File size: 2,532 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 65 66 67 68 69 70 71 72 73 74 75 | # 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 torch
from parameterized import parameterized
from monai.transforms import AsDiscreted
TEST_CASE_1 = [
{
"keys": ["pred", "label"],
"argmax": [True, False],
"to_onehot": True,
"n_classes": 2,
"threshold_values": False,
"logit_thresh": 0.5,
},
{"pred": torch.tensor([[[[0.0, 1.0]], [[2.0, 3.0]]]]), "label": torch.tensor([[[[0, 1]]]])},
{"pred": torch.tensor([[[[0.0, 0.0]], [[1.0, 1.0]]]]), "label": torch.tensor([[[[1.0, 0.0]], [[0.0, 1.0]]]])},
(1, 2, 1, 2),
]
TEST_CASE_2 = [
{
"keys": ["pred", "label"],
"argmax": False,
"to_onehot": False,
"n_classes": None,
"threshold_values": [True, False],
"logit_thresh": 0.6,
},
{"pred": torch.tensor([[[[0.0, 1.0], [2.0, 3.0]]]]), "label": torch.tensor([[[[0, 1], [1, 1]]]])},
{"pred": torch.tensor([[[[0.0, 1.0], [1.0, 1.0]]]]), "label": torch.tensor([[[[0.0, 1.0], [1.0, 1.0]]]])},
(1, 1, 2, 2),
]
TEST_CASE_3 = [
{
"keys": ["pred"],
"argmax": True,
"to_onehot": True,
"n_classes": 2,
"threshold_values": False,
"logit_thresh": 0.5,
},
{"pred": torch.tensor([[[[0.0, 1.0]], [[2.0, 3.0]]]])},
{"pred": torch.tensor([[[[0.0, 0.0]], [[1.0, 1.0]]]])},
(1, 2, 1, 2),
]
class TestAsDiscreted(unittest.TestCase):
@parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
def test_value_shape(self, input_param, test_input, output, expected_shape):
result = AsDiscreted(**input_param)(test_input)
torch.testing.assert_allclose(result["pred"], output["pred"])
self.assertTupleEqual(result["pred"].shape, expected_shape)
if "label" in result:
torch.testing.assert_allclose(result["label"], output["label"])
self.assertTupleEqual(result["label"].shape, expected_shape)
if __name__ == "__main__":
unittest.main()
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