wehub-resource-sync
410 lines
17 KiB
Python
410 lines
17 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.functional.vision
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# 自动生成的单测，覆盖 paddle.nn.functional.vision 模块中未覆盖的代码
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# Target: cover uncovered lines in python/paddle/nn/functional/vision.py
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# NOTE: test_ai_vision_ops.py already covers yolo_loss and yolo_box from paddle.vision.ops.
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# This test covers affine_grid, pixel_unshuffle, and channel_shuffle.
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"""
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测试模块：paddle.nn.functional.vision
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Test Module: paddle.nn.functional.vision
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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. affine_grid - 仿射变换坐标网格生成 / Affine transformation coordinate grid generation
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- 2D 仿射网格 / 2D affine grid
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- align_corners=True/False / align_corners option
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- 3D 仿射网格 / 3D affine grid
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- 输出形状验证 / Output shape verification
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2. pixel_unshuffle - 像素反混洗 / Pixel unshuffle operation
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- downscale_factor=2, 3 / Different downscale factors
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- NCHW 和 NHWC 格式 / NCHW and NHWC formats
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- 输出形状验证 / Output shape verification
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3. channel_shuffle - 通道混洗 / Channel shuffle operation
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- 不同 groups 参数 / Different groups
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- NCHW 和 NHWC 格式 / NCHW and NHWC formats
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- 输出形状等于输入形状 / Output shape equals input shape
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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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class TestAffineGrid2D(unittest.TestCase):
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"""测试 2D affine_grid
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Test 2D affine_grid"""
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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_2d_affine_grid_batch1(self):
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"""测试 batch=1 的 2D 仿射网格
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Test 2D affine grid with batch=1"""
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theta = paddle.to_tensor(
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[[[-0.7, -0.4, 0.3], [0.6, 0.5, 1.5]]],
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dtype="float32",
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)
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grid = F.affine_grid(theta, [1, 2, 3, 3])
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# 输出形状应为 [1, 3, 3, 2] / Output shape should be [1, 3, 3, 2]
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self.assertEqual(list(grid.shape), [1, 3, 3, 2])
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def test_2d_affine_grid_align_corners_false(self):
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"""测试 align_corners=False 的 2D 仿射网格
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Test 2D affine grid with align_corners=False"""
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theta = paddle.to_tensor(
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[[[-0.7, -0.4, 0.3], [0.6, 0.5, 1.5]]],
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dtype="float32",
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)
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grid = F.affine_grid(theta, [1, 2, 3, 3], align_corners=False)
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self.assertEqual(list(grid.shape), [1, 3, 3, 2])
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# 验证网格值范围合理 / Verify grid values are in reasonable range
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grid_np = grid.numpy()
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self.assertTrue(np.all(np.isfinite(grid_np)))
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def test_2d_affine_grid_align_corners_true(self):
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"""测试 align_corners=True 的 2D 仿射网格
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Test 2D affine grid with align_corners=True"""
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theta = paddle.to_tensor(
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[[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]],
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dtype="float32",
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)
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# 单位变换应该生成标准坐标网格 / Identity should generate standard grid
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grid = F.affine_grid(theta, [1, 1, 4, 4], align_corners=True)
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self.assertEqual(list(grid.shape), [1, 4, 4, 2])
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# align_corners=True 时，角点应为 -1 和 1
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# With align_corners=True, corners should be -1 and 1
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grid_np = grid.numpy()
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np.testing.assert_allclose(grid_np[0, 0, 0, :], [-1.0, -1.0], atol=1e-5)
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np.testing.assert_allclose(grid_np[0, 3, 3, :], [1.0, 1.0], atol=1e-5)
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def test_2d_affine_grid_batch2(self):
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"""测试 batch=2 的 2D 仿射网格
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Test 2D affine grid with batch=2"""
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theta = paddle.randn([2, 2, 3], dtype='float32')
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grid = F.affine_grid(theta, [2, 3, 5, 5])
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self.assertEqual(list(grid.shape), [2, 5, 5, 2])
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def test_2d_affine_grid_float64(self):
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"""测试 float64 类型的 2D 仿射网格
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Test 2D affine grid with float64 dtype"""
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theta = paddle.to_tensor(
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[[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]],
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dtype="float64",
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)
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grid = F.affine_grid(theta, [1, 1, 2, 2])
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self.assertEqual(grid.dtype, paddle.float64)
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self.assertEqual(list(grid.shape), [1, 2, 2, 2])
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def test_2d_affine_grid_identity_transform(self):
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"""测试单位变换生成标准坐标网格
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Test identity transform generates standard coordinate grid"""
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theta = paddle.to_tensor(
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[[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]],
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dtype="float32",
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)
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grid = F.affine_grid(theta, [1, 1, 2, 2], align_corners=True)
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grid_np = grid.numpy()
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# 四个角点 / Four corners
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expected = [[[-1.0, -1.0], [1.0, -1.0]], [[-1.0, 1.0], [1.0, 1.0]]]
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np.testing.assert_allclose(grid_np[0], expected, atol=1e-5)
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class TestAffineGrid3D(unittest.TestCase):
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"""测试 3D affine_grid (体积仿射变换)
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Test 3D affine_grid (volumetric affine transformation)"""
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def setUp(self):
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paddle.disable_static()
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def test_3d_affine_grid(self):
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"""测试 3D 仿射网格 (batch, depth, height, width)
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Test 3D affine grid"""
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theta = paddle.randn([1, 3, 4], dtype='float32')
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grid = F.affine_grid(theta, [1, 2, 4, 4, 4])
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# 输出形状应为 [batch, D, H, W, 3] / Output shape should be [batch, D, H, W, 3]
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self.assertEqual(list(grid.shape), [1, 4, 4, 4, 3])
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def test_3d_affine_grid_identity(self):
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"""测试 3D 单位变换
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Test 3D identity transform"""
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theta = paddle.to_tensor(
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[[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]],
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dtype='float32',
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)
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grid = F.affine_grid(theta, [1, 1, 2, 2, 2], align_corners=True)
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self.assertEqual(list(grid.shape), [1, 2, 2, 2, 3])
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# 验证角点坐标 / Verify corner coordinates
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grid_np = grid.numpy()
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np.testing.assert_allclose(
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grid_np[0, 0, 0, 0, :], [-1.0, -1.0, -1.0], atol=1e-5
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)
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np.testing.assert_allclose(
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grid_np[0, 1, 1, 1, :], [1.0, 1.0, 1.0], atol=1e-5
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)
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def test_3d_affine_grid_different_sizes(self):
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"""测试 3D 仿射网格不同尺寸
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Test 3D affine grid with different sizes"""
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theta = paddle.randn([2, 3, 4], dtype='float32')
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grid = F.affine_grid(theta, [2, 3, 8, 6, 4])
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self.assertEqual(list(grid.shape), [2, 8, 6, 4, 3])
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class TestPixelUnshuffle(unittest.TestCase):
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"""测试 pixel_unshuffle 操作
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Test pixel_unshuffle operation"""
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def setUp(self):
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paddle.disable_static()
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def test_downscale_factor_2_nchw(self):
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"""测试 downscale_factor=2 在 NCHW 格式下
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Test downscale_factor=2 with NCHW format"""
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x = paddle.randn([2, 4, 6, 6], dtype='float32')
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out = F.pixel_unshuffle(x, 2, data_format='NCHW')
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# 输出形状应为 [N, C*r*r, H/r, W/r]
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# Output shape should be [N, C*r*r, H/r, W/r]
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self.assertEqual(list(out.shape), [2, 16, 3, 3])
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def test_downscale_factor_3_nchw(self):
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"""测试 downscale_factor=3 在 NCHW 格式下
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Test downscale_factor=3 with NCHW format"""
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x = paddle.randn([1, 9, 6, 6], dtype='float32')
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out = F.pixel_unshuffle(x, 3, data_format='NCHW')
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# [1, 9*3*3, 6/3, 6/3] = [1, 81, 2, 2]
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self.assertEqual(list(out.shape), [1, 81, 2, 2])
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def test_downscale_factor_2_nhwc(self):
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"""测试 downscale_factor=2 在 NHWC 格式下
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Test downscale_factor=2 with NHWC format"""
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x = paddle.randn([2, 6, 6, 4], dtype='float32')
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out = F.pixel_unshuffle(x, 2, data_format='NHWC')
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# NHWC: [N, H/r, W/r, C*r*r]
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self.assertEqual(list(out.shape), [2, 3, 3, 16])
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def test_downscale_factor_4_nchw(self):
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"""测试 downscale_factor=4 在 NCHW 格式下
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Test downscale_factor=4 with NCHW format"""
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x = paddle.randn([1, 16, 8, 8], dtype='float32')
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out = F.pixel_unshuffle(x, 4, data_format='NCHW')
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self.assertEqual(list(out.shape), [1, 256, 2, 2])
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def test_pixel_unshuffle_float64(self):
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"""测试 float64 类型的 pixel_unshuffle
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Test pixel_unshuffle with float64 dtype"""
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x = paddle.randn([1, 4, 4, 4], dtype='float64')
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out = F.pixel_unshuffle(x, 2, data_format='NCHW')
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self.assertEqual(out.dtype, paddle.float64)
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self.assertEqual(list(out.shape), [1, 16, 2, 2])
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def test_pixel_unshuffle_values_preserved(self):
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"""测试 pixel_unshuffle 是否正确重新排列像素值
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Test that pixel_unshuffle correctly rearranges pixel values"""
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# 创建简单的输入来验证数据正确性
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# Create simple input to verify data correctness
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x = paddle.arange(0, 36, dtype='float32').reshape([1, 1, 6, 6])
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out = F.pixel_unshuffle(x, 2, data_format='NCHW')
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self.assertEqual(list(out.shape), [1, 4, 3, 3])
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# 所有原始值应保留（仅重新排列）
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# All original values should be preserved (just rearranged)
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x_flat = paddle.sort(x.flatten())
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out_flat = paddle.sort(out.flatten())
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np.testing.assert_array_equal(x_flat.numpy(), out_flat.numpy())
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class TestPixelUnshuffleErrors(unittest.TestCase):
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"""测试 pixel_unshuffle 的错误处理
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Test pixel_unshuffle error handling"""
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def setUp(self):
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paddle.disable_static()
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def test_invalid_downscale_factor_type(self):
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"""测试非整数 downscale_factor 应报错
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Test that non-integer downscale_factor raises error"""
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x = paddle.randn([1, 4, 4, 4], dtype='float32')
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with self.assertRaises(TypeError):
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F.pixel_unshuffle(x, 2.0, data_format='NCHW')
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def test_zero_downscale_factor(self):
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"""测试 downscale_factor=0 应报错
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Test that downscale_factor=0 raises error"""
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x = paddle.randn([1, 4, 4, 4], dtype='float32')
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with self.assertRaises(ValueError):
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F.pixel_unshuffle(x, 0, data_format='NCHW')
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def test_negative_downscale_factor(self):
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"""测试负数 downscale_factor 应报错
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Test that negative downscale_factor raises error"""
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x = paddle.randn([1, 4, 4, 4], dtype='float32')
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with self.assertRaises(ValueError):
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F.pixel_unshuffle(x, -2, data_format='NCHW')
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def test_invalid_data_format(self):
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"""测试无效的 data_format 应报错
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Test that invalid data_format raises error"""
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x = paddle.randn([1, 4, 4, 4], dtype='float32')
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with self.assertRaises(ValueError):
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F.pixel_unshuffle(x, 2, data_format='NCHWD')
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def test_3d_input_raises_error(self):
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"""测试 3D 输入应报错
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Test that 3D input raises error"""
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x = paddle.randn([1, 4, 4], dtype='float32')
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with self.assertRaises(ValueError):
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F.pixel_unshuffle(x, 2, data_format='NCHW')
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class TestChannelShuffle(unittest.TestCase):
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"""测试 channel_shuffle 操作
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Test channel_shuffle operation"""
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def setUp(self):
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paddle.disable_static()
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def test_groups_2_4channel_nchw(self):
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"""测试 groups=2 在 4 通道 NCHW 输入
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Test groups=2 on 4-channel NCHW input"""
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x = paddle.arange(0, 4, dtype='float32').reshape([1, 4, 1, 1])
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out = F.channel_shuffle(x, 2, data_format='NCHW')
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# 输出形状应与输入相同 / Output shape should equal input shape
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self.assertEqual(list(out.shape), [1, 4, 1, 1])
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# 验证值被正确重新排列
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# Verify values are correctly rearranged
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# groups=2: 原始 [0,1,2,3] -> 分组 [[0,1],[2,3]] -> 交错 [0,2,1,3]
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expected = paddle.to_tensor([0, 2, 1, 3], dtype='float32').reshape(
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[1, 4, 1, 1]
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)
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np.testing.assert_array_equal(out.numpy(), expected.numpy())
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def test_groups_4_8channel_nchw(self):
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"""测试 groups=4 在 8 通道 NCHW 输入
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Test groups=4 on 8-channel NCHW input"""
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x = paddle.arange(0, 8, dtype='float32').reshape([1, 8, 1, 1])
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out = F.channel_shuffle(x, 4, data_format='NCHW')
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self.assertEqual(list(out.shape), [1, 8, 1, 1])
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# groups=4: 分组 [[0,1],[2,3],[4,5],[6,7]] -> 交错 [0,2,4,6,1,3,5,7]
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expected = paddle.to_tensor(
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[0, 2, 4, 6, 1, 3, 5, 7], dtype='float32'
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).reshape([1, 8, 1, 1])
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np.testing.assert_array_equal(out.numpy(), expected.numpy())
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def test_groups_1(self):
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"""测试 groups=1 (无混洗)
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Test groups=1 (no shuffle)"""
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x = paddle.arange(0, 6, dtype='float32').reshape([1, 6, 1, 1])
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out = F.channel_shuffle(x, 1, data_format='NCHW')
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# groups=1: 无变化 / groups=1: no change
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np.testing.assert_array_equal(out.numpy(), x.numpy())
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def test_groups_2_nhwc(self):
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"""测试 groups=2 在 NHWC 格式下
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Test groups=2 in NHWC format"""
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x = paddle.arange(0, 4, dtype='float32').reshape([1, 1, 1, 4])
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out = F.channel_shuffle(x, 2, data_format='NHWC')
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self.assertEqual(list(out.shape), [1, 1, 1, 4])
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# groups=2 on NHWC with channels [0,1,2,3] -> [0,2,1,3]
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expected = paddle.to_tensor([0, 2, 1, 3], dtype='float32').reshape(
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[1, 1, 1, 4]
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)
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np.testing.assert_array_equal(out.numpy(), expected.numpy())
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def test_groups_2_spatial_input(self):
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"""测试 groups=2 在有空间维度的输入上
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Test groups=2 on input with spatial dimensions"""
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x = paddle.arange(0, 24, dtype='float32').reshape([1, 4, 2, 3])
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out = F.channel_shuffle(x, 2, data_format='NCHW')
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self.assertEqual(list(out.shape), [1, 4, 2, 3])
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# 所有原始值应保留 / All original values should be preserved
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np.testing.assert_array_equal(
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np.sort(x.flatten().numpy()),
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np.sort(out.flatten().numpy()),
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)
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def test_channel_shuffle_float64(self):
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"""测试 float64 类型的 channel_shuffle
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Test channel_shuffle with float64 dtype"""
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x = paddle.arange(0, 8, dtype='float64').reshape([1, 8, 1, 1])
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out = F.channel_shuffle(x, 4, data_format='NCHW')
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self.assertEqual(out.dtype, paddle.float64)
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self.assertEqual(list(out.shape), [1, 8, 1, 1])
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class TestChannelShuffleErrors(unittest.TestCase):
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"""测试 channel_shuffle 的错误处理
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Test channel_shuffle error handling"""
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def setUp(self):
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paddle.disable_static()
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def test_invalid_groups_type(self):
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"""测试非整数 groups 应报错
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Test that non-integer groups raises error"""
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x = paddle.randn([1, 4, 2, 2], dtype='float32')
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with self.assertRaises(TypeError):
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F.channel_shuffle(x, 2.0, data_format='NCHW')
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def test_zero_groups(self):
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"""测试 groups=0 应报错
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Test that groups=0 raises error"""
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x = paddle.randn([1, 4, 2, 2], dtype='float32')
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with self.assertRaises(ValueError):
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F.channel_shuffle(x, 0, data_format='NCHW')
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def test_negative_groups(self):
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"""测试负数 groups 应报错
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Test that negative groups raises error"""
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x = paddle.randn([1, 4, 2, 2], dtype='float32')
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with self.assertRaises(ValueError):
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F.channel_shuffle(x, -1, data_format='NCHW')
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def test_invalid_data_format(self):
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"""测试无效的 data_format 应报错
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Test that invalid data_format raises error"""
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x = paddle.randn([1, 4, 2, 2], dtype='float32')
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with self.assertRaises(ValueError):
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|
F.channel_shuffle(x, 2, data_format='invalid')
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|
|
|
def test_3d_input_raises_error(self):
|
|
"""测试 3D 输入应报错
|
|
Test that 3D input raises error"""
|
|
x = paddle.randn([1, 4, 2], dtype='float32')
|
|
with self.assertRaises(ValueError):
|
|
F.channel_shuffle(x, 2, data_format='NCHW')
|
|
|
|
|
|
class TestAffineGridErrors(unittest.TestCase):
|
|
"""测试 affine_grid 的错误处理
|
|
Test affine_grid error handling"""
|
|
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
|
|
def test_non_tensor_theta(self):
|
|
"""测试非 Tensor 的 theta 应报错
|
|
Test that non-Tensor theta raises error"""
|
|
with self.assertRaises(TypeError):
|
|
F.affine_grid([[1, 0, 0], [0, 1, 0]], [1, 1, 2, 2])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|