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| | import unittest |
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| | import numpy as np |
| | from parameterized import parameterized |
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|
| | from monai.transforms import AsChannelFirstd |
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|
| | TEST_CASE_1 = [{"keys": ["image", "label", "extra"], "channel_dim": -1}, (4, 1, 2, 3)] |
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| | TEST_CASE_2 = [{"keys": ["image", "label", "extra"], "channel_dim": 3}, (4, 1, 2, 3)] |
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| | TEST_CASE_3 = [{"keys": ["image", "label", "extra"], "channel_dim": 2}, (3, 1, 2, 4)] |
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| | class TestAsChannelFirstd(unittest.TestCase): |
| | @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) |
| | def test_shape(self, input_param, expected_shape): |
| | test_data = { |
| | "image": np.random.randint(0, 2, size=[1, 2, 3, 4]), |
| | "label": np.random.randint(0, 2, size=[1, 2, 3, 4]), |
| | "extra": np.random.randint(0, 2, size=[1, 2, 3, 4]), |
| | } |
| | result = AsChannelFirstd(**input_param)(test_data) |
| | self.assertTupleEqual(result["image"].shape, expected_shape) |
| | self.assertTupleEqual(result["label"].shape, expected_shape) |
| | self.assertTupleEqual(result["extra"].shape, expected_shape) |
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| | if __name__ == "__main__": |
| | unittest.main() |
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