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133 lines
4.4 KiB
133 lines
4.4 KiB
# Copyright (c) 2020 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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# when test, you should add hapi root path to the PYTHONPATH,
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# export PYTHONPATH=PATH_TO_HAPI:$PYTHONPATH
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import unittest
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import os
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import tempfile
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import cv2
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import shutil
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import numpy as np
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from paddle.incubate.hapi.datasets import DatasetFolder
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from paddle.incubate.hapi.vision.transforms import transforms
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class TestTransforms(unittest.TestCase):
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def setUp(self):
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self.data_dir = tempfile.mkdtemp()
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for i in range(2):
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sub_dir = os.path.join(self.data_dir, 'class_' + str(i))
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if not os.path.exists(sub_dir):
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os.makedirs(sub_dir)
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for j in range(2):
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if j == 0:
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fake_img = (np.random.random(
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(280, 350, 3)) * 255).astype('uint8')
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else:
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fake_img = (np.random.random(
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(400, 300, 3)) * 255).astype('uint8')
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cv2.imwrite(os.path.join(sub_dir, str(j) + '.jpg'), fake_img)
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def tearDown(self):
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shutil.rmtree(self.data_dir)
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def do_transform(self, trans):
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dataset_folder = DatasetFolder(self.data_dir, transform=trans)
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for _ in dataset_folder:
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pass
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def test_trans_all(self):
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normalize = transforms.Normalize(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375])
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trans = transforms.Compose([
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transforms.RandomResizedCrop(224), transforms.GaussianNoise(),
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transforms.ColorJitter(
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brightness=0.4, contrast=0.4, saturation=0.4,
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hue=0.4), transforms.RandomHorizontalFlip(),
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transforms.Permute(mode='CHW'), normalize
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])
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self.do_transform(trans)
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def test_trans_resize(self):
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trans = transforms.Compose([
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transforms.Resize(300, [0, 1]),
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transforms.RandomResizedCrop((280, 280)),
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transforms.Resize(280, [0, 1]),
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transforms.Resize((256, 200)),
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transforms.Resize((180, 160)),
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transforms.CenterCrop(128),
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transforms.CenterCrop((128, 128)),
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])
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self.do_transform(trans)
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def test_trans_centerCrop(self):
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trans = transforms.Compose([
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transforms.CenterCropResize(224),
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transforms.CenterCropResize(128, 160),
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])
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self.do_transform(trans)
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def test_flip(self):
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trans = transforms.Compose([
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transforms.RandomHorizontalFlip(1.0),
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transforms.RandomHorizontalFlip(0.0),
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transforms.RandomVerticalFlip(0.0),
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transforms.RandomVerticalFlip(1.0),
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])
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self.do_transform(trans)
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def test_color_jitter(self):
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trans = transforms.BatchCompose([
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transforms.BrightnessTransform(0.0),
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transforms.HueTransform(0.0),
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transforms.SaturationTransform(0.0),
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transforms.ContrastTransform(0.0),
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])
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self.do_transform(trans)
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def test_exception(self):
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trans = transforms.Compose([transforms.Resize(-1)])
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trans_batch = transforms.BatchCompose([transforms.Resize(-1)])
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with self.assertRaises(Exception):
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self.do_transform(trans)
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with self.assertRaises(Exception):
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self.do_transform(trans_batch)
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with self.assertRaises(ValueError):
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transforms.ContrastTransform(-1.0)
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with self.assertRaises(ValueError):
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transforms.SaturationTransform(-1.0),
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with self.assertRaises(ValueError):
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transforms.HueTransform(-1.0)
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with self.assertRaises(ValueError):
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transforms.BrightnessTransform(-1.0)
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def test_info(self):
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str(transforms.Compose([transforms.Resize((224, 224))]))
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str(transforms.BatchCompose([transforms.Resize((224, 224))]))
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if __name__ == '__main__':
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unittest.main()
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