# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ 激活函数单元测试 / Activation Function Unit Tests 测试目标 / Test Target: paddle.nn.functional 激活函数 覆盖的模块 / Covered Modules: - F.relu/relu6/leaky_relu/prelu - F.sigmoid/tanh/swish/silu - F.gelu/elu/selu/celu - F.softplus/softsign/mish - F.log_softmax/log_sigmoid 作用 / Purpose: 补充函数式激活函数API的测试，提升覆盖率。 """ import unittest import numpy as np import paddle import paddle.nn.functional as F paddle.disable_static() class TestReluFamily(unittest.TestCase): """测试ReLU系列激活函数 / Test ReLU family activations""" def test_relu(self): """测试ReLU / Test ReLU""" x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0]) result = F.relu(x) np.testing.assert_allclose(result.numpy(), [0.0, 0.0, 0.0, 1.0, 2.0]) def test_relu6(self): """测试ReLU6 / Test ReLU6""" x = paddle.to_tensor([-1.0, 0.0, 3.0, 6.0, 8.0]) result = F.relu6(x) np.testing.assert_allclose(result.numpy(), [0.0, 0.0, 3.0, 6.0, 6.0]) def test_leaky_relu(self): """测试LeakyReLU / Test LeakyReLU""" x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0]) result = F.leaky_relu(x, negative_slope=0.1) np.testing.assert_allclose( result.numpy(), [-0.2, -0.1, 0.0, 1.0, 2.0], rtol=1e-5 ) def test_elu(self): """测试ELU / Test ELU""" x = paddle.to_tensor([-1.0, 0.0, 1.0]) result = F.elu(x, alpha=1.0) self.assertEqual(result.shape, [3]) # For x < 0: alpha*(exp(x)-1), for x >= 0: x np.testing.assert_allclose( float(result[0].numpy()), 1.0 * (np.exp(-1.0) - 1), rtol=1e-5 ) def test_selu(self): """测试SELU / Test SELU""" x = paddle.to_tensor([-1.0, 0.0, 1.0]) result = F.selu(x) self.assertEqual(result.shape, [3]) def test_prelu(self): """测试PReLU / Test PReLU""" weight = paddle.to_tensor([0.25]) x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0]) result = F.prelu(x, weight) np.testing.assert_allclose( result.numpy(), [-0.5, -0.25, 0.0, 1.0, 2.0], rtol=1e-5 ) class TestSigmoidFamily(unittest.TestCase): """测试Sigmoid系列激活函数 / Test Sigmoid family activations""" def test_sigmoid(self): """测试Sigmoid / Test Sigmoid""" x = paddle.to_tensor([0.0]) result = F.sigmoid(x) self.assertAlmostEqual(float(result.item()), 0.5, places=5) def test_tanh(self): """测试Tanh / Test Tanh""" x = paddle.to_tensor([0.0, 1.0, -1.0]) result = F.tanh(x) np.testing.assert_allclose( result.numpy(), np.tanh([0.0, 1.0, -1.0]), rtol=1e-5 ) def test_hardtanh(self): """测试HardTanh / Test HardTanh""" x = paddle.to_tensor([-3.0, -0.5, 0.0, 0.5, 3.0]) result = F.hardtanh(x, min=-1.0, max=1.0) np.testing.assert_allclose(result.numpy(), [-1.0, -0.5, 0.0, 0.5, 1.0]) def test_hardsigmoid(self): """测试HardSigmoid / Test HardSigmoid""" x = paddle.to_tensor([-3.0, 0.0, 3.0]) result = F.hardsigmoid(x) self.assertEqual(result.shape, [3]) self.assertAlmostEqual(float(result[0].numpy()), 0.0, places=5) self.assertAlmostEqual(float(result[2].numpy()), 1.0, places=5) def test_log_sigmoid(self): """测试LogSigmoid / Test LogSigmoid""" x = paddle.to_tensor([0.0, 1.0, -1.0]) result = F.log_sigmoid(x) expected = np.log(1 / (1 + np.exp(-np.array([0.0, 1.0, -1.0])))) np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5) class TestSwishAndMish(unittest.TestCase): """测试Swish/Mish激活函数 / Test Swish/Mish activations""" def test_swish(self): """测试Swish / Test Swish""" x = paddle.to_tensor([0.0, 1.0, -1.0]) result = F.swish(x) self.assertEqual(result.shape, [3]) # swish(x) = x * sigmoid(x) expected = np.array([0.0, 1.0, -1.0]) * ( 1 / (1 + np.exp(-np.array([0.0, 1.0, -1.0]))) ) np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5) def test_silu(self): """测试SiLU (Swish) / Test SiLU""" x = paddle.to_tensor([0.0, 1.0, -1.0]) result = F.silu(x) self.assertEqual(result.shape, [3]) def test_mish(self): """测试Mish / Test Mish""" x = paddle.to_tensor([0.0, 1.0, -1.0]) result = F.mish(x) self.assertEqual(result.shape, [3]) def test_gelu(self): """测试GELU / Test GELU""" x = paddle.to_tensor([0.0, 1.0, -1.0]) result = F.gelu(x) self.assertEqual(result.shape, [3]) def test_gelu_approximate(self): """测试近似GELU / Test approximate GELU""" x = paddle.to_tensor([0.0, 1.0, -1.0]) result = F.gelu(x, approximate=True) self.assertEqual(result.shape, [3]) class TestSoftmaxFamily(unittest.TestCase): """测试Softmax系列 / Test Softmax family""" def test_softmax(self): """测试Softmax / Test Softmax""" x = paddle.to_tensor([[1.0, 2.0, 3.0]]) result = F.softmax(x, axis=1) self.assertAlmostEqual(float(paddle.sum(result).numpy()), 1.0, places=5) def test_log_softmax(self): """测试LogSoftmax / Test LogSoftmax""" x = paddle.to_tensor([[1.0, 2.0, 3.0]]) result = F.log_softmax(x, axis=1) # exp(log_softmax) should sum to 1 np.testing.assert_allclose( float(paddle.exp(result).sum().numpy()), 1.0, rtol=1e-5 ) def test_softplus(self): """测试Softplus / Test Softplus""" x = paddle.to_tensor([-1.0, 0.0, 1.0]) result = F.softplus(x) self.assertEqual(result.shape, [3]) # All outputs should be positive self.assertTrue(bool((result > 0).all().numpy())) def test_softsign(self): """测试Softsign / Test Softsign""" x = paddle.to_tensor([-1.0, 0.0, 1.0]) result = F.softsign(x) # softsign(x) = x / (1 + |x|) expected = np.array([-1.0, 0.0, 1.0]) / (1 + np.abs([-1.0, 0.0, 1.0])) np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5) if __name__ == '__main__': unittest.main()