text: # Copyright (c) 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 import eval_mode
  from monai.networks.blocks import ChannelSELayer, ResidualSELayer
  from tests.utils import test_script_save
  
  TEST_CASES = [  # single channel 3D, batch 16
      [{"spatial_dims": 2, "in_channels": 4, "r": 3}, (7, 4, 64, 48), (7, 4, 64, 48)],  # 4-channel 2D, batch 7
      [  # 4-channel 1D, batch 16
          {"spatial_dims": 1, "in_channels": 4, "r": 3, "acti_type_1": "relu"},
          (16, 4, 63),
          (16, 4, 63),
      ],
  ]
  
  TEST_CASES_3D = []
  for type_1 in {"relu", "relu6", "leakyrelu"}:
      for type_2 in {"prelu", "sigmoid", "relu"}:
          test_case = [
              {"spatial_dims": 3, "in_channels": 10, "r": 3, "acti_type_1": type_1, "acti_type_2": type_2},
              (16, 10, 32, 24, 48),
              (16, 10, 32, 24, 48),
          ]
          TEST_CASES_3D.append(test_case)
  
  
  class TestChannelSELayer(unittest.TestCase):
      @parameterized.expand(TEST_CASES + TEST_CASES_3D)
      def test_shape(self, input_param, input_shape, expected_shape):
          net = ChannelSELayer(**input_param)
          with eval_mode(net):
              result = net(torch.randn(input_shape))
              self.assertEqual(result.shape, expected_shape)
  
      def test_script(self):
          input_param, input_shape, _ = TEST_CASES[0]
          net = ChannelSELayer(**input_param)
          test_data = torch.randn(input_shape)
          test_script_save(net, test_data)
  
      def test_ill_arg(self):
          with self.assertRaises(ValueError):
              ChannelSELayer(spatial_dims=1, in_channels=4, r=100)
  
  
  class TestResidualSELayer(unittest.TestCase):
      @parameterized.expand(TEST_CASES[:1])
      def test_shape(self, input_param, input_shape, expected_shape):
          net = ResidualSELayer(**input_param)
          with eval_mode(net):
              result = net(torch.randn(input_shape))
              self.assertEqual(result.shape, expected_shape)
  
      def test_script(self):
          input_param, input_shape, _ = TEST_CASES[0]
          net = ResidualSELayer(**input_param)
          test_data = torch.randn(input_shape)
          test_script_save(net, test_data)
  
  
  if __name__ == "__main__":
      unittest.main()
  