initial unit tests for dice loss

.github/workflows/pythonapp.yml

@@ -25,7 +25,6 @@ jobs:
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
         # exit-zero treats all errors as warnings.
         flake8 . --count --statistics
-#     - name: Test with pytest
-#       run: |
-#         pip install pytest
-#         pytest
+    - name: Test and coverage
+      run: |
+        ./runtests.sh --coverage

requirements.txt

@@ -12,3 +12,4 @@ pillow
 pandas
 coverage
 nibabel
+parameterized

runtests.sh

@@ -1,4 +1,5 @@
 #! /bin/bash
+set -e
 # Test script for running all tests
 
 
@@ -52,8 +53,8 @@ done
 if [ "$doDryRun" = 'true' ]
 then
     echo "Dry run commands:"
-    cmdprefix="dryrun " 
-    
+    cmdprefix="dryrun "
+
     # create a dry run function which prints the command prepended with spaces for neatness
     function dryrun { echo "  " $* ; }
 fi

tests/test_dice_loss.py

@@ -0,0 +1,53 @@
+# 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.losses.dice import DiceLoss
+
+TEST_CASE_1 = [
+    {
+        'include_background': False,
+    },
+    {
+        'pred': torch.tensor([[[[1., -1.], [-1., 1.]]]]),
+        'ground': torch.tensor([[[[1., 0.], [1., 1.]]]]),
+        'smooth': 1e-6,
+    },
+    0.307576,
+]
+
+TEST_CASE_2 = [
+    {
+        'include_background': True,
+    },
+    {
+        'pred': torch.tensor([[[[1., -1.], [-1., 1.]]], [[[1., -1.], [-1., 1.]]]]),
+        'ground': torch.tensor([[[[1., 1.], [1., 1.]]], [[[1., 0.], [1., 0.]]]]),
+        'smooth': 1e-4,
+    },
+    0.416636,
+]
+
+
+class TestDiceLoss(unittest.TestCase):
+
+    @parameterized.expand([TEST_CASE_1, TEST_CASE_2])
+    def test_shape(self, input_param, input_data, expected_val):
+        result = DiceLoss(**input_param).forward(**input_data)
+        self.assertAlmostEqual(result.item(), expected_val, places=5)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_unet.py

@@ -0,0 +1,68 @@
+# 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.unet import UNet
+
+TEST_CASE_1 = [  # single channel 2D, batch 16
+    {
+        'dimensions': 2,
+        'in_channels': 1,
+        'num_classes': 3,
+        'channels': (16, 32, 64),
+        'strides': (2, 2),
+        'num_res_units': 1,
+    },
+    torch.randn(16, 1, 32, 32),
+    (16, 32, 32),
+]
+
+TEST_CASE_2 = [  # single channel 3D, batch 16
+    {
+        'dimensions': 3,
+        'in_channels': 1,
+        'num_classes': 3,
+        'channels': (16, 32, 64),
+        'strides': (2, 2),
+        'num_res_units': 1,
+    },
+    torch.randn(16, 1, 32, 24, 48),
+    (16, 32, 24, 48),
+]
+
+TEST_CASE_3 = [  # 4-channel 3D, batch 16
+    {
+        'dimensions': 3,
+        'in_channels': 4,
+        'num_classes': 3,
+        'channels': (16, 32, 64),
+        'strides': (2, 2),
+        'num_res_units': 1,
+    },
+    torch.randn(16, 4, 32, 64, 48),
+    (16, 32, 64, 48),
+]
+
+
+class TestUNET(unittest.TestCase):
+
+    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
+    def test_shape(self, input_param, input_data, expected_shape):
+        result = UNet(**input_param).forward(input_data)[1]
+        self.assertEqual(result.shape, expected_shape)
+
+
+if __name__ == '__main__':
+    unittest.main()