# Copyright (c) 2020 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. # when test, you should add hapi root path to the PYTHONPATH, # export PYTHONPATH=PATH_TO_HAPI:$PYTHONPATH import unittest import os import tempfile import cv2 import shutil import numpy as np from paddle.incubate.hapi.datasets import DatasetFolder from paddle.incubate.hapi.vision.transforms import transforms import paddle.incubate.hapi.vision.transforms.functional as F class TestTransforms(unittest.TestCase): def setUp(self): self.data_dir = tempfile.mkdtemp() for i in range(2): sub_dir = os.path.join(self.data_dir, 'class_' + str(i)) if not os.path.exists(sub_dir): os.makedirs(sub_dir) for j in range(2): if j == 0: fake_img = (np.random.random( (280, 350, 3)) * 255).astype('uint8') else: fake_img = (np.random.random( (400, 300, 3)) * 255).astype('uint8') cv2.imwrite(os.path.join(sub_dir, str(j) + '.jpg'), fake_img) def tearDown(self): shutil.rmtree(self.data_dir) def do_transform(self, trans): dataset_folder = DatasetFolder(self.data_dir, transform=trans) for _ in dataset_folder: pass def test_trans_all(self): normalize = transforms.Normalize( mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375]) trans = transforms.Compose([ transforms.RandomResizedCrop(224), transforms.GaussianNoise(), transforms.ColorJitter( brightness=0.4, contrast=0.4, saturation=0.4, hue=0.4), transforms.RandomHorizontalFlip(), transforms.Permute(mode='CHW'), normalize ]) self.do_transform(trans) def test_normalize(self): normalize = transforms.Normalize(mean=0.5, std=0.5) trans = transforms.Compose([transforms.Permute(mode='CHW'), normalize]) self.do_transform(trans) def test_trans_resize(self): trans = transforms.Compose([ transforms.Resize(300, [0, 1]), transforms.RandomResizedCrop((280, 280)), transforms.Resize(280, [0, 1]), transforms.Resize((256, 200)), transforms.Resize((180, 160)), transforms.CenterCrop(128), transforms.CenterCrop((128, 128)), ]) self.do_transform(trans) def test_trans_centerCrop(self): trans = transforms.Compose([ transforms.CenterCropResize(224), transforms.CenterCropResize(128, 160), ]) self.do_transform(trans) def test_flip(self): trans = transforms.Compose([ transforms.RandomHorizontalFlip(1.0), transforms.RandomHorizontalFlip(0.0), transforms.RandomVerticalFlip(0.0), transforms.RandomVerticalFlip(1.0), ]) self.do_transform(trans) def test_color_jitter(self): trans = transforms.BatchCompose([ transforms.BrightnessTransform(0.0), transforms.HueTransform(0.0), transforms.SaturationTransform(0.0), transforms.ContrastTransform(0.0), ]) self.do_transform(trans) def test_rotate(self): trans = transforms.Compose([ transforms.RandomRotate(90), transforms.RandomRotate([-10, 10]), transforms.RandomRotate( 45, expand=True), transforms.RandomRotate( 10, expand=True, center=(60, 80)), ]) self.do_transform(trans) def test_pad(self): trans = transforms.Compose([transforms.Pad(2)]) self.do_transform(trans) fake_img = np.random.rand(200, 150, 3).astype('float32') trans_pad = transforms.Pad(10) fake_img_padded = trans_pad(fake_img) np.testing.assert_equal(fake_img_padded.shape, (220, 170, 3)) trans_pad1 = transforms.Pad([1, 2]) trans_pad2 = transforms.Pad([1, 2, 3, 4]) img = trans_pad1(fake_img) img = trans_pad2(img) def test_erase(self): trans = transforms.Compose( [transforms.RandomErasing(), transforms.RandomErasing(value=0.0)]) self.do_transform(trans) def test_random_crop(self): trans = transforms.Compose([ transforms.RandomCrop(200), transforms.RandomCrop((140, 160)), ]) self.do_transform(trans) trans_random_crop1 = transforms.RandomCrop(224) trans_random_crop2 = transforms.RandomCrop((140, 160)) fake_img = np.random.rand(500, 400, 3).astype('float32') fake_img_crop1 = trans_random_crop1(fake_img) fake_img_crop2 = trans_random_crop2(fake_img_crop1) np.testing.assert_equal(fake_img_crop1.shape, (224, 224, 3)) np.testing.assert_equal(fake_img_crop2.shape, (140, 160, 3)) trans_random_crop_same = transforms.RandomCrop((140, 160)) img = trans_random_crop_same(fake_img_crop2) trans_random_crop_bigger = transforms.RandomCrop((180, 200)) img = trans_random_crop_bigger(img) trans_random_crop_pad = transforms.RandomCrop((224, 256), 2, True) img = trans_random_crop_pad(img) def test_grayscale(self): trans = transforms.Compose([transforms.Grayscale()]) self.do_transform(trans) trans_gray = transforms.Grayscale() fake_img = np.random.rand(500, 400, 3).astype('float32') fake_img_gray = trans_gray(fake_img) np.testing.assert_equal(len(fake_img_gray.shape), 3) np.testing.assert_equal(fake_img_gray.shape[0], 500) np.testing.assert_equal(fake_img_gray.shape[1], 400) trans_gray3 = transforms.Grayscale(3) fake_img = np.random.rand(500, 400, 3).astype('float32') fake_img_gray = trans_gray3(fake_img) def test_exception(self): trans = transforms.Compose([transforms.Resize(-1)]) trans_batch = transforms.BatchCompose([transforms.Resize(-1)]) with self.assertRaises(Exception): self.do_transform(trans) with self.assertRaises(Exception): self.do_transform(trans_batch) with self.assertRaises(ValueError): transforms.ContrastTransform(-1.0) with self.assertRaises(ValueError): transforms.SaturationTransform(-1.0), with self.assertRaises(ValueError): transforms.HueTransform(-1.0) with self.assertRaises(ValueError): transforms.BrightnessTransform(-1.0) with self.assertRaises(ValueError): transforms.Pad([1.0, 2.0, 3.0]) with self.assertRaises(TypeError): fake_img = np.random.rand(100, 120, 3).astype('float32') F.pad(fake_img, '1') with self.assertRaises(TypeError): fake_img = np.random.rand(100, 120, 3).astype('float32') F.pad(fake_img, 1, {}) with self.assertRaises(TypeError): fake_img = np.random.rand(100, 120, 3).astype('float32') F.pad(fake_img, 1, padding_mode=-1) with self.assertRaises(ValueError): fake_img = np.random.rand(100, 120, 3).astype('float32') F.pad(fake_img, [1.0, 2.0, 3.0]) with self.assertRaises(ValueError): transforms.RandomRotate(-2) with self.assertRaises(ValueError): transforms.RandomRotate([1, 2, 3]) with self.assertRaises(ValueError): trans_gray = transforms.Grayscale(5) fake_img = np.random.rand(100, 120, 3).astype('float32') trans_gray(fake_img) def test_info(self): str(transforms.Compose([transforms.Resize((224, 224))])) str(transforms.BatchCompose([transforms.Resize((224, 224))])) if __name__ == '__main__': unittest.main()