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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | # Copyright 2020 MONAI Consortium
# 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 os
import tempfile
import unittest
import nibabel as nib
import numpy as np
from parameterized import parameterized
from torch.utils.data import DataLoader
from monai.data import ArrayDataset
from monai.transforms import AddChannel, Compose, LoadNifti, RandAdjustContrast, RandGaussianNoise, Spacing
TEST_CASE_1 = [
Compose([LoadNifti(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]),
Compose([LoadNifti(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]),
(0, 1),
(1, 128, 128, 128),
]
TEST_CASE_2 = [
Compose([LoadNifti(image_only=True), AddChannel(), RandAdjustContrast(prob=1.0)]),
Compose([LoadNifti(image_only=True), AddChannel(), RandAdjustContrast(prob=1.0)]),
(0, 1),
(1, 128, 128, 128),
]
class TestCompose(Compose):
def __call__(self, input_):
img, metadata = self.transforms[0](input_)
img = self.transforms[1](img)
img, _, _ = self.transforms[2](img, metadata["affine"])
return self.transforms[3](img), metadata
TEST_CASE_3 = [
TestCompose([LoadNifti(image_only=False), AddChannel(), Spacing(pixdim=(2, 2, 4)), RandAdjustContrast(prob=1.0)]),
TestCompose([LoadNifti(image_only=False), AddChannel(), Spacing(pixdim=(2, 2, 4)), RandAdjustContrast(prob=1.0)]),
(0, 2),
(1, 64, 64, 33),
]
TEST_CASE_4 = [Compose([LoadNifti(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]), (1, 128, 128, 128)]
class TestArrayDataset(unittest.TestCase):
@parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
def test_shape(self, img_transform, label_transform, indices, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
test_image1 = os.path.join(tempdir, "test_image1.nii.gz")
test_seg1 = os.path.join(tempdir, "test_seg1.nii.gz")
test_image2 = os.path.join(tempdir, "test_image2.nii.gz")
test_seg2 = os.path.join(tempdir, "test_seg2.nii.gz")
nib.save(test_image, test_image1)
nib.save(test_image, test_seg1)
nib.save(test_image, test_image2)
nib.save(test_image, test_seg2)
test_images = [test_image1, test_image2]
test_segs = [test_seg1, test_seg2]
test_labels = [1, 1]
dataset = ArrayDataset(test_images, img_transform, test_segs, label_transform, test_labels, None)
self.assertEqual(len(dataset), 2)
dataset.set_random_state(1234)
data1 = dataset[0]
data2 = dataset[1]
self.assertTupleEqual(data1[indices[0]].shape, expected_shape)
self.assertTupleEqual(data1[indices[1]].shape, expected_shape)
np.testing.assert_allclose(data1[indices[0]], data1[indices[1]])
self.assertTupleEqual(data2[indices[0]].shape, expected_shape)
self.assertTupleEqual(data2[indices[1]].shape, expected_shape)
np.testing.assert_allclose(data2[indices[0]], data2[indices[0]])
dataset = ArrayDataset(test_images, img_transform, test_segs, label_transform, test_labels, None)
dataset.set_random_state(1234)
_ = dataset[0]
data2_new = dataset[1]
np.testing.assert_allclose(data2[indices[0]], data2_new[indices[0]], atol=1e-3)
@parameterized.expand([TEST_CASE_4])
def test_default_none(self, img_transform, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
test_image1 = os.path.join(tempdir, "test_image1.nii.gz")
test_image2 = os.path.join(tempdir, "test_image2.nii.gz")
nib.save(test_image, test_image1)
nib.save(test_image, test_image2)
test_images = [test_image1, test_image2]
dataset = ArrayDataset(test_images, img_transform)
self.assertEqual(len(dataset), 2)
dataset.set_random_state(1234)
data1 = dataset[0]
data2 = dataset[1]
self.assertTupleEqual(data1.shape, expected_shape)
self.assertTupleEqual(data2.shape, expected_shape)
dataset = ArrayDataset(test_images, img_transform)
dataset.set_random_state(1234)
_ = dataset[0]
data2_new = dataset[1]
np.testing.assert_allclose(data2, data2_new, atol=1e-3)
@parameterized.expand([TEST_CASE_4])
def test_dataloading_img(self, img_transform, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
test_image1 = os.path.join(tempdir, "test_image1.nii.gz")
test_image2 = os.path.join(tempdir, "test_image2.nii.gz")
nib.save(test_image, test_image1)
nib.save(test_image, test_image2)
test_images = [test_image1, test_image2]
dataset = ArrayDataset(test_images, img_transform)
self.assertEqual(len(dataset), 2)
dataset.set_random_state(1234)
loader = DataLoader(dataset, batch_size=10, num_workers=1)
imgs = next(iter(loader)) # test batching
np.testing.assert_allclose(imgs.shape, [2] + list(expected_shape))
dataset.set_random_state(1234)
new_imgs = next(iter(loader)) # test batching
np.testing.assert_allclose(imgs, new_imgs, atol=1e-3)
@parameterized.expand([TEST_CASE_4])
def test_dataloading_img_label(self, img_transform, expected_shape):
test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)), np.eye(4))
with tempfile.TemporaryDirectory() as tempdir:
test_image1 = os.path.join(tempdir, "test_image1.nii.gz")
test_image2 = os.path.join(tempdir, "test_image2.nii.gz")
test_label1 = os.path.join(tempdir, "test_label1.nii.gz")
test_label2 = os.path.join(tempdir, "test_label2.nii.gz")
nib.save(test_image, test_image1)
nib.save(test_image, test_image2)
nib.save(test_image, test_label1)
nib.save(test_image, test_label2)
test_images = [test_image1, test_image2]
test_labels = [test_label1, test_label2]
dataset = ArrayDataset(test_images, img_transform, test_labels, img_transform)
self.assertEqual(len(dataset), 2)
dataset.set_random_state(1234)
loader = DataLoader(dataset, batch_size=10, num_workers=1)
data = next(iter(loader)) # test batching
np.testing.assert_allclose(data[0].shape, [2] + list(expected_shape))
dataset.set_random_state(1234)
new_data = next(iter(loader)) # test batching
np.testing.assert_allclose(data[0], new_data[0], atol=1e-3)
if __name__ == "__main__":
unittest.main()
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