# 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. """ 视觉变换单元测试 / Vision Transforms Unit Tests 测试目标 / Test Target: paddle.vision.transforms 模块 覆盖的模块 / Covered Modules: - paddle.vision.transforms: 图像变换操作 - Resize, RandomCrop, CenterCrop, Normalize, ToTensor等 作用 / Purpose: 覆盖图像预处理变换的各种代码路径，补充视觉变换功能的测试。 """ import unittest import numpy as np from PIL import Image import paddle from paddle.vision.transforms import ( CenterCrop, ColorJitter, Compose, Grayscale, Normalize, Pad, RandomCrop, RandomHorizontalFlip, RandomRotation, RandomVerticalFlip, Resize, ToTensor, ) paddle.disable_static() def create_test_image(height=64, width=64, channels=3): """创建测试图像 / Create test image""" arr = np.random.randint(0, 255, (height, width, channels), dtype=np.uint8) return Image.fromarray(arr) def create_numpy_image(height=64, width=64, channels=3): """创建numpy测试图像 / Create numpy test image""" return np.random.randint(0, 255, (height, width, channels), dtype=np.uint8) class TestResize(unittest.TestCase): """测试Resize变换 / Test Resize transform""" def test_resize_pil(self): """测试PIL图像Resize / Test PIL image resize""" resize = Resize(32) img = create_test_image(64, 64) result = resize(img) self.assertEqual(min(result.size), 32) def test_resize_tuple(self): """测试指定尺寸Resize / Test resize to specific size""" resize = Resize((32, 48)) img = create_test_image(64, 64) result = resize(img) self.assertEqual(result.size, (48, 32)) def test_resize_numpy(self): """测试numpy图像Resize (PIL模式) / Test numpy resize via PIL""" resize = Resize(32) img = create_test_image(64, 64) result = resize(img) self.assertEqual(min(result.size), 32) class TestCrop(unittest.TestCase): """测试裁剪变换 / Test crop transforms""" def test_center_crop(self): """测试中心裁剪 / Test center crop""" crop = CenterCrop(32) img = create_test_image(64, 64) result = crop(img) self.assertEqual(result.size, (32, 32)) def test_random_crop(self): """测试随机裁剪 / Test random crop""" crop = RandomCrop(32) img = create_test_image(64, 64) result = crop(img) self.assertEqual(result.size, (32, 32)) def test_random_crop_with_padding(self): """测试带填充的随机裁剪 / Test random crop with padding""" crop = RandomCrop(32, padding=4) img = create_test_image(32, 32) result = crop(img) self.assertEqual(result.size, (32, 32)) class TestFlipAndRotate(unittest.TestCase): """测试翻转和旋转变换 / Test flip and rotate transforms""" def test_random_horizontal_flip(self): """测试随机水平翻转 / Test random horizontal flip""" flip = RandomHorizontalFlip(prob=1.0) # Always flip img = create_test_image(32, 32) result = flip(img) self.assertEqual(result.size, img.size) def test_random_vertical_flip(self): """测试随机垂直翻转 / Test random vertical flip""" flip = RandomVerticalFlip(prob=1.0) # Always flip img = create_test_image(32, 32) result = flip(img) self.assertEqual(result.size, img.size) def test_random_rotation(self): """测试随机旋转 / Test random rotation""" rotation = RandomRotation(45) img = create_test_image(64, 64) result = rotation(img) self.assertIsNotNone(result) class TestNormalize(unittest.TestCase): """测试归一化变换 / Test normalize transform""" def test_normalize_tensor(self): """测试张量归一化 / Test tensor normalization""" normalize = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) x = paddle.to_tensor(np.random.rand(3, 32, 32).astype('float32')) result = normalize(x) self.assertEqual(result.shape, [3, 32, 32]) def test_normalize_numpy(self): """测试numpy归一化 / Test numpy normalization""" normalize = Normalize( mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], data_format='HWC', ) img = create_numpy_image(32, 32).astype('float32') result = normalize(img) self.assertEqual(result.shape, (32, 32, 3)) class TestToTensorAndTranspose(unittest.TestCase): """测试ToTensor / Test ToTensor""" def test_to_tensor_pil(self): """测试PIL图像转张量 / Test PIL image to tensor""" to_tensor = ToTensor() img = create_test_image(32, 32) result = to_tensor(img) self.assertEqual(result.shape, [3, 32, 32]) # Values should be in [0, 1] self.assertTrue(float(result.max().numpy()) <= 1.0) def test_to_tensor_numpy(self): """测试numpy图像转张量 / Test numpy image to tensor""" to_tensor = ToTensor() img = create_numpy_image(32, 32).astype('float32') result = to_tensor(img) self.assertIsNotNone(result) class TestColorTransforms(unittest.TestCase): """测试颜色变换 / Test color transforms""" def test_grayscale(self): """测试灰度化 / Test grayscale""" grayscale = Grayscale() img = create_test_image(32, 32, 3) result = grayscale(img) self.assertEqual(result.mode, 'L') def test_grayscale_keep_channels(self): """测试保持通道数的灰度化 / Test grayscale with num_output_channels=3""" grayscale = Grayscale(num_output_channels=3) img = create_test_image(32, 32, 3) result = grayscale(img) self.assertEqual(result.mode, 'RGB') def test_color_jitter(self): """测试颜色抖动 / Test color jitter""" jitter = ColorJitter( brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1 ) img = create_test_image(32, 32) result = jitter(img) self.assertEqual(result.size, img.size) class TestCompose(unittest.TestCase): """测试Compose组合变换 / Test Compose transformation""" def test_compose_basic(self): """测试基本Compose / Test basic compose""" transform = Compose( [ Resize(64), CenterCrop(32), ToTensor(), Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ] ) img = create_test_image(128, 128) result = transform(img) self.assertEqual(result.shape, [3, 32, 32]) def test_pad_transform(self): """测试Pad填充变换 / Test Pad transform""" pad = Pad(4) img = create_test_image(32, 32) result = pad(img) self.assertEqual(result.size, (40, 40)) if __name__ == '__main__': unittest.main()