Unit tests written for SavitskyGolayFilter()

Signed-off-by: Christian Baker <[email protected]>

tests/test_savitsky_golay_filter.py

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+# 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.networks.layers import SavitskyGolayFilter
+from tests.utils import skip_if_no_cuda
+
+# Zero-padding trivial tests
+
+TEST_CASE_SINGLE_VALUE = [
+    {"window_length": 3, "order": 1},
+    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Input data: Single value
+    torch.Tensor([1 / 3]).unsqueeze(0).unsqueeze(0),  # Expected output: With a window length of 3 and polyorder 1
+    # output should be equal to mean of 0, 1 and 0 = 1/3 (because input will be zero-padded and a linear fit performed)
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_1D = [
+    {"window_length": 3, "order": 1},
+    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Input data
+    torch.Tensor([2 / 3, 1.0, 2 / 3])
+    .unsqueeze(0)
+    .unsqueeze(0),  # Expected output: zero padded, so linear interpolation
+    # over length-3 windows will result in output of [2/3, 1, 2/3].
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_2D_AXIS_2 = [
+    {"window_length": 3, "order": 1},  # along default axis (2, first spatial dim)
+    torch.ones((3, 2)).unsqueeze(0).unsqueeze(0),
+    torch.Tensor([[2 / 3, 2 / 3], [1.0, 1.0], [2 / 3, 2 / 3]]).unsqueeze(0).unsqueeze(0),
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_2D_AXIS_3 = [
+    {"window_length": 3, "order": 1, "axis": 3},  # along axis 3 (second spatial dim)
+    torch.ones((2, 3)).unsqueeze(0).unsqueeze(0),
+    torch.Tensor([[2 / 3, 1.0, 2 / 3], [2 / 3, 1.0, 2 / 3]]).unsqueeze(0).unsqueeze(0),
+    1e-15,  # absolute tolerance
+]
+
+# Replicated-padding trivial tests
+
+TEST_CASE_SINGLE_VALUE_REP = [
+    {"window_length": 3, "order": 1, "mode": "replicate"},
+    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Input data: Single value
+    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Expected output: With a window length of 3 and polyorder 1
+    # output will be equal to mean of [1, 1, 1] = 1 (input will be nearest-neighbour-padded and a linear fit performed)
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_1D_REP = [
+    {"window_length": 3, "order": 1, "mode": "replicate"},
+    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Input data
+    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Expected output: zero padded, so linear interpolation
+    # over length-3 windows will result in output of [2/3, 1, 2/3].
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_2D_AXIS_2_REP = [
+    {"window_length": 3, "order": 1, "mode": "replicate"},  # along default axis (2, first spatial dim)
+    torch.ones((3, 2)).unsqueeze(0).unsqueeze(0),
+    torch.Tensor([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]).unsqueeze(0).unsqueeze(0),
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_2D_AXIS_3_REP = [
+    {"window_length": 3, "order": 1, "axis": 3, "mode": "replicate"},  # along axis 3 (second spatial dim)
+    torch.ones((2, 3)).unsqueeze(0).unsqueeze(0),
+    torch.Tensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]).unsqueeze(0).unsqueeze(0),
+    1e-15,  # absolute tolerance
+]
+
+# Sine smoothing
+
+TEST_CASE_SINE_SMOOTH = [
+    {"window_length": 3, "order": 1},
+    # Sine wave with period equal to savgol window length (windowed to reduce edge effects).
+    torch.as_tensor(np.sin(2 * np.pi * 1 / 3 * np.arange(100)) * np.hanning(100)).unsqueeze(0).unsqueeze(0),
+    # Should be smoothed out to zeros
+    torch.zeros(100).unsqueeze(0).unsqueeze(0),
+    # tolerance chosen by examining output of SciPy.signal.savgol_filter when provided the above input
+    2e-2,  # absolute tolerance
+]
+
+
+class TestSavitskyGolayCPU(unittest.TestCase):
+    @parameterized.expand(
+        [
+            TEST_CASE_SINGLE_VALUE,
+            TEST_CASE_1D,
+            TEST_CASE_2D_AXIS_2,
+            TEST_CASE_2D_AXIS_3,
+            TEST_CASE_SINGLE_VALUE_REP,
+            TEST_CASE_1D_REP,
+            TEST_CASE_2D_AXIS_2_REP,
+            TEST_CASE_2D_AXIS_3_REP,
+            TEST_CASE_SINE_SMOOTH,
+        ]
+    )
+    def test_value(self, arguments, image, expected_data, atol):
+        result = SavitskyGolayFilter(**arguments)(image)
+        np.testing.assert_allclose(result, expected_data, atol=atol)
+
+
+@skip_if_no_cuda
+class TestSavitskyGolayGPU(unittest.TestCase):
+    @parameterized.expand(
+        [
+            TEST_CASE_SINGLE_VALUE,
+            TEST_CASE_1D,
+            TEST_CASE_2D_AXIS_2,
+            TEST_CASE_2D_AXIS_3,
+            TEST_CASE_SINGLE_VALUE_REP,
+            TEST_CASE_1D_REP,
+            TEST_CASE_2D_AXIS_2_REP,
+            TEST_CASE_2D_AXIS_3_REP,
+            TEST_CASE_SINE_SMOOTH,
+        ]
+    )
+    def test_value(self, arguments, image, expected_data, atol):
+        result = SavitskyGolayFilter(**arguments)(image.to(device="cuda"))
+        np.testing.assert_allclose(result.cpu(), expected_data, atol=atol)