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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 monai.networks.nets import Discriminator
TEST_CASE_0 = [
{"in_shape": (1, 64, 64), "channels": (2, 4, 8), "strides": (2, 2, 2), "num_res_units": 0},
torch.rand(16, 1, 64, 64),
(16, 1),
]
TEST_CASE_1 = [
{"in_shape": (1, 64, 64), "channels": (2, 4, 8), "strides": (2, 2, 2), "num_res_units": 2},
torch.rand(16, 1, 64, 64),
(16, 1),
]
TEST_CASE_2 = [
{"in_shape": (1, 64, 64), "channels": (2, 4), "strides": (2, 2), "num_res_units": 0},
torch.rand(16, 1, 64, 64),
(16, 1),
]
CASES = [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2]
class TestDiscriminator(unittest.TestCase):
@parameterized.expand(CASES)
def test_shape(self, input_param, input_data, expected_shape):
net = Discriminator(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
if __name__ == "__main__":
unittest.main()
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