wehub-resource-sync
213 lines
9.4 KiB
Python
213 lines
9.4 KiB
Python
# Copyright (c) 2024 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] Unit test for paddle.nn.initializer.Orthogonal
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# 自动生成的单测，覆盖 paddle.nn.initializer.orthogonal 模块中未覆盖的代码
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# Target: cover uncovered lines 157-271 in paddle/python/paddle/nn/initializer/orthogonal.py
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"""
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测试模块：paddle.nn.initializer.Orthogonal
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Test Module: paddle.nn.initializer.Orthogonal
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本测试覆盖以下功能：
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This test covers the following functions:
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1. Orthogonal 初始化器在动态图模式下的使用 / Orthogonal initializer in dynamic graph mode
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- rows > cols 情况：列向量正交 / rows > cols case: columns are orthogonal
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- rows < cols 情况：行向量正交 / rows < cols case: rows are orthogonal
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- rows == cols 情况：行列均正交 / rows == cols case: both rows and columns are orthogonal
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- gain 参数测试 / gain parameter test
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2. Orthogonal 初始化器在静态图模式下的使用 / Orthogonal initializer in static graph mode
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- 覆盖静态图代码路径 lines 157-271 / covers static graph code path
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覆盖的未覆盖行：157, 161, 167, 181, 187, 193, 204, 210, 218-219, 226, 234, 240-241, 247, 254, 256, 264, 271
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注意：paddle.nn.Linear(in, out) 的weight形状为 [in, out]
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Note: paddle.nn.Linear(in, out) weight shape is [in, out]
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所以 Linear(10, 15) → weight [10, 15]，rows=10 < cols=15
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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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class TestOrthogonalInitializerDynamic(unittest.TestCase):
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"""测试动态图模式下Orthogonal初始化器
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Test Orthogonal initializer in dynamic graph mode"""
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def setUp(self):
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"""设置测试环境 / Set up test environment"""
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paddle.disable_static()
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def test_rows_greater_than_cols(self):
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"""测试rows > cols的情况，weight [15, 10]，列向量应正交
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Test rows > cols case, weight [15, 10], columns should be orthogonal
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Linear(10, 15) → weight shape [10, 15], rows=10 < cols=15
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So we use Linear(15, 10) → weight shape [15, 10], rows=15 > cols=10"""
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weight_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Orthogonal()
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)
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# Linear(out=10, in=15) → weight [15, 10], but Paddle Linear(in, out)
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# Linear(15, 10) → weight [15, 10], rows=15 > cols=10
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linear = paddle.nn.Linear(15, 10, weight_attr=weight_attr)
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weight = linear.weight.numpy()
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self.assertEqual(weight.shape, (15, 10))
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# W^T * W 应接近单位矩阵 / W^T * W should be close to identity (10x10)
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wtw = np.matmul(weight.T, weight)
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identity = np.eye(10)
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np.testing.assert_allclose(wtw, identity, atol=1e-5)
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def test_rows_less_than_cols(self):
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"""测试rows < cols的情况，weight [10, 15]，行向量应正交
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Test rows < cols case, weight [10, 15], rows should be orthogonal
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Linear(10, 15) → weight [10, 15], rows=10 < cols=15"""
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weight_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Orthogonal()
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)
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linear = paddle.nn.Linear(10, 15, weight_attr=weight_attr)
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weight = linear.weight.numpy()
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self.assertEqual(weight.shape, (10, 15))
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# W * W^T 应接近单位矩阵 / W * W^T should be close to identity (10x10)
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wwt = np.matmul(weight, weight.T)
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identity = np.eye(10)
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np.testing.assert_allclose(wwt, identity, atol=1e-5)
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def test_square_matrix(self):
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"""测试rows == cols的情况（10x10），行列均正交
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Test rows == cols case (10x10), both rows and columns are orthogonal"""
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weight_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Orthogonal()
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)
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linear = paddle.nn.Linear(10, 10, weight_attr=weight_attr)
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weight = linear.weight.numpy()
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self.assertEqual(weight.shape, (10, 10))
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# W * W^T 应接近单位矩阵 / W * W^T should be close to identity
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wwt = np.matmul(weight, weight.T)
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identity = np.eye(10)
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np.testing.assert_allclose(wwt, identity, atol=1e-5)
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def test_with_gain(self):
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"""测试带gain参数的Orthogonal初始化
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Test Orthogonal initialization with gain parameter"""
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gain = 2.0
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weight_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Orthogonal(gain=gain)
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)
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linear = paddle.nn.Linear(10, 10, weight_attr=weight_attr)
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weight = linear.weight.numpy()
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# W * W^T / gain^2 应接近单位矩阵
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# W * W^T / gain^2 should be close to identity
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wwt = np.matmul(weight, weight.T) / (gain**2)
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identity = np.eye(10)
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np.testing.assert_allclose(wwt, identity, atol=1e-5)
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def test_conv2d_orthogonal(self):
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"""测试在Conv2D上使用Orthogonal初始化（维度>2）
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Test Orthogonal initialization on Conv2D (dimension > 2)"""
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weight_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Orthogonal()
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)
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conv = paddle.nn.Conv2D(3, 8, 3, weight_attr=weight_attr)
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weight = conv.weight.numpy()
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self.assertEqual(len(weight.shape), 4) # [out_c, in_c, kH, kW]
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class TestOrthogonalInitializerStatic(unittest.TestCase):
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"""测试静态图模式下的Orthogonal初始化器，覆盖未覆盖的静态图代码路径
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Test Orthogonal initializer in static graph mode, covering uncovered static paths"""
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def test_static_graph_rows_greater_cols(self):
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"""测试静态图模式下rows > cols的Orthogonal初始化
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Test Orthogonal init with rows > cols in static graph mode
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Linear(15, 8) → weight [15, 8], rows=15 > cols=8"""
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paddle.enable_static()
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try:
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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weight_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Orthogonal()
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)
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linear = paddle.nn.Linear(15, 8, weight_attr=weight_attr)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(startup_prog)
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result = exe.run(main_prog, fetch_list=[linear.weight])
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weight = result[0]
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self.assertEqual(weight.shape, (15, 8))
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# W^T * W 应接近单位矩阵 / W^T * W should be close to identity (8x8)
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wtw = np.matmul(weight.T, weight)
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identity = np.eye(8)
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np.testing.assert_allclose(wtw, identity, atol=1e-4)
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finally:
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paddle.disable_static()
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def test_static_graph_rows_less_cols(self):
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"""测试静态图模式下rows < cols的Orthogonal初始化（触发transpose分支）
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Test Orthogonal init with rows < cols in static graph (triggers transpose branch)
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Linear(8, 15) → weight [8, 15], rows=8 < cols=15"""
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paddle.enable_static()
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try:
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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weight_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Orthogonal()
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)
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linear = paddle.nn.Linear(8, 15, weight_attr=weight_attr)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(startup_prog)
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result = exe.run(main_prog, fetch_list=[linear.weight])
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weight = result[0]
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self.assertEqual(weight.shape, (8, 15))
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# W * W^T 应接近单位矩阵 / W * W^T should be close to identity (8x8)
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wwt = np.matmul(weight, weight.T)
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identity = np.eye(8)
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np.testing.assert_allclose(wwt, identity, atol=1e-4)
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finally:
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paddle.disable_static()
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def test_static_graph_with_gain(self):
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"""测试静态图模式下带gain参数的Orthogonal初始化
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Test Orthogonal init with gain parameter in static graph mode"""
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paddle.enable_static()
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try:
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gain = 1.5
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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weight_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Orthogonal(gain=gain)
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)
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linear = paddle.nn.Linear(6, 6, weight_attr=weight_attr)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(startup_prog)
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result = exe.run(main_prog, fetch_list=[linear.weight])
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weight = result[0]
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# W * W^T / gain^2 应接近单位矩阵
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# W * W^T / gain^2 should be close to identity
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wwt = np.matmul(weight, weight.T) / (gain**2)
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identity = np.eye(6)
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np.testing.assert_allclose(wwt, identity, atol=1e-4)
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finally:
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paddle.disable_static()
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if __name__ == '__main__':
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unittest.main()
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