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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 | # 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 monai.transforms import ConcatItemsd
class TestConcatItemsd(unittest.TestCase):
def test_tensor_values(self):
device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu:0")
input_data = {
"img1": torch.tensor([[0, 1], [1, 2]], device=device),
"img2": torch.tensor([[0, 1], [1, 2]], device=device),
}
result = ConcatItemsd(keys=["img1", "img2"], name="cat_img")(input_data)
self.assertTrue("cat_img" in result)
result["cat_img"] += 1
torch.testing.assert_allclose(result["img1"], torch.tensor([[0, 1], [1, 2]], device=device))
torch.testing.assert_allclose(result["cat_img"], torch.tensor([[1, 2], [2, 3], [1, 2], [2, 3]], device=device))
def test_numpy_values(self):
input_data = {"img1": np.array([[0, 1], [1, 2]]), "img2": np.array([[0, 1], [1, 2]])}
result = ConcatItemsd(keys=["img1", "img2"], name="cat_img")(input_data)
self.assertTrue("cat_img" in result)
result["cat_img"] += 1
np.testing.assert_allclose(result["img1"], np.array([[0, 1], [1, 2]]))
np.testing.assert_allclose(result["cat_img"], np.array([[1, 2], [2, 3], [1, 2], [2, 3]]))
if __name__ == "__main__":
unittest.main()
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