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