From 6363a3362b4716c93f3e2ba282dc071a22d8c2a8 Mon Sep 17 00:00:00 2001 From: Shang Zhizhou Date: Tue, 29 Sep 2020 12:19:11 +0800 Subject: [PATCH] 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 --- .../tests/unittests/test_activation_op.py | 90 ++++++++++++++++++- 1 file changed, 89 insertions(+), 1 deletion(-) diff --git a/python/paddle/fluid/tests/unittests/test_activation_op.py b/python/paddle/fluid/tests/unittests/test_activation_op.py index ad7539e76e..68a5fa5e8f 100755 --- a/python/paddle/fluid/tests/unittests/test_activation_op.py +++ b/python/paddle/fluid/tests/unittests/test_activation_op.py @@ -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) -- GitLab