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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 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | # 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 numpy as np
import torch
from parameterized import parameterized
from monai.losses import DiceLoss
TEST_CASES = [
[ # shape: (1, 1, 2, 2), (1, 1, 2, 2)
{"include_background": True, "sigmoid": True},
{
"input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]]),
"target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]]),
"smooth": 1e-6,
},
0.307576,
],
[ # shape: (2, 1, 2, 2), (2, 1, 2, 2)
{"include_background": True, "sigmoid": 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]]]]),
"smooth": 1e-4,
},
0.416657,
],
[ # shape: (2, 2, 3), (2, 1, 3)
{"include_background": False, "to_onehot_y": True},
{
"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]]]),
"smooth": 0.0,
},
0.0,
],
[ # shape: (2, 2, 3), (2, 1, 3)
{"include_background": True, "to_onehot_y": True, "sigmoid": 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]]]),
"smooth": 1e-4,
},
0.435050,
],
[ # shape: (2, 2, 3), (2, 1, 3)
{"include_background": True, "to_onehot_y": True, "sigmoid": True, "reduction": "none"},
{
"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]]]),
"smooth": 1e-4,
},
[[0.296529, 0.415136], [0.599976, 0.428559]],
],
[ # shape: (2, 2, 3), (2, 1, 3)
{"include_background": True, "to_onehot_y": True, "softmax": 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]]]),
"smooth": 1e-4,
},
0.383713,
],
[ # shape: (2, 2, 3), (2, 1, 3)
{"include_background": True, "to_onehot_y": True, "softmax": True, "reduction": "sum"},
{
"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]]]),
"smooth": 1e-4,
},
1.534853,
],
[ # shape: (1, 1, 2, 2), (1, 1, 2, 2)
{"include_background": True, "sigmoid": True},
{
"input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]]),
"target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]]),
"smooth": 1e-6,
},
0.307576,
],
[ # shape: (1, 1, 2, 2), (1, 1, 2, 2)
{"include_background": True, "sigmoid": True, "squared_pred": True},
{
"input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]]),
"target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]]),
"smooth": 1e-5,
},
0.178337,
],
[ # shape: (1, 1, 2, 2), (1, 1, 2, 2)
{"include_background": True, "sigmoid": True, "jaccard": True},
{
"input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]]),
"target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]]),
"smooth": 1e-5,
},
0.470451,
],
[ # shape: (2, 1, 2, 2), (2, 1, 2, 2)
{"include_background": True, "other_act": torch.tanh},
{
"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]]]]),
"smooth": 1e-4,
},
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)},
{
"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]]]),
"smooth": 1e-4,
},
-8.522593,
],
]
class TestDiceLoss(unittest.TestCase):
@parameterized.expand(TEST_CASES)
def test_shape(self, input_param, input_data, expected_val):
result = DiceLoss(**input_param).forward(**input_data)
np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)
def test_ill_shape(self):
loss = DiceLoss()
with self.assertRaisesRegex(AssertionError, ""):
loss.forward(torch.ones((1, 2, 3)), torch.ones((4, 5, 6)))
def test_ill_opts(self):
with self.assertRaisesRegex(ValueError, ""):
DiceLoss(sigmoid=True, softmax=True)
chn_input = torch.ones((1, 1, 3))
chn_target = torch.ones((1, 1, 3))
with self.assertRaisesRegex(ValueError, ""):
DiceLoss(reduction="unknown")(chn_input, chn_target)
with self.assertRaisesRegex(ValueError, ""):
DiceLoss(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 = DiceLoss(include_background=False)
loss.forward(chn_input, chn_target)
with self.assertWarns(Warning):
loss = DiceLoss(softmax=True)
loss.forward(chn_input, chn_target)
with self.assertWarns(Warning):
loss = DiceLoss(to_onehot_y=True)
loss.forward(chn_input, chn_target)
if __name__ == "__main__":
unittest.main()
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