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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 | # 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 unet_pipe import UNetPipe
TEST_CASES = [
[ # 1-channel 3D, batch 12
{"spatial_dims": 3, "out_channels": 2, "in_channels": 1, "depth": 3, "n_feat": 8},
torch.randn(12, 1, 32, 64, 48),
(12, 2, 32, 64, 48),
],
[ # 1-channel 3D, batch 16
{"spatial_dims": 3, "out_channels": 2, "in_channels": 1, "depth": 3},
torch.randn(16, 1, 32, 64, 48),
(16, 2, 32, 64, 48),
],
[ # 4-channel 3D, batch 16, batch normalisation
{"spatial_dims": 3, "out_channels": 3, "in_channels": 2},
torch.randn(16, 2, 64, 64, 64),
(16, 3, 64, 64, 64),
],
]
class TestUNETPipe(unittest.TestCase):
@parameterized.expand(TEST_CASES)
def test_shape(self, input_param, input_data, expected_shape):
net = UNetPipe(**input_param)
if torch.cuda.is_available():
net = net.to(torch.device("cuda"))
input_data = input_data.to(torch.device("cuda"))
net.eval()
with torch.no_grad():
result = net.forward(input_data.float())
self.assertEqual(result.shape, expected_shape)
if __name__ == "__main__":
unittest.main()
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