wehub-resource-sync
238 lines
8.1 KiB
Python
238 lines
8.1 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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# [AUTO-GENERATED] Test file for paddle.nn.functional.activation
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# 覆盖模块: paddle/nn/functional/activation.py
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# 未覆盖行: 98,101,102,103,109,152,155,156,157,163,221,224,225,226,232,277,281,282,283,289,363,367,368,369,375,417,431,432,433,434
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# Covered module: paddle/nn/functional/activation.py
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# Uncovered lines: 98,101,102,103,109,152,155,156,157,163,221,224,225,226,232,277,281,282,283,289,363,367,368,369,375,417,431,432,433,434
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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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class TestSilu(unittest.TestCase):
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"""测试 silu 激活函数
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Test silu activation function"""
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def test_silu_basic(self):
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"""测试基本的 silu
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Test basic silu"""
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x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
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result = F.silu(x)
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# silu(x) = x * sigmoid(x)
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expected = x.numpy() * (1.0 / (1.0 + np.exp(-x.numpy())))
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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def test_silu_2d(self):
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"""测试2D输入的 silu
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Test silu with 2D input"""
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x = paddle.randn([3, 4])
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result = F.silu(x)
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self.assertEqual(result.shape, [3, 4])
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class TestLogSigmoid(unittest.TestCase):
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"""测试 log_sigmoid 激活函数
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Test log_sigmoid activation function"""
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def test_log_sigmoid_basic(self):
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"""测试基本的 log_sigmoid
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Test basic log_sigmoid"""
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x = paddle.to_tensor([-1.0, 0.0, 1.0])
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result = F.log_sigmoid(x)
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expected = np.log(1.0 / (1.0 + np.exp(-x.numpy())))
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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def test_log_sigmoid_2d(self):
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"""测试2D输入的 log_sigmoid
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Test log_sigmoid with 2D input"""
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x = paddle.randn([2, 3])
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result = F.log_sigmoid(x)
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self.assertEqual(result.shape, [2, 3])
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class TestTanhshrink(unittest.TestCase):
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"""测试 tanhshrink 激活函数
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Test tanhshrink activation function"""
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def test_tanhshrink_basic(self):
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"""测试基本的 tanhshrink
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Test basic tanhshrink"""
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x = paddle.to_tensor([-1.0, 0.0, 1.0])
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result = F.tanhshrink(x)
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# tanhshrink(x) = x - tanh(x)
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expected = x.numpy() - np.tanh(x.numpy())
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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def test_tanhshrink_2d(self):
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"""测试2D输入的 tanhshrink
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Test tanhshrink with 2D input"""
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x = paddle.randn([2, 3])
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result = F.tanhshrink(x)
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self.assertEqual(result.shape, [2, 3])
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class TestHardshrink(unittest.TestCase):
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"""测试 hardshrink 激活函数
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Test hardshrink activation function"""
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def test_hardshrink_basic(self):
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"""测试基本的 hardshrink
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Test basic hardshrink"""
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x = paddle.to_tensor([-2.0, -0.5, 0.0, 0.5, 2.0])
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result = F.hardshrink(x)
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# hardshrink(x) = x if |x| > 0.5, else 0
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expected = [-2.0, 0.0, 0.0, 0.0, 2.0]
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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def test_hardshrink_custom_threshold(self):
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"""测试自定义阈值的 hardshrink
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Test hardshrink with custom threshold"""
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x = paddle.to_tensor([-1.0, -0.8, 0.0, 0.8, 1.0])
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result = F.hardshrink(x, threshold=0.9)
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expected = [-1.0, 0.0, 0.0, 0.0, 1.0]
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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class TestSoftshrink(unittest.TestCase):
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"""测试 softshrink 激活函数
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Test softshrink activation function"""
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def test_softshrink_basic(self):
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"""测试基本的 softshrink
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Test basic softshrink"""
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x = paddle.to_tensor([-2.0, -0.5, 0.0, 0.5, 2.0])
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result = F.softshrink(x)
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# softshrink(x, lambda=0.5) = x - 0.5 if x > 0.5, x + 0.5 if x < -0.5, 0 otherwise
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expected = [-1.5, 0.0, 0.0, 0.0, 1.5]
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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def test_softshrink_custom_lambda(self):
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"""测试自定义 lambda 的 softshrink
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Test softshrink with custom lambda"""
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x = paddle.to_tensor([-2.0, -0.5, 0.0, 0.5, 2.0])
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result = F.softshrink(x, threshold=1.0)
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expected = [-1.0, 0.0, 0.0, 0.0, 1.0]
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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class TestSoftsign(unittest.TestCase):
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"""测试 softsign 激活函数
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Test softsign activation function"""
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def test_softsign_basic(self):
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"""测试基本的 softsign
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Test basic 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 = [-0.5, 0.0, 0.5]
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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class TestHardtanh(unittest.TestCase):
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"""测试 hardtanh 激活函数
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Test hardtanh activation function"""
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def test_hardtanh_basic(self):
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"""测试基本的 hardtanh
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Test basic hardtanh"""
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x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
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result = F.hardtanh(x)
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expected = [-1.0, -1.0, 0.0, 1.0, 1.0]
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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def test_hardtanh_custom_range(self):
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"""测试自定义范围的 hardtanh
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Test hardtanh with custom range"""
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x = paddle.to_tensor([-3.0, -1.0, 0.0, 1.0, 3.0])
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result = F.hardtanh(x, min=-2.0, max=2.0)
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expected = [-2.0, -1.0, 0.0, 1.0, 2.0]
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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class TestHardsigmoid(unittest.TestCase):
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"""测试 hardsigmoid 激活函数
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Test hardsigmoid activation function"""
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def test_hardsigmoid_basic(self):
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"""测试基本的 hardsigmoid
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Test basic hardsigmoid"""
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x = paddle.to_tensor([-4.0, -2.0, 0.0, 2.0, 4.0])
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result = F.hardsigmoid(x)
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# hardsigmoid(x) = clip(x/6 + 0.5, 0, 1)
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expected = [0.0, 1.0 / 6.0, 0.5, 5.0 / 6.0, 1.0]
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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class TestHardswish(unittest.TestCase):
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"""测试 hardswish 激活函数
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Test hardswish activation function"""
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def test_hardswish_basic(self):
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"""测试基本的 hardswish
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Test basic hardswish"""
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x = paddle.to_tensor([-4.0, -2.0, 0.0, 2.0, 4.0])
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result = F.hardswish(x)
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self.assertEqual(result.shape, [5])
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class TestPrelu(unittest.TestCase):
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"""测试 prelu 激活函数
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Test prelu activation function"""
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def test_prelu_basic(self):
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"""测试基本的 prelu
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Test basic prelu"""
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x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
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w = paddle.to_tensor([0.25])
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result = F.prelu(x, w)
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# prelu(x) = max(0, x) + w * min(0, x)
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expected = [0.25 * (-2.0), 0.25 * (-1.0), 0.0, 1.0, 2.0]
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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class TestRelu6(unittest.TestCase):
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"""测试 relu6 激活函数
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Test relu6 activation function"""
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def test_relu6_basic(self):
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"""测试基本的 relu6
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Test basic relu6"""
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x = paddle.to_tensor([-2.0, 0.0, 3.0, 6.0, 8.0])
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result = F.relu6(x)
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expected = [0.0, 0.0, 3.0, 6.0, 6.0]
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np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
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class TestSelu(unittest.TestCase):
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"""测试 selu 激活函数
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Test selu activation function"""
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def test_selu_basic(self):
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"""测试基本的 selu
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Test basic selu"""
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x = paddle.to_tensor([-2.0, 0.0, 2.0])
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result = F.selu(x)
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self.assertEqual(result.shape, [3])
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if __name__ == '__main__':
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unittest.main()
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