self.init_dtype()
approximate = True
np.random.seed(1024)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
out = gelu(x, approximate)
......@@ -1134,6 +1186,7 @@ class TestGelu(TestActivation):
self.op_type = "gelu"
self.init_dtype()
approximate = False
np.random.seed(2048)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
out = gelu(x, approximate)
......@@ -1150,11 +1203,13 @@ class TestGelu(TestActivation):
class TestGELUAPI(unittest.TestCase):
# test paddle.nn.GELU, paddle.nn.functional.gelu
def setUp(self):
np.random.seed(1024)
self.x_np = np.random.uniform(-1, 1, [11, 17]).astype('float32')
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', [11, 17])
out1 = F.gelu(x)
......@@ -1185,6 +1240,7 @@ class TestGELUAPI(unittest.TestCase):
paddle.enable_static()
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.gelu, 1)
......@@ -1202,6 +1258,7 @@ class TestBRelu(TestActivation):
self.op_type = "brelu"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-5, 10, [10, 12]).astype(self.dtype)
t_min = 1.0
t_max = 4.0
......@@ -1250,6 +1307,7 @@ class TestRelu6(TestActivation):
self.op_type = "relu6"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-1, 10, [10, 12]).astype(self.dtype)
x[np.abs(x) < 0.005] = 0.02
out = ref_relu6(x)
......@@ -1267,12 +1325,14 @@ class TestRelu6(TestActivation):
class TestRelu6API(unittest.TestCase):
# test paddle.nn.ReLU6, paddle.nn.functional.relu6
def setUp(self):
np.random.seed(1024)
self.x_np = np.random.uniform(-1, 10, [10, 12]).astype(np.float64)
self.x_np[np.abs(self.x_np) < 0.005] = 0.02
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.relu6(x)
......@@ -1296,6 +1356,7 @@ class TestRelu6API(unittest.TestCase):
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.relu6(x)
......@@ -1305,6 +1366,7 @@ class TestRelu6API(unittest.TestCase):
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.relu6, 1)
......@@ -1322,6 +1384,7 @@ class TestHardSwish(TestActivation):
self.op_type = 'hard_swish'
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-6, 6, [10, 12]).astype(self.dtype)
threshold = 6.0
scale = 6.0
......@@ -1361,6 +1424,7 @@ class TestSoftRelu(TestActivation):
self.op_type = "soft_relu"
self.init_dtype()
np.random.seed(4096)
x = np.random.uniform(-3, 3, [4, 4]).astype(self.dtype)
threshold = 2.0
# The same reason with TestAbs
......@@ -1406,6 +1470,7 @@ class TestELU(TestActivation):
self.op_type = "elu"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-3, 3, [10, 12]).astype(self.dtype)
alpha = 1.
out = elu(x, alpha)
......@@ -1424,11 +1489,13 @@ class TestELU(TestActivation):
class TestELUAPI(unittest.TestCase):
# test paddle.nn.ELU, paddle.nn.functional.elu
def setUp(self):
np.random.seed(1024)
self.x_np = np.random.uniform(-3, 3, [10, 12]).astype('float32')
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', [10, 12])
out1 = F.elu(x)
......@@ -1459,6 +1526,7 @@ class TestELUAPI(unittest.TestCase):
paddle.enable_static()
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.elu, 1)
......@@ -1476,6 +1544,7 @@ class TestReciprocal(TestActivation):
self.op_type = "reciprocal"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
out = np.reciprocal(x)
......@@ -1494,6 +1563,7 @@ class TestLog(TestActivation):
self.op_type = "log"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.log(x)
......@@ -1521,6 +1591,7 @@ class TestLog1p(TestActivation):
self.op_type = "log1p"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.log1p(x)
......@@ -1566,6 +1637,7 @@ class TestSquare(TestActivation):
self.op_type = "square"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.square(x)
......@@ -1584,6 +1656,7 @@ class TestPow(TestActivation):
self.op_type = "pow"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
out = np.power(x, 3)
......@@ -1603,6 +1676,7 @@ class TestPow_factor_tensor(TestActivation):
self.op_type = "pow"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
out = np.power(x, 3)
......@@ -1680,6 +1754,7 @@ class TestSTanh(TestActivation):
self.op_type = "stanh"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
scale_a = 2.0 / 3.0
scale_b = 1.7159
......@@ -1725,6 +1800,7 @@ class TestSoftplus(TestActivation):
beta = 2
threshold = 15
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
out = ref_softplus(x, beta, threshold)
self.inputs = {'X': x}
......@@ -1742,11 +1818,13 @@ class TestSoftplusAPI(unittest.TestCase):
def setUp(self):
self.beta = 2
self.threshold = 15
np.random.seed(1024)
self.x_np = np.random.uniform(-1, 1, [10, 12]).astype(np.float64)
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.softplus(x, self.beta, self.threshold)
......@@ -1770,6 +1848,7 @@ class TestSoftplusAPI(unittest.TestCase):
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.softplus(x)
......@@ -1779,6 +1858,7 @@ class TestSoftplusAPI(unittest.TestCase):
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.softplus, 1)
......@@ -1801,6 +1881,7 @@ class TestSoftsign(TestActivation):
self.op_type = "softsign"
self.init_dtype()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
out = ref_softsign(x)
self.inputs = {'X': x}
......@@ -1815,11 +1896,13 @@ class TestSoftsign(TestActivation):
class TestSoftsignAPI(unittest.TestCase):
# test paddle.nn.Softsign, paddle.nn.functional.softsign
def setUp(self):
np.random.seed(1024)
self.x_np = np.random.uniform(-1, 1, [10, 12]).astype(np.float64)
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.softsign(x)
......@@ -1843,6 +1926,7 @@ class TestSoftsignAPI(unittest.TestCase):
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.softsign(x)
......@@ -1852,6 +1936,7 @@ class TestSoftsignAPI(unittest.TestCase):
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.softsign, 1)
......@@ -1871,6 +1956,7 @@ class TestThresholdedRelu(TestActivation):
threshold = 0.25
self.delta = 0.005
np.random.seed(1024)
X = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
# Same reason as TestAbs
......@@ -1907,6 +1993,7 @@ class TestHardSigmoid(TestActivation):
self.op_type = "hard_sigmoid"
self.init_dtype()
np.random.seed(1024)
X = np.random.uniform(-5, 5, [10, 12]).astype("float32")
slope = 0.2
offset = 0.5
......@@ -1951,6 +2038,7 @@ class TestSwish(TestActivation):
self.op_type = "swish"
self.init_dtype()
np.random.seed(1024)
X = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
beta = 2.3
out = X * expit(beta * X)
......
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