File size: 2,663 Bytes
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 | # 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 unittest
import numpy as np
import torch
from parameterized import parameterized
from monai.transforms import CopyItemsd
from monai.utils import ensure_tuple
TEST_CASE_1 = ["img", 1, "img_1"]
TEST_CASE_2 = [["img", "seg"], 1, ["img_1", "seg_1"]]
TEST_CASE_3 = ["img", 2, ["img_1", "img_2"]]
TEST_CASE_4 = [["img", "seg"], 2, ["img_1", "seg_1", "img_2", "seg_2"]]
class TestCopyItemsd(unittest.TestCase):
@parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])
def test_numpy_values(self, keys, times, names):
input_data = {"img": np.array([[0, 1], [1, 2]]), "seg": np.array([[0, 1], [1, 2]])}
result = CopyItemsd(keys=keys, times=times, names=names)(input_data)
for name in ensure_tuple(names):
self.assertTrue(name in result)
result[name] += 1
np.testing.assert_allclose(result[name], np.array([[1, 2], [2, 3]]))
np.testing.assert_allclose(result["img"], np.array([[0, 1], [1, 2]]))
def test_tensor_values(self):
device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu:0")
input_data = {
"img": torch.tensor([[0, 1], [1, 2]], device=device),
"seg": torch.tensor([[0, 1], [1, 2]], device=device),
}
result = CopyItemsd(keys="img", times=1, names="img_1")(input_data)
self.assertTrue("img_1" in result)
result["img_1"] += 1
torch.testing.assert_allclose(result["img"], torch.tensor([[0, 1], [1, 2]], device=device))
torch.testing.assert_allclose(result["img_1"], torch.tensor([[1, 2], [2, 3]], device=device))
def test_array_values(self):
input_data = {"img": [[0, 1], [1, 2]], "seg": [[0, 1], [1, 2]]}
result = CopyItemsd(keys="img", times=1, names="img_1")(input_data)
self.assertTrue("img_1" in result)
result["img_1"][0][0] += 1
np.testing.assert_allclose(result["img"], [[0, 1], [1, 2]])
np.testing.assert_allclose(result["img_1"], [[1, 1], [1, 2]])
if __name__ == "__main__":
unittest.main()
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