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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 | # 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 Affine
TEST_CASES = [
[
dict(padding_mode="zeros", as_tensor_output=False, device=None),
{"img": np.arange(9).reshape((1, 3, 3)), "spatial_size": (-1, 0)},
np.arange(9).reshape(1, 3, 3),
],
[
dict(padding_mode="zeros", as_tensor_output=False, device=None),
{"img": np.arange(4).reshape((1, 2, 2))},
np.arange(4).reshape(1, 2, 2),
],
[
dict(padding_mode="zeros", as_tensor_output=False, device=None),
{"img": np.arange(4).reshape((1, 2, 2)), "spatial_size": (4, 4)},
np.array([[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 2.0, 3.0, 0.0], [0.0, 0.0, 0.0, 0.0]]]),
],
[
dict(rotate_params=[np.pi / 2], padding_mode="zeros", as_tensor_output=False, device=None),
{"img": np.arange(4).reshape((1, 2, 2)), "spatial_size": (4, 4)},
np.array([[[0.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 3.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]]),
],
[
dict(padding_mode="zeros", as_tensor_output=False, device=None),
{"img": np.arange(27).reshape((1, 3, 3, 3)), "spatial_size": (-1, 0, 0)},
np.arange(27).reshape(1, 3, 3, 3),
],
[
dict(padding_mode="zeros", as_tensor_output=False, device=None),
{"img": np.arange(8).reshape((1, 2, 2, 2)), "spatial_size": (4, 4, 4)},
np.array(
[
[
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 2.0, 3.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 4.0, 5.0, 0.0], [0.0, 6.0, 7.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
]
]
),
],
[
dict(rotate_params=[np.pi / 2], padding_mode="zeros", as_tensor_output=False, device=None),
{"img": np.arange(8).reshape((1, 2, 2, 2)), "spatial_size": (4, 4, 4)},
np.array(
[
[
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 3.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 6.0, 4.0, 0.0], [0.0, 7.0, 5.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
]
]
),
],
]
class TestAffine(unittest.TestCase):
@parameterized.expand(TEST_CASES)
def test_affine(self, input_param, input_data, expected_val):
g = Affine(**input_param)
result = g(**input_data)
self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected_val))
if torch.is_tensor(result):
np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4)
else:
np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)
if __name__ == "__main__":
unittest.main()
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