You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
239 lines
8.1 KiB
239 lines
8.1 KiB
# 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.
|
|
|
|
import unittest
|
|
import os
|
|
import tempfile
|
|
import cv2
|
|
import shutil
|
|
import numpy as np
|
|
|
|
from paddle.vision.datasets import DatasetFolder
|
|
from paddle.vision.transforms import transforms
|
|
import paddle.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()
|