text: # Copyright 2020 - 2021 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.losses import TverskyLoss
  from tests.utils import SkipIfBeforePyTorchVersion, test_script_save
  
  TEST_CASES = [
      [  # shape: (1, 1, 2, 2), (1, 1, 2, 2)
          {"include_background": True, "sigmoid": True, "smooth_nr": 1e-6, "smooth_dr": 1e-6},
          {"input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]]), "target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])},
          0.307576,
      ],
      [  # shape: (2, 1, 2, 2), (2, 1, 2, 2)
          {"include_background": True, "sigmoid": True, "smooth_nr": 1e-4, "smooth_dr": 1e-4},
          {
              "input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]], [[[1.0, -1.0], [-1.0, 1.0]]]]),
              "target": torch.tensor([[[[1.0, 1.0], [1.0, 1.0]]], [[[1.0, 0.0], [1.0, 0.0]]]]),
          },
          0.416657,
      ],
      [  # shape: (2, 2, 3), (2, 1, 3)
          {"include_background": False, "to_onehot_y": True, "smooth_nr": 0, "smooth_dr": 0},
          {
              "input": torch.tensor([[[1.0, 1.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]]),
              "target": torch.tensor([[[0.0, 0.0, 1.0]], [[0.0, 1.0, 0.0]]]),
          },
          0.0,
      ],
      [  # shape: (2, 2, 3), (2, 1, 3)
          {"include_background": False, "to_onehot_y": True, "smooth_nr": 0, "smooth_dr": 1e-3},
          {
              "input": torch.tensor([[[1.0, 1.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]]),
              "target": torch.tensor([[[0.0, 0.0, 1.0]], [[0.0, 1.0, 0.0]]]),
          },
          0.000999,
      ],
      [  # shape: (2, 2, 3), (2, 1, 3)
          {"include_background": True, "to_onehot_y": True, "sigmoid": True, "smooth_nr": 1e-4, "smooth_dr": 1e-4},
          {
              "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]),
              "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]),
          },
          0.435050,
      ],
      [  # shape: (2, 2, 3), (2, 1, 3)
          {"include_background": True, "to_onehot_y": True, "sigmoid": True, "batch": True},
          {
              "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]),
              "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]),
          },
          0.422979,
      ],
      [  # shape: (2, 2, 3), (2, 1, 3)
          {
              "include_background": True,
              "to_onehot_y": True,
              "sigmoid": True,
              "reduction": "sum",
              "smooth_nr": 1e-4,
              "smooth_dr": 1e-4,
          },
          {
              "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]),
              "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]),
          },
          1.74013,
      ],
      [  # shape: (2, 2, 3), (2, 1, 3)
          {"include_background": True, "to_onehot_y": True, "softmax": True, "smooth_nr": 1e-4, "smooth_dr": 1e-4},
          {
              "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]),
              "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]),
          },
          0.383713,
      ],
      [  # shape: (2, 2, 3), (2, 1, 3)
          {
              "include_background": True,
              "to_onehot_y": True,
              "softmax": True,
              "reduction": "none",
              "smooth_nr": 1e-4,
              "smooth_dr": 1e-4,
          },
          {
              "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]),
              "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]),
          },
          [[0.210961, 0.295339], [0.599952, 0.428547]],
      ],
      [  # shape: (1, 1, 2, 2), (1, 1, 2, 2)
          {"include_background": True, "sigmoid": True, "alpha": 0.3, "beta": 0.7, "smooth_nr": 1e-6, "smooth_dr": 1e-6},
          {"input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]]), "target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])},
          0.3589,
      ],
      [  # shape: (1, 1, 2, 2), (1, 1, 2, 2)
          {"include_background": True, "sigmoid": True, "alpha": 0.7, "beta": 0.3, "smooth_nr": 1e-6, "smooth_dr": 1e-6},
          {"input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]]), "target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])},
          0.247366,
      ],
      [  # shape: (2, 1, 2, 2), (2, 1, 2, 2)
          {"include_background": True, "other_act": torch.tanh, "smooth_nr": 1e-4, "smooth_dr": 1e-4},
          {
              "input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]], [[[1.0, -1.0], [-1.0, 1.0]]]]),
              "target": torch.tensor([[[[1.0, 1.0], [1.0, 1.0]]], [[[1.0, 0.0], [1.0, 0.0]]]]),
          },
          0.999963,
      ],
      [  # shape: (2, 1, 2, 2), (2, 1, 2, 2)
          {"include_background": True, "other_act": torch.tanh, "smooth_nr": 0, "smooth_dr": 1e-3, "batch": True},
          {
              "input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]], [[[1.0, -1.0], [-1.0, 1.0]]]]),
              "target": torch.tensor([[[[1.0, 1.0], [1.0, 1.0]]], [[[1.0, 0.0], [1.0, 0.0]]]]),
          },
          0.999963,
      ],
      [  # shape: (2, 2, 3), (2, 1, 3)
          {
              "include_background": True,
              "to_onehot_y": True,
              "other_act": lambda x: torch.log_softmax(x, dim=1),
              "smooth_nr": 1e-4,
              "smooth_dr": 1e-4,
          },
          {
              "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]),
              "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]),
          },
          -8.533317,
      ],
  ]
  
  
  class TestTverskyLoss(unittest.TestCase):
      @parameterized.expand(TEST_CASES)
      def test_shape(self, input_param, input_data, expected_val):
          result = TverskyLoss(**input_param).forward(**input_data)
          np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-4)
  
      def test_ill_shape(self):
          loss = TverskyLoss()
          with self.assertRaisesRegex(AssertionError, ""):
              loss.forward(torch.ones((2, 2, 3)), torch.ones((4, 5, 6)))
          chn_input = torch.ones((1, 1, 3))
          chn_target = torch.ones((1, 1, 3))
          with self.assertRaisesRegex(ValueError, ""):
              TverskyLoss(reduction="unknown")(chn_input, chn_target)
          with self.assertRaisesRegex(ValueError, ""):
              TverskyLoss(reduction=None)(chn_input, chn_target)
  
      def test_input_warnings(self):
          chn_input = torch.ones((1, 1, 3))
          chn_target = torch.ones((1, 1, 3))
          with self.assertWarns(Warning):
              loss = TverskyLoss(include_background=False)
              loss.forward(chn_input, chn_target)
          with self.assertWarns(Warning):
              loss = TverskyLoss(softmax=True)
              loss.forward(chn_input, chn_target)
          with self.assertWarns(Warning):
              loss = TverskyLoss(to_onehot_y=True)
              loss.forward(chn_input, chn_target)
  
      @SkipIfBeforePyTorchVersion((1, 7, 0))
      def test_script(self):
          loss = TverskyLoss()
          test_input = torch.ones(2, 1, 8, 8)
          test_script_save(loss, test_input, test_input)
  
  
  if __name__ == "__main__":
      unittest.main()
  